From c1fcc4edc585efd8b6b3bb41cbf4df920496763c Mon Sep 17 00:00:00 2001 From: Jiaji Date: Tue, 9 Dec 2025 15:49:49 +0800 Subject: [PATCH] Add trading agent (#45) --- README.md | 12 + README_zh.md | 12 + evotraders/.eslintrc | 24 + evotraders/.gitignore | 63 + evotraders/.pre-commit-config.yaml | 122 ++ evotraders/.stylelintrc | 6 + evotraders/README.md | 236 +++ evotraders/README_zh.md | 243 +++ evotraders/backend/__init__.py | 0 evotraders/backend/agents/__init__.py | 6 + evotraders/backend/agents/analyst.py | 133 ++ .../backend/agents/portfolio_manager.py | 188 ++ evotraders/backend/agents/prompt_loader.py | 184 ++ .../agents/prompts/analyst/personas.yaml | 117 + .../backend/agents/prompts/analyst/system.md | 23 + .../prompts/portfolio_manager/system.md | 31 + .../agents/prompts/risk_manager/system.md | 21 + evotraders/backend/agents/risk_manager.py | 88 + evotraders/backend/cli.py | 623 ++++++ evotraders/backend/config/__init__.py | 0 evotraders/backend/config/constants.py | 76 + 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Trading System | ------ @@ -134,6 +135,17 @@ Beyond being a ready-to-use agent, we envision Alias as a foundational template 📖 **Documentation**: [English](alias/README.md) | [中文](alias/README_ZH.md) +### EvoTraders + +*EvoTraders* is a financial trading agent framework that builds a trading system capable of continuous learning and evolution in real markets through multi-agent collaboration and memory systems. Key features include: + +- **Multi-Agent Collaboration**: A team of specialized analysts (Fundamentals, Technical, Sentiment, Valuation) and managers collaborating like a real trading team. +- **Memory Enhancement & Evolution**: Agents reflect and summarize after trades using the ReMe memory framework, evolving their trading styles over time. +- **Real-Time & Backtesting**: Supports both real-time market data integration for live trading and backtesting modes. +- **Visualized Dashboard**: A comprehensive frontend to observe analysis processes, communication, and performance tracking. + +📖 **Documentation**: [English](evotraders/README.md) | [中文](evotraders/README_zh.md) + ------ ## ℹ️ Getting Help diff --git a/README_zh.md b/README_zh.md index 4b0957e..04c949e 100644 --- a/README_zh.md +++ b/README_zh.md @@ -105,6 +105,7 @@ AgentScope Runtime 是一个**全面的运行时框架**，主要解决部署和 | | conversational_agents/multiagent_debate | ✅ | ❌ | Agent 辩论 | | **评估** | evaluation/ace_bench | ✅ | ❌ | ACE Bench 基准测试 | | **通用智能体** | alias/ | ✅ | ✅ | 在沙盒中运行的可以解决真实问题的智能体程序 | +| **金融交易** | evotraders/ | ✅ | ❌ | 自我进化的多智能体交易系统 | --- @@ -133,6 +134,17 @@ AgentScope Runtime 是一个**全面的运行时框架**，主要解决部署和 📖 **文档**：[English](alias/README.md) | [中文](alias/README_ZH.md) +### EvoTraders + +*EvoTraders* 是一个开源的金融交易智能体框架，通过多智能体协作和记忆系统，构建能够在真实市场中持续学习与进化的交易系统。主要特性包括： + +- **多智能体协作交易**：包含基本面、技术面、情绪、估值等专业分析师角色以及基金经理和风控专家的团队协作。 +- **记忆增强与进化**：基于 ReMe 记忆框架，智能体在交易后反思总结，形成独特的投资方法论。 +- **实盘与回测支持**：支持实时行情接入的实盘模式以及基于历史数据的回测模式。 +- **可视化交易大厅**：提供可视化界面实时观察智能体的分析过程、沟通记录和决策演化。 + +📖 **文档**：[English](evotraders/README.md) | [中文](evotraders/README_zh.md) + --- ## ℹ️ 获取帮助 diff --git a/evotraders/.eslintrc b/evotraders/.eslintrc new file mode 100644 index 0000000..0c2fdb7 --- /dev/null +++ b/evotraders/.eslintrc @@ -0,0 +1,24 @@ +{ + "env": { + "browser": true, + "es2021": true + }, + "parserOptions": { + "ecmaVersion": 2021, + "sourceType": "module", + "ecmaFeatures": { + "jsx": true + } + }, + "rules": { + "semi": ["error", "always"], + "quotes": ["error", "double"], + "indent": ["error", 2], + "linebreak-style": ["error", "unix"], + "brace-style": ["error", "1tbs"], + "curly": ["error", "all"], + "no-eval": ["error"], + "prefer-const": ["error"], + "arrow-spacing": ["error", { "before": true, "after": true }] + } +} \ No newline at end of file diff --git a/evotraders/.gitignore b/evotraders/.gitignore new file mode 100644 index 0000000..3fea7b9 --- /dev/null +++ b/evotraders/.gitignore @@ -0,0 +1,63 @@ +# Python +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +build/ +develop-eggs/ +dist/ +eggs/ +.eggs/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg + +# Virtual Environment +/.venv/ +ENV/ + +# Environment Variables +.env + +# IDE +.idea/ +.vscode/ +*.swp +*.swo +.cursorrules +.cursorignore + +# OS +.DS_Store + +# Txt files +*.txt + +# PDF files +*.pdf + +# Frontend +node_modules + +# Outputs +outputs/ + +# Data files +backend/data/ret_data/ + +# Database files (users will have their own local databases) +*.db +*.db-journal +*.db-wal +*.db-shm +*.sqlite +*.sqlite3 + +*.ipynb +*.log \ No newline at end of file diff --git a/evotraders/.pre-commit-config.yaml b/evotraders/.pre-commit-config.yaml new file mode 100644 index 0000000..ea2c6e1 --- /dev/null +++ b/evotraders/.pre-commit-config.yaml @@ -0,0 +1,122 @@ +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.3.0 + hooks: + - id: check-ast + - id: sort-simple-yaml + - id: check-yaml + exclude: | + (?x)^( + meta.yaml + )$ + - id: check-xml + - id: check-toml + - id: check-docstring-first + - id: check-json + - id: fix-encoding-pragma + - id: detect-private-key + - id: trailing-whitespace + - repo: https://github.com/asottile/add-trailing-comma + rev: v3.1.0 + hooks: + - id: add-trailing-comma + - repo: https://github.com/pre-commit/mirrors-mypy + rev: v1.7.0 + hooks: + - id: mypy + exclude: + (?x)( + pb2\.py$ + | grpc\.py$ + | ^docs + | \.html$ + ) + args: [ + --ignore-missing-imports, + --disable-error-code=var-annotated, + --disable-error-code=union-attr, + --disable-error-code=assignment, + --disable-error-code=attr-defined, + --disable-error-code=import-untyped, + --disable-error-code=truthy-function, + --follow-imports=skip, + --explicit-package-bases, + ] + - repo: https://github.com/psf/black + rev: 23.3.0 + hooks: + - id: black + args: [ --line-length=79 ] + - repo: https://github.com/PyCQA/flake8 + rev: 6.1.0 + hooks: + - id: flake8 + args: [ "--extend-ignore=E203"] + - repo: https://github.com/pylint-dev/pylint + rev: v3.0.2 + hooks: + - id: pylint + exclude: + (?x)( + ^docs + | pb2\.py$ + | grpc\.py$ + | \.demo$ + | \.md$ + | \.html$ + ) + args: [ + "--init-hook=import sys; sys.path.insert(0, 'alias/src')", + --disable=W0511, + --disable=W0718, + --disable=W0122, + --disable=C0103, + --disable=R0913, + --disable=E0401, + --disable=E1101, + --disable=C0415, + --disable=W0603, + --disable=R1705, + --disable=R0914, + --disable=E0601, + --disable=W0602, + --disable=W0604, + --disable=R0801, + --disable=R0902, + --disable=R0903, + --disable=C0123, + --disable=W0231, + --disable=W1113, + --disable=W0221, + --disable=R0401, + --disable=W0632, + --disable=W0123, + --disable=C3001, + --disable=W0201, + --disable=C0302, + --disable=W1203, + --disable=C2801, + --disable=C0114, # Disable missing module docstring for quick dev + --disable=C0115, # Disable missing class docstring for quick dev + --disable=C0116, # Disable missing function or method docstring for quick dev + ] + - repo: https://github.com/pre-commit/mirrors-eslint + rev: v7.32.0 + hooks: + - id: eslint + files: \.(js|jsx)$ + exclude: '.*js_third_party.*' + args: [ '--fix' ] + - repo: https://github.com/thibaudcolas/pre-commit-stylelint + rev: v14.4.0 + hooks: + - id: stylelint + files: \.(css)$ + exclude: '.*css_third_party.*' + args: [ '--fix' ] + - repo: https://github.com/pre-commit/mirrors-prettier + rev: 'v3.0.0' + hooks: + - id: prettier + additional_dependencies: [ 'prettier@3.0.0' ] + files: \.(tsx?)$ \ No newline at end of file diff --git a/evotraders/.stylelintrc b/evotraders/.stylelintrc new file mode 100644 index 0000000..a5d2a39 --- /dev/null +++ b/evotraders/.stylelintrc @@ -0,0 +1,6 @@ +{ + "rules": { + "indentation": 2, + "string-quotes": "double" + } +} \ No newline at end of file diff --git a/evotraders/README.md b/evotraders/README.md new file mode 100644 index 0000000..52b6f85 --- /dev/null +++ b/evotraders/README.md @@ -0,0 +1,236 @@ +

+ +

+ +

EvoTraders: A Self-Evolving Multi-Agent Trading System

+ +

+ 📌 Visit us at EvoTraders website ! +

+ +![System Demo](./docs/assets/evotraders_demo.gif) + +EvoTraders is an open-source financial trading agent framework that builds a trading system capable of continuous learning and evolution in real markets through multi-agent collaboration and memory systems. + +--- + +## Core Features + +**Multi-Agent Collaborative Trading** +A team of 6 members, including 4 specialized analyst roles (fundamentals, technical, sentiment, valuation) + portfolio manager + risk management, collaborating to make decisions like a real trading team. + +You can customize your Agents here: [Custom Configuration](#custom-configuration) + +**Continuous Learning and Evolution** +Based on the ReMe memory framework, agents reflect and summarize after each trade, preserving experience across rounds, and forming unique investment methodologies. + +Through this design, we hope that when AI Agents form a team and enter the real-time market, they will gradually develop their own trading styles and decision preferences, rather than one-time random inference. + +**Real-Time Market Trading** +Supports real-time market data integration, providing backtesting mode and live trading mode, allowing AI Agents to learn and make decisions in real market fluctuations. + +**Visualized Trading Information** +Observe agents' analysis processes, communication records, and decision evolution in real-time, with complete tracking of return curves and analyst performance. + +

+ + +

+ +--- + +## Quick Start + +### Installation + +```bash +# Clone repository +git clone https://github.com/agentscope-ai/agentscope-samples +cd agentscope-samples/EvoTraders + +# Install dependencies (Recommend uv!) +uv pip install -e . +# optional: pip install -e . + + +# Configure environment variables +cp env.template .env +# Edit .env file and add your API Keys. The following config are required: + +# finance data API: At minimum, FINANCIAL_DATASETS_API_KEY is required, corresponding to FIN_DATA_SOURCE=financial_datasets; It is recommended to add FINNHUB_API_KEY, corresponding to FIN_DATA_SOURCE=finnhub; If using live mode, FINNHUB_API_KEY must be added +FIN_DATA_SOURCE = #finnhub or financial_datasets +FINANCIAL_DATASETS_API_KEY= #Required +FINNHUB_API_KEY= #Optional + +# LLM API for Agents +OPENAI_API_KEY= +OPENAI_BASE_URL= +MODEL_NAME=qwen3-max-preview + +# LLM & embedding API for Memory +MEMORY_API_KEY= +``` + +### Running + +**Backtest Mode:** +```bash +evotraders backtest --start 2025-11-01 --end 2025-12-01 +evotraders backtest --start 2025-11-01 --end 2025-12-01 --enable-memory # Use Memory +``` + +If you do not have market data APIs and just want to try the backtest demo, download the offline data and unzip it into `backend/data`: +```bash +wget "https://agentscope-open.oss-cn-beijing.aliyuncs.com/ret_data.zip" +unzip ret_data.zip -d backend/data +``` +The zip includes basic stock price data so you can run the backtest demo out of the box. + +**Live Trading:** +```bash +evotraders live # Run immediately (default) +evotraders live --enable-memory # Use memory +evotraders live --mock # Mock mode (testing) +evotraders live -t 22:30 # Run daily at 22:30 local time (auto-converts to NYSE timezone) +``` + +**Get Help:** +```bash +evotraders --help # View global CLI help +evotraders backtest --help # View backtest mode parameters +evotraders live --help # View live/mock run parameters +``` + +**Launch Visualization Interface:** +```bash +# Ensure npm is installed, otherwise install it: +# npm install +evotraders frontend # Default connects to port 8765, you can modify the address in ./frontend/env.local to change the port number +``` + +Visit `http://localhost:5173/` to view the trading room, select a date and click Run/Replay to observe the decision-making process. + +--- + +## System Architecture + +![Architecture Diagram](docs/assets/evotraders_pipeline.jpg) + +### Agent Design + +**Analyst Team:** +- **Fundamentals Analyst**: Financial health, profitability, growth quality +- **Technical Analyst**: Price trends, technical indicators, momentum analysis +- **Sentiment Analyst**: Market sentiment, news sentiment, insider trading +- **Valuation Analyst**: DCF, residual income, EV/EBITDA + +**Decision Layer:** +- **Portfolio Manager**: Integrates analysis signals from analysts, executes communication strategies, combines analyst and team historical performance, recent investment memories, and long-term investment experience to make final decisions +- **Risk Management**: Real-time price and volatility monitoring, position limits, multi-layer risk warnings + +### Decision Process + +``` +Real-time Market Data → Independent Analysis → Intelligent Communication (1v1/1vN/NvN) → Decision Execution → Performance Evaluation → Learning and Evolution (Memory Update) +``` + +Each trading day goes through five stages: + +1. **Analysis Stage**: Each agent independently analyzes based on their respective tools and historical experience +2. **Communication Stage**: Exchange views through private chats, notifications, meetings, etc. +3. **Decision Stage**: Portfolio manager makes comprehensive judgments and provides final trades +4. **Evaluation Stage** + - **Performance Charts**: Track portfolio return curves vs. benchmark strategies (equal-weighted, market-cap weighted, momentum). Used to evaluate overall strategy effectiveness. + + - **Analyst Rankings**: Click on avatars in the Trading Room to view analyst performance (win rate, bull/bear market win rate). Used to understand which analysts provide the most valuable insights. + + - **Statistics**: Detailed position and trading history. Used for in-depth analysis of position management and execution quality. + +5. **Review Stage**: Agents reflect on decisions and summarize experiences based on actual returns of the day, and store them in the ReMe memory framework for continuous improvement + +--- + +### Module Support + +- **Agent Framework**: [AgentScope](https://github.com/agentscope-ai/agentscope) +- **Memory System**: [ReMe](https://github.com/agentscope-ai/reme) +- **LLM Support**: OpenAI, DeepSeek, Qwen, Moonshot, Zhipu AI, etc. + +--- + +## Custom Configuration + +### Custom Analyst Roles + +1. Register role information in [./backend/agents/prompts/analyst/personas.yaml](./backend/agents/prompts/analyst/personas.yaml), for example: + +```yaml +comprehensive_analyst: + name: "Comprehensive Analyst" + focus: + - ... + preferred_tools: # Flexibly select based on situation + description: | + As a comprehensive analyst ... +``` + +2. Add role definition in [./backend/config/constants.py](./backend/config/constants.py) +```python +ANALYST_TYPES = { + # Add new analyst + "comprehensive_analyst": { + "display_name": "Comprehensive Analyst", + "agent_id": "comprehensive_analyst", + "description": "Uses LLM to intelligently select analysis tools, performs comprehensive analysis", + "order": 15 + } +} +``` + +3. Introduce new role in frontend configuration [./frontend/src/config/constants.js](./frontend/src/config/constants.js) (optional) +```javascript +export const AGENTS = [ + // Override one of the agents + { + id: "comprehensive_analyst", + name: "Comprehensive Analyst", + role: "Comprehensive Analyst", + avatar: `${ASSET_BASE_URL}/...`, + colors: { bg: '#F9FDFF', text: '#1565C0', accent: '#1565C0' } + } +] +``` + +### Custom Models + +Configure models used by different agents in the [.env](.env) file: + +```bash +AGENT_SENTIMENT_ANALYST_MODEL_NAME=qwen3-max-preview +AGENT_FUNDAMENTALS_ANALYST_MODEL_NAME=deepseek-chat +AGENT_TECHNICAL_ANALYST_MODEL_NAME=glm-4-plus +AGENT_VALUATION_ANALYST_MODEL_NAME=moonshot-v1-32k +``` + +### Project Structure + +``` +EvoTraders/ +├── backend/ +│ ├── agents/ # Agent implementation +│ ├── communication/ # Communication system +│ ├── memory/ # Memory system (ReMe) +│ ├── tools/ # Analysis toolset +│ ├── servers/ # WebSocket services +│ └── cli.py # CLI entry point +├── frontend/ # React visualization interface +└── logs_and_memory/ # Logs and memory data +``` + +--- + +## License and Disclaimer + +EvoTraders is a research and educational project, open-sourced under the Apache 2.0 license. + +**Risk Warning**: Before trading with real funds, please conduct thorough testing and risk assessment. Past performance does not guarantee future returns. Investment involves risks, and decisions should be made with caution. diff --git a/evotraders/README_zh.md b/evotraders/README_zh.md new file mode 100644 index 0000000..5a6c1f9 --- /dev/null +++ b/evotraders/README_zh.md @@ -0,0 +1,243 @@ +

+ +

+ +

EvoTraders：自我进化的多智能体交易系统

+ + +

+ 📌 Visit us at EvoTraders website ! +

+ +![系统演示](./docs/assets/evotraders_demo.gif) + +EvoTraders是一个开源的金融交易智能体框架，通过多智能体协作和记忆系统，构建能够在真实市场中持续学习与进化的交易系统。 + +--- + +## 核心特性 + +**多智能体协作交易** +6名成员，包含4种专业分析师角色（基本面、技术面、情绪、估值）+ 投资组合经理 + 风险管理，像真实交易团队一样协作决策。 + +你可以在这里自定义你的Agents，支持配置不同大模型（如 Qwen、DeepSeek、GPT、Claude等）协同分析：[自定义配置](#自定义配置) + +**持续学习与进化** +基于 ReMe 记忆框架，智能体在每次交易后反思总结，跨回合保留经验，形成独特的投资方法论。 + +通过这样的设计，我们希望当 AI Agents 组成团队进入实时市场，它们会逐渐形成自己的交易风格和决策偏好，而不是一次性的随机推理 + + +**实时市场交易** +支持实时行情接入，提供回测模式和实盘模式，让 AI Agents 在真实市场波动中学习和决策。 + +**可视化交易信息** +实时观察 Agents 的分析过程、沟通记录和决策演化，完整追踪收益曲线和分析师表现。 + + +

+ + +

+ + +--- + +## 快速开始 + +### 安装 + +```bash +# 克隆仓库 +git clone https://github.com/agentscope-ai/agentscope-samples +cd agentscope-samples/EvoTraders + +# 安装依赖(推荐使用uv） +uv pip install -e . +# (可选）pip install -e . + +# 配置环境变量 +cp env.template .env +# 编辑 .env 文件，添加你的 API Keys,以下的配置项为必填项 + +# finance data API:至少需要FINANCIAL_DATASETS_API_KEY，对应FIN_DATA_SOURCE=financial_datasets；推荐添加FINNHUB_API_KEY，对应至少需要FINANCIAL_DATASETS_API_KEY，对应FIN_DATA_SOURCE填为finnhub;如果使用live 模式必须添加FINNHUB_API_KEY +FIN_DATA_SOURCE= #finnhub or financial_datasets +FINANCIAL_DATASETS_API_KEY= #必需 +FINNHUB_API_KEY= #可选 + +# LLM API for Agents +OPENAI_API_KEY= +OPENAI_BASE_URL= +MODEL_NAME=qwen3-max-preview + +# LLM & embedding API for Memory +MEMORY_API_KEY= +``` + +### 运行 + +**回测模式：** +```bash +evotraders backtest --start 2025-11-01 --end 2025-12-01 +evotraders backtest --start 2025-11-01 --end 2025-12-01 --enable-memory # 使用记忆 + +``` + +如果没有可用的行情 API，想快速体验回测 demo，可直接下载离线数据并解压到 `backend/data`： +```bash +wget "https://agentscope-open.oss-cn-beijing.aliyuncs.com/ret_data.zip" +unzip ret_data.zip -d backend/data +``` +该压缩包提供基础的股票行情数据，解压后即可直接用于回测演示。 + +**实盘交易：** +```bash +evotraders live # 立即运行（默认） +evotraders live --enable-memory # 使用记忆 +evotraders live --mock # Mock 模式（测试） +evotraders live -t 22:30 # 每天本地时间 22:30 运行（自动转换为 NYSE 时区） +``` + +**获取帮助：** +```bash +evotraders --help # 查看整体命令行帮助 +evotraders backtest --help # 查看回测模式的参数说明 +evotraders live --help # 查看实盘/Mock 运行的参数说明 +``` + +**启动可视化界面：** +```bash +# 确保已安装 npm, 否则请安装： +# npm install +evotraders frontend # 默认连接 8765 端口, 你可以修改 ./frontend/env.local 中的地址从而修改端口号 +``` + +访问 `http://localhost:5173/` 查看交易大厅，选择日期并点击 Run/Replay 观察决策过程。 + +--- + +## 系统架构 + +![架构图](docs/assets/evotraders_pipeline.jpg) + +### 智能体设计 + +**分析师团队：** +- **基本面分析师**：财务健康度、盈利能力、增长质量 +- **技术分析师**：价格趋势、技术指标、动量分析 +- **情绪分析师**：市场情绪、新闻舆情、内部人交易 +- **估值分析师**：DCF、剩余收益、EV/EBITDA + +**决策层：** +- **投资组合经理**：整合来自分析师的分析信号，执行沟通策略，结合分析师和团队历史表现、近期投资记忆和长期投资经验，进行最终决策 +- **风险管理**：实时价格与波动率监控、头寸限制，多层风险预警 + +### 决策流程 + +``` +实时行情 → 独立分析 → 智能沟通 (1v1/1vN/NvN) → 决策执行 → 收益评估 → 学习与进化（记忆更新） +``` + +每个交易日经历五个阶段： + +1. **分析阶段**：各智能体基于各自工具和历史经验独立分析 +2. **沟通阶段**：通过私聊、通知、会议等方式交换观点 +3. **决策阶段**：投资组合经理综合判断，给出最终交易 +4. **评估阶段** + - **业绩图表**: 追踪组合收益曲线 vs. 基准策略（等权、市值加权、动量）。用于评估整体策略有效性。 + + - **分析师排名**: 在 Trading Room 点击头像查看分析师表现（胜率、牛/熊市胜率）。用于了解哪些分析师提供最有价值的洞察。 + + - **统计数据**: 详细的持仓和交易历史。用于深入分析仓位管理和执行质量。 + +4. **复盘阶段**：Agents 根据当日实际收益反思决策、总结经验，并存入 ReMe 记忆框架以持续改进 + +--- + +### 模块支持 + +- **智能体框架**：[AgentScope](https://github.com/agentscope-ai/agentscope) +- **记忆系统**：[ReMe](https://github.com/agentscope-ai/reme) +- **LLM 支持**：OpenAI、DeepSeek、Qwen、Moonshot、Zhipu AI 等 + + +--- + +## 自定义配置 + +### 自定义分析师角色 + +1. 在 [./backend/agents/prompts/analyst/personas.yaml](./backend/agents/prompts/analyst/personas.yaml) 中注册角色信息，例如： + +```yaml +comprehensive_analyst: + name: "Comprehensive Analyst" + focus: + - ... + preferred_tools: # Flexibly select based on situation + description: | + As a comprehensive analyst ... +``` + +2. 在 [./backend/config/constants.py](./backend/config/constants.py) 添加角色定义 +```python +ANALYST_TYPES = { + # 增加新的分析师 + "comprehensive_analyst": { + "display_name": "Comprehensive Analyst", + "agent_id": "comprehensive_analyst", + "description": "Uses LLM to intelligently select analysis tools, performs comprehensive analysis", + "order": 15 + } +} +``` + +3. 在前端配置 [./frontend/src/config/constants.js](./frontend/src/config/constants.js) 中引入新角色（可选） +```javascript +export const AGENTS = [ + // 覆盖掉其中某一个agent + { + id: "comprehensive_analyst", + name: "Comprehensive Analyst", + role: "Comprehensive Analyst", + avatar: `${ASSET_BASE_URL}/...`, + colors: { bg: '#F9FDFF', text: '#1565C0', accent: '#1565C0' } + } + ] +``` + + + +### 自定义模型 + +在 [.env](.env) 文件中配置不同智能体使用的模型： + +```bash +AGENT_SENTIMENT_ANALYST_MODEL_NAME=qwen3-max-preview +AGENT_FUNDAMENTAL_ANALYST_MODEL_NAME=deepseek-chat +AGENT_TECHNICAL_ANALYST_MODEL_NAME=glm-4-plus +AGENT_VALUATION_ANALYST_MODEL_NAME=moonshot-v1-32k +``` + +### 项目结构 + +``` +EvoTraders/ +├── backend/ +│ ├── agents/ # 智能体实现 +│ ├── communication/ # 通信系统 +│ ├── memory/ # 记忆系统 (ReMe) +│ ├── tools/ # 分析工具集 +│ ├── servers/ # WebSocket 服务 +│ └── cli.py # CLI 入口 +├── frontend/ # React 可视化界面 +└── logs_and_memory/ # 日志和记忆数据 +``` + +--- + +## 许可与免责 + +EvoTraders 是一个研究和教育项目，采用 Apache 2.0 许可协议开源。 + +**风险提示**：在实际资金交易前，请务必进行充分的测试和风险评估。历史表现不代表未来收益，投资有风险，决策需谨慎。 diff --git a/evotraders/backend/__init__.py b/evotraders/backend/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/evotraders/backend/agents/__init__.py b/evotraders/backend/agents/__init__.py new file mode 100644 index 0000000..9347bf6 --- /dev/null +++ b/evotraders/backend/agents/__init__.py @@ -0,0 +1,6 @@ +# -*- coding: utf-8 -*- +from .analyst import AnalystAgent +from .portfolio_manager import PMAgent +from .risk_manager import RiskAgent + +__all__ = ["AnalystAgent", "PMAgent", "RiskAgent"] diff --git a/evotraders/backend/agents/analyst.py b/evotraders/backend/agents/analyst.py new file mode 100644 index 0000000..a524a11 --- /dev/null +++ b/evotraders/backend/agents/analyst.py @@ -0,0 +1,133 @@ +# -*- coding: utf-8 -*- +""" +Analyst Agent - Based on AgentScope ReActAgent +Performs analysis using tools and LLM +""" +from typing import Any, Dict, Optional + +from agentscope.agent import ReActAgent +from agentscope.memory import InMemoryMemory, LongTermMemoryBase +from agentscope.message import Msg + +from ..config.constants import ANALYST_TYPES +from ..utils.progress import progress +from .prompt_loader import PromptLoader + +_prompt_loader = PromptLoader() + + +class AnalystAgent(ReActAgent): + """ + Analyst Agent - Uses LLM for tool selection and analysis + Inherits from AgentScope's ReActAgent + """ + + def __init__( + self, + analyst_type: str, + toolkit: Any, + model: Any, + formatter: Any, + agent_id: Optional[str] = None, + config: Optional[Dict[str, Any]] = None, + long_term_memory: Optional[LongTermMemoryBase] = None, + ): + """ + Initialize Analyst Agent + + Args: + analyst_type: Type of analyst (e.g., "fundamentals", etc.) + toolkit: AgentScope Toolkit instance + model: LLM model instance + formatter: Message formatter instance + agent_id: Agent ID (defaults to "{analyst_type}_analyst") + config: Configuration dictionary + long_term_memory: Optional ReMeTaskLongTermMemory instance + """ + if analyst_type not in ANALYST_TYPES: + raise ValueError( + f"Unknown analyst type: {analyst_type}. " + f"Must be one of: {list(ANALYST_TYPES.keys())}", + ) + + self.analyst_type_key = analyst_type + self.analyst_persona = ANALYST_TYPES[analyst_type]["display_name"] + + if agent_id is None: + agent_id = analyst_type + + self.config = config or {} + + sys_prompt = self._load_system_prompt() + + kwargs = { + "name": agent_id, + "sys_prompt": sys_prompt, + "model": model, + "formatter": formatter, + "toolkit": toolkit, + "memory": InMemoryMemory(), + "max_iters": 10, + } + if long_term_memory: + kwargs["long_term_memory"] = long_term_memory + kwargs["long_term_memory_mode"] = "static_control" + + super().__init__(**kwargs) + + def _load_system_prompt(self) -> str: + """Load system prompt for analyst""" + personas_config = _prompt_loader.load_yaml_config( + "analyst", + "personas", + ) + persona = personas_config.get(self.analyst_type_key, {}) + + # Get focus items and format as bullet points + focus_items = persona.get("focus", []) + focus_text = "\n".join(f"- {item}" for item in focus_items) + + # Get description + description = persona.get("description", "").strip() + + return _prompt_loader.load_prompt( + "analyst", + "system", + variables={ + "analyst_type": self.analyst_persona, + "focus": focus_text, + "description": description, + }, + ) + + async def reply(self, x: Msg = None) -> Msg: + """ + Override reply method to add progress tracking + + Args: + x: Input message (content must be str) + + Returns: + Response message (content is str) + """ + ticker = None + if x and hasattr(x, "metadata") and x.metadata: + ticker = x.metadata.get("ticker") + + if ticker: + progress.update_status( + self.name, + ticker, + f"Starting {self.analyst_persona} analysis", + ) + + result = await super().reply(x) + + if ticker: + progress.update_status( + self.name, + ticker, + "Analysis completed", + ) + + return result diff --git a/evotraders/backend/agents/portfolio_manager.py b/evotraders/backend/agents/portfolio_manager.py new file mode 100644 index 0000000..9699189 --- /dev/null +++ b/evotraders/backend/agents/portfolio_manager.py @@ -0,0 +1,188 @@ +# -*- coding: utf-8 -*- +""" +Portfolio Manager Agent - Based on AgentScope ReActAgent +Responsible for decision-making (NOT trade execution) +""" + +from typing import Any, Dict, Optional + +from agentscope.agent import ReActAgent +from agentscope.memory import InMemoryMemory, LongTermMemoryBase +from agentscope.message import Msg, TextBlock +from agentscope.tool import Toolkit, ToolResponse + +from ..utils.progress import progress +from .prompt_loader import PromptLoader + +_prompt_loader = PromptLoader() + + +class PMAgent(ReActAgent): + """ + Portfolio Manager Agent - Makes investment decisions + + Key features: + 1. PM outputs decisions only (action + quantity per ticker) + 2. Trade execution happens externally (in pipeline/executor) + 3. Supports both backtest and live modes + """ + + def __init__( + self, + name: str = "portfolio_manager", + model: Any = None, + formatter: Any = None, + initial_cash: float = 100000.0, + margin_requirement: float = 0.25, + config: Optional[Dict[str, Any]] = None, + long_term_memory: Optional[LongTermMemoryBase] = None, + ): + self.config = config or {} + + # Portfolio state + self.portfolio = { + "cash": initial_cash, + "positions": {}, + "margin_used": 0.0, + "margin_requirement": margin_requirement, + } + + # Decisions made in current cycle + self._decisions: Dict[str, Dict] = {} + + # Create toolkit + toolkit = self._create_toolkit() + + sys_prompt = _prompt_loader.load_prompt("portfolio_manager", "system") + + kwargs = { + "name": name, + "sys_prompt": sys_prompt, + "model": model, + "formatter": formatter, + "toolkit": toolkit, + "memory": InMemoryMemory(), + "max_iters": 10, + } + if long_term_memory: + kwargs["long_term_memory"] = long_term_memory + kwargs["long_term_memory_mode"] = "both" + + super().__init__(**kwargs) + + def _create_toolkit(self) -> Toolkit: + """Create toolkit with decision recording tool""" + toolkit = Toolkit() + toolkit.register_tool_function(self._make_decision) + return toolkit + + def _make_decision( + self, + ticker: str, + action: str, + quantity: int, + confidence: int = 50, + reasoning: str = "", + ) -> ToolResponse: + """ + Record a trading decision for a ticker. + + Args: + ticker: Stock ticker symbol (e.g., "AAPL") + action: Decision - "long", "short" or "hold" + quantity: Number of shares to trade (0 for hold) + confidence: Confidence level 0-100 + reasoning: Explanation for this decision + + Returns: + ToolResponse confirming decision recorded + """ + if action not in ["long", "short", "hold"]: + return ToolResponse( + content=[ + TextBlock( + type="text", + text=f"Invalid action: {action}. " + "Must be 'long', 'short', or 'hold'.", + ), + ], + ) + + self._decisions[ticker] = { + "action": action, + "quantity": quantity if action != "hold" else 0, + "confidence": confidence, + "reasoning": reasoning, + } + + return ToolResponse( + content=[ + TextBlock( + type="text", + text=f"Decision recorded: {action} " + f"{quantity} shares of {ticker}" + f" (confidence: {confidence}%)", + ), + ], + ) + + async def reply(self, x: Msg = None) -> Msg: + """ + Make investment decisions + + Returns: + Msg with decisions in metadata + """ + if x is None: + return Msg( + name=self.name, + content="No input provided", + role="assistant", + ) + + # Clear previous decisions + self._decisions = {} + + progress.update_status( + self.name, + None, + "Analyzing and making decisions", + ) + + result = await super().reply(x) + + progress.update_status(self.name, None, "Completed") + + # Attach decisions to metadata + if result.metadata is None: + result.metadata = {} + result.metadata["decisions"] = self._decisions.copy() + result.metadata["portfolio"] = self.portfolio.copy() + + return result + + def get_decisions(self) -> Dict[str, Dict]: + """Get decisions from current cycle""" + return self._decisions.copy() + + def get_portfolio_state(self) -> Dict[str, Any]: + """Get current portfolio state""" + return self.portfolio.copy() + + def load_portfolio_state(self, portfolio: Dict[str, Any]): + """Load portfolio state""" + if not portfolio: + return + self.portfolio = { + "cash": portfolio.get("cash", self.portfolio["cash"]), + "positions": portfolio.get("positions", {}).copy(), + "margin_used": portfolio.get("margin_used", 0.0), + "margin_requirement": portfolio.get( + "margin_requirement", + self.portfolio["margin_requirement"], + ), + } + + def update_portfolio(self, portfolio: Dict[str, Any]): + """Update portfolio after external execution""" + self.portfolio.update(portfolio) diff --git a/evotraders/backend/agents/prompt_loader.py b/evotraders/backend/agents/prompt_loader.py new file mode 100644 index 0000000..af649bc --- /dev/null +++ b/evotraders/backend/agents/prompt_loader.py @@ -0,0 +1,184 @@ +# -*- coding: utf-8 -*- +""" +Prompt Loader - Unified management and loading of Agent Prompts +Supports Markdown and YAML formats +Uses simple string replacement, does not depend on Jinja2 +""" +import re +from pathlib import Path +from typing import Any, Dict, Optional + +import yaml + + +class PromptLoader: + """Unified Prompt loader""" + + def __init__(self, prompts_dir: Optional[Path] = None): + """ + Initialize Prompt loader + + Args: + prompts_dir: Prompts directory path, + defaults to prompts/ directory of current file + """ + if prompts_dir is None: + self.prompts_dir = Path(__file__).parent / "prompts" + else: + self.prompts_dir = Path(prompts_dir) + + # Cache loaded prompts + self._prompt_cache: Dict[str, str] = {} + self._yaml_cache: Dict[str, Dict] = {} + + def load_prompt( + self, + agent_type: str, + prompt_name: str, + variables: Optional[Dict[str, Any]] = None, + ) -> str: + """ + Load and render Prompt + + Args: + agent_type: Agent type (analyst, portfolio_manager, risk_manager) + prompt_name: Prompt file name (without extension) + variables: Variable dictionary for rendering Prompt + + Returns: + Rendered prompt string + + Examples: + loader = PromptLoader() + prompt = loader.load_prompt("analyst", "tool_selection", + {"analyst_persona": "Technical Analyst"}) + """ + cache_key = f"{agent_type}/{prompt_name}" + + # Try to load from cache + if cache_key not in self._prompt_cache: + prompt_path = self.prompts_dir / agent_type / f"{prompt_name}.md" + + if not prompt_path.exists(): + raise FileNotFoundError( + f"Prompt file not found: {prompt_path}\n" + f"Please create the prompt file or check the path.", + ) + + with open(prompt_path, "r", encoding="utf-8") as f: + self._prompt_cache[cache_key] = f.read() + + prompt_template = self._prompt_cache[cache_key] + + # If variables provided, use simple string replacement + if variables: + rendered = self._render_template(prompt_template, variables) + else: + rendered = prompt_template + + # Smart escaping: escape braces in JSON code blocks + # rendered = self._escape_json_braces(rendered) + return rendered + + def _render_template( + self, + template: str, + variables: Dict[str, Any], + ) -> str: + """ + Render template using simple string replacement + Supports {{ variable }} syntax (compatible with previous Jinja2 format) + + Args: + template: Template string + variables: Variable dictionary + + Returns: + Rendered string + """ + rendered = template + + # Replace {{ variable }} format + for key, value in variables.items(): + # Support both {{ key }} and {{key}} formats + pattern1 = f"{{{{ {key} }}}}" + pattern2 = f"{{{{{key}}}}}" + rendered = rendered.replace(pattern1, str(value)) + rendered = rendered.replace(pattern2, str(value)) + + return rendered + + def _escape_json_braces(self, text: str) -> str: + """ + Escape braces in JSON code blocks, treating them as literals + + Args: + text: Text to process + + Returns: + Processed text + """ + + def replace_code_block(match): + code_content = match.group(1) + # Escape all braces within code block + escaped = code_content.replace("{", "{{").replace("}", "}}") + return f"```json\n{escaped}\n```" + + # Replace all braces in JSON code blocks + text = re.sub( + r"```json\n(.*?)\n```", + replace_code_block, + text, + flags=re.DOTALL, + ) + return text + + def load_yaml_config( + self, + agent_type: str, + config_name: str, + ) -> Dict[str, Any]: + """ + Load YAML configuration file + + Args: + agent_type: Agent type + config_name: Configuration file name (without extension) + + Returns: + Configuration dictionary + + Examples: + >>> loader = PromptLoader() + >>> config = loader.load_yaml_config("analyst", "personas") + """ + cache_key = f"{agent_type}/{config_name}" + + if cache_key not in self._yaml_cache: + yaml_path = self.prompts_dir / agent_type / f"{config_name}.yaml" + + if not yaml_path.exists(): + raise FileNotFoundError(f"YAML config not found: {yaml_path}") + + with open(yaml_path, "r", encoding="utf-8") as f: + self._yaml_cache[cache_key] = yaml.safe_load(f) + + return self._yaml_cache[cache_key] + + def clear_cache(self): + """Clear cache (for hot reload)""" + self._prompt_cache.clear() + self._yaml_cache.clear() + + def reload_prompt(self, agent_type: str, prompt_name: str): + """Reload specified prompt (force cache refresh)""" + cache_key = f"{agent_type}/{prompt_name}" + if cache_key in self._prompt_cache: + del self._prompt_cache[cache_key] + + def reload_config(self, agent_type: str, config_name: str): + """Reload specified configuration (force cache refresh)""" + cache_key = f"{agent_type}/{config_name}" + if cache_key in self._yaml_cache: + del self._yaml_cache[cache_key] diff --git a/evotraders/backend/agents/prompts/analyst/personas.yaml b/evotraders/backend/agents/prompts/analyst/personas.yaml new file mode 100644 index 0000000..73cea6e --- /dev/null +++ b/evotraders/backend/agents/prompts/analyst/personas.yaml @@ -0,0 +1,117 @@ +# Analyst Personas Configuration + +fundamentals_analyst: + name: "Fundamental Analyst" + focus: + - "Company financial health and profitability" + - "Business model sustainability and competitive advantages" + - "Management quality and corporate governance" + - "Industry position and market share" + - "Long-term investment value assessment" + tools: + - "analyze_profitability" + - "analyze_growth" + - "analyze_financial_health" + - "analyze_valuation_ratios" + - "analyze_efficiency_ratios" + description: | + As a fundamental analyst, you focus on: + - Company financial health and profitability + - Business model sustainability and competitive advantages + - Management quality and corporate governance + - Industry position and market share + - Long-term investment value assessment + You tend to select tools that provide deep insights into company intrinsic value, preferring fundamental and valuation tools. + +technical_analyst: + name: "Technical Analyst" + focus: + - "Price trends and chart patterns" + - "Technical indicators and trading signals" + - "Market sentiment and capital flows" + - "Support/resistance levels and key price points" + - "Short to medium-term trading opportunities" + description: | + As a technical analyst, you focus on: + - Price trends and chart patterns + - Technical indicators and trading signals + - Market sentiment and capital flows + - Support/resistance levels and key price points + - Short to medium-term trading opportunities + You tend to select tools that capture price dynamics and market trends, preferring technical analysis tools. + tools: + - "analyze_trend_following" + - "analyze_momentum" + - "analyze_mean_reversion" + - "analyze_volatility" + +sentiment_analyst: + name: "Sentiment Analyst" + focus: + - "Market participant sentiment changes" + - "News opinion and media influence" + - "Insider trading behavior" + - "Investor panic and greed emotions" + - "Market expectations and psychological factors" + description: | + As a sentiment analyst, you focus on: + - Market participant sentiment changes + - News opinion and media influence + - Insider trading behavior + - Investor panic and greed emotions + - Market expectations and psychological factors + You tend to select tools that reflect market sentiment and investor behavior, preferring sentiment and behavioral tools. + tools: + - "analyze_news_sentiment" + - "analyze_insider_trading" + +valuation_analyst: + name: "Valuation Analyst" + focus: + - "Company intrinsic value calculation" + - "Comparison of different valuation methods" + - "Valuation model assumptions and sensitivity" + - "Relative and absolute valuation" + - "Investment margin of safety assessment" + description: | + As a valuation analyst, you focus on: + - Company intrinsic value calculation + - Comparison of different valuation methods + - Valuation model assumptions and sensitivity + - Relative and absolute valuation + - Investment margin of safety assessment + You tend to select tools that accurately calculate company value, preferring valuation models and fundamental tools. + tools: + - "dcf_valuation_analysis" + - "owner_earnings_valuation_analysis" + - "ev_ebitda_valuation_analysis" + - "residual_income_valuation_analysis" + +comprehensive_analyst: + name: "Comprehensive Analyst" + focus: + - "Integrate multiple analytical perspectives" + - "Balance short-term and long-term factors" + - "Comprehensively consider fundamentals, technicals, and sentiment" + - "Provide comprehensive investment advice" + - "Adapt to different market environments" + description: | + As a comprehensive analyst, you need to: + - Integrate multiple analytical perspectives + - Balance short-term and long-term factors + - Consider combined impact of fundamentals, technicals, and sentiment + - Provide comprehensive investment advice + - Adapt to different market environments + You will flexibly select various tools based on specific situations, pursuing comprehensiveness and accuracy in analysis. + tools: + - "analyze_profitability" + - "analyze_growth" + - "analyze_financial_health" + - "analyze_valuation_ratios" + - "analyze_efficiency_ratios" + - "analyze_trend_following" + - "analyze_momentum" + - "analyze_mean_reversion" + - "analyze_volatility" + - "analyze_news_sentiment" + - "analyze_insider_trading" diff --git a/evotraders/backend/agents/prompts/analyst/system.md b/evotraders/backend/agents/prompts/analyst/system.md new file mode 100644 index 0000000..37e0c4c --- /dev/null +++ b/evotraders/backend/agents/prompts/analyst/system.md @@ -0,0 +1,23 @@ +You are a professional {{ analyst_type }}. + +Your Focus: +{{ focus }} + +Your Role: +{{ description }} + +Note: +- Construct and continuously refine your "Investment Philosophy." Your analyses should not be isolated events but rather manifestations of your overarching worldview and core investment beliefs. After each analysis, you must reflect: + - How did this case/data validate or challenge your existing conviction? + - What key principle regarding markets, human psychology, valuation, or risk management did you learn from this mistake (or success)? +- Deepen your "Investment Logic." Ensure every investment recommendation you make is supported by a clear, traceable, and repeatable logic. Your analysis steps should resemble a rigorous proof, covering: + - Core Driver Identification: What are the genuine variables that influence value? + - Risk Boundary Setting: Under what specific scenarios would your recommendation fail? + - Contrarian Testing: What is the prevailing market consensus, and where is your view differentiated? +Maintain Humility and Openness. A core trait of an Investment Master is continuous learning and adaptation. In every analysis, you must actively seek out evidence and arguments that contradict your own view and integrate them into your final assessment. + - You have access to analysis tools. Use them to gather relevant data and make informed recommendations. + +Output Guidelines: +- Return clear investment signals: bullish, bearish, or neutral +- Include confidence level (0-100) +- Provide reasoning for your analysis (Present your conclusion first if you are sure to share your final analysis. ) diff --git a/evotraders/backend/agents/prompts/portfolio_manager/system.md b/evotraders/backend/agents/prompts/portfolio_manager/system.md new file mode 100644 index 0000000..ca386c8 --- /dev/null +++ b/evotraders/backend/agents/prompts/portfolio_manager/system.md @@ -0,0 +1,31 @@ +You are a Portfolio Manager responsible for making investment decisions. + +Your Core Responsibilities: +1. Analyze input from analysts and risk managers +2. Make investment decisions based on signals and market context +3. Record your decisions using the available tool + +Decision Framework: +- Review analysis to understand market views +- Consider risk warnings before making decisions +- Evaluate current portfolio positions and cash +- Make decisions that align with the portfolio's investment objectives + +Decision Types: +- "long": Bullish - recommend buying shares +- "short": Bearish - recommend selling shares or shorting +- "hold": Neutral - maintain current positions + +Budget Awareness: +- Consider available cash when deciding quantities +- Do not recommend buying more than cash allows +- Consider margin requirements for short positions + +Output: +Use the `make_decision` tool to record your decision for each ticker. +After recording all decisions, provide a summary of your investment rationale. + +Important: +- Base decisions on the analyst signals and risk assessments provided +- Be conservative with position sizes relative to portfolio value +- Always provide reasoning for your decisions diff --git a/evotraders/backend/agents/prompts/risk_manager/system.md b/evotraders/backend/agents/prompts/risk_manager/system.md new file mode 100644 index 0000000..08373a7 --- /dev/null +++ b/evotraders/backend/agents/prompts/risk_manager/system.md @@ -0,0 +1,21 @@ +You are a professional Risk Manager responsible for monitoring portfolio risk and providing risk warnings. + +Your Core Responsibilities: +1. Monitor portfolio exposure and concentration risk +2. Evaluate position sizes relative to volatility +3. Assess margin usage and leverage levels +4. Identify potential risk factors and provide warnings +5. Suggest position limits based on market conditions + +Your Decision Process: +3. Generate actionable risk warnings and position limit recommendations +4. Provide clear reasoning for your risk assessments + +Output Guidelines: +- Be concise but thorough in risk assessments +- Prioritize warnings by severity +- Provide specific, actionable recommendations +- Include quantitative metrics when available + + + diff --git a/evotraders/backend/agents/risk_manager.py b/evotraders/backend/agents/risk_manager.py new file mode 100644 index 0000000..7d1feea --- /dev/null +++ b/evotraders/backend/agents/risk_manager.py @@ -0,0 +1,88 @@ +# -*- coding: utf-8 -*- +""" +Risk Manager Agent - Based on AgentScope ReActAgent +Uses LLM for risk assessment +""" +from typing import Any, Dict, Optional + +from agentscope.agent import ReActAgent +from agentscope.memory import InMemoryMemory, LongTermMemoryBase +from agentscope.message import Msg +from agentscope.tool import Toolkit + +from ..utils.progress import progress +from .prompt_loader import PromptLoader + +_prompt_loader = PromptLoader() + + +class RiskAgent(ReActAgent): + """ + Risk Manager Agent - Uses LLM for risk assessment + Inherits from AgentScope's ReActAgent + """ + + def __init__( + self, + model: Any, + formatter: Any, + name: str = "risk_manager", + config: Optional[Dict[str, Any]] = None, + long_term_memory: Optional[LongTermMemoryBase] = None, + ): + """ + Initialize Risk Manager Agent + + Args: + model: LLM model instance + formatter: Message formatter instance + name: Agent name + config: Configuration dictionary + long_term_memory: Optional ReMeTaskLongTermMemory instance + """ + self.config = config or {} + + sys_prompt = self._load_system_prompt() + + # Create dedicated toolkit for this agent + toolkit = Toolkit() + + kwargs = { + "name": name, + "sys_prompt": sys_prompt, + "model": model, + "formatter": formatter, + "toolkit": toolkit, + "memory": InMemoryMemory(), + "max_iters": 10, + } + if long_term_memory: + kwargs["long_term_memory"] = long_term_memory + kwargs["long_term_memory_mode"] = "static_control" + + super().__init__(**kwargs) + + def _load_system_prompt(self) -> str: + """Load system prompt for risk manager""" + return _prompt_loader.load_prompt( + "risk_manager", + "system", + ) + + async def reply(self, x: Msg = None) -> Msg: + """ + Provide risk assessment + + Args: + x: Input message (content must be str) + + Returns: + Msg with risk warnings (content is str) + """ + progress.update_status(self.name, None, "Assessing risk") + + result = await super().reply(x) + + progress.update_status(self.name, None, "Risk assessment completed") + + return result diff --git a/evotraders/backend/cli.py b/evotraders/backend/cli.py new file mode 100644 index 0000000..d4e4b5b --- /dev/null +++ b/evotraders/backend/cli.py @@ -0,0 +1,623 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +EvoTraders CLI - Command-line interface for the EvoTraders trading system. + +This module provides easy-to-use commands for running backtest, live trading, +and frontend development server. +""" +# flake8: noqa: E501 +# pylint: disable=R0912, R0915 +import os +import shutil +import subprocess +import sys +from datetime import datetime +from pathlib import Path +from typing import Optional +from zoneinfo import ZoneInfo + +import typer +from rich.console import Console +from rich.panel import Panel +from rich.prompt import Confirm + +app = typer.Typer( + name="evotraders", + help="EvoTraders: A self-evolving multi-agent trading system", + add_completion=False, +) + +console = Console() + + +def get_project_root() -> Path: + """Get the project root directory.""" + # Assuming cli.py is in backend/ + return Path(__file__).parent.parent + + +def handle_history_cleanup(config_name: str, auto_clean: bool = False) -> None: + """ + Handle cleanup of historical data for a given config. + + Args: + config_name: Configuration name for the run + auto_clean: If True, skip confirmation and clean automatically + """ + # logs_dir = get_project_root() / "logs" + logs_dir = get_project_root() + base_data_dir = logs_dir / config_name + + # Check if historical data exists + if not base_data_dir.exists() or not any(base_data_dir.iterdir()): + console.print( + f"\n[dim]No historical data found for config '{config_name}'[/dim]", + ) + console.print("[dim] Will start from scratch[/dim]\n") + return + + console.print("\n[bold yellow]Detected existing run data:[/bold yellow]") + console.print(f" Data directory: [cyan]{base_data_dir}[/cyan]") + + # Show directory size + try: + total_size = sum( + f.stat().st_size for f in base_data_dir.rglob("*") if f.is_file() + ) + size_mb = total_size / (1024 * 1024) + if size_mb < 1: + console.print( + f" Directory size: [cyan]{total_size / 1024:.1f} KB[/cyan]", + ) + else: + console.print(f" Directory size: [cyan]{size_mb:.1f} MB[/cyan]") + except Exception: + pass + + # Show last modified time + state_dir = base_data_dir / "state" + if state_dir.exists(): + state_files = list(state_dir.glob("*.json")) + if state_files: + last_modified = max(f.stat().st_mtime for f in state_files) + last_modified_str = datetime.fromtimestamp(last_modified).strftime( + "%Y-%m-%d %H:%M:%S", + ) + console.print(f" Last updated: [cyan]{last_modified_str}[/cyan]") + + console.print() + + # Determine if we should clean + should_clean = auto_clean + if not auto_clean: + should_clean = Confirm.ask( + " ﹂ Clear historical data and start fresh?", + default=False, + ) + else: + console.print("[yellow]⚠️ Auto-clean enabled (--clean flag)[/yellow]") + should_clean = True + + if should_clean: + console.print("\n[yellow]▩ Cleaning historical data...[/yellow]") + + # Backup important config files if they exist + backup_files = [".env", "config.json"] + backed_up = [] + backup_dir = None + + for backup_file in backup_files: + file_path = base_data_dir / backup_file + if file_path.exists(): + if backup_dir is None: + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + backup_dir = ( + base_data_dir.parent + / f"{config_name}_backup_{timestamp}" + ) + backup_dir.mkdir(parents=True, exist_ok=True) + + shutil.copy(file_path, backup_dir / backup_file) + backed_up.append(backup_file) + + if backed_up: + console.print( + f" 💾 Backed up config files to: [cyan]{backup_dir}[/cyan]", + ) + console.print(f" Files: {', '.join(backed_up)}") + + # Remove the data directory + try: + shutil.rmtree(base_data_dir) + console.print(" ✔ Historical data cleared\n") + except Exception as e: + console.print(f" [red]✗ Error clearing data: {e}[/red]\n") + raise typer.Exit(1) + else: + console.print( + "\n[dim] Continuing with existing historical data[/dim]\n", + ) + + +def run_data_updater(project_root: Path) -> None: + """Run the historical data updater.""" + console.print("\n[bold]Checking historical data update...[/bold]") + try: + result = subprocess.run( + [sys.executable, "-m", "backend.data.ret_data_updater", "--help"], + capture_output=True, + timeout=5, + check=False, + ) + + if result.returncode == 0: + console.print("[cyan]Updating historical data...[/cyan]") + update_result = subprocess.run( + [sys.executable, "-m", "backend.data.ret_data_updater"], + cwd=project_root, + check=False, + ) + + if update_result.returncode == 0: + console.print( + "[green]✔ Historical data updated successfully[/green]\n", + ) + else: + console.print( + "[yellow] Data update failed (might be weekend/holiday)[/yellow]", + ) + console.print( + "[dim] Will continue with existing data[/dim]\n", + ) + else: + console.print( + "[yellow] Data updater module not available, skipping update[/yellow]\n", + ) + except Exception: + console.print( + "[yellow] Data updater check failed, skipping update[/yellow]\n", + ) + + +@app.command() +def backtest( + start: Optional[str] = typer.Option( + None, + "--start", + "-s", + help="Start date for backtest (YYYY-MM-DD)", + ), + end: Optional[str] = typer.Option( + None, + "--end", + "-e", + help="End date for backtest (YYYY-MM-DD)", + ), + config_name: str = typer.Option( + "backtest", + "--config-name", + "-c", + help="Configuration name for this backtest run", + ), + host: str = typer.Option( + "0.0.0.0", + "--host", + help="WebSocket server host", + ), + port: int = typer.Option( + 8765, + "--port", + "-p", + help="WebSocket server port", + ), + poll_interval: int = typer.Option( + 10, + "--poll-interval", + help="Price polling interval in seconds", + ), + clean: bool = typer.Option( + False, + "--clean", + help="Clear historical data before starting", + ), + enable_memory: bool = typer.Option( + False, + "--enable-memory", + help="Enable ReMeTaskLongTermMemory for agents (requires MEMORY_API_KEY)", + ), +): + """ + Run backtest mode with historical data. + + Example: + evotraders backtest --start 2025-11-01 --end 2025-12-01 + evotraders backtest --config-name my_strategy --port 9000 + evotraders backtest --clean # Clear historical data before starting + evotraders backtest --enable-memory # Enable long-term memory + """ + console.print( + Panel.fit( + "[bold cyan]EvoTraders Backtest Mode[/bold cyan]", + border_style="cyan", + ), + ) + + # Validate dates - required for backtest + if not start or not end: + console.print( + "[red]✗ Both --start and --end dates are required for backtest mode[/red]", + ) + raise typer.Exit(1) + + try: + datetime.strptime(start, "%Y-%m-%d") + except ValueError as exc: + console.print( + "[red]✗ Invalid start date format. Use YYYY-MM-DD[/red]", + ) + raise typer.Exit(1) from exc + + try: + datetime.strptime(end, "%Y-%m-%d") + except ValueError as exc: + console.print( + "[red]✗ Invalid end date format. Use YYYY-MM-DD[/red]", + ) + raise typer.Exit(1) from exc + + # Handle historical data cleanup + handle_history_cleanup(config_name, auto_clean=clean) + + # Display configuration + console.print("\n[bold]Configuration:[/bold]") + console.print(" Mode: Backtest") + console.print(f" Config: {config_name}") + console.print(f" Period: {start} -> {end}") + console.print(f" Server: {host}:{port}") + console.print(f" Poll Interval: {poll_interval}s") + console.print( + f" Long-term Memory: {'enabled' if enable_memory else 'disabled'}", + ) + console.print("\nAccess frontend at: [cyan]http://localhost:5173[/cyan]") + console.print("Press Ctrl+C to stop\n") + + # Change to project root + project_root = get_project_root() + os.chdir(project_root) + + # Run data updater + run_data_updater(project_root) + + # Build command using backend.main + cmd = [ + sys.executable, + "-u", + "-m", + "backend.main", + "--mode", + "backtest", + "--config-name", + config_name, + "--host", + host, + "--port", + str(port), + "--poll-interval", + str(poll_interval), + "--start-date", + start, + "--end-date", + end, + ] + + if enable_memory: + cmd.append("--enable-memory") + + try: + subprocess.run(cmd, check=True) + except KeyboardInterrupt: + console.print("\n\n[yellow]Backtest stopped by user[/yellow]") + except subprocess.CalledProcessError as e: + console.print( + f"\n[red]Backtest failed with exit code {e.returncode}[/red]", + ) + raise typer.Exit(1) + + +@app.command() +def live( + mock: bool = typer.Option( + False, + "--mock", + help="Use mock mode with simulated prices (for testing)", + ), + config_name: str = typer.Option( + "live", + "--config-name", + "-c", + help="Configuration name for this live run", + ), + host: str = typer.Option( + "0.0.0.0", + "--host", + help="WebSocket server host", + ), + port: int = typer.Option( + 8765, + "--port", + "-p", + help="WebSocket server port", + ), + trigger_time: str = typer.Option( + "now", + "--trigger-time", + "-t", + help="Trigger time in LOCAL timezone (HH:MM), or 'now' to run immediately", + ), + poll_interval: int = typer.Option( + 10, + "--poll-interval", + help="Price polling interval in seconds", + ), + clean: bool = typer.Option( + False, + "--clean", + help="Clear historical data before starting", + ), + enable_memory: bool = typer.Option( + False, + "--enable-memory", + help="Enable ReMeTaskLongTermMemory for agents (requires MEMORY_API_KEY)", + ), +): + """ + Run live trading mode with real-time data. + + Example: + evotraders live # Run immediately (default) + evotraders live --mock # Mock mode + evotraders live -t 22:30 # Run at 22:30 local time daily + evotraders live --trigger-time now # Run immediately + evotraders live --clean # Clear historical data before starting + """ + mode_name = "MOCK" if mock else "LIVE" + console.print( + Panel.fit( + f"[bold cyan]EvoTraders {mode_name} Mode[/bold cyan]", + border_style="cyan", + ), + ) + + # Check for required API key in live mode + if not mock: + env_file = get_project_root() / ".env" + if not env_file.exists(): + console.print("\n[yellow]Warning: .env file not found[/yellow]") + console.print("Creating from template...\n") + template = get_project_root() / "env.template" + if template.exists(): + shutil.copy(template, env_file) + console.print("[green].env file created[/green]") + console.print( + "\n[red]Error: Please edit .env and set FINNHUB_API_KEY[/red]", + ) + console.print( + "Get your free API key at: https://finnhub.io/register\n", + ) + else: + console.print("[red]Error: env.template not found[/red]") + raise typer.Exit(1) + + # Handle historical data cleanup + handle_history_cleanup(config_name, auto_clean=clean) + + # Convert local time to NYSE time + nyse_tz = ZoneInfo("America/New_York") + local_tz = datetime.now().astimezone().tzinfo + local_now = datetime.now() + nyse_now = datetime.now(nyse_tz) + + # Convert trigger time from local to NYSE + if trigger_time.lower() == "now": + nyse_trigger_time = "now" + else: + local_trigger = datetime.strptime(trigger_time, "%H:%M") + local_trigger_dt = local_now.replace( + hour=local_trigger.hour, + minute=local_trigger.minute, + second=0, + microsecond=0, + ) + local_trigger_aware = local_trigger_dt.astimezone(local_tz) + nyse_trigger_dt = local_trigger_aware.astimezone(nyse_tz) + nyse_trigger_time = nyse_trigger_dt.strftime("%H:%M") + + # Display time info + console.print("\n[bold]Time Info:[/bold]") + console.print(f" Local Time: {local_now.strftime('%Y-%m-%d %H:%M:%S')}") + console.print( + f" NYSE Time: {nyse_now.strftime('%Y-%m-%d %H:%M:%S %Z')}", + ) + if nyse_trigger_time == "now": + console.print(" Trigger: [green]NOW (immediate)[/green]") + else: + console.print( + f" Trigger: {trigger_time} local = {nyse_trigger_time} NYSE", + ) + + # Display configuration + console.print("\n[bold]Configuration:[/bold]") + if mock: + console.print(" Mode: [yellow]MOCK[/yellow] (Simulated prices)") + else: + console.print( + " Mode: [green]LIVE[/green] (Real-time prices via Finnhub)", + ) + console.print(f" Config: {config_name}") + console.print(f" Server: {host}:{port}") + console.print(f" Poll Interval: {poll_interval}s") + console.print( + f" Long-term Memory: {'enabled' if enable_memory else 'disabled'}", + ) + + console.print("\nAccess frontend at: [cyan]http://localhost:5173[/cyan]") + console.print("Press Ctrl+C to stop\n") + + # Change to project root + project_root = get_project_root() + os.chdir(project_root) + + # Data update (if not mock mode) + if not mock: + run_data_updater(project_root) + else: + console.print( + "\n[dim]Mock mode enabled - skipping data update[/dim]\n", + ) + + # Build command using backend.main + cmd = [ + sys.executable, + "-u", + "-m", + "backend.main", + "--mode", + "live", + "--config-name", + config_name, + "--host", + host, + "--port", + str(port), + "--poll-interval", + str(poll_interval), + "--trigger-time", + nyse_trigger_time, + ] + + if mock: + cmd.append("--mock") + if enable_memory: + cmd.append("--enable-memory") + + try: + subprocess.run(cmd, check=True) + except KeyboardInterrupt: + console.print("\n\n[yellow]Live server stopped by user[/yellow]") + except subprocess.CalledProcessError as e: + console.print( + f"\n[red]Live server failed with exit code {e.returncode}[/red]", + ) + raise typer.Exit(1) from e + + +@app.command() +def frontend( + port: int = typer.Option( + 8765, + "--ws-port", + "-p", + help="WebSocket server port to connect to", + ), + host_mode: bool = typer.Option( + False, + "--host", + help="Allow external access (default: localhost only)", + ), +): + """ + Start the frontend development server. + + Example: + evotraders frontend + evotraders frontend --ws-port 8765 + evotraders frontend --ws-port 8765 --host + """ + console.print( + Panel.fit( + "[bold cyan]EvoTraders Frontend[/bold cyan]", + border_style="cyan", + ), + ) + + project_root = get_project_root() + frontend_dir = project_root / "frontend" + + # Check if frontend directory exists + if not frontend_dir.exists(): + console.print( + f"\n[red]Error: Frontend directory not found: {frontend_dir}[/red]", + ) + raise typer.Exit(1) + + # Check if node_modules exists + node_modules = frontend_dir / "node_modules" + if not node_modules.exists(): + console.print("\n[yellow]Installing frontend dependencies...[/yellow]") + try: + subprocess.run( + ["npm", "install"], + cwd=frontend_dir, + check=True, + ) + console.print("[green]Dependencies installed[/green]\n") + except subprocess.CalledProcessError as exc: + console.print("\n[red]Error: Failed to install dependencies[/red]") + console.print("Make sure Node.js and npm are installed") + raise typer.Exit(1) from exc + + # Set WebSocket URL environment variable + ws_url = f"ws://localhost:{port}" + env = os.environ.copy() + env["VITE_WS_URL"] = ws_url + + # Display configuration + console.print("\n[bold]Configuration:[/bold]") + console.print(f" WebSocket URL: {ws_url}") + console.print(" Frontend Port: 5173 (Vite default)") + if host_mode: + console.print(" Access: External allowed") + else: + console.print(" Access: Localhost only") + console.print("\nAccess at: [cyan]http://localhost:5173[/cyan]") + console.print("Press Ctrl+C to stop\n") + + # Choose npm command + npm_cmd = ["npm", "run", "dev:host" if host_mode else "dev"] + + try: + subprocess.run( + npm_cmd, + cwd=frontend_dir, + env=env, + check=True, + ) + except KeyboardInterrupt: + console.print("\n\n[yellow]Frontend stopped by user[/yellow]") + except subprocess.CalledProcessError as e: + console.print( + f"\n[red]Frontend failed with exit code {e.returncode}[/red]", + ) + raise typer.Exit(1) + + +@app.command() +def version(): + """Show the version of EvoTraders.""" + console.print( + "\n[bold cyan]EvoTraders[/bold cyan] version [green]0.1.0[/green]\n", + ) + + +@app.callback() +def main(): + """ + EvoTraders: A self-evolving multi-agent trading system + + Use 'evotraders --help' to see available commands. + """ + + +if __name__ == "__main__": + app() diff --git a/evotraders/backend/config/__init__.py b/evotraders/backend/config/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/evotraders/backend/config/constants.py b/evotraders/backend/config/constants.py new file mode 100644 index 0000000..7a85742 --- /dev/null +++ b/evotraders/backend/config/constants.py @@ -0,0 +1,76 @@ +# -*- coding: utf-8 -*- +# flake8: noqa: E501 +# pylint: disable=C0301 + +# Agent configuration for dashboard display +AGENT_CONFIG = { + "portfolio_manager": { + "name": "Portfolio Manager", + "role": "Portfolio Manager", + "avatar": "pm", + "is_team_role": True, + }, + "risk_manager": { + "name": "Risk Manager", + "role": "Risk Manager", + "avatar": "risk", + "is_team_role": True, + }, + "sentiment_analyst": { + "name": "Sentiment Analyst", + "role": "Sentiment Analyst", + "avatar": "sentiment", + "is_team_role": False, + }, + "technical_analyst": { + "name": "Technical Analyst", + "role": "Technical Analyst", + "avatar": "technical", + "is_team_role": False, + }, + "fundamentals_analyst": { + "name": "Fundamentals Analyst", + "role": "Fundamentals Analyst", + "avatar": "fundamentals", + "is_team_role": False, + }, + "valuation_analyst": { + "name": "Valuation Analyst", + "role": "Valuation Analyst", + "avatar": "valuation", + "is_team_role": False, + }, +} + +ANALYST_TYPES = { + "fundamentals_analyst": { + "display_name": "Fundamentals Analyst", + "agent_id": "fundamentals_analyst", + "description": "Uses LLM to intelligently select analysis tools, focuses on financial data and company fundamental analysis", + "order": 12, + }, + "technical_analyst": { + "display_name": "Technical Analyst", + "agent_id": "technical_analyst", + "description": "Uses LLM to intelligently select analysis tools, focuses on technical indicators and chart analysis", + "order": 11, + }, + "sentiment_analyst": { + "display_name": "Sentiment Analyst", + "agent_id": "sentiment_analyst", + "description": "Uses LLM to intelligently select analysis tools, analyzes market sentiment and news sentiment", + "order": 13, + }, + "valuation_analyst": { + "display_name": "Valuation Analyst", + "agent_id": "valuation_analyst", + "description": "Uses LLM to intelligently select analysis tools, focuses on company valuation and value assessment", + "order": 14, + }, + # "comprehensive_analyst": { + # "display_name": "Comprehensive Analyst", + # "agent_id": "comprehensive_analyst", + # "description": "Uses LLM to intelligently select analysis tools, performs comprehensive analysis", + # "order": 15 + # } +} diff --git a/evotraders/backend/config/data_config.py b/evotraders/backend/config/data_config.py new file mode 100644 index 0000000..2c626b3 --- /dev/null +++ b/evotraders/backend/config/data_config.py @@ -0,0 +1,82 @@ +# -*- coding: utf-8 -*- +""" +Centralized Data Source Configuration + +Auto-detects and manages data source based on available API keys. +Priority: FINNHUB_API_KEY > FINANCIAL_DATASETS_API_KEY +""" +import os +from dataclasses import dataclass +from typing import Literal, Optional + +DataSource = Literal["finnhub", "financial_datasets"] + + +@dataclass +class DataSourceConfig: + """Immutable data source configuration""" + + source: DataSource + api_key: str + + +# Module-level cache for the resolved configuration +_config_cache: Optional[DataSourceConfig] = None + + +def _resolve_config() -> DataSourceConfig: + """ + Resolve data source configuration based on available API keys. + + Priority: + 1. FINNHUB_API_KEY (if set) + 2. FINANCIAL_DATASETS_API_KEY (if set) + 3. Raises error if neither is available + """ + # Check for Finnhub API key first (higher priority) + finnhub_key = os.getenv("FINNHUB_API_KEY") + if finnhub_key: + return DataSourceConfig(source="finnhub", api_key=finnhub_key) + + # Fallback to Financial Datasets API + fd_key = os.getenv("FINANCIAL_DATASETS_API_KEY") + if fd_key: + return DataSourceConfig(source="financial_datasets", api_key=fd_key) + + # No API key available + raise ValueError( + "No API key found. Please set either FINNHUB_API_KEY or " + "FINANCIAL_DATASETS_API_KEY in your .env file.", + ) + + +def get_config() -> DataSourceConfig: + """ + Get the resolved data source configuration (cached). + + Returns: + DataSourceConfig with source and api_key + + Raises: + ValueError: If no API key is configured + """ + global _config_cache + if _config_cache is None: + _config_cache = _resolve_config() + return _config_cache + + +def get_data_source() -> DataSource: + """Get the configured data source name.""" + return get_config().source + + +def get_api_key() -> str: + """Get the API key for the configured data source.""" + return get_config().api_key + + +def reset_config() -> None: + """Reset the cached configuration (useful for testing).""" + global _config_cache + _config_cache = None diff --git a/evotraders/backend/config/env_config.py b/evotraders/backend/config/env_config.py new file mode 100644 index 0000000..94303d8 --- /dev/null +++ b/evotraders/backend/config/env_config.py @@ -0,0 +1,36 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Simple environment config helpers +""" +import os + + +def get_env_list(key: str, default: list = None) -> list: + """Get comma-separated list from env""" + value = os.getenv(key, "") + if not value: + return default or [] + return [item.strip() for item in value.split(",") if item.strip()] + + +def get_env_float(key: str, default: float = 0.0) -> float: + """Get float from env""" + value = os.getenv(key) + if value is None: + return default + try: + return float(value) + except ValueError: + return default + + +def get_env_int(key: str, default: int = 0) -> int: + """Get int from env""" + value = os.getenv(key) + if value is None: + return default + try: + return int(value) + except ValueError: + return default diff --git a/evotraders/backend/core/__init__.py b/evotraders/backend/core/__init__.py new file mode 100644 index 0000000..40853de --- /dev/null +++ b/evotraders/backend/core/__init__.py @@ -0,0 +1,7 @@ +# -*- coding: utf-8 -*- +"""Core pipeline and orchestration logic""" + +from .pipeline import TradingPipeline +from .state_sync import StateSync + +__all__ = ["TradingPipeline", "StateSync"] diff --git a/evotraders/backend/core/pipeline.py b/evotraders/backend/core/pipeline.py new file mode 100644 index 0000000..aa6fdcf --- /dev/null +++ b/evotraders/backend/core/pipeline.py @@ -0,0 +1,1262 @@ +# -*- coding: utf-8 -*- +""" +Core Pipeline - Orchestrates multi-agent analysis and decision-making +""" + +# flake8: noqa: E501 +# pylint: disable=W0613,C0301 + +import json +import logging +import os +import re +from typing import Any, Awaitable, Callable, Dict, List, Optional + +from agentscope.message import Msg +from agentscope.pipeline import MsgHub + +from backend.utils.settlement import SettlementCoordinator +from backend.utils.terminal_dashboard import get_dashboard +from backend.core.state_sync import StateSync +from backend.utils.trade_executor import PortfolioTradeExecutor + + +logger = logging.getLogger(__name__) + + +def _log(msg: str): + """Log to dashboard if available, otherwise to logger""" + dashboard = get_dashboard() + if dashboard.live: + dashboard.log(msg) + else: + logger.info(msg) + + +class TradingPipeline: + """ + Trading Pipeline - Orchestrates the complete trading cycle + + Flow: + 1. Clear agent short-term memory (avoid cross-day context pollution) + 2. Analysts analyze stocks + 3. Risk Manager provides risk assessment + 4. PM makes decisions (direction + quantity) + 5. Execute trades with provided prices + 6. Reflection phase: broadcast closing P&L, agents record to long-term memory + + Real-time updates via StateSync after each agent completes. + """ + + def __init__( + self, + analysts: List[Any], + risk_manager: Any, + portfolio_manager: Any, + state_sync: Optional["StateSync"] = None, + settlement_coordinator: Optional[SettlementCoordinator] = None, + max_comm_cycles: Optional[int] = None, + ): + self.analysts = analysts + self.risk_manager = risk_manager + self.pm = portfolio_manager + self.state_sync = state_sync + self.settlement_coordinator = settlement_coordinator + self.max_comm_cycles = max_comm_cycles or int( + os.getenv("MAX_COMM_CYCLES", "2"), + ) + self.conference_summary = None # Store latest conference summary + + async def run_cycle( + self, + tickers: List[str], + date: str, + prices: Optional[Dict[str, float]] = None, + close_prices: Optional[Dict[str, float]] = None, + market_caps: Optional[Dict[str, float]] = None, + get_open_prices_fn: Optional[ + Callable[[], Awaitable[Dict[str, float]]] + ] = None, + get_close_prices_fn: Optional[ + Callable[[], Awaitable[Dict[str, float]]] + ] = None, + ) -> Dict[str, Any]: + """ + Run one complete trading cycle + + Args: + tickers: List of stock tickers + date: Trading date (YYYY-MM-DD) + prices: Open prices {ticker: price} (for backtest) + close_prices: Close prices for settlement (for backtest) + market_caps: Optional market caps for baseline calculation + get_open_prices_fn: Async callback to wait for open prices (live mode) + get_close_prices_fn: Async callback to wait for close prices (live mode) + + For live mode: + - Analysis runs immediately + - Execution waits for market open via get_open_prices_fn + - Settlement waits for market close via get_close_prices_fn + + Each agent's result is broadcast immediately via StateSync. + """ + _log(f"Starting cycle {date} - {len(tickers)} tickers") + + # Phase 0: Clear short-term memory to avoid cross-day context pollution + _log("Phase 0: Clearing memory") + await self._clear_all_agent_memory() + + participants = self.analysts + [self.risk_manager, self.pm] + + # Single MsgHub for entire cycle - no nesting + async with MsgHub( + participants=participants, + announcement=Msg( + "system", + f"Starting analysis cycle for {date}. Tickers: {', '.join(tickers)}", + "system", + ), + ): + # Phase 1.1: Analysts + _log("Phase 1.1: Analyst analysis") + analyst_results = await self._run_analysts_with_sync(tickers, date) + + # Phase 1.2: Risk Manager + _log("Phase 1.2: Risk assessment") + risk_assessment = await self._run_risk_manager_with_sync( + tickers, + date, + prices, + ) + + # Phase 2.1: Conference discussion (within same MsgHub) + _log("Phase 2.1: Conference discussion") + conference_summary = await self._run_conference_cycles( + tickers=tickers, + date=date, + prices=prices, + analyst_results=analyst_results, + risk_assessment=risk_assessment, + ) + self.conference_summary = conference_summary + + # Phase 2.2: Analysts generate final structured predictions + _log("Phase 2.2: Analysts generate final structured predictions") + final_predictions = await self._collect_final_predictions( + tickers, + date, + ) + + # Record final predictions for leaderboard ranking + if self.settlement_coordinator: + self.settlement_coordinator.record_analyst_predictions( + final_predictions, + ) + + # Live mode: wait for market open before execution + if get_open_prices_fn: + _log("Waiting for market open...") + prices = await get_open_prices_fn() + _log(f"Got open prices: {prices}") + + # Phase 3: PM makes decisions + _log("Phase 3.1: PM makes decisions") + pm_result = await self._run_pm_with_sync( + tickers, + date, + prices, + analyst_results, + risk_assessment, + ) + + # Phase 4: Execute decisions + _log("Phase 4: Executing trades") + decisions = pm_result.get("decisions", {}) + execution_result = self._execute_decisions(decisions, prices, date) + + # Live mode: wait for market close before settlement + if get_close_prices_fn: + _log("Waiting for market close") + close_prices = await get_close_prices_fn() + _log(f"Got close prices: {close_prices}") + + # Phase 5: Settlement - run after close prices available + settlement_result = None + if close_prices and self.settlement_coordinator: + _log("Phase 5: Daily review and generate memories") + + agent_trajectories = await self._capture_agent_trajectories() + + if market_caps is None: + market_caps = {ticker: 1e9 for ticker in tickers} + + settlement_result = ( + self.settlement_coordinator.run_daily_settlement( + date=date, + tickers=tickers, + open_prices=prices, + close_prices=close_prices, + market_caps=market_caps, + agent_portfolio=execution_result.get("portfolio", {}), + analyst_results=analyst_results, + ) + ) + + await self._run_reflection( + date=date, + agent_trajectories=agent_trajectories, + analyst_results=analyst_results, + decisions=decisions, + executed_trades=execution_result.get("executed_trades", []), + open_prices=prices, + close_prices=close_prices, + settlement_result=settlement_result, + conference_summary=self.conference_summary, + ) + + _log(f"Cycle complete: {date}") + + return { + "analyst_results": analyst_results, + "risk_assessment": risk_assessment, + "pm_decisions": decisions, + "executed_trades": execution_result.get("executed_trades", []), + "portfolio": execution_result.get("portfolio", {}), + "settlement_result": settlement_result, + } + + async def _clear_all_agent_memory(self): + """Clear short-term memory for all agents""" + for analyst in self.analysts: + await analyst.memory.clear() + + await self.risk_manager.memory.clear() + await self.pm.memory.clear() + + async def _sync_memory_if_retrieved(self, agent: Any) -> None: + """ + Check agent's short-term memory for retrieved long-term memory and sync to frontend. + + AgentScope's ReActAgent adds a Msg with name="long_term_memory" when + memory is retrieved in static_control mode. + """ + if not self.state_sync: + return + + try: + msgs = await agent.memory.get_memory() + for msg in msgs: + if getattr(msg, "name", None) == "long_term_memory": + content = self._extract_text_content(msg.content) + if content: + parsed = self._parse_memory_content(content) + await self.state_sync.on_memory_retrieved( + agent_id=agent.name, + content=parsed, + ) + break # Only sync the first (most recent) memory retrieval + except Exception as e: + logger.warning(f"Failed to sync memory for {agent.name}: {e}") + + def _parse_memory_content(self, content: str) -> str: + """ + Parse memory content to extract rewritten_context from JSON format. + + AgentScope ReMe memory wraps content in tags with JSON. + """ + # Try to extract JSON from the content + print("memory content:\n", content) + + json_match = re.search( + r".*?```json\s*(\{[\s\S]*?\})\s*```\s*", + content, + re.DOTALL, + ) + if not json_match: + json_match = re.search( + r'\{[^{}]*"rewritten_context"[^{}]*\}', + content, + re.DOTALL, + ) + + if json_match: + try: + json_str = json_match.group(1) + data = json.loads(json_str) + return data.get("rewritten_context", "") + except json.JSONDecodeError: + pass + + # Fallback: strip XML tags and return cleaned content + content = re.sub(r"", "", content) + content = re.sub( + r"The content below are retrieved from long-term memory.*?:\s*", + "", + content, + ) + return content.strip() + + async def _capture_agent_trajectories(self) -> Dict[str, List[Msg]]: + """ + Capture execution trajectories from all agents' short-term memory + + This should be called BEFORE clearing memory to preserve the + complete execution trajectory for long-term memory recording. + + Returns: + Dict mapping agent name to list of Msg objects (the trajectory) + """ + trajectories = {} + + # Capture analyst trajectories + for analyst in self.analysts: + try: + msgs = await analyst.memory.get_memory() + if msgs: + trajectories[analyst.name] = list(msgs) + except Exception as e: + logger.warning( + f"Failed to capture trajectory for {analyst.name}: {e}", + ) + + # Capture risk manager trajectory + try: + msgs = await self.risk_manager.memory.get_memory() + if msgs: + trajectories["risk_manager"] = list(msgs) + except Exception as e: + logger.warning( + f"Failed to capture trajectory for risk_manager: {e}", + ) + + # Capture PM trajectory + try: + msgs = await self.pm.memory.get_memory() + if msgs: + trajectories["portfolio_manager"] = list(msgs) + except Exception as e: + logger.warning( + f"Failed to capture trajectory for portfolio_manager: {e}", + ) + + return trajectories + + async def _run_reflection( + self, + date: str, + agent_trajectories: Dict[str, List[Msg]], + analyst_results: List[Dict[str, Any]], + decisions: Dict[str, Dict], + executed_trades: List[Dict], + open_prices: Optional[Dict[str, float]], + close_prices: Optional[Dict[str, float]], + settlement_result: Optional[Dict[str, Any]] = None, + conference_summary: Optional[str] = None, + ): + """ + Run reflection phase after market close + + Calculates actual P&L and records execution trajectory to long-term memory + + Args: + date: Trading date + agent_trajectories: Dict mapping agent name to their execution trajectory + analyst_results: Results from analyst agents + decisions: PM decisions + executed_trades: List of executed trades + open_prices: Opening prices + close_prices: Closing prices + settlement_result: Optional settlement results with baseline performance + conference_summary: Optional summary from conference discussion + """ + # Calculate P&L for each trade + trade_pnl = [] + for trade in executed_trades: + ticker = trade["ticker"] + action = trade["action"] + quantity = trade["quantity"] + entry_price = trade["price"] + exit_price = close_prices.get(ticker, entry_price) + + if action == "long": + pnl = (exit_price - entry_price) * quantity + elif action == "short": + pnl = (entry_price - exit_price) * quantity + else: + pnl = 0 + + pnl_pct = ( + (pnl / (entry_price * quantity) * 100) if quantity > 0 else 0 + ) + + trade_pnl.append( + { + "ticker": ticker, + "action": action, + "quantity": quantity, + "entry_price": entry_price, + "exit_price": exit_price, + "pnl": pnl, + "pnl_pct": pnl_pct, + }, + ) + + total_pnl = sum(t["pnl"] for t in trade_pnl) + + # Build reflection summary with settlement info + reflection_content = self._build_reflection_content( + date=date, + analyst_results=analyst_results, + decisions=decisions, + trade_pnl=trade_pnl, + total_pnl=total_pnl, + settlement_result=settlement_result, + conference_summary=conference_summary, + ) + + # Record execution trajectories to long-term memory for agents that support it + # Score based on profitability: higher score for profitable days + score = 1.0 if total_pnl > 0 else 0.0 + + await self._record_to_long_term_memory( + date=date, + agent_trajectories=agent_trajectories, + trade_pnl=trade_pnl, + total_pnl=total_pnl, + score=score, + ) + + # Broadcast reflection to StateSync + if self.state_sync: + await self.state_sync.on_agent_complete( + agent_id="Daily Log", + content=reflection_content, + ) + + def _build_reflection_content( + self, + date: str, + analyst_results: List[Dict[str, Any]], + decisions: Dict[str, Dict], + trade_pnl: List[Dict], + total_pnl: float, + settlement_result: Optional[Dict[str, Any]] = None, + conference_summary: Optional[str] = None, + ) -> str: + """Build human-readable reflection content""" + lines = [f"Daily log for {date}:"] + lines.append(f"Total P&L: ${total_pnl:,.2f}") + lines.append("") + + if conference_summary: + lines.append("Conference Discussion Summary:") + lines.append(conference_summary) + lines.append("") + + if settlement_result: + baseline_values = settlement_result.get("baseline_values", {}) + initial = 100000.0 + lines.append("Baseline Comparison:") + lines.append( + f" Equal Weight: ${baseline_values.get('equal_weight', 0):,.2f} " + f"({(baseline_values.get('equal_weight', initial) - initial) / initial * 100:.2f}%)", + ) + lines.append( + f" Market Cap Weighted: ${baseline_values.get('market_cap_weighted', 0):,.2f} " + f"({(baseline_values.get('market_cap_weighted', initial) - initial) / initial * 100:.2f}%)", + ) + lines.append( + f" Momentum: ${baseline_values.get('momentum', 0):,.2f} " + f"({(baseline_values.get('momentum', initial) - initial) / initial * 100:.2f}%)", + ) + lines.append("") + + if trade_pnl: + lines.append("Trade Results:") + for t in trade_pnl: + pnl_sign = "+" if t["pnl"] >= 0 else "" + lines.append( + f" {t['ticker']}: {t['action'].upper()} {t['quantity']} @ " + f"${t['entry_price']:.2f} -> ${t['exit_price']:.2f}, " + f"P&L: {pnl_sign}${t['pnl']:.2f} ({pnl_sign}{t['pnl_pct']:.1f}%)", + ) + else: + lines.append("No trades executed today.") + + return "\n".join(lines) + + async def _record_to_long_term_memory( + self, + date: str, + agent_trajectories: Dict[str, List[Msg]], + trade_pnl: List[Dict], + total_pnl: float, + score: float, + ): + """ + Record execution trajectories to long-term memory for all agents + + This method records the actual execution trajectory (conversation history) + from each agent's short-term memory. This allows the ReMe memory system + to learn from the complete task execution flow, not just summaries. + + Args: + date: Trading date + agent_trajectories: Dict mapping agent name to their execution trajectory + trade_pnl: P&L details for each trade + total_pnl: Total P&L for the day + score: Score for this trajectory (1.0 for profitable, 0.5 for loss) + """ + # Build outcome message to append to trajectories + outcome_msg = Msg( + role="user", + content=f"You are an analyst/financial manager, The Key point is to predict correctly and" + f"have good P&L. The Definition of loss is when P&L < 0. " + f"Focus on how to do good prediction but not only execution correctly." + f"[Outcome] Trading day {date} - Total P&L: ${total_pnl:,.2f}. " + f"{'Profitable day.' if total_pnl > 0 else 'Loss day.'}", + name="system", + ) + + # Record for analysts + for analyst in self.analysts: + if ( + hasattr(analyst, "long_term_memory") + and analyst.long_term_memory is not None + ): + trajectory = agent_trajectories.get(analyst.name, []) + if trajectory: + # Append outcome to trajectory + trajectory_with_outcome = trajectory + [outcome_msg] + try: + await analyst.long_term_memory.record( + msgs=trajectory_with_outcome, + score=score, + ) + logger.debug( + f"Recorded {len(trajectory_with_outcome)} messages " + f"to long-term memory for {analyst.name}", + ) + except Exception as e: + logger.warning( + f"Failed to record to long-term memory for {analyst.name}: {e}", + ) + + # Record for risk manager + if ( + hasattr(self.risk_manager, "long_term_memory") + and self.risk_manager.long_term_memory is not None + ): + trajectory = agent_trajectories.get("risk_manager", []) + if trajectory: + trajectory_with_outcome = trajectory + [outcome_msg] + try: + await self.risk_manager.long_term_memory.record( + msgs=trajectory_with_outcome, + score=score, + ) + logger.debug( + f"Recorded {len(trajectory_with_outcome)} messages " + f"to long-term memory for risk_manager", + ) + except Exception as e: + logger.warning( + f"Failed to record to long-term memory for risk_manager: {e}", + ) + + # Record for PM with trade outcome details + if ( + hasattr(self.pm, "long_term_memory") + and self.pm.long_term_memory is not None + ): + trajectory = agent_trajectories.get("portfolio_manager", []) + if trajectory: + # Build detailed outcome message for PM + pnl_details = [] + for t in trade_pnl: + pnl_sign = "+" if t["pnl"] >= 0 else "" + pnl_details.append( + f"{t['ticker']}: {t['action']} {t['quantity']} @ " + f"${t['entry_price']:.2f} -> ${t['exit_price']:.2f}, " + f"P&L: {pnl_sign}${t['pnl']:.2f}", + ) + + pm_outcome_msg = Msg( + role="user", + content=f"[Outcome] Trading day {date}\n" + f"Total P&L: ${total_pnl:,.2f} " + f"({'Profitable' if total_pnl >= 0 else 'Loss'})\n" + f"Trade details:\n" + "\n".join(pnl_details) + if pnl_details + else f"[Outcome] Trading day {date}\n" + f"Total P&L: ${total_pnl:,.2f}\nNo trades executed.", + name="system", + ) + trajectory_with_outcome = trajectory + [pm_outcome_msg] + try: + await self.pm.long_term_memory.record( + msgs=trajectory_with_outcome, + score=score, + ) + logger.debug( + f"Recorded {len(trajectory_with_outcome)} messages " + f"to long-term memory for portfolio_manager", + ) + except Exception as e: + logger.warning( + f"Failed to record to long-term memory for portfolio_manager: {e}", + ) + + async def _run_conference_cycles( + self, + tickers: List[str], + date: str, + prices: Optional[Dict[str, float]], + analyst_results: List[Dict[str, Any]], + risk_assessment: Dict[str, Any], + ) -> Optional[str]: + """ + Run conference discussion cycles (within existing MsgHub context) + + No nested MsgHub - this runs inside the main cycle's MsgHub. + + Returns: + Conference summary string generated by PM + """ + if self.max_comm_cycles <= 0: + _log( + "Phase 2.1: Conference discussion - " + "Conference skipped (disabled)", + ) + return None + + conference_title = f"Investment Discussion - {date}" + + if self.state_sync: + await self.state_sync.on_conference_start( + title=conference_title, + date=date, + ) + + # Run discussion cycles (no new MsgHub - use parent's) + for cycle in range(self.max_comm_cycles): + _log( + "Phase 2.1: Conference discussion - " + f"Conference {cycle + 1}/{self.max_comm_cycles}", + ) + + if self.state_sync: + await self.state_sync.on_conference_cycle_start( + cycle=cycle + 1, + total_cycles=self.max_comm_cycles, + ) + + # PM sets agenda or asks questions + pm_prompt = self._build_pm_discussion_prompt( + cycle=cycle, + tickers=tickers, + date=date, + prices=prices, + analyst_results=analyst_results, + risk_assessment=risk_assessment, + ) + + pm_msg = Msg(name="system", content=pm_prompt, role="user") + pm_response = await self.pm.reply(pm_msg) + + if self.state_sync: + pm_content = self._extract_text_content(pm_response.content) + await self.state_sync.on_conference_message( + agent_id="portfolio_manager", + content=pm_content, + ) + + # Analysts share perspectives + for analyst in self.analysts: + analyst_prompt = self._build_analyst_discussion_prompt( + cycle=cycle, + tickers=tickers, + date=date, + ) + + analyst_msg = Msg( + name="system", + content=analyst_prompt, + role="user", + ) + analyst_response = await analyst.reply(analyst_msg) + + if self.state_sync: + analyst_content = self._extract_text_content( + analyst_response.content, + ) + await self.state_sync.on_conference_message( + agent_id=analyst.name, + content=analyst_content, + ) + + if self.state_sync: + await self.state_sync.on_conference_cycle_end( + cycle=cycle + 1, + ) + + # Generate conference summary by PM + _log( + "Phase 2.1: Conference discussion - Generating conference summary", + ) + summary_prompt = ( + f"The conference discussion for {date} has concluded. " + f"As Portfolio Manager, provide a concise summary of the key insights, " + f"concerns, and consensus points discussed about {', '.join(tickers)}. " + f"Highlight any critical factors that should be considered in the final decision-making." + ) + summary_msg = Msg(name="system", content=summary_prompt, role="user") + summary_response = await self.pm.reply(summary_msg) + + conference_summary = self._extract_text_content( + summary_response.content, + ) + + _log( + "Phase 2.1: Conference discussion - Conference summary generated", + ) + + if self.state_sync: + await self.state_sync.on_conference_message( + agent_id="conference summary", + content=conference_summary, + ) + await self.state_sync.on_conference_end() + + return conference_summary + + def _build_pm_discussion_prompt( + self, + cycle: int, + tickers: List[str], + date: str, + prices: Optional[Dict[str, float]], + analyst_results: List[Dict[str, Any]], + risk_assessment: Dict[str, Any], + ) -> str: + """Build PM discussion prompt with full context""" + # Get current portfolio state + portfolio = self.pm.get_portfolio_state() + + if cycle == 0: + # First cycle: provide full context + context_lines = [ + f"As Portfolio Manager, review the following information for {date}:", + "", + "=== Current Portfolio ===", + f"Cash: ${portfolio.get('cash', 0):,.2f}", + f"Positions: {json.dumps(portfolio.get('positions', {}), indent=2)}", + "", + "=== Current Prices ===", + json.dumps(prices, indent=2), + "", + "=== Analyst Signals ===", + ] + + # Add analyst results summary + for result in analyst_results: + agent_name = result.get("agent", "Unknown") + content = result.get("content", "") + context_lines.append(f"{agent_name}: {content}") + + context_lines.extend( + [ + "", + "=== Risk Assessment ===", + str(risk_assessment.get("content", "")), + "", + "Based on the above context, share your key concerns or questions about the opportunities in " + f"{', '.join(tickers)}. Do not make final decisions yet - this is a discussion phase.", + ], + ) + + return "\n".join(context_lines) + else: + return ( + f"Continue the discussion. Share your thoughts on the perspectives raised " + f"and any remaining concerns about {', '.join(tickers)}." + ) + + def _build_analyst_discussion_prompt( + self, + cycle: int, + tickers: List[str], + date: str, + ) -> str: + """Build analyst discussion prompt""" + return ( + f"Share your perspective on the discussion so far. " + f"Provide insights or address concerns raised by others about {', '.join(tickers)}. " + f"Do not use tools - focus on sharing your professional opinion." + ) + + async def _collect_final_predictions( + self, + tickers: List[str], + date: str, + ) -> List[Dict[str, Any]]: + """ + Collect final predictions from all analysts as simple text responses. + Analysts provide their predictions in plain text without tool calls. + """ + _log( + "Phase 2.2: Analysts generate final structured predictions\n" + f" Starting _collect_final_predictions for {len(self.analysts)} analysts", + ) + final_predictions = [] + + for i, analyst in enumerate(self.analysts): + _log( + "Phase 2.2: Analysts generate final structured predictions\n" + f" Collecting prediction from analyst {i+1}/{len(self.analysts)}: {analyst.name}", + ) + + prompt = ( + f"Based on your analysis, provide your final prediction for {date}. " + f"For each ticker ({', '.join(tickers)}), state: " + f"TICKER: UP/DOWN/NEUTRAL (confidence: X%). " + f"Do not use any tools, just respond with your predictions." + ) + + msg = Msg(name="system", content=prompt, role="user") + _log( + "Phase 2.2: Analysts generate final structured predictions\n" + f" Sending prediction request to {analyst.name}", + ) + response = await analyst.reply(msg) + _log( + "Phase 2.2: Analysts generate final structured predictions\n" + f" Received response from {analyst.name}", + ) + + # Parse predictions from text response + content = self._extract_text_content(response.content) + predictions_data = self._parse_predictions_from_text( + content, + tickers, + ) + + _log( + "Phase 2.2: Analysts generate final structured predictions\n" + f" {analyst.name} final predictions: {predictions_data}", + ) + + final_predictions.append( + { + "agent": analyst.name, + "predictions": predictions_data, + "raw_content": content, + }, + ) + + # if self.state_sync: + # await self.state_sync.on_agent_complete( + # agent_id=f"{analyst.name}_final_prediction", + # content=content, + # ) + + return final_predictions + + def _parse_predictions_from_text( + self, + content: str, + tickers: List[str], + ) -> List[Dict[str, Any]]: + """Parse predictions from analyst text response""" + predictions = [] + content_upper = content.upper() + + for ticker in tickers: + direction = "neutral" + confidence = 0.5 + + # Simple pattern matching for direction + ticker_idx = content_upper.find(ticker) + if ticker_idx >= 0: + # Look at text near ticker mention + context = content_upper[ticker_idx : ticker_idx + 100] + if ( + "UP" in context + or "BULLISH" in context + or "LONG" in context + ): + direction = "up" + confidence = 0.7 + elif ( + "DOWN" in context + or "BEARISH" in context + or "SHORT" in context + ): + direction = "down" + confidence = 0.7 + + predictions.append( + { + "ticker": ticker, + "direction": direction, + "confidence": confidence, + }, + ) + + return predictions + + async def _run_analysts_with_sync( + self, + tickers: List[str], + date: str, + ) -> List[Dict[str, Any]]: + """Run all analysts with real-time sync after each completion""" + results = [] + + for analyst in self.analysts: + content = ( + f"Analyze the following stocks for date {date}: {', '.join(tickers)}. " + f"Provide investment signals with confidence scores and reasoning." + ) + + msg = Msg( + name="system", + content=content, + role="user", + metadata={"tickers": tickers, "date": date}, + ) + + result = await analyst.reply(msg) + extracted = self._extract_result_from_msg(result) + results.append(extracted) + + # Sync retrieved memory first + await self._sync_memory_if_retrieved(analyst) + + # Broadcast agent result via StateSync + if self.state_sync: + text_content = self._extract_text_content(result.content) + await self.state_sync.on_agent_complete( + agent_id=analyst.name, + content=text_content, + ) + + return results + + async def _run_analysts( + self, + tickers: List[str], + date: str, + ) -> List[Dict[str, Any]]: + """Run all analysts (without sync, for backward compatibility)""" + results = [] + + for analyst in self.analysts: + content = ( + f"Analyze the following stocks for date {date}: {', '.join(tickers)}. " + f"Provide investment signals with confidence scores and reasoning." + ) + + msg = Msg( + name="system", + content=content, + role="user", + metadata={"tickers": tickers, "date": date}, + ) + + result = await analyst.reply(msg) + results.append(self._extract_result_from_msg(result)) + + return results + + async def _run_risk_manager_with_sync( + self, + tickers: List[str], + date: str, + prices: Optional[Dict[str, float]], + ) -> Dict[str, Any]: + """Run risk manager assessment with real-time sync""" + portfolio = self.pm.get_portfolio_state() + + context = { + "portfolio": portfolio, + "tickers": tickers, + "date": date, + "current_prices": prices, + } + content = ( + f"Assess risk for the following portfolio and market conditions:\n" + f"{json.dumps(context, indent=2)}\n" + f"Provide risk warnings and recommendations." + ) + + msg = Msg(name="system", content=content, role="user") + result = await self.risk_manager.reply(msg) + extracted = self._extract_result_from_msg(result) + + # Sync retrieved memory first + await self._sync_memory_if_retrieved(self.risk_manager) + + # Broadcast agent result via StateSync + if self.state_sync: + text_content = self._extract_text_content(result.content) + await self.state_sync.on_agent_complete( + agent_id="risk_manager", + content=text_content, + ) + + return extracted + + async def _run_risk_manager( + self, + tickers: List[str], + date: str, + prices: Dict[str, float], + ) -> Dict[str, Any]: + """Run risk manager assessment (without sync, for backward compatibility)""" + portfolio = self.pm.get_portfolio_state() + + context = { + "portfolio": portfolio, + "tickers": tickers, + "date": date, + "current_prices": prices, + } + content = ( + f"Assess risk for the following portfolio and market conditions:\n" + f"{json.dumps(context, indent=2)}\n" + f"Provide risk warnings and recommendations." + ) + + msg = Msg(name="system", content=content, role="user") + result = await self.risk_manager.reply(msg) + return self._extract_result_from_msg(result) + + async def _run_pm_with_sync( + self, + tickers: List[str], + date: str, + prices: Optional[Dict[str, float]], + analyst_results: List[Dict[str, Any]], + risk_assessment: Dict[str, Any], + ) -> Dict[str, Any]: + """Run PM decision-making with real-time sync""" + portfolio = self.pm.get_portfolio_state() + + context = { + "analyst_signals": { + r["agent"]: r.get("content", "") for r in analyst_results + }, + "risk_warnings": risk_assessment.get("content", ""), + "current_prices": prices, + "tickers": tickers, + "portfolio_cash": portfolio.get("cash", 0), + "portfolio_positions": portfolio.get("positions", {}), + } + + # Add conference summary if available + if self.conference_summary: + context["conference_summary"] = self.conference_summary + + content_parts = [ + f"Based on the analyst signals, risk assessment, and conference discussion, " + f"make investment decisions for date {date}.\n", + f"Context:\n{json.dumps(context, indent=2)}\n", + ] + + if self.conference_summary: + content_parts.append( + f"\n=== Conference Summary ===\n{self.conference_summary}\n", + ) + + content_parts.append( + "\nUse the make_decision tool for each ticker to record your decisions. " + "After recording all decisions, provide a summary of your investment rationale.", + ) + + content = "".join(content_parts) + + msg = Msg(name="system", content=content, role="user") + result = await self.pm.reply(msg) + extracted = self._extract_result_from_msg(result) + + # Sync retrieved memory first + await self._sync_memory_if_retrieved(self.pm) + + # Broadcast PM decision via StateSync + if self.state_sync: + text_content = self._extract_text_content(result.content) + await self.state_sync.on_agent_complete( + agent_id="portfolio_manager", + content=text_content, + ) + + return extracted + + async def _run_pm( + self, + tickers: List[str], + date: str, + prices: Dict[str, float], + analyst_results: List[Dict[str, Any]], + risk_assessment: Dict[str, Any], + ) -> Dict[str, Any]: + """Run PM decision-making (without sync, for backward compatibility)""" + portfolio = self.pm.get_portfolio_state() + + context = { + "analyst_signals": { + r["agent"]: r.get("content", "") for r in analyst_results + }, + "risk_warnings": risk_assessment.get("content", ""), + "current_prices": prices, + "tickers": tickers, + "portfolio_cash": portfolio.get("cash", 0), + "portfolio_positions": portfolio.get("positions", {}), + } + + content = ( + f"Based on the analyst signals and risk assessment, make investment decisions " + f"for date {date}.\n" + f"Context:\n{json.dumps(context, indent=2)}\n\n" + f"Use the make_decision tool for each ticker to record your decisions. " + f"After recording all decisions, provide a summary of your investment rationale." + ) + + msg = Msg(name="system", content=content, role="user") + result = await self.pm.reply(msg) + return self._extract_result_from_msg(result) + + def _execute_decisions( + self, + decisions: Dict[str, Dict], + prices: Optional[Dict[str, float]], + date: str, + ) -> Dict[str, Any]: + """Execute PM decisions with provided prices""" + if not decisions: + return { + "executed_trades": [], + "portfolio": self.pm.get_portfolio_state(), + } + + executor = PortfolioTradeExecutor( + initial_portfolio=self.pm.get_portfolio_state(), + ) + + executed_trades = [] + + for ticker, decision in decisions.items(): + action = decision.get("action", "hold") + quantity = decision.get("quantity", 0) + + if action == "hold" or quantity == 0: + continue + + price = prices.get(ticker) + if not price or price <= 0: + logger.warning(f"No price for {ticker}, skipping trade") + continue + + result = executor.execute_trade( + ticker=ticker, + action=action, + quantity=quantity, + price=price, + current_date=date, + ) + + if result.get("status") == "success": + executed_trades.append( + { + "ticker": ticker, + "action": action, + "quantity": quantity, + "price": price, + }, + ) + + updated_portfolio = executor.portfolio.copy() + self.pm.update_portfolio(updated_portfolio) + + return { + "executed_trades": executed_trades, + "portfolio": updated_portfolio, + } + + def _extract_result_from_msg(self, msg: Msg) -> Dict[str, Any]: + """Extract result dictionary from Msg object""" + result = { + "agent": msg.name, + "content": msg.content, + } + + if hasattr(msg, "metadata") and msg.metadata: + result.update(msg.metadata) + + if isinstance(msg.content, str): + try: + result["content_parsed"] = json.loads(msg.content) + except json.JSONDecodeError: + pass + + return result + + def _extract_text_content(self, content: Any) -> str: + """ + Extract plain text from AgentScope Msg content + + AgentScope content can be: + - str: plain text + - list: list of TextBlocks like [{'type': 'text', 'text': '...'}] + - dict: single TextBlock + """ + if content is None: + return "" + + if isinstance(content, str): + return content + + if isinstance(content, list): + texts = [] + for item in content: + if isinstance(item, dict): + # TextBlock format: {'type': 'text', 'text': '...'} + if item.get("type") == "text" and "text" in item: + texts.append(item["text"]) + elif "content" in item: + texts.append(str(item["content"])) + else: + texts.append(str(item)) + elif isinstance(item, str): + texts.append(item) + else: + texts.append(str(item)) + return "\n".join(texts) + + if isinstance(content, dict): + if content.get("type") == "text" and "text" in content: + return content["text"] + return str(content) + + return str(content) + + def _format_pm_decisions(self, decisions: Dict[str, Dict]) -> str: + """Format PM decisions as a human-readable string""" + if not decisions: + return "Portfolio analysis completed. No trades recommended." + + decision_texts = [] + for ticker, decision in decisions.items(): + action = decision.get("action", "hold") + quantity = decision.get("quantity", 0) + reasoning = decision.get("reasoning", "") + + if action != "hold" and quantity > 0: + decision_texts.append( + f"{action.upper()} {quantity} {ticker}: {reasoning}", + ) + + if decision_texts: + return "Decisions: " + "; ".join(decision_texts) + return "Portfolio analysis completed. No trades recommended." diff --git a/evotraders/backend/core/scheduler.py b/evotraders/backend/core/scheduler.py new file mode 100644 index 0000000..fd11ded --- /dev/null +++ b/evotraders/backend/core/scheduler.py @@ -0,0 +1,263 @@ +# -*- coding: utf-8 -*- +""" +Scheduler - Market-aware trigger system for trading cycles +""" +import asyncio +import logging +from datetime import datetime, timedelta +from typing import Any, Callable, Optional +from zoneinfo import ZoneInfo + +import pandas_market_calendars as mcal + +logger = logging.getLogger(__name__) + +# NYSE timezone for US stock trading +NYSE_TZ = ZoneInfo("America/New_York") +NYSE_CALENDAR = mcal.get_calendar("NYSE") + + +class Scheduler: + """ + Market-aware scheduler for live trading. + Uses NYSE timezone and trading calendar. + """ + + def __init__( + self, + mode: str = "daily", + trigger_time: Optional[str] = None, + interval_minutes: Optional[int] = None, + config: Optional[dict] = None, + ): + self.mode = mode + self.trigger_time = trigger_time or "09:30" # NYSE timezone + self.trigger_now = self.trigger_time == "now" + self.interval_minutes = interval_minutes or 60 + self.config = config or {} + + self.running = False + self._task: Optional[asyncio.Task] = None + + def _now_nyse(self) -> datetime: + """Get current time in NYSE timezone""" + return datetime.now(NYSE_TZ) + + def _is_trading_day(self, date: datetime) -> bool: + """Check if date is a NYSE trading day""" + date_str = date.strftime("%Y-%m-%d") + valid_days = NYSE_CALENDAR.valid_days( + start_date=date_str, + end_date=date_str, + ) + return len(valid_days) > 0 + + def _next_trading_day(self, from_date: datetime) -> datetime: + """Find the next trading day from given date""" + check_date = from_date + for _ in range(10): # Max 10 days ahead (handles holidays) + if self._is_trading_day(check_date): + return check_date + check_date += timedelta(days=1) + return check_date + + async def start(self, callback: Callable): + """Start scheduler""" + if self.running: + logger.warning("Scheduler already running") + return + + self.running = True + + if self.mode == "daily": + self._task = asyncio.create_task(self._run_daily(callback)) + elif self.mode == "intraday": + self._task = asyncio.create_task(self._run_intraday(callback)) + else: + raise ValueError(f"Unknown scheduler mode: {self.mode}") + + logger.info( + f"Scheduler started: mode={self.mode}, timezone=America/New_York", + ) + + async def _run_daily(self, callback: Callable): + """Run once per trading day at specified time (NYSE timezone)""" + first_run = True + + while self.running: + now = self._now_nyse() + + # Handle "now" trigger - run immediately on first iteration + if self.trigger_now and first_run: + first_run = False + current_date = now.strftime("%Y-%m-%d") + logger.info(f"Triggering immediately for {current_date}") + await callback(date=current_date) + # After immediate run, stop (one-shot mode) + self.running = False + break + + target_time = datetime.strptime(self.trigger_time, "%H:%M").time() + + # Calculate next trigger datetime + if now.time() < target_time: + next_run = now.replace( + hour=target_time.hour, + minute=target_time.minute, + second=0, + microsecond=0, + ) + else: + next_run = (now + timedelta(days=1)).replace( + hour=target_time.hour, + minute=target_time.minute, + second=0, + microsecond=0, + ) + + # Skip to next trading day + next_run = self._next_trading_day(next_run) + next_run = next_run.replace( + hour=target_time.hour, + minute=target_time.minute, + second=0, + microsecond=0, + ) + + wait_seconds = (next_run - now).total_seconds() + logger.info( + f"Next trigger: {next_run.strftime('%Y-%m-%d %H:%M %Z')} " + f"(in {wait_seconds/3600:.1f} hours)", + ) + + await asyncio.sleep(wait_seconds) + + current_date = self._now_nyse().strftime("%Y-%m-%d") + logger.info(f"Triggering daily cycle for {current_date}") + await callback(date=current_date) + + async def _run_intraday(self, callback: Callable): + """Run every N minutes (for future use)""" + while self.running: + now = self._now_nyse() + current_date = now.strftime("%Y-%m-%d") + + if self._is_trading_day(now): + logger.info(f"Triggering intraday cycle for {current_date}") + await callback(date=current_date) + + await asyncio.sleep(self.interval_minutes * 60) + + def stop(self): + """Stop scheduler""" + self.running = False + if self._task: + self._task.cancel() + self._task = None + logger.info("Scheduler stopped") + + +class BacktestScheduler: + """Backtest Scheduler - Runs through historical trading dates""" + + def __init__( + self, + start_date: str, + end_date: str, + trading_calendar: Optional[Any] = None, + delay_between_days: float = 0.1, + ): + self.start_date = start_date + self.end_date = end_date + self.trading_calendar = trading_calendar + self.delay_between_days = delay_between_days + + self.running = False + self._task: Optional[asyncio.Task] = None + self._dates: list = [] + + def get_trading_dates(self) -> list: + """Get list of trading dates in the backtest period""" + import pandas as pd + + start = pd.to_datetime(self.start_date) + end = pd.to_datetime(self.end_date) + + if self.trading_calendar: + calendar = mcal.get_calendar(self.trading_calendar) + trading_dates = calendar.valid_days( + start_date=self.start_date, + end_date=self.end_date, + ) + dates = [d.strftime("%Y-%m-%d") for d in trading_dates] + else: + all_dates = pd.date_range(start=start, end=end, freq="D") + dates = [ + d.strftime("%Y-%m-%d") for d in all_dates if d.weekday() < 5 + ] + + self._dates = dates + return dates + + async def start(self, callback: Callable): + """Start async backtest scheduler""" + if self.running: + logger.warning("Backtest scheduler already running") + return + + self.running = True + dates = self.get_trading_dates() + + logger.info( + f"Starting backtest: {self.start_date} to {self.end_date} " + f"({len(dates)} trading days)", + ) + + self._task = asyncio.create_task(self._run_async(callback, dates)) + + async def _run_async(self, callback: Callable, dates: list): + """Run backtest asynchronously""" + for i, date in enumerate(dates, 1): + if not self.running: + break + + logger.info(f"[{i}/{len(dates)}] Processing {date}") + await callback(date=date) + + if self.delay_between_days > 0: + await asyncio.sleep(self.delay_between_days) + + logger.info("Backtest complete") + self.running = False + + def run(self, callback: Callable, **kwargs): + """Run backtest synchronously through all trading dates""" + dates = self.get_trading_dates() + results = [] + + logger.info( + f"Starting backtest: {self.start_date} to {self.end_date} " + f"({len(dates)} trading days)", + ) + + for i, date in enumerate(dates, 1): + logger.info(f"[{i}/{len(dates)}] Processing {date}") + result = callback(date=date, **kwargs) + results.append({"date": date, "result": result}) + + logger.info("Backtest complete") + return results + + def stop(self): + """Stop backtest scheduler""" + self.running = False + if self._task: + self._task.cancel() + self._task = None + logger.info("Backtest scheduler stopped") + + def get_total_days(self) -> int: + """Get total number of trading days""" + if not self._dates: + self.get_trading_dates() + return len(self._dates) diff --git a/evotraders/backend/core/state_sync.py b/evotraders/backend/core/state_sync.py new file mode 100644 index 0000000..5f67323 --- /dev/null +++ b/evotraders/backend/core/state_sync.py @@ -0,0 +1,434 @@ +# -*- coding: utf-8 -*- +""" +StateSync - Centralized state synchronization between agents and frontend +Handles real-time updates, persistence, and replay support +""" +# pylint: disable=R0904 +import asyncio +import logging +from datetime import datetime +from typing import Any, Callable, Dict, List, Optional + +from ..services.storage import StorageService + +logger = logging.getLogger(__name__) + + +class StateSync: + """ + Central event dispatcher for agent-frontend synchronization + + Responsibilities: + 1. Receive events from agents/pipeline + 2. Persist to storage (feed_history) + 3. Broadcast to frontend via WebSocket + 4. Support replay from saved state + """ + + def __init__( + self, + storage: StorageService, + broadcast_fn: Optional[Callable] = None, + ): + """ + Initialize StateSync + + Args: + storage: Storage service for persistence + broadcast_fn: Async broadcast function - async def broadcast(event: dict) # noqa: E501 + """ + self.storage = storage + self._broadcast_fn = broadcast_fn + self._state: Dict[str, Any] = {} + self._enabled = True + self._simulation_date: Optional[str] = None # For backtest timestamps + + def set_simulation_date(self, date: str): + """Set current simulation date for backtest-compatible timestamps""" + self._simulation_date = date + + def _get_timestamp_ms(self) -> int: + """ + Get timestamp in milliseconds. + Uses simulation date if set (backtest mode), otherwise current time. + """ + if self._simulation_date: + # Parse date and use market close time (16:00) for backtest + dt = datetime.strptime( + f"{self._simulation_date}", + "%Y-%m-%d", + ) + return int(dt.timestamp() * 1000) + return int(datetime.now().timestamp() * 1000) + + def load_state(self): + """Load server state from storage""" + self._state = self.storage.load_server_state() + self.storage.update_server_state_from_dashboard(self._state) + logger.info( + f"StateSync loaded: {len(self._state.get('feed_history', []))} feeds", # noqa: E501 + ) + + def save_state(self): + """Save current state to storage""" + self.storage.save_server_state(self._state) + + @property + def state(self) -> Dict[str, Any]: + """Get current state""" + return self._state + + def set_broadcast_fn(self, fn: Callable): + """Set broadcast function (supports late binding)""" + self._broadcast_fn = fn + + def update_state(self, key: str, value: Any): + """Update a state field""" + self._state[key] = value + + async def emit(self, event: Dict[str, Any], persist: bool = True): + """ + Emit an event - persists and broadcasts + + Args: + event: Event dictionary, must contain "type" + persist: Whether to persist to feed_history + """ + if not self._enabled: + return + + # Ensure timestamp exists (use simulation date if in backtest mode) + if "timestamp" not in event: + if self._simulation_date: + event["timestamp"] = f"{self._simulation_date}" + else: + event["timestamp"] = datetime.now().isoformat() + + # Persist to feed_history + if persist: + self.storage.add_feed_message(self._state, event) + self.save_state() + + # Broadcast to frontend + if self._broadcast_fn: + await self._broadcast_fn(event) + + # ========== Agent Events ========== + + async def on_agent_complete( + self, + agent_id: str, + content: str, + **extra, + ): + """ + Called when an agent finishes its reply + + Args: + agent_id: Agent identifier (e.g., "fundamentals_analyst") + content: Agent's output content + **extra: Additional fields to include + """ + ts_ms = self._get_timestamp_ms() + + await self.emit( + { + "type": "agent_message", + "agentId": agent_id, + "content": content, + "ts": ts_ms, + **extra, + }, + ) + + logger.info(f"Agent complete: {agent_id}") + + async def on_memory_retrieved( + self, + agent_id: str, + content: str, + ): + """ + Called when long-term memory is retrieved for an agent + + Args: + agent_id: Agent identifier + content: Retrieved memory content + """ + ts_ms = self._get_timestamp_ms() + + await self.emit( + { + "type": "memory", + "agentId": agent_id, + "content": content, + "ts": ts_ms, + }, + ) + + logger.info(f"Memory retrieved for: {agent_id}") + + # ========== Conference Events ========== + + async def on_conference_start(self, title: str, date: str): + """Called when conference discussion starts""" + ts_ms = self._get_timestamp_ms() + + await self.emit( + { + "type": "conference_start", + "title": title, + "date": date, + "ts": ts_ms, + }, + ) + + logger.info(f"Conference started: {title}") + + async def on_conference_cycle_start(self, cycle: int, total_cycles: int): + """Called when a conference cycle starts""" + await self.emit( + { + "type": "conference_cycle_start", + "cycle": cycle, + "totalCycles": total_cycles, + }, + persist=False, + ) + + async def on_conference_message(self, agent_id: str, content: str): + """Called when an agent speaks during conference""" + ts_ms = self._get_timestamp_ms() + + await self.emit( + { + "type": "conference_message", + "agentId": agent_id, + "content": content, + "ts": ts_ms, + }, + ) + + async def on_conference_cycle_end(self, cycle: int): + """Called when a conference cycle ends""" + await self.emit( + { + "type": "conference_cycle_end", + "cycle": cycle, + }, + persist=False, + ) + + async def on_conference_end(self): + """Called when conference discussion ends""" + ts_ms = self._get_timestamp_ms() + + await self.emit( + { + "type": "conference_end", + "ts": ts_ms, + }, + ) + + logger.info("Conference ended") + + # ========== Cycle Events ========== + + async def on_cycle_start(self, date: str): + """Called at start of trading cycle""" + self._state["current_date"] = date + self._state["status"] = "running" + self.set_simulation_date( + date, + ) # Set for backtest-compatible timestamps + + await self.emit( + { + "type": "day_start", + "date": date, + "progress": self._calculate_progress(), + }, + ) + # await self.emit( + # { + # "type": "system", + # "content": f"Starting trading analysis for {date}", + # }, + # ) + + async def on_cycle_end(self, date: str, portfolio_summary: Dict = None): + """Called at end of trading cycle""" + # Update completed count + self._state["trading_days_completed"] = ( + self._state.get("trading_days_completed", 0) + 1 + ) + + # Broadcast team_summary if available + if portfolio_summary: + summary_data = { + "type": "team_summary", + "balance": portfolio_summary.get( + "balance", + portfolio_summary.get("total_value", 0), + ), + "pnlPct": portfolio_summary.get( + "pnlPct", + portfolio_summary.get("pnl_percent", 0), + ), + "equity": portfolio_summary.get("equity", []), + "baseline": portfolio_summary.get("baseline", []), + "baseline_vw": portfolio_summary.get("baseline_vw", []), + "momentum": portfolio_summary.get("momentum", []), + } + + # Include live returns if available + if portfolio_summary.get("equity_return"): + summary_data["equity_return"] = portfolio_summary[ + "equity_return" + ] + if portfolio_summary.get("baseline_return"): + summary_data["baseline_return"] = portfolio_summary[ + "baseline_return" + ] + if portfolio_summary.get("baseline_vw_return"): + summary_data["baseline_vw_return"] = portfolio_summary[ + "baseline_vw_return" + ] + if portfolio_summary.get("momentum_return"): + summary_data["momentum_return"] = portfolio_summary[ + "momentum_return" + ] + + await self.emit(summary_data, persist=True) + + await self.emit( + { + "type": "day_complete", + "date": date, + "progress": self._calculate_progress(), + }, + ) + + self.save_state() + + # ========== Portfolio Events ========== + + async def on_holdings_update(self, holdings: List[Dict]): + """Called when holdings change""" + self._state["holdings"] = holdings + await self.emit( + { + "type": "team_holdings", + "data": holdings, + }, + persist=False, + ) # Holdings change frequently, don't store all in feed_history + + async def on_trades_executed(self, trades: List[Dict]): + """Called when trades are executed""" + # Update state with new trades + existing = self._state.get("trades", []) + self._state["trades"] = trades + existing + + await self.emit( + { + "type": "team_trades", + "mode": "incremental", + "data": trades, + }, + persist=False, + ) + + async def on_stats_update(self, stats: Dict): + """Called when stats are updated""" + self._state["stats"] = stats + await self.emit( + { + "type": "team_stats", + "data": stats, + }, + persist=False, + ) + + # ========== System Events ========== + + async def on_system_message(self, content: str): + """Emit a system message""" + await self.emit( + { + "type": "system", + "content": content, + }, + ) + + # ========== Replay Support ========== + + async def replay_feed_history(self, delay_ms: int = 100): + """ + Replay events from feed_history + + Useful for: frontend reconnection or restoring from saved state + """ + feed_history = self._state.get("feed_history", []) + + # feed_history is newest-first, need to reverse for chronological replay # noqa: E501 + for event in reversed(feed_history): + if self._broadcast_fn: + await self._broadcast_fn(event) + await asyncio.sleep(delay_ms / 1000) + + logger.info(f"Replayed {len(feed_history)} events") + + def get_initial_state_payload( + self, + include_dashboard: bool = True, + ) -> Dict[str, Any]: + """ + Build initial state payload for new client connections + + Args: + include_dashboard: Whether to load dashboard files + + Returns: + Dictionary suitable for sending to frontend + """ + payload = { + "server_mode": self._state.get("server_mode", "live"), + "is_mock_mode": self._state.get("is_mock_mode", False), + "is_backtest": self._state.get("is_backtest", False), + "feed_history": self._state.get("feed_history", []), + "current_date": self._state.get("current_date"), + "trading_days_total": self._state.get("trading_days_total", 0), + "trading_days_completed": self._state.get( + "trading_days_completed", + 0, + ), + "holdings": self._state.get("holdings", []), + "trades": self._state.get("trades", []), + "stats": self._state.get("stats", {}), + "leaderboard": self._state.get("leaderboard", []), + "portfolio": self._state.get("portfolio", {}), + "realtime_prices": self._state.get("realtime_prices", {}), + } + + if include_dashboard: + payload["dashboard"] = { + "summary": self.storage.load_file("summary"), + "holdings": self.storage.load_file("holdings"), + "stats": self.storage.load_file("stats"), + "trades": self.storage.load_file("trades"), + "leaderboard": self.storage.load_file("leaderboard"), + } + + return payload + + def _calculate_progress(self) -> float: + """Calculate backtest progress percentage""" + total = self._state.get("trading_days_total", 0) + completed = self._state.get("trading_days_completed", 0) + return completed / total if total > 0 else 0.0 + + def set_backtest_dates(self, dates: List[str]): + """Set total trading days for backtest progress tracking""" + self._state["trading_days_total"] = len(dates) + self._state["trading_days_completed"] = 0 diff --git a/evotraders/backend/data/__init__.py b/evotraders/backend/data/__init__.py new file mode 100644 index 0000000..eb90e8e --- /dev/null +++ b/evotraders/backend/data/__init__.py @@ -0,0 +1,6 @@ +# -*- coding: utf-8 -*- +from backend.data.historical_price_manager import HistoricalPriceManager +from backend.data.mock_price_manager import MockPriceManager +from backend.data.polling_price_manager import PollingPriceManager + +__all__ = ["MockPriceManager", "PollingPriceManager", "HistoricalPriceManager"] diff --git a/evotraders/backend/data/cache.py b/evotraders/backend/data/cache.py new file mode 100644 index 0000000..375c9b3 --- /dev/null +++ b/evotraders/backend/data/cache.py @@ -0,0 +1,107 @@ +# -*- coding: utf-8 -*- + +from typing_extensions import Any + + +class Cache: + """In-memory cache for API responses.""" + + def __init__(self): + self._prices_cache = {} + self._financial_metrics_cache = {} + self._line_items_cache = {} + self._insider_trades_cache = {} + self._company_news_cache = {} + + def _merge_data( + self, + existing: list[dict] | None, + new_data: list[dict], + key_field: str, + ) -> list[dict]: + """Merge existing and new data""" + if not existing: + return new_data + + # Create a set of existing keys for O(1) lookup + existing_keys = {item[key_field] for item in existing} + + # Only add items that don't exist yet + merged = existing.copy() + merged.extend( + [ + item + for item in new_data + if item[key_field] not in existing_keys + ], + ) + return merged + + def get_prices(self, ticker: str) -> list[dict[str, Any]] | None: + """Get cached price data if available.""" + return self._prices_cache.get(ticker) + + def set_prices(self, ticker: str, data: list[dict[str, Any]]): + """Append new price data to cache.""" + self._prices_cache[ticker] = self._merge_data( + self._prices_cache.get(ticker), + data, + key_field="time", + ) + + def get_financial_metrics(self, ticker: str) -> list[dict[str, Any]]: + """Get cached financial metrics if available.""" + return self._financial_metrics_cache.get(ticker) + + def set_financial_metrics(self, ticker: str, data: list[dict[str, Any]]): + """Append new financial metrics to cache.""" + self._financial_metrics_cache[ticker] = self._merge_data( + self._financial_metrics_cache.get(ticker), + data, + key_field="report_period", + ) + + def get_line_items(self, ticker: str) -> list[dict[str, Any]] | None: + """Get cached line items if available.""" + return self._line_items_cache.get(ticker) + + def set_line_items(self, ticker: str, data: list[dict[str, Any]]): + """Append new line items to cache.""" + self._line_items_cache[ticker] = self._merge_data( + self._line_items_cache.get(ticker), + data, + key_field="report_period", + ) + + def get_insider_trades(self, ticker: str) -> list[dict[str, Any]] | None: + """Get cached insider trades if available.""" + return self._insider_trades_cache.get(ticker) + + def set_insider_trades(self, ticker: str, data: list[dict[str, Any]]): + """Append new insider trades to cache.""" + self._insider_trades_cache[ticker] = self._merge_data( + self._insider_trades_cache.get(ticker), + data, + key_field="filing_date", + ) # Could also use transaction_date if preferred + + def get_company_news(self, ticker: str) -> list[dict[str, Any]] | None: + """Get cached company news if available.""" + return self._company_news_cache.get(ticker) + + def set_company_news(self, ticker: str, data: list[dict[str, Any]]): + """Append new company news to cache.""" + self._company_news_cache[ticker] = self._merge_data( + self._company_news_cache.get(ticker), + data, + key_field="date", + ) + + +# Global cache instance +_cache = Cache() + + +def get_cache() -> Cache: + """Get the global cache instance.""" + return _cache diff --git a/evotraders/backend/data/historical_price_manager.py b/evotraders/backend/data/historical_price_manager.py new file mode 100644 index 0000000..4db80da --- /dev/null +++ b/evotraders/backend/data/historical_price_manager.py @@ -0,0 +1,233 @@ +# -*- coding: utf-8 -*- +""" +Historical Price Manager for backtest mode +""" +import logging +from datetime import datetime +from pathlib import Path +from typing import Callable, Dict, List, Optional + +import pandas as pd + +logger = logging.getLogger(__name__) + +# Path to local CSV data directory +_DATA_DIR = Path(__file__).parent / "ret_data" + + +class HistoricalPriceManager: + """Provides historical prices for backtest mode""" + + def __init__(self): + self.subscribed_symbols = [] + self.price_callbacks = [] + self._price_cache = {} + self._current_date = None + self.latest_prices = {} + self.open_prices = {} + self.close_prices = {} + self.running = False + + def subscribe( + self, + symbols: List[str], + ): + """Subscribe to symbols""" + for symbol in symbols: + if symbol not in self.subscribed_symbols: + self.subscribed_symbols.append(symbol) + + def unsubscribe(self, symbols: List[str]): + """Unsubscribe from symbols""" + for symbol in symbols: + if symbol in self.subscribed_symbols: + self.subscribed_symbols.remove(symbol) + self._price_cache.pop(symbol, None) + + def add_price_callback(self, callback: Callable): + """Add price update callback""" + self.price_callbacks.append(callback) + + def _load_from_csv(self, symbol: str) -> Optional[pd.DataFrame]: + """Load price data from local CSV file.""" + csv_path = _DATA_DIR / f"{symbol}.csv" + if not csv_path.exists(): + return None + + try: + df = pd.read_csv(csv_path) + if df.empty or "time" not in df.columns: + return None + + df["Date"] = pd.to_datetime(df["time"]) + df.set_index("Date", inplace=True) + df.sort_index(inplace=True) + return df + except Exception as e: + logger.warning(f"Failed to load CSV for {symbol}: {e}") + return None + + def preload_data(self, start_date: str, end_date: str): + """Preload historical data from local CSV files.""" + logger.info(f"Preloading data: {start_date} to {end_date}") + + for symbol in self.subscribed_symbols: + if symbol in self._price_cache: + continue + + # Load from local CSV file directly + df = self._load_from_csv(symbol) + if df is not None and not df.empty: + self._price_cache[symbol] = df + logger.info(f"Loaded {symbol} from CSV: {len(df)} records") + else: + logger.warning(f"No CSV data for {symbol}") + + def set_date(self, date: str): + """Set current trading date and update prices""" + self._current_date = date + date_dt = pd.Timestamp(date) + + for symbol in self.subscribed_symbols: + df = self._price_cache.get(symbol) + if df is None or df.empty: + # Keep previous prices if no data available + logger.warning(f"No cached data for {symbol} on {date}") + continue + + # Find exact date or closest earlier date + if date_dt in df.index: + row = df.loc[date_dt] + else: + valid_dates = df.index[df.index <= date_dt] + if len(valid_dates) == 0: + logger.warning(f"No data for {symbol} on or before {date}") + continue + row = df.loc[valid_dates[-1]] + + open_price = float(row["open"]) + close_price = float(row["close"]) + + self.open_prices[symbol] = open_price + self.close_prices[symbol] = close_price + self.latest_prices[symbol] = open_price + + logger.debug( + f"{symbol} @ {date}: open={open_price:.2f}, close={close_price:.2f}", # noqa: E501 + ) + + def emit_open_prices(self): + """Emit open prices to callbacks""" + if not self._current_date: + return + + timestamp = int( + datetime.strptime(self._current_date, "%Y-%m-%d").timestamp() + * 1000, + ) + + for symbol in self.subscribed_symbols: + price = self.open_prices.get(symbol) + if price is None or price <= 0: + logger.warning(f"Invalid open price for {symbol}: {price}") + continue + + self.latest_prices[symbol] = price + self._emit_price(symbol, price, timestamp) + + def emit_close_prices(self): + """Emit close prices to callbacks""" + if not self._current_date: + return + + timestamp = int( + datetime.strptime(self._current_date, "%Y-%m-%d").timestamp() + * 1000, + ) + timestamp += 23400000 # Add 6.5 hours + + for symbol in self.subscribed_symbols: + price = self.close_prices.get(symbol) + if price is None or price <= 0: + logger.warning(f"Invalid close price for {symbol}: {price}") + continue + + self.latest_prices[symbol] = price + self._emit_price(symbol, price, timestamp) + + def _emit_price(self, symbol: str, price: float, timestamp: int): + """Emit single price to callbacks""" + open_price = self.open_prices.get(symbol, price) + close_price = self.close_prices.get(symbol, price) + ret = ( + ((price - open_price) / open_price) * 100 if open_price > 0 else 0 + ) + + price_data = { + "symbol": symbol, + "price": price, + "timestamp": timestamp, + "open": open_price, + "close": close_price, + "high": max(open_price, close_price), + "low": min(open_price, close_price), + "ret": ret, + } + + for callback in self.price_callbacks: + try: + callback(price_data) + except Exception as e: + logger.error(f"Callback error for {symbol}: {e}") + + def get_price_for_date( + self, + symbol: str, + date: str, + price_type: str = "close", + ) -> Optional[float]: + """Get price for a specific date""" + df = self._price_cache.get(symbol) + if df is None or df.empty: + return self.latest_prices.get(symbol) + + date_dt = pd.Timestamp(date) + if date_dt in df.index: + return float(df.loc[date_dt, price_type]) + + valid_dates = df.index[df.index <= date_dt] + if len(valid_dates) == 0: + return self.latest_prices.get(symbol) + return float(df.loc[valid_dates[-1], price_type]) + + def start(self): + """Start manager""" + self.running = True + + def stop(self): + """Stop manager""" + self.running = False + + def get_latest_price(self, symbol: str) -> Optional[float]: + return self.latest_prices.get(symbol) + + def get_all_latest_prices(self) -> Dict[str, float]: + return self.latest_prices.copy() + + def get_open_price(self, symbol: str) -> Optional[float]: + # Return open price, fallback to latest if not set + price = self.open_prices.get(symbol) + if price is None or price <= 0: + return self.latest_prices.get(symbol) + return price + + def get_close_price(self, symbol: str) -> Optional[float]: + # Return close price, fallback to latest if not set + price = self.close_prices.get(symbol) + if price is None or price <= 0: + return self.latest_prices.get(symbol) + return price + + def reset_open_prices(self): + # Don't clear prices - keep them for continuity + pass diff --git a/evotraders/backend/data/mock_price_manager.py b/evotraders/backend/data/mock_price_manager.py new file mode 100644 index 0000000..d54b7ea --- /dev/null +++ b/evotraders/backend/data/mock_price_manager.py @@ -0,0 +1,241 @@ +# -*- coding: utf-8 -*- +""" +Mock Price Manager - For testing during non-trading hours +Generates virtual real-time price data +""" +import logging +import os +import random +import threading +import time +from typing import Callable, Dict, List, Optional + +logger = logging.getLogger(__name__) + + +class MockPriceManager: + """Mock Price Manager - Generates virtual prices for testing""" + + def __init__(self, poll_interval: int = 10, volatility: float = 0.5): + """ + Args: + poll_interval: Price update interval in seconds + volatility: Price volatility percentage + """ + if poll_interval is None: + poll_interval = int(os.getenv("MOCK_POLL_INTERVAL", "5")) + if volatility is None: + volatility = float(os.getenv("MOCK_VOLATILITY", "0.5")) + + self.poll_interval = poll_interval + self.volatility = volatility + + self.subscribed_symbols: List[str] = [] + self.base_prices: Dict[str, float] = {} + self.open_prices: Dict[str, float] = {} + self.latest_prices: Dict[str, float] = {} + self.price_callbacks: List[Callable] = [] + + self.running = False + self._thread: Optional[threading.Thread] = None + + self.default_base_prices = { + "AAPL": 237.50, + "MSFT": 425.30, + "GOOGL": 161.50, + "AMZN": 218.45, + "NVDA": 950.00, + "META": 573.22, + "TSLA": 342.15, + "AMD": 168.90, + "NFLX": 688.25, + "INTC": 42.18, + "COIN": 285.50, + "PLTR": 45.80, + "BABA": 88.30, + "DIS": 112.50, + "BKNG": 4850.00, + } + + logger.info( + f"MockPriceManager initialized (interval: {self.poll_interval}s, " + f"volatility: {self.volatility}%)", + ) + + def subscribe( + self, + symbols: List[str], + base_prices: Dict[str, float] = None, + ): + """Subscribe to stock symbols""" + for symbol in symbols: + if symbol not in self.subscribed_symbols: + self.subscribed_symbols.append(symbol) + + if base_prices and symbol in base_prices: + base_price = base_prices[symbol] + elif symbol in self.default_base_prices: + base_price = self.default_base_prices[symbol] + else: + base_price = random.uniform(50, 500) + + self.base_prices[symbol] = base_price + self.open_prices[symbol] = base_price + self.latest_prices[symbol] = base_price + + logger.info( + f"Subscribed to mock price: {symbol} (base: ${base_price:.2f})", # noqa: E501 + ) + + def unsubscribe(self, symbols: List[str]): + """Unsubscribe from symbols""" + for symbol in symbols: + if symbol in self.subscribed_symbols: + self.subscribed_symbols.remove(symbol) + self.base_prices.pop(symbol, None) + self.open_prices.pop(symbol, None) + self.latest_prices.pop(symbol, None) + logger.info(f"Unsubscribed: {symbol}") + + def add_price_callback(self, callback: Callable): + """Add price update callback""" + self.price_callbacks.append(callback) + + def _generate_price_update(self, symbol: str) -> float: + """Generate price update based on random walk""" + current_price = self.latest_prices.get( + symbol, + self.base_prices[symbol], + ) + + change_percent = random.uniform(-self.volatility, self.volatility) + new_price = current_price * (1 + change_percent / 100) + + # 10% chance of larger movement + if random.random() < 0.1: + trend_factor = random.uniform(-2, 2) + new_price = new_price * (1 + trend_factor / 100) + + # Limit intraday movement to +/-10% + open_price = self.open_prices[symbol] + max_price = open_price * 1.10 + min_price = open_price * 0.90 + new_price = max(min_price, min(max_price, new_price)) + + return new_price + + def _update_prices(self): + """Update prices for all subscribed stocks""" + timestamp = int(time.time() * 1000) + + for symbol in self.subscribed_symbols: + try: + new_price = self._generate_price_update(symbol) + self.latest_prices[symbol] = new_price + + open_price = self.open_prices[symbol] + ret = ((new_price - open_price) / open_price) * 100 + + price_data = { + "symbol": symbol, + "price": new_price, + "timestamp": timestamp, + "volume": random.randint(1000000, 10000000), + "open": open_price, + "high": max(new_price, open_price), + "low": min(new_price, open_price), + "previous_close": open_price, + "ret": ret, + } + + for callback in self.price_callbacks: + try: + callback(price_data) + except Exception as e: + logger.error( + f"Mock price callback error ({symbol}): {e}", + ) + + logger.debug( + f"Mock {symbol}: ${new_price:.2f} [ret: {ret:+.2f}%]", + ) + + except Exception as e: + logger.error(f"Failed to generate mock price ({symbol}): {e}") + + def _polling_loop(self): + """Main polling loop""" + logger.info( + f"Mock price generation started (interval: {self.poll_interval}s)", + ) + + while self.running: + try: + start_time = time.time() + self._update_prices() + + elapsed = time.time() - start_time + sleep_time = max(0, self.poll_interval - elapsed) + if sleep_time > 0: + time.sleep(sleep_time) + + except Exception as e: + logger.error(f"Mock polling loop error: {e}") + time.sleep(5) + + def start(self): + """Start mock price generation""" + if self.running: + logger.warning("Mock price manager already running") + return + + if not self.subscribed_symbols: + logger.warning("No stocks subscribed") + return + + self.running = True + self._thread = threading.Thread(target=self._polling_loop, daemon=True) + self._thread.start() + + logger.info( + f"Mock price manager started: {', '.join(self.subscribed_symbols)}", # noqa: E501 + ) + + def stop(self): + """Stop mock price generation""" + self.running = False + if self._thread: + self._thread.join(timeout=5) + logger.info("Mock price manager stopped") + + def get_latest_price(self, symbol: str) -> Optional[float]: + """Get latest price for symbol""" + return self.latest_prices.get(symbol) + + def get_all_latest_prices(self) -> Dict[str, float]: + """Get all latest prices""" + return self.latest_prices.copy() + + def get_open_price(self, symbol: str) -> Optional[float]: + """Get open price for symbol""" + return self.open_prices.get(symbol) + + def reset_open_prices(self): + """Reset open prices for new trading day""" + for symbol in self.subscribed_symbols: + last_close = self.latest_prices[symbol] + gap_percent = random.uniform(-1, 1) + new_open = last_close * (1 + gap_percent / 100) + self.open_prices[symbol] = new_open + self.latest_prices[symbol] = new_open + logger.info("Open prices reset") + + def set_base_price(self, symbol: str, price: float): + """Manually set base price for testing""" + if symbol in self.subscribed_symbols: + self.base_prices[symbol] = price + self.open_prices[symbol] = price + self.latest_prices[symbol] = price + logger.info(f"{symbol} base price set to: ${price:.2f}") + else: + logger.warning(f"{symbol} not subscribed") diff --git a/evotraders/backend/data/polling_price_manager.py b/evotraders/backend/data/polling_price_manager.py new file mode 100644 index 0000000..101d9f1 --- /dev/null +++ b/evotraders/backend/data/polling_price_manager.py @@ -0,0 +1,175 @@ +# -*- coding: utf-8 -*- +""" +Polling-based Price Manager - Uses Finnhub REST API +Supports real-time price fetching via polling +""" +import logging +import threading +import time +from typing import Callable, Dict, List, Optional + +import finnhub + +logger = logging.getLogger(__name__) + + +class PollingPriceManager: + """Polling-based price manager using Finnhub Quote API""" + + def __init__(self, api_key: str, poll_interval: int = 30): + """ + Args: + api_key: Finnhub API Key + poll_interval: Polling interval in seconds (default 30s) + """ + self.api_key = api_key + self.poll_interval = poll_interval + self.finnhub_client = finnhub.Client(api_key=api_key) + + self.subscribed_symbols: List[str] = [] + self.latest_prices: Dict[str, float] = {} + self.open_prices: Dict[str, float] = {} + self.price_callbacks: List[Callable] = [] + + self.running = False + self._thread: Optional[threading.Thread] = None + + logger.info( + f"PollingPriceManager initialized (interval: {poll_interval}s)", + ) + + def subscribe(self, symbols: List[str]): + """Subscribe to stock symbols""" + for symbol in symbols: + if symbol not in self.subscribed_symbols: + self.subscribed_symbols.append(symbol) + logger.info(f"Subscribed to: {symbol}") + + def unsubscribe(self, symbols: List[str]): + """Unsubscribe from symbols""" + for symbol in symbols: + if symbol in self.subscribed_symbols: + self.subscribed_symbols.remove(symbol) + logger.info(f"Unsubscribed: {symbol}") + + def add_price_callback(self, callback: Callable): + """Add price update callback""" + self.price_callbacks.append(callback) + + def _fetch_prices(self): + """Fetch latest prices for all subscribed stocks""" + for symbol in self.subscribed_symbols: + try: + quote_data = self.finnhub_client.quote(symbol) + + current_price = quote_data.get("c") + open_price = quote_data.get("o") + timestamp = quote_data.get("t", int(time.time())) + + if not current_price or current_price <= 0: + logger.warning(f"{symbol}: Invalid price data") + continue + + # Store open price on first fetch + if ( + symbol not in self.open_prices + and open_price + and open_price > 0 + ): + self.open_prices[symbol] = open_price + logger.info(f"{symbol} open price: ${open_price:.2f}") + + stored_open = self.open_prices.get(symbol, open_price) + ret = ( + ((current_price - stored_open) / stored_open) * 100 + if stored_open > 0 + else 0 + ) + + self.latest_prices[symbol] = current_price + + price_data = { + "symbol": symbol, + "price": current_price, + "timestamp": timestamp * 1000, + "open": stored_open, + "high": quote_data.get("h"), + "low": quote_data.get("l"), + "previous_close": quote_data.get("pc"), + "ret": ret, + "change": quote_data.get("d"), + "change_percent": quote_data.get("dp"), + } + + for callback in self.price_callbacks: + try: + callback(price_data) + except Exception as e: + logger.error(f"Price callback error ({symbol}): {e}") + + logger.debug( + f"{symbol}: ${current_price:.2f} [ret: {ret:+.2f}%]", + ) + + except Exception as e: + logger.error(f"Failed to fetch {symbol} price: {e}") + + def _polling_loop(self): + """Main polling loop""" + logger.info(f"Price polling started (interval: {self.poll_interval}s)") + + while self.running: + try: + start_time = time.time() + self._fetch_prices() + + elapsed = time.time() - start_time + sleep_time = max(0, self.poll_interval - elapsed) + if sleep_time > 0: + time.sleep(sleep_time) + + except Exception as e: + logger.error(f"Polling loop error: {e}") + time.sleep(5) + + def start(self): + """Start price polling""" + if self.running: + logger.warning("Price polling already running") + return + + if not self.subscribed_symbols: + logger.warning("No stocks subscribed") + return + + self.running = True + self._thread = threading.Thread(target=self._polling_loop, daemon=True) + self._thread.start() + + logger.info( + f"Price polling started: {', '.join(self.subscribed_symbols)}", + ) + + def stop(self): + """Stop price polling""" + self.running = False + if self._thread: + self._thread.join(timeout=5) + logger.info("Price polling stopped") + + def get_latest_price(self, symbol: str) -> Optional[float]: + """Get latest price for symbol""" + return self.latest_prices.get(symbol) + + def get_all_latest_prices(self) -> Dict[str, float]: + """Get all latest prices""" + return self.latest_prices.copy() + + def get_open_price(self, symbol: str) -> Optional[float]: + """Get open price for symbol""" + return self.open_prices.get(symbol) + + def reset_open_prices(self): + """Reset open prices for new trading day""" + self.open_prices.clear() + logger.info("Open prices reset") diff --git a/evotraders/backend/data/ret_data_updater.py b/evotraders/backend/data/ret_data_updater.py new file mode 100644 index 0000000..3c9a096 --- /dev/null +++ b/evotraders/backend/data/ret_data_updater.py @@ -0,0 +1,387 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Automatic Incremental Historical Data Update Module + +Features: +1. Fetch stock historical data from configured API (Finnhub or Financial Datasets) +2. Incrementally update CSV files in ret_data directory +3. Automatically detect last update date, only download new data +4. Calculate returns (ret) +5. Support batch updates for multiple stocks +""" + +# flake8: noqa: E501 + +import logging +import os +import sys +import time +from datetime import datetime, timedelta +from pathlib import Path +from typing import Dict, List, Optional + +import exchange_calendars as xcals +import pandas as pd +import pandas_market_calendars as mcal +from dotenv import load_dotenv + +from backend.config.data_config import ( + get_config, +) +from backend.tools.data_tools import get_prices, prices_to_df + +# Add project root directory to path +BASE_DIR = Path(__file__).resolve().parents[2] +if str(BASE_DIR) not in sys.path: + sys.path.append(str(BASE_DIR)) + +# Configure logging +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s [%(levelname)s] %(message)s", +) +logger = logging.getLogger(__name__) + + +class DataUpdater: + """Data updater""" + + data_dir: Path + + def __init__( + self, + data_dir: str = None, + start_date: str = "2022-01-01", + ): + """ + Initialize data updater + + Args: + data_dir: Data storage directory, defaults to backend/data/ret_data + start_date: Historical data start date (YYYY-MM-DD) + """ + # Get config from centralized source + config = get_config() + self.data_source = config.source + self.api_key = config.api_key + + # Set data directory + if data_dir is None: + self.data_dir = BASE_DIR / "backend" / "data" / "ret_data" + else: + self.data_dir = Path(data_dir) + + # Ensure directory exists + self.data_dir.mkdir(parents=True, exist_ok=True) + + self.start_date = start_date + + # Initialize Finnhub client if needed + if self.data_source == "finnhub": + import finnhub + + self.client = finnhub.Client(api_key=self.api_key) + logger.info("Finnhub client initialized") + else: + self.client = None + logger.info("Financial Datasets API configured") + + def get_trading_dates(self, start_date: str, end_date: str) -> List[str]: + """Get US stock market trading date sequence.""" + try: + if mcal is not None: + nyse = mcal.get_calendar("NYSE") + trading_dates = nyse.valid_days( + start_date=start_date, + end_date=end_date, + ) + return [date.strftime("%Y-%m-%d") for date in trading_dates] + + elif xcals is not None: + nyse = xcals.get_calendar("XNYS") + trading_dates = nyse.sessions_in_range(start_date, end_date) + return [date.strftime("%Y-%m-%d") for date in trading_dates] + + except Exception as e: + logger.warning( + f"Failed to get US trading calendar, using business days: {e}", + ) + + # Fallback to simple business day method + date_range = pd.date_range(start_date, end_date, freq="B") + return [date.strftime("%Y-%m-%d") for date in date_range] + + def get_last_date_from_csv(self, ticker: str) -> Optional[datetime]: + """Get last data date from CSV file.""" + csv_path = self.data_dir / f"{ticker}.csv" + + if not csv_path.exists(): + logger.info(f"{ticker}.csv does not exist, will create new file") + return None + + try: + df = pd.read_csv(csv_path) + if df.empty or "time" not in df.columns: + return None + + last_date_str = df["time"].iloc[-1] + last_date = datetime.strptime(last_date_str, "%Y-%m-%d") + logger.info(f"{ticker} last data date: {last_date_str}") + return last_date + except Exception as e: + logger.warning(f"Failed to read {ticker}.csv: {e}") + return None + + def fetch_data_from_api( + self, + ticker: str, + start_date: datetime, + end_date: datetime, + ) -> Optional[pd.DataFrame]: + """Fetch data from configured API.""" + start_date_str = start_date.strftime("%Y-%m-%d") + end_date_str = end_date.strftime("%Y-%m-%d") + + logger.info( + f"Fetching {ticker} data from {self.data_source}: {start_date_str} to {end_date_str}", + ) + + prices = get_prices( + ticker=ticker, + start_date=start_date_str, + end_date=end_date_str, + ) + + if not prices: + logger.warning(f"{ticker} no data returned from API") + return None + + # Convert to DataFrame + df = prices_to_df(prices) + df = df.reset_index() + df["time"] = df["Date"].dt.strftime("%Y-%m-%d") + + # Calculate returns (next day return) + df["ret"] = df["close"].pct_change().shift(-1) + + # Select needed columns + df = df[["open", "close", "high", "low", "volume", "time", "ret"]] + + logger.info(f"Successfully fetched {ticker} data: {len(df)} records") + return df + + def merge_and_save(self, ticker: str, new_data: pd.DataFrame) -> bool: + """Merge old and new data and save.""" + csv_path = self.data_dir / f"{ticker}.csv" + + try: + if csv_path.exists(): + old_data = pd.read_csv(csv_path) + logger.info(f"{ticker} existing data: {len(old_data)} records") + + # Merge and deduplicate + combined = pd.concat([old_data, new_data], ignore_index=True) + combined = combined.drop_duplicates( + subset=["time"], + keep="last", + ) + combined = combined.sort_values("time").reset_index(drop=True) + + # Recalculate returns + combined["ret"] = combined["close"].pct_change().shift(-1) + + logger.info(f"{ticker} merged data: {len(combined)} records") + else: + combined = new_data + logger.info(f"{ticker} new file: {len(combined)} records") + + combined.to_csv(csv_path, index=False) + logger.info(f"{ticker} data saved to: {csv_path}") + return True + + except Exception as e: + logger.error(f"Failed to save {ticker} data: {e}") + return False + + def update_ticker( + self, + ticker: str, + force_full_update: bool = False, + ) -> bool: + """Update data for a single stock.""" + logger.info(f"{'='*60}") + logger.info(f"Starting update for {ticker}") + logger.info(f"{'='*60}") + + # Determine start date + if force_full_update: + start_date = datetime.strptime(self.start_date, "%Y-%m-%d") + logger.info(f"Force full update, start date: {start_date.date()}") + else: + last_date = self.get_last_date_from_csv(ticker) + if last_date: + start_date = last_date + timedelta(days=1) + logger.info( + f"Incremental update, start date: {start_date.date()}", + ) + else: + start_date = datetime.strptime(self.start_date, "%Y-%m-%d") + logger.info(f"First update, start date: {start_date.date()}") + + end_date = datetime.now() + + if start_date.date() >= end_date.date(): + logger.info(f"{ticker} data is up to date, no update needed") + return True + + new_data = self.fetch_data_from_api(ticker, start_date, end_date) + + if new_data is None or new_data.empty: + days_diff = (end_date - start_date).days + if days_diff <= 3: + logger.info( + f"{ticker} has no new data (may be weekend/holiday)", + ) + return True + else: + logger.warning(f"{ticker} has no new data") + return False + + success = self.merge_and_save(ticker, new_data) + + if success: + logger.info(f"{ticker} update completed") + else: + logger.error(f"{ticker} update failed") + + return success + + def update_all_tickers( + self, + tickers: List[str], + force_full_update: bool = False, + ) -> Dict[str, bool]: + """Batch update multiple stocks.""" + results = {} + + logger.info(f"{'='*60}") + logger.info(f"Starting batch update for {len(tickers)} stocks") + logger.info(f"Stock list: {', '.join(tickers)}") + logger.info(f"{'='*60}") + + for i, ticker in enumerate(tickers, 1): + logger.info(f"[{i}/{len(tickers)}] Processing {ticker}") + results[ticker] = self.update_ticker(ticker, force_full_update) + + # API rate limiting + if i < len(tickers): + time.sleep(1) + + # Print summary + logger.info(f"{'='*60}") + logger.info("Update Summary") + logger.info(f"{'='*60}") + + success_count = sum(results.values()) + fail_count = len(results) - success_count + + logger.info(f"Success: {success_count}") + logger.info(f"Failed: {fail_count}") + + if fail_count > 0: + failed_tickers = [t for t, s in results.items() if not s] + logger.warning(f"Failed stocks: {', '.join(failed_tickers)}") + + logger.info(f"{'='*60}\n") + + return results + + +def main(): + """Command line entry point""" + import argparse + + parser = argparse.ArgumentParser( + description="Automatically update stock historical data", + ) + parser.add_argument( + "--tickers", + type=str, + help="Stock ticker list (comma-separated), e.g.: AAPL,MSFT,GOOGL", + ) + parser.add_argument( + "--data-dir", + type=str, + help="Data storage directory (default: backend/data/ret_data)", + ) + parser.add_argument( + "--start-date", + type=str, + default="2022-01-01", + help="Historical data start date (YYYY-MM-DD, default: 2022-01-01)", + ) + parser.add_argument( + "--force", + action="store_true", + help="Force full update (re-download all data)", + ) + + args = parser.parse_args() + + # Load environment variables + load_dotenv() + + # Validate API key is available + try: + config = get_config() + logger.info(f"Using data source: {config.source}") + except ValueError as e: + logger.error(str(e)) + sys.exit(1) + + # Get stock list + if args.tickers: + tickers = [t.strip().upper() for t in args.tickers.split(",")] + else: + tickers_env = os.getenv("TICKERS", "") + if tickers_env: + tickers = [t.strip().upper() for t in tickers_env.split(",")] + else: + logger.error("Stock list not provided") + logger.error( + "Please set via --tickers parameter or TICKERS environment variable", + ) + sys.exit(1) + + # Create updater + updater = DataUpdater( + data_dir=args.data_dir, + start_date=args.start_date, + ) + + # Execute update + try: + results = updater.update_all_tickers( + tickers, + force_full_update=args.force, + ) + except Exception: + # API error (e.g., weekend/holiday with no data) + sys.exit(1) + + # Return status code + success_count = sum(results.values()) + if success_count == len(results): + logger.info("All stocks updated successfully!") + sys.exit(0) + elif success_count == 0: + logger.warning("All stocks have no new data (may be weekend/holiday)") + sys.exit(0) + else: + logger.warning("Some stocks failed to update, but will continue") + sys.exit(0) + + +if __name__ == "__main__": + main() diff --git a/evotraders/backend/data/schema.py b/evotraders/backend/data/schema.py new file mode 100644 index 0000000..e73dade --- /dev/null +++ b/evotraders/backend/data/schema.py @@ -0,0 +1,184 @@ +# -*- coding: utf-8 -*- +from pydantic import BaseModel + + +class Price(BaseModel): + open: float + close: float + high: float + low: float + volume: int + time: str + + +class PriceResponse(BaseModel): + ticker: str + prices: list[Price] + + +class FinancialMetrics(BaseModel): + ticker: str + report_period: str + period: str + currency: str + market_cap: float | None + enterprise_value: float | None + price_to_earnings_ratio: float | None + price_to_book_ratio: float | None + price_to_sales_ratio: float | None + enterprise_value_to_ebitda_ratio: float | None + enterprise_value_to_revenue_ratio: float | None + free_cash_flow_yield: float | None + peg_ratio: float | None + gross_margin: float | None + operating_margin: float | None + net_margin: float | None + return_on_equity: float | None + return_on_assets: float | None + return_on_invested_capital: float | None + asset_turnover: float | None + inventory_turnover: float | None + receivables_turnover: float | None + days_sales_outstanding: float | None + operating_cycle: float | None + working_capital_turnover: float | None + current_ratio: float | None + quick_ratio: float | None + cash_ratio: float | None + operating_cash_flow_ratio: float | None + debt_to_equity: float | None + debt_to_assets: float | None + interest_coverage: float | None + revenue_growth: float | None + earnings_growth: float | None + book_value_growth: float | None + earnings_per_share_growth: float | None + free_cash_flow_growth: float | None + operating_income_growth: float | None + ebitda_growth: float | None + payout_ratio: float | None + earnings_per_share: float | None + book_value_per_share: float | None + free_cash_flow_per_share: float | None + + +class FinancialMetricsResponse(BaseModel): + financial_metrics: list[FinancialMetrics] + + +class LineItem(BaseModel): + ticker: str + report_period: str + period: str + currency: str + + # Allow additional fields dynamically + model_config = {"extra": "allow"} + + +class LineItemResponse(BaseModel): + search_results: list[LineItem] + + +class InsiderTrade(BaseModel): + ticker: str + issuer: str | None + name: str | None + title: str | None + is_board_director: bool | None + transaction_date: str | None + transaction_shares: float | None + transaction_price_per_share: float | None + transaction_value: float | None + shares_owned_before_transaction: float | None + shares_owned_after_transaction: float | None + security_title: str | None + filing_date: str + + +class InsiderTradeResponse(BaseModel): + insider_trades: list[InsiderTrade] + + +class CompanyNews(BaseModel): + category: str | None = None + ticker: str + title: str + related: str | None = None + source: str + date: str | None = None + url: str + summary: str | None = None + + +class CompanyNewsResponse(BaseModel): + news: list[CompanyNews] + + +class CompanyFacts(BaseModel): + ticker: str + name: str + cik: str | None = None + industry: str | None = None + sector: str | None = None + category: str | None = None + exchange: str | None = None + is_active: bool | None = None + listing_date: str | None = None + location: str | None = None + market_cap: float | None = None + number_of_employees: int | None = None + sec_filings_url: str | None = None + sic_code: str | None = None + sic_industry: str | None = None + sic_sector: str | None = None + website_url: str | None = None + weighted_average_shares: int | None = None + + +class CompanyFactsResponse(BaseModel): + company_facts: CompanyFacts + + +class Position(BaseModel): + """Position information - for Portfolio mode""" + + long: int = 0 # Long position quantity (shares) + short: int = 0 # Short position quantity (shares) + long_cost_basis: float = 0.0 # Long position average cost + short_cost_basis: float = 0.0 # Short position average cost + + +class Portfolio(BaseModel): + """Portfolio - for Portfolio mode""" + + cash: float = 100000.0 # Available cash + positions: dict[str, Position] = {} # ticker -> Position mapping + # Margin requirement (0.0 means shorting disabled, 0.5 means 50% margin) + margin_requirement: float = 0.0 + margin_used: float = 0.0 # Margin used + + +class AnalystSignal(BaseModel): + signal: str | None = None + confidence: float | None = None + reasoning: dict | str | None = None + max_position_size: float | None = None # For risk management signals + + +class TickerAnalysis(BaseModel): + ticker: str + analyst_signals: dict[str, AnalystSignal] # agent_name -> signal mapping + + +class AgentStateData(BaseModel): + tickers: list[str] + portfolio: Portfolio + start_date: str + end_date: str + ticker_analyses: dict[str, TickerAnalysis] # ticker -> analysis mapping + + +class AgentStateMetadata(BaseModel): + show_reasoning: bool = False + model_config = {"extra": "allow"} diff --git a/evotraders/backend/llm/__init__.py b/evotraders/backend/llm/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/evotraders/backend/llm/models.py b/evotraders/backend/llm/models.py new file mode 100644 index 0000000..f8edfdd --- /dev/null +++ b/evotraders/backend/llm/models.py @@ -0,0 +1,243 @@ +# -*- coding: utf-8 -*- +""" +AgentScope Native Model Factory +Uses native AgentScope model classes for LLM calls +""" +import os +from enum import Enum +from typing import Optional, Tuple +from agentscope.formatter import ( + AnthropicChatFormatter, + DashScopeChatFormatter, + GeminiChatFormatter, + OllamaChatFormatter, + OpenAIChatFormatter, +) +from agentscope.model import ( + AnthropicChatModel, + DashScopeChatModel, + GeminiChatModel, + OllamaChatModel, + OpenAIChatModel, +) + + +class ModelProvider(Enum): + """Supported model providers""" + + OPENAI = "OPENAI" + ANTHROPIC = "ANTHROPIC" + DASHSCOPE = "DASHSCOPE" + ALIBABA = "ALIBABA" + GEMINI = "GEMINI" + GOOGLE = "GOOGLE" + OLLAMA = "OLLAMA" + DEEPSEEK = "DEEPSEEK" + GROQ = "GROQ" + OPENROUTER = "OPENROUTER" + + +# Provider to AgentScope model class mapping +PROVIDER_MODEL_MAP = { + "OPENAI": OpenAIChatModel, + "ANTHROPIC": AnthropicChatModel, + "DASHSCOPE": DashScopeChatModel, + "ALIBABA": DashScopeChatModel, + "GEMINI": GeminiChatModel, + "GOOGLE": GeminiChatModel, + "OLLAMA": OllamaChatModel, + # OpenAI-compatible providers use OpenAIChatModel with custom base_url + "DEEPSEEK": OpenAIChatModel, + "GROQ": OpenAIChatModel, + "OPENROUTER": OpenAIChatModel, +} + +# Provider to formatter mapping +PROVIDER_FORMATTER_MAP = { + "OPENAI": OpenAIChatFormatter, + "ANTHROPIC": AnthropicChatFormatter, + "DASHSCOPE": DashScopeChatFormatter, + "ALIBABA": DashScopeChatFormatter, + "GEMINI": GeminiChatFormatter, + "GOOGLE": GeminiChatFormatter, + "OLLAMA": OllamaChatFormatter, + # OpenAI-compatible providers use OpenAIChatFormatter + "DEEPSEEK": OpenAIChatFormatter, + "GROQ": OpenAIChatFormatter, + "OPENROUTER": OpenAIChatFormatter, +} + +# Provider-specific base URLs +PROVIDER_BASE_URLS = { + "DEEPSEEK": "https://api.deepseek.com/v1", + "GROQ": "https://api.groq.com/openai/v1", + "OPENROUTER": "https://openrouter.ai/api/v1", +} + +# Provider-specific API key environment variable names +PROVIDER_API_KEY_ENV = { + "OPENAI": "OPENAI_API_KEY", + "ANTHROPIC": "ANTHROPIC_API_KEY", + "DASHSCOPE": "DASHSCOPE_API_KEY", + "ALIBABA": "DASHSCOPE_API_KEY", + "GEMINI": "GOOGLE_API_KEY", + "GOOGLE": "GOOGLE_API_KEY", + "DEEPSEEK": "DEEPSEEK_API_KEY", + "GROQ": "GROQ_API_KEY", + "OPENROUTER": "OPENROUTER_API_KEY", +} + + +def create_model( + model_name: str, + provider: str, + api_key: Optional[str] = None, + stream: bool = False, + **kwargs, +): + """ + Create an AgentScope model instance + + Args: + model_name: Model name (e.g., "gpt-4o", "claude-3-opus") + provider: Provider name (e.g., "OPENAI", "ANTHROPIC") + api_key: API key (optional, will read from env if not provided) + stream: Whether to use streaming mode + **kwargs: Additional model-specific arguments + + Returns: + AgentScope model instance + """ + provider = provider.upper() + + model_class = PROVIDER_MODEL_MAP.get(provider) + if model_class is None: + raise ValueError(f"Unsupported provider: {provider}") + + # Get API key from env if not provided + if api_key is None: + env_key = PROVIDER_API_KEY_ENV.get(provider) + if env_key: + api_key = os.getenv(env_key) + + # Build model kwargs + model_kwargs = { + "model_name": model_name, + "stream": stream, + **kwargs, + } + + # Add API key if needed (Ollama doesn't need it) + if provider != "OLLAMA" and api_key: + model_kwargs["api_key"] = api_key + + # Handle OpenAI-compatible providers with custom base_url + if provider in PROVIDER_BASE_URLS: + base_url = PROVIDER_BASE_URLS[provider] + model_kwargs["client_args"] = {"base_url": base_url} + + # Handle custom OpenAI base URL + if provider == "OPENAI": + base_url = os.getenv("OPENAI_BASE_URL") or os.getenv("OPENAI_API_BASE") + if base_url: + model_kwargs["client_args"] = {"base_url": base_url} + + # Handle DashScope base URL (uses different parameter) + if provider in ("DASHSCOPE", "ALIBABA"): + base_url = os.getenv("DASHSCOPE_BASE_URL") + if base_url: + model_kwargs["base_http_api_url"] = base_url + + # Handle Ollama host + if provider == "OLLAMA": + host = os.getenv("OLLAMA_HOST") + if host: + model_kwargs["host"] = host + + return model_class(**model_kwargs) + + +def get_agent_model(agent_id: str, stream: bool = False): + """ + Get model for a specific agent based on environment variables + + Environment variable pattern: + AGENT_{AGENT_ID}_MODEL_NAME: Model name + AGENT_{AGENT_ID}_MODEL_PROVIDER: Provider name + + fallback to global MODEL_NAME & MODEL_PROVIDER if agent-specific not given + + Args: + agent_id: Agent ID (e.g., "sentiment_analyst", "portfolio_manager") + stream: Whether to use streaming mode + + Returns: + AgentScope model instance + """ + # Normalize agent_id to uppercase for env var lookup + agent_key = agent_id.upper().replace("-", "_") + + # Try agent-specific config first + model_name = os.getenv(f"AGENT_{agent_key}_MODEL_NAME") + provider = os.getenv(f"AGENT_{agent_key}_MODEL_PROVIDER") + + print(f"Using specific model {model_name} for agent {agent_key}") + # Fall back to global config + if not model_name: + model_name = os.getenv("MODEL_NAME", "gpt-4o") + if not provider: + provider = os.getenv("MODEL_PROVIDER", "OPENAI") + + return create_model( + model_name=model_name, + provider=provider, + stream=stream, + ) + + +def get_agent_formatter(agent_id: str): + """ + Get formatter for a specific agent based on environment variables + + Args: + agent_id: Agent ID (e.g., "sentiment_analyst", "portfolio_manager") + + Returns: + AgentScope formatter instance + """ + # Normalize agent_id to uppercase for env var lookup + agent_key = agent_id.upper().replace("-", "_") + + # Try agent-specific config first + provider = os.getenv(f"AGENT_{agent_key}_MODEL_PROVIDER") + + # Fall back to global config + if not provider: + provider = os.getenv("MODEL_PROVIDER", "OPENAI") + + provider = provider.upper() + formatter_class = PROVIDER_FORMATTER_MAP.get(provider, OpenAIChatFormatter) + return formatter_class() + + +def get_agent_model_info(agent_id: str) -> Tuple[str, str]: + """ + Get model name and provider for a specific agent + + Args: + agent_id: Agent ID (e.g., "sentiment_analyst", "portfolio_manager") + + Returns: + Tuple of (model_name, provider_name) + """ + agent_key = agent_id.upper().replace("-", "_") + + model_name = os.getenv(f"AGENT_{agent_key}_MODEL_NAME") + provider = os.getenv(f"AGENT_{agent_key}_MODEL_PROVIDER") + + if not model_name: + model_name = os.getenv("MODEL_NAME", "gpt-4o") + if not provider: + provider = os.getenv("MODEL_PROVIDER", "OPENAI") + + return model_name, provider.upper() diff --git a/evotraders/backend/main.py b/evotraders/backend/main.py new file mode 100644 index 0000000..79338eb --- /dev/null +++ b/evotraders/backend/main.py @@ -0,0 +1,332 @@ +# -*- coding: utf-8 -*- +""" +Main Entry Point +Supports: backtest, live, mock modes +""" +import argparse +import asyncio +import logging +import os +from contextlib import AsyncExitStack +from pathlib import Path +import loguru + +from dotenv import load_dotenv + +from backend.agents import AnalystAgent, PMAgent, RiskAgent +from backend.config.constants import ANALYST_TYPES +from backend.config.env_config import get_env_float, get_env_int, get_env_list +from backend.core.pipeline import TradingPipeline +from backend.core.scheduler import BacktestScheduler, Scheduler +from backend.utils.settlement import SettlementCoordinator +from backend.llm.models import get_agent_formatter, get_agent_model +from backend.services.gateway import Gateway +from backend.services.market import MarketService +from backend.services.storage import StorageService + +load_dotenv() +logger = logging.getLogger(__name__) +loguru.logger.disable("flowllm") +loguru.logger.disable("reme_ai") + + +def create_long_term_memory(agent_name: str, config_name: str): + """ + Create ReMeTaskLongTermMemory for an agent + + Requires DASHSCOPE_API_KEY env var + """ + from agentscope.memory import ReMeTaskLongTermMemory + from agentscope.model import DashScopeChatModel + from agentscope.embedding import DashScopeTextEmbedding + + api_key = os.getenv("MEMORY_API_KEY") + if not api_key: + logger.warning("MEMORY_API_KEY not set, long-term memory disabled") + return None + + memory_dir = str(Path(config_name) / "memory") + + return ReMeTaskLongTermMemory( + agent_name=agent_name, + user_name=agent_name, + model=DashScopeChatModel( + model_name=os.getenv("MEMORY_MODEL_NAME", "qwen3-max"), + api_key=api_key, + stream=False, + ), + embedding_model=DashScopeTextEmbedding( + model_name=os.getenv( + "MEMORY_EMBEDDING_MODEL", + "text-embedding-v4", + ), + api_key=api_key, + dimensions=1024, + ), + **{ + "vector_store.default.backend": "local", + "vector_store.default.params.store_dir": memory_dir, + }, + ) + + +def create_agents( + config_name: str, + initial_cash: float, + margin_requirement: float, + enable_long_term_memory: bool = False, +): + """Create all agents for the system + + Returns: + tuple: (analysts, risk_manager, portfolio_manager, long_term_memories) + long_term_memories is a list of memory + """ + analysts = [] + long_term_memories = [] + + for analyst_type in ANALYST_TYPES: + model = get_agent_model(analyst_type) + formatter = get_agent_formatter(analyst_type) + toolkit = create_toolkit(analyst_type) + + long_term_memory = None + if enable_long_term_memory: + long_term_memory = create_long_term_memory( + analyst_type, + config_name, + ) + if long_term_memory: + long_term_memories.append(long_term_memory) + + analyst = AnalystAgent( + analyst_type=analyst_type, + toolkit=toolkit, + model=model, + formatter=formatter, + agent_id=analyst_type, + config={"config_name": config_name}, + long_term_memory=long_term_memory, + ) + analysts.append(analyst) + + risk_long_term_memory = None + if enable_long_term_memory: + risk_long_term_memory = create_long_term_memory( + "risk_manager", + config_name, + ) + if risk_long_term_memory: + long_term_memories.append(risk_long_term_memory) + + risk_manager = RiskAgent( + model=get_agent_model("risk_manager"), + formatter=get_agent_formatter("risk_manager"), + name="risk_manager", + config={"config_name": config_name}, + long_term_memory=risk_long_term_memory, + ) + + pm_long_term_memory = None + if enable_long_term_memory: + pm_long_term_memory = create_long_term_memory( + "portfolio_manager", + config_name, + ) + if pm_long_term_memory: + long_term_memories.append(pm_long_term_memory) + + portfolio_manager = PMAgent( + name="portfolio_manager", + model=get_agent_model("portfolio_manager"), + formatter=get_agent_formatter("portfolio_manager"), + initial_cash=initial_cash, + margin_requirement=margin_requirement, + config={"config_name": config_name}, + long_term_memory=pm_long_term_memory, + ) + + return analysts, risk_manager, portfolio_manager, long_term_memories + + +def create_toolkit(analyst_type: str): + """Create AgentScope Toolkit with tools for specific analyst type""" + from agentscope.tool import Toolkit + from backend.agents.prompt_loader import PromptLoader + from backend.tools.analysis_tools import TOOL_REGISTRY + + # Load analyst persona config + prompt_loader = PromptLoader() + personas_config = prompt_loader.load_yaml_config("analyst", "personas") + persona = personas_config.get(analyst_type, {}) + + # Get tool names for this analyst type + tool_names = persona.get("tools", []) + + # Create toolkit and register tools + toolkit = Toolkit() + for tool_name in tool_names: + tool_func = TOOL_REGISTRY.get(tool_name) + if tool_func: + toolkit.register_tool_function(tool_func) + + return toolkit + + +async def run_with_gateway(args): + """Run with WebSocket gateway""" + is_backtest = args.mode == "backtest" + + # Load config from env, override with args + tickers = get_env_list("TICKERS", ["AAPL", "MSFT"]) + initial_cash = get_env_float("INITIAL_CASH", 100000.0) + margin_requirement = get_env_float("MARGIN_REQUIREMENT", 0.0) + config_name = args.config_name + + # Create market service + market_service = MarketService( + tickers=tickers, + poll_interval=args.poll_interval, + mock_mode=args.mock and not is_backtest, + backtest_mode=is_backtest, + api_key=os.getenv("FINNHUB_API_KEY") + if not args.mock and not is_backtest + else None, + backtest_start_date=args.start_date if is_backtest else None, + backtest_end_date=args.end_date if is_backtest else None, + ) + + # Create storage service + storage_service = StorageService( + dashboard_dir=Path(config_name) / "team_dashboard", + initial_cash=initial_cash, + config_name=config_name, + ) + + if not storage_service.files["summary"].exists(): + storage_service.initialize_empty_dashboard() + else: + storage_service.update_leaderboard_model_info() + + # Create agents and pipeline + analysts, risk_manager, pm, long_term_memories = create_agents( + config_name=config_name, + initial_cash=initial_cash, + margin_requirement=margin_requirement, + enable_long_term_memory=args.enable_memory, + ) + portfolio_state = storage_service.load_portfolio_state() + pm.load_portfolio_state(portfolio_state) + + settlement_coordinator = SettlementCoordinator( + storage=storage_service, + initial_capital=initial_cash, + ) + + pipeline = TradingPipeline( + analysts=analysts, + risk_manager=risk_manager, + portfolio_manager=pm, + settlement_coordinator=settlement_coordinator, + max_comm_cycles=get_env_int("MAX_COMM_CYCLES", 2), + ) + + # Create scheduler callback + scheduler_callback = None + trading_dates = [] + + if is_backtest: + backtest_scheduler = BacktestScheduler( + start_date=args.start_date, + end_date=args.end_date, + trading_calendar="NYSE", + delay_between_days=0.5, + ) + trading_dates = backtest_scheduler.get_trading_dates() + + async def scheduler_callback_fn(callback): + await backtest_scheduler.start(callback) + + scheduler_callback = scheduler_callback_fn + else: + # Live mode: use daily scheduler with NYSE timezone + live_scheduler = Scheduler( + mode="daily", + trigger_time=args.trigger_time, + config={"config_name": config_name}, + ) + + async def scheduler_callback_fn(callback): + await live_scheduler.start(callback) + + scheduler_callback = scheduler_callback_fn + + # Create gateway + gateway = Gateway( + market_service=market_service, + storage_service=storage_service, + pipeline=pipeline, + scheduler_callback=scheduler_callback, + config={ + "mode": args.mode, + "mock_mode": args.mock, + "backtest_mode": is_backtest, + "tickers": tickers, + "config_name": config_name, + }, + ) + + if is_backtest: + gateway.set_backtest_dates(trading_dates) + + # Start long-term memory contexts and run gateway + async with AsyncExitStack() as stack: + for memory in long_term_memories: + await stack.enter_async_context(memory) + await gateway.start(host=args.host, port=args.port) + + +def main(): + """Main entry point""" + parser = argparse.ArgumentParser(description="Trading System") + parser.add_argument("--mode", choices=["live", "backtest"], default="live") + parser.add_argument("--mock", action="store_true") + parser.add_argument("--config-name", default="mock") + parser.add_argument("--host", default="0.0.0.0") + parser.add_argument("--port", type=int, default=8765) + parser.add_argument("--trigger-time", default="09:30") # NYSE market open + parser.add_argument("--poll-interval", type=int, default=10) + parser.add_argument("--start-date") + parser.add_argument("--end-date") + parser.add_argument( + "--enable-memory", + action="store_true", + help="Enable ReMeTaskLongTermMemory for agents", + ) + + args = parser.parse_args() + + # Load config from env for logging + tickers = get_env_list("TICKERS", ["AAPL", "MSFT"]) + initial_cash = get_env_float("INITIAL_CASH", 100000.0) + + logger.info("=" * 60) + logger.info(f"Mode: {args.mode}, Config: {args.config_name}") + logger.info(f"Tickers: {tickers}") + logger.info(f"Initial Cash: ${initial_cash:,.2f}") + logger.info( + f"Long-term Memory: {'enabled' if args.enable_memory else 'disabled'}", + ) + if args.mode == "backtest": + if not args.start_date or not args.end_date: + parser.error( + "--start-date and --end-date required for backtest mode", + ) + logger.info(f"Backtest: {args.start_date} to {args.end_date}") + logger.info("=" * 60) + + asyncio.run(run_with_gateway(args)) + + +if __name__ == "__main__": + main() diff --git a/evotraders/backend/services/__init__.py b/evotraders/backend/services/__init__.py new file mode 100644 index 0000000..249960b --- /dev/null +++ b/evotraders/backend/services/__init__.py @@ -0,0 +1,2 @@ +# -*- coding: utf-8 -*- +"""Services layer for infrastructure components""" diff --git a/evotraders/backend/services/gateway.py b/evotraders/backend/services/gateway.py new file mode 100644 index 0000000..3fdd744 --- /dev/null +++ b/evotraders/backend/services/gateway.py @@ -0,0 +1,542 @@ +# -*- coding: utf-8 -*- +""" +WebSocket Gateway for frontend communication +""" +import asyncio +import json +import logging +from datetime import datetime +from typing import Any, Callable, Dict, List, Optional, Set + +import websockets +from websockets.server import WebSocketServerProtocol + +from backend.utils.msg_adapter import FrontendAdapter +from backend.utils.terminal_dashboard import get_dashboard +from backend.core.pipeline import TradingPipeline +from backend.core.state_sync import StateSync +from backend.services.market import MarketService +from backend.services.storage import StorageService + +logger = logging.getLogger(__name__) + + +class Gateway: + """WebSocket Gateway for frontend communication""" + + def __init__( + self, + market_service: MarketService, + storage_service: StorageService, + pipeline: TradingPipeline, + state_sync: Optional[StateSync] = None, + scheduler_callback: Optional[Callable] = None, + config: Dict[str, Any] = None, + ): + self.market_service = market_service + self.storage = storage_service + self.pipeline = pipeline + self.scheduler_callback = scheduler_callback + self.config = config or {} + + self.mode = self.config.get("mode", "live") + self.is_backtest = self.mode == "backtest" or self.config.get( + "backtest_mode", + False, + ) + + self.state_sync = state_sync or StateSync(storage=storage_service) + # self.state_sync.set_mode(self.is_backtest) + self.state_sync.set_broadcast_fn(self.broadcast) + self.pipeline.state_sync = self.state_sync + + self.connected_clients: Set[WebSocketServerProtocol] = set() + self.lock = asyncio.Lock() + self._backtest_task: Optional[asyncio.Task] = None + self._backtest_start_date: Optional[str] = None + self._backtest_end_date: Optional[str] = None + self._dashboard = get_dashboard() + self._market_status_task: Optional[asyncio.Task] = None + + # Session tracking for live returns + self._session_start_portfolio_value: Optional[float] = None + + async def start(self, host: str = "0.0.0.0", port: int = 8766): + """Start gateway server""" + logger.info(f"Starting gateway on {host}:{port}") + + # Initialize terminal dashboard + self._dashboard.set_config( + mode=self.mode, + config_name=self.config.get("config_name", "default"), + host=host, + port=port, + poll_interval=self.config.get("poll_interval", 10), + mock=self.config.get("mock_mode", False), + tickers=self.config.get("tickers", []), + initial_cash=self.storage.initial_cash, + start_date=self._backtest_start_date or "", + end_date=self._backtest_end_date or "", + ) + self._dashboard.start() + + self.state_sync.load_state() + self.state_sync.update_state("status", "running") + self.state_sync.update_state("server_mode", self.mode) + self.state_sync.update_state("is_backtest", self.is_backtest) + self.state_sync.update_state( + "is_mock_mode", + self.config.get("mock_mode", False), + ) + + # Load and display existing portfolio state if available + summary = self.storage.load_file("summary") + if summary: + holdings = self.storage.load_file("holdings") or [] + trades = self.storage.load_file("trades") or [] + current_date = self.state_sync.state.get("current_date") + self._dashboard.update( + date=current_date or "-", + status="running", + portfolio=summary, + holdings=holdings, + trades=trades, + ) + logger.info( + "Loaded existing portfolio: $%s", + f"{summary.get('totalAssetValue', 0):,.2f}", + ) + + await self.market_service.start(broadcast_func=self.broadcast) + + if self.scheduler_callback: + await self.scheduler_callback(callback=self.on_strategy_trigger) + + # Start market status monitoring (only for live mode) + if not self.is_backtest: + self._market_status_task = asyncio.create_task( + self._market_status_monitor(), + ) + + async with websockets.serve( + self.handle_client, + host, + port, + ping_interval=30, + ping_timeout=60, + ): + logger.info( + f"Gateway started: ws://{host}:{port}, mode={self.mode}", + ) + await asyncio.Future() + + @property + def state(self) -> Dict[str, Any]: + return self.state_sync.state + + async def handle_client(self, websocket: WebSocketServerProtocol): + """Handle WebSocket client connection""" + async with self.lock: + self.connected_clients.add(websocket) + + await self._send_initial_state(websocket) + await self._handle_client_messages(websocket) + + async with self.lock: + self.connected_clients.discard(websocket) + + async def _send_initial_state(self, websocket: WebSocketServerProtocol): + state_payload = self.state_sync.get_initial_state_payload( + include_dashboard=True, + ) + # Include market status in initial state + state_payload[ + "market_status" + ] = self.market_service.get_market_status() + + # Include live returns if session is active + if self.storage.is_live_session_active: + live_returns = self.storage.get_live_returns() + if "portfolio" in state_payload: + state_payload["portfolio"].update(live_returns) + + await websocket.send( + json.dumps( + {"type": "initial_state", "state": state_payload}, + ensure_ascii=False, + default=str, + ), + ) + + async def _handle_client_messages( + self, + websocket: WebSocketServerProtocol, + ): + try: + async for message in websocket: + data = json.loads(message) + msg_type = data.get("type", "unknown") + + if msg_type == "ping": + await websocket.send( + json.dumps( + { + "type": "pong", + "timestamp": datetime.now().isoformat(), + }, + ensure_ascii=False, + ), + ) + elif msg_type == "get_state": + await self._send_initial_state(websocket) + elif msg_type == "start_backtest": + await self._handle_start_backtest(data) + + except websockets.ConnectionClosed: + pass + except json.JSONDecodeError: + pass + + async def _handle_start_backtest(self, data: Dict[str, Any]): + if not self.is_backtest: + return + dates = data.get("dates", []) + if dates and self._backtest_task is None: + self._backtest_task = asyncio.create_task( + self._run_backtest_dates(dates), + ) + + async def broadcast(self, message: Dict[str, Any]): + """Broadcast message to all connected clients""" + if not self.connected_clients: + return + + message_json = json.dumps(message, ensure_ascii=False, default=str) + + async with self.lock: + tasks = [ + self._send_to_client(client, message_json) + for client in self.connected_clients.copy() + ] + + if tasks: + await asyncio.gather(*tasks, return_exceptions=True) + + async def _send_to_client( + self, + client: WebSocketServerProtocol, + message: str, + ): + try: + await client.send(message) + except websockets.ConnectionClosed: + async with self.lock: + self.connected_clients.discard(client) + + async def _market_status_monitor(self): + """Periodically check and broadcast market status changes""" + while True: + try: + await self.market_service.check_and_broadcast_market_status() + + # On market open, start live session tracking + status = self.market_service.get_market_status() + if ( + status["status"] == "open" + and not self.storage.is_live_session_active + ): + self.storage.start_live_session() + summary = self.storage.load_file("summary") or {} + self._session_start_portfolio_value = summary.get( + "totalAssetValue", + self.storage.initial_cash, + ) + logger.info( + "Session start portfolio: " + f"${self._session_start_portfolio_value:,.2f}", + ) + elif ( + status["status"] != "open" + and self.storage.is_live_session_active + ): + self.storage.end_live_session() + self._session_start_portfolio_value = None + + # Update and broadcast live returns if session is active + if self.storage.is_live_session_active: + await self._update_and_broadcast_live_returns() + + await asyncio.sleep(60) # Check every minute + except asyncio.CancelledError: + break + except Exception as e: + logger.error(f"Market status monitor error: {e}") + await asyncio.sleep(60) + + async def _update_and_broadcast_live_returns(self): + """Calculate and broadcast live returns for current session""" + if not self.storage.is_live_session_active: + return + + # Get current prices and calculate portfolio value + prices = self.market_service.get_all_prices() + if not prices or not any(p > 0 for p in prices.values()): + return + + # Load current internal state to get baseline values + state = self.storage.load_internal_state() + + # Get latest values from history (if available) + equity_history = state.get("equity_history", []) + baseline_history = state.get("baseline_history", []) + baseline_vw_history = state.get("baseline_vw_history", []) + momentum_history = state.get("momentum_history", []) + + current_equity = equity_history[-1]["v"] if equity_history else None + current_baseline = ( + baseline_history[-1]["v"] if baseline_history else None + ) + current_baseline_vw = ( + baseline_vw_history[-1]["v"] if baseline_vw_history else None + ) + current_momentum = ( + momentum_history[-1]["v"] if momentum_history else None + ) + + # Update live returns with current values + point = self.storage.update_live_returns( + current_equity=current_equity, + current_baseline=current_baseline, + current_baseline_vw=current_baseline_vw, + current_momentum=current_momentum, + ) + + # Broadcast if we have new data + if point: + live_returns = self.storage.get_live_returns() + await self.broadcast( + { + "type": "team_summary", + "equity_return": live_returns["equity_return"], + "baseline_return": live_returns["baseline_return"], + "baseline_vw_return": live_returns["baseline_vw_return"], + "momentum_return": live_returns["momentum_return"], + }, + ) + + async def on_strategy_trigger(self, date: str): + """Handle trading cycle trigger""" + logger.info(f"Strategy triggered for {date}") + + tickers = self.config.get("tickers", []) + + if self.is_backtest: + await self._run_backtest_cycle(date, tickers) + else: + await self._run_live_cycle(date, tickers) + + async def _run_backtest_cycle(self, date: str, tickers: List[str]): + """Run backtest cycle with pre-loaded prices""" + self.market_service.set_backtest_date(date) + await self.market_service.emit_market_open() + + await self.state_sync.on_cycle_start(date) + self._dashboard.update(date=date, status="Analyzing...") + + prices = self.market_service.get_open_prices() + close_prices = self.market_service.get_close_prices() + market_caps = self._get_market_caps(tickers, date) + + result = await self.pipeline.run_cycle( + tickers=tickers, + date=date, + prices=prices, + close_prices=close_prices, + market_caps=market_caps, + ) + + await self.market_service.emit_market_close() + settlement_result = result.get("settlement_result") + self._save_cycle_results(result, date, close_prices, settlement_result) + await self._broadcast_portfolio_updates(result, close_prices) + await self._finalize_cycle(date) + + async def _run_live_cycle(self, date: str, tickers: List[str]): + """ + Run live cycle with real market timing. + + - Analysis runs immediately + - Execution waits for market open + (or uses current prices if already open) + - Settlement waits for market close + """ + # Get actual trading date (might be next trading day if weekend) + trading_date = self.market_service.get_live_trading_date() + logger.info( + f"Live cycle: triggered={date}, trading_date={trading_date}", + ) + + await self.state_sync.on_cycle_start(trading_date) + self._dashboard.update(date=trading_date, status="Analyzing...") + + market_caps = self._get_market_caps(tickers, trading_date) + + # Run pipeline with async price callbacks + result = await self.pipeline.run_cycle( + tickers=tickers, + date=trading_date, + market_caps=market_caps, + get_open_prices_fn=self.market_service.wait_for_open_prices, + get_close_prices_fn=self.market_service.wait_for_close_prices, + ) + + close_prices = self.market_service.get_all_prices() + settlement_result = result.get("settlement_result") + self._save_cycle_results( + result, + trading_date, + close_prices, + settlement_result, + ) + await self._broadcast_portfolio_updates(result, close_prices) + await self._finalize_cycle(trading_date) + + async def _finalize_cycle(self, date: str): + """Finalize cycle: broadcast state and update dashboard""" + summary = self.storage.load_file("summary") or {} + + # Include live returns if session is active + if self.storage.is_live_session_active: + live_returns = self.storage.get_live_returns() + summary.update(live_returns) + + await self.state_sync.on_cycle_end(date, portfolio_summary=summary) + + holdings = self.storage.load_file("holdings") or [] + trades = self.storage.load_file("trades") or [] + self._dashboard.update( + date=date, + status="Running", + portfolio=summary, + holdings=holdings, + trades=trades, + ) + + def _get_market_caps( + self, + tickers: List[str], + date: str, + ) -> Dict[str, float]: + """ + Get market caps for tickers (stub implementation) + + Args: + tickers: List of tickers + date: Trading date + + Returns: + Dict mapping ticker to market cap + """ + from ..tools.data_tools import get_market_cap + + market_caps = {} + for ticker in tickers: + try: + market_cap = get_market_cap(ticker, date) + if market_cap: + market_caps[ticker] = market_cap + else: + market_caps[ticker] = 1e9 + except Exception: + market_caps[ticker] = 1e9 + + return market_caps + + async def _broadcast_portfolio_updates( + self, + result: Dict[str, Any], + prices: Dict[str, float], + ): + portfolio = result.get("portfolio", {}) + + if portfolio: + holdings = FrontendAdapter.build_holdings(portfolio, prices) + if holdings: + await self.state_sync.on_holdings_update(holdings) + + stats = FrontendAdapter.build_stats(portfolio, prices) + if stats: + await self.state_sync.on_stats_update(stats) + + executed_trades = result.get("executed_trades", []) + if executed_trades: + await self.state_sync.on_trades_executed(executed_trades) + + def _save_cycle_results( + self, + result: Dict[str, Any], + date: str, + prices: Dict[str, float], + settlement_result: Optional[Dict[str, Any]] = None, + ): + portfolio = result.get("portfolio", {}) + executed_trades = result.get("executed_trades", []) + + # Extract baseline values from settlement result + baseline_values = None + if settlement_result: + baseline_values = settlement_result.get("baseline_values") + + if portfolio: + self.storage.update_dashboard_after_cycle( + portfolio=portfolio, + prices=prices, + date=date, + executed_trades=executed_trades, + baseline_values=baseline_values, + ) + + async def _run_backtest_dates(self, dates: List[str]): + self.state_sync.set_backtest_dates(dates) + self._dashboard.update(days_total=len(dates), days_completed=0) + + await self.state_sync.on_system_message( + f"Starting backtest - {len(dates)} trading days", + ) + + for i, date in enumerate(dates): + self._dashboard.update(days_completed=i) + await self.on_strategy_trigger(date=date) + await asyncio.sleep(0.1) + + await self.state_sync.on_system_message( + f"Backtest complete - {len(dates)} days", + ) + + # Update dashboard with final state + summary = self.storage.load_file("summary") or {} + self._dashboard.update( + status="Complete", + portfolio=summary, + days_completed=len(dates), + ) + self._dashboard.stop() + self._dashboard.print_final_summary() + + self._backtest_task = None + + def set_backtest_dates(self, dates: List[str]): + self.state_sync.set_backtest_dates(dates) + if dates: + self._backtest_start_date = dates[0] + self._backtest_end_date = dates[-1] + self._dashboard.days_total = len(dates) + + def stop(self): + self.state_sync.save_state() + self.market_service.stop() + if self._backtest_task: + self._backtest_task.cancel() + if self._market_status_task: + self._market_status_task.cancel() + self._dashboard.stop() diff --git a/evotraders/backend/services/market.py b/evotraders/backend/services/market.py new file mode 100644 index 0000000..7a337e2 --- /dev/null +++ b/evotraders/backend/services/market.py @@ -0,0 +1,625 @@ +# -*- coding: utf-8 -*- +""" +Market Data Service +Supports live, mock, and backtest modes +""" +import asyncio +import logging +from datetime import datetime, timedelta +from typing import Any, Callable, Dict, List, Optional +from zoneinfo import ZoneInfo + +import pandas_market_calendars as mcal + +logger = logging.getLogger(__name__) + +# NYSE timezone and calendar +NYSE_TZ = ZoneInfo("America/New_York") +NYSE_CALENDAR = mcal.get_calendar("NYSE") + + +class MarketStatus: + """Market status enum-like class""" + + OPEN = "open" + CLOSED = "closed" + PREMARKET = "premarket" + AFTERHOURS = "afterhours" + + +class MarketService: + """Market data service for price management""" + + def __init__( + self, + tickers: List[str], + poll_interval: int = 10, + mock_mode: bool = False, + backtest_mode: bool = False, + api_key: Optional[str] = None, + backtest_start_date: Optional[str] = None, + backtest_end_date: Optional[str] = None, + ): + self.tickers = tickers + self.poll_interval = poll_interval + self.mock_mode = mock_mode + self.backtest_mode = backtest_mode + self.api_key = api_key + self.backtest_start_date = backtest_start_date + self.backtest_end_date = backtest_end_date + + self.cache: Dict[str, Dict[str, Any]] = {} + self.running = False + self._loop: Optional[asyncio.AbstractEventLoop] = None + self._broadcast_func: Optional[Callable] = None + self._price_manager: Optional[Any] = None + self._current_date: Optional[str] = None + + # Market status tracking + self._last_market_status: Optional[str] = None + + # Session tracking for live returns + self._session_start_values: Optional[Dict[str, float]] = None + self._session_start_timestamp: Optional[int] = None + + @property + def mode_name(self) -> str: + if self.backtest_mode: + return "BACKTEST" + elif self.mock_mode: + return "MOCK" + return "LIVE" + + async def start(self, broadcast_func: Callable): + """Start market data service""" + if self.running: + return + + self.running = True + self._loop = asyncio.get_running_loop() + self._broadcast_func = broadcast_func + + if self.backtest_mode: + self._start_backtest_mode() + elif self.mock_mode: + self._start_mock_mode() + else: + self._start_real_mode() + + logger.info( + f"Market service started: {self.mode_name}, tickers={self.tickers}", # noqa: E501 + ) + + def _make_price_callback(self) -> Callable: + """Create thread-safe price callback""" + + def callback(price_data: Dict[str, Any]): + symbol = price_data["symbol"] + self.cache[symbol] = price_data + + loop = self._loop + if loop and loop.is_running() and self._broadcast_func: + asyncio.run_coroutine_threadsafe( + self._broadcast_price_update(price_data), + loop, + ) + + return callback + + def _start_mock_mode(self): + from backend.data.mock_price_manager import MockPriceManager + + self._price_manager = MockPriceManager( + poll_interval=self.poll_interval, + volatility=0.5, + ) + self._price_manager.add_price_callback(self._make_price_callback()) + self._price_manager.subscribe( + self.tickers, + base_prices={t: 100.0 for t in self.tickers}, + ) + self._price_manager.start() + + def _start_real_mode(self): + from backend.data.polling_price_manager import PollingPriceManager + + if not self.api_key: + raise ValueError("API key required for live mode") + self._price_manager = PollingPriceManager( + api_key=self.api_key, + poll_interval=self.poll_interval, + ) + self._price_manager.add_price_callback(self._make_price_callback()) + self._price_manager.subscribe(self.tickers) + self._price_manager.start() + + def _start_backtest_mode(self): + from backend.data.historical_price_manager import ( + HistoricalPriceManager, + ) + + self._price_manager = HistoricalPriceManager() + self._price_manager.add_price_callback(self._make_price_callback()) + self._price_manager.subscribe(self.tickers) + + if self.backtest_start_date and self.backtest_end_date: + self._price_manager.preload_data( + self.backtest_start_date, + self.backtest_end_date, + ) + + self._price_manager.start() + + async def _broadcast_price_update(self, price_data: Dict[str, Any]): + """Broadcast price update to frontend""" + if not self._broadcast_func: + return + + symbol = price_data["symbol"] + price = price_data["price"] + open_price = price_data.get("open", price) + ret = ( + ((price - open_price) / open_price) * 100 if open_price > 0 else 0 + ) + + await self._broadcast_func( + { + "type": "price_update", + "symbol": symbol, + "price": price, + "open": open_price, + "ret": ret, + "timestamp": price_data.get("timestamp"), + "realtime_prices": { + t: self._get_cached_price(t) for t in self.tickers + }, + }, + ) + + def _get_cached_price(self, ticker: str) -> Dict[str, Any]: + """Get cached price data for a ticker""" + if ticker in self.cache: + return self.cache[ticker] + # Return from price manager if not in cache + if self._price_manager: + price = self._price_manager.get_latest_price(ticker) + if price: + return {"price": price, "symbol": ticker} + return {"price": 0, "symbol": ticker} + + def stop(self): + """Stop market service""" + if not self.running: + return + self.running = False + if self._price_manager: + self._price_manager.stop() + self._price_manager = None + self._loop = None + self._broadcast_func = None + + # Backtest methods + def set_backtest_date(self, date: str): + """Set current backtest date""" + if not self.backtest_mode or not self._price_manager: + return + self._current_date = date + self._price_manager.set_date(date) + logger.info(f"Backtest date: {date}") + + async def emit_market_open(self): + """Emit market open prices""" + if self.backtest_mode and self._price_manager: + self._price_manager.emit_open_prices() + # Log prices for debugging + prices = self.get_open_prices() + logger.info(f"Open prices: {prices}") + + async def emit_market_close(self): + """Emit market close prices""" + if self.backtest_mode and self._price_manager: + self._price_manager.emit_close_prices() + # Log prices for debugging + prices = self.get_close_prices() + logger.info(f"Close prices: {prices}") + + def get_open_prices(self) -> Dict[str, float]: + """Get open prices for all tickers""" + prices = {} + for ticker in self.tickers: + price = None + # Try price manager first + if self.backtest_mode and self._price_manager: + price = self._price_manager.get_open_price(ticker) + # Fallback to cache + if price is None or price <= 0: + cached = self.cache.get(ticker, {}) + price = cached.get("open") or cached.get("price") + prices[ticker] = price if price and price > 0 else 0.0 + return prices + + def get_close_prices(self) -> Dict[str, float]: + """Get close prices for all tickers""" + prices = {} + for ticker in self.tickers: + price = None + # Try price manager first + if self.backtest_mode and self._price_manager: + price = self._price_manager.get_close_price(ticker) + # Fallback to cache + if price is None or price <= 0: + cached = self.cache.get(ticker, {}) + price = cached.get("close") or cached.get("price") + prices[ticker] = price if price and price > 0 else 0.0 + return prices + + def get_price_for_date( + self, + ticker: str, + date: str, + price_type: str = "close", + ) -> Optional[float]: + """Get price for a specific date""" + if self.backtest_mode and self._price_manager: + return self._price_manager.get_price_for_date( + ticker, + date, + price_type, + ) + return self.get_price_sync(ticker) + + # Common methods + def get_price_sync(self, ticker: str) -> Optional[float]: + """Get latest price synchronously""" + # Try cache first + data = self.cache.get(ticker) + if data and data.get("price"): + return data["price"] + # Try price manager + if self._price_manager: + return self._price_manager.get_latest_price(ticker) + return None + + def get_all_prices(self) -> Dict[str, float]: + """Get all latest prices""" + prices = {} + for ticker in self.tickers: + price = self.get_price_sync(ticker) + prices[ticker] = price if price and price > 0 else 0.0 + return prices + + # Live mode async waiting methods + + def _now_nyse(self) -> datetime: + """Get current time in NYSE timezone""" + return datetime.now(NYSE_TZ) + + def _is_trading_day(self, date: datetime) -> bool: + """Check if date is a NYSE trading day""" + date_str = date.strftime("%Y-%m-%d") + valid_days = NYSE_CALENDAR.valid_days( + start_date=date_str, + end_date=date_str, + ) + return len(valid_days) > 0 + + def _get_market_hours(self, date: datetime) -> tuple: + """Get market open and close times for a given date""" + date_str = date.strftime("%Y-%m-%d") + schedule = NYSE_CALENDAR.schedule( + start_date=date_str, + end_date=date_str, + ) + if schedule.empty: + return None, None + market_open = schedule.iloc[0]["market_open"].to_pydatetime() + market_close = schedule.iloc[0]["market_close"].to_pydatetime() + return market_open, market_close + + def _next_trading_day(self, from_date: datetime) -> datetime: + """Find the next trading day from given date""" + check_date = from_date + timedelta(days=1) + for _ in range(10): # Max 10 days ahead (handles holidays) + if self._is_trading_day(check_date): + return check_date + check_date += timedelta(days=1) + return check_date + + def _get_trading_date_for_execution(self) -> tuple: + """ + Determine the trading date for execution. + + Returns: + (trading_date, market_open_time, market_close_time) + + Logic: + - If today is a trading day and market has opened: use today + - If today is a trading day but market hasn't opened: wait for open + - If today is not a trading day: use next trading day + """ + now = self._now_nyse() + today = now.replace(hour=0, minute=0, second=0, microsecond=0) + + if self._is_trading_day(today): + market_open, market_close = self._get_market_hours(today) + return today, market_open, market_close + else: + # Weekend or holiday - find next trading day + next_day = self._next_trading_day(today) + market_open, market_close = self._get_market_hours(next_day) + return next_day, market_open, market_close + + async def wait_for_open_prices(self) -> Dict[str, float]: + """ + Wait for market open and return open prices. + + Behavior: + - If market is already open today: return current prices immediately + - If market hasn't opened yet today: wait until open + - If not a trading day: wait until next trading day opens + """ + now = self._now_nyse() + trading_date, market_open, _ = self._get_trading_date_for_execution() + + if market_open is None: + logger.warning("Could not determine market hours") + return self.get_all_prices() + + trading_date_str = trading_date.strftime("%Y-%m-%d") + + # Check if we need to wait + if now < market_open: + wait_seconds = (market_open - now).total_seconds() + logger.info( + f"Waiting {wait_seconds/60:.1f} min for market open " + f"({trading_date_str} {market_open.strftime('%H:%M')} ET)", + ) + await asyncio.sleep(wait_seconds) + # Small delay to ensure prices are available + await asyncio.sleep(5) + else: + logger.info( + f"Market already open for {trading_date_str}, " + f"getting current prices", + ) + + # Poll until we have valid prices + prices = await self._poll_for_prices() + logger.info(f"Got open prices for {trading_date_str}: {prices}") + return prices + + async def wait_for_close_prices(self) -> Dict[str, float]: + """ + Wait for market close and return close prices. + + Behavior: + - If market is already closed today: return current prices immediately + - If market hasn't closed yet: wait until close + """ + now = self._now_nyse() + trading_date, _, market_close = self._get_trading_date_for_execution() + + if market_close is None: + logger.warning("Could not determine market hours") + return self.get_all_prices() + + trading_date_str = trading_date.strftime("%Y-%m-%d") + + # Check if we need to wait + if now < market_close: + wait_seconds = (market_close - now).total_seconds() + logger.info( + f"Waiting {wait_seconds/60:.1f} min for market close " + f"({trading_date_str} {market_close.strftime('%H:%M')} ET)", + ) + await asyncio.sleep(wait_seconds) + # Small delay to ensure final prices settle + await asyncio.sleep(10) + else: + logger.info( + f"Market already closed for {trading_date_str}, " + f"getting close prices", + ) + + # Get final prices + prices = await self._poll_for_prices() + logger.info(f"Got close prices for {trading_date_str}: {prices}") + return prices + + def get_live_trading_date(self) -> str: + """Get the trading date that will be used for live execution""" + trading_date, _, _ = self._get_trading_date_for_execution() + return trading_date.strftime("%Y-%m-%d") + + async def _poll_for_prices( + self, + max_retries: int = 12, + ) -> Dict[str, float]: + """Poll until all prices are available""" + for _ in range(max_retries): + prices = self.get_all_prices() + if all(p > 0 for p in prices.values()): + return prices + logger.debug("Waiting for prices to be available...") + await asyncio.sleep(5) + # Return whatever we have + return self.get_all_prices() + + # ========== Market Status Methods ========== + + def get_market_status(self) -> Dict[str, Any]: + """ + Get current market status + + Returns: + Dict with status info: + - status: 'open' | 'closed' | 'premarket' | 'afterhours' + - status_text: Human readable status + - is_trading_day: Whether today is a trading day + - market_open: Market open time (if trading day) + - market_close: Market close time (if trading day) + """ + if self.backtest_mode: + # In backtest mode, always return open + return { + "status": MarketStatus.OPEN, + "status_text": "Backtest Mode", + "is_trading_day": True, + } + + now = self._now_nyse() + today = now.replace(hour=0, minute=0, second=0, microsecond=0) + + is_trading = self._is_trading_day(today) + + if not is_trading: + return { + "status": MarketStatus.CLOSED, + "status_text": "Market Closed (Non-trading Day)", + "is_trading_day": False, + } + + market_open, market_close = self._get_market_hours(today) + + if market_open is None or market_close is None: + return { + "status": MarketStatus.CLOSED, + "status_text": "Market Closed", + "is_trading_day": is_trading, + } + + # Determine status based on current time + if now < market_open: + return { + "status": MarketStatus.PREMARKET, + "status_text": "Pre-Market", + "is_trading_day": True, + "market_open": market_open.isoformat(), + "market_close": market_close.isoformat(), + } + elif now > market_close: + return { + "status": MarketStatus.CLOSED, + "status_text": "Market Closed", + "is_trading_day": True, + "market_open": market_open.isoformat(), + "market_close": market_close.isoformat(), + } + else: + return { + "status": MarketStatus.OPEN, + "status_text": "Market Open", + "is_trading_day": True, + "market_open": market_open.isoformat(), + "market_close": market_close.isoformat(), + } + + async def check_and_broadcast_market_status(self): + """Check market status and broadcast if changed""" + status = self.get_market_status() + current_status = status["status"] + + if current_status != self._last_market_status: + self._last_market_status = current_status + await self._broadcast_market_status(status) + + # Handle session transitions + if current_status == MarketStatus.OPEN: + await self._on_session_start() + elif ( + current_status == MarketStatus.CLOSED + and self._session_start_values is not None + ): + self._on_session_end() + + async def _broadcast_market_status(self, status: Dict[str, Any]): + """Broadcast market status update to frontend""" + if not self._broadcast_func: + return + + await self._broadcast_func( + { + "type": "market_status_update", + "market_status": status, + "timestamp": datetime.now(NYSE_TZ).isoformat(), + }, + ) + logger.info(f"Market status: {status['status_text']}") + + async def _on_session_start(self): + """Called when market session starts - capture baseline values""" + # Wait briefly for prices to be available + await asyncio.sleep(2) + + prices = self.get_all_prices() + if prices and any(p > 0 for p in prices.values()): + self._session_start_values = prices.copy() + self._session_start_timestamp = int( + datetime.now().timestamp() * 1000, + ) + logger.info(f"Session started with prices: {prices}") + + def _on_session_end(self): + """Called when market session ends - clear session data""" + self._session_start_values = None + self._session_start_timestamp = None + logger.info("Session ended, cleared session data") + + def get_session_returns( + self, + current_prices: Dict[str, float], + portfolio_value: Optional[float] = None, + session_start_portfolio_value: Optional[float] = None, + ) -> Optional[Dict[str, Any]]: + """ + Calculate session returns (from session start to now) + + Args: + current_prices: Current prices for tickers + portfolio_value: Current portfolio value (optional) + session_start_portfolio_value: + + Returns: + Dict with return data or None if session not started + """ + if self._session_start_values is None: + return None + + timestamp = int(datetime.now().timestamp() * 1000) + returns = {} + + # Calculate individual ticker returns + for ticker, start_price in self._session_start_values.items(): + current = current_prices.get(ticker) + if current and start_price and start_price > 0: + ret = ((current - start_price) / start_price) * 100 + returns[ticker] = round(ret, 4) + + result = { + "timestamp": timestamp, + "ticker_returns": returns, + } + + # Calculate portfolio return if values provided + if ( + portfolio_value is not None + and session_start_portfolio_value is not None + ): + if session_start_portfolio_value > 0: + portfolio_ret = ( + (portfolio_value - session_start_portfolio_value) + / session_start_portfolio_value + ) * 100 + result["portfolio_return"] = round(portfolio_ret, 4) + + return result + + @property + def session_start_values(self) -> Optional[Dict[str, float]]: + """Get session start values for external use""" + return self._session_start_values + + @property + def session_start_timestamp(self) -> Optional[int]: + """Get session start timestamp""" + return self._session_start_timestamp diff --git a/evotraders/backend/services/storage.py b/evotraders/backend/services/storage.py new file mode 100644 index 0000000..30608d8 --- /dev/null +++ b/evotraders/backend/services/storage.py @@ -0,0 +1,1099 @@ +# -*- coding: utf-8 -*- +""" +Storage Service - Data persistence and dashboard file management +Handles reading/writing dashboard JSON files and portfolio state +""" +# pylint: disable=R0904 +import json +import logging +from datetime import datetime +from pathlib import Path +from typing import Any, Dict, List, Optional + +logger = logging.getLogger(__name__) + + +class StorageService: + """ + Storage service for data persistence + + Responsibilities: + 1. Load/save dashboard JSON files + (summary, holdings, stats, trades, leaderboard) + 2. Load/save internal state (_internal_state.json) + 3. Load/save server state (server_state.json) with feed history + 4. Manage portfolio state persistence + 5. Support loading from saved state to resume execution + """ + + def __init__( + self, + dashboard_dir: Path, + initial_cash: float = 100000.0, + config_name: str = "mock", + ): + """ + Initialize storage service + + Args: + dashboard_dir: Directory for dashboard files + initial_cash: Initial cash amount + config_name: Configuration name for state directory + """ + self.dashboard_dir = Path(dashboard_dir) + self.dashboard_dir.mkdir(parents=True, exist_ok=True) + self.initial_cash = initial_cash + self.config_name = config_name + + # Dashboard file paths + self.files = { + "summary": self.dashboard_dir / "summary.json", + "holdings": self.dashboard_dir / "holdings.json", + "stats": self.dashboard_dir / "stats.json", + "trades": self.dashboard_dir / "trades.json", + "leaderboard": self.dashboard_dir / "leaderboard.json", + } + + # Internal state file + self.internal_state_file = self.dashboard_dir / "_internal_state.json" + + # Server state directory and file + self.state_dir = self.dashboard_dir.parent / "state" + self.state_dir.mkdir(parents=True, exist_ok=True) + self.server_state_file = self.state_dir / "server_state.json" + + # Feed history (for agent messages) + self.max_feed_history = 200 + + # File modification time cache (for change detection) + self.file_mtimes: Dict[str, float] = {} + + # Live returns tracking (for current trading session) + self._session_start_equity: Optional[float] = None + self._session_start_baseline: Optional[float] = None + self._session_start_baseline_vw: Optional[float] = None + self._session_start_momentum: Optional[float] = None + self._live_return_history: List[Dict[str, Any]] = [] + + logger.info(f"Storage service initialized: {self.dashboard_dir}") + + def load_file(self, file_type: str) -> Optional[Any]: + """ + Load dashboard JSON file + + Args: + file_type: One of: summary, holdings, stats, trades, leaderboard + + Returns: + Loaded data or None if file doesn't exist + """ + file_path = self.files.get(file_type) + if not file_path or not file_path.exists(): + return None + + try: + with open(file_path, "r", encoding="utf-8") as f: + return json.load(f) + except Exception as e: + logger.error(f"Failed to load {file_type}.json: {e}") + return None + + def save_file(self, file_type: str, data: Any): + """ + Save dashboard JSON file + + Args: + file_type: One of: summary, holdings, stats, trades, leaderboard + data: Data to save + """ + file_path = self.files.get(file_type) + if not file_path: + logger.error(f"Unknown file type: {file_type}") + return + + try: + with open(file_path, "w", encoding="utf-8") as f: + json.dump(data, f, indent=2, ensure_ascii=False) + except Exception as e: + logger.error(f"Failed to save {file_type}.json: {e}") + + def check_file_updates(self) -> Dict[str, bool]: + """ + Check which dashboard files have been updated since last check + + Returns: + Dictionary mapping file_type to whether it was updated + """ + updated = {} + + for file_type, file_path in self.files.items(): + if not file_path.exists(): + updated[file_type] = False + continue + + try: + current_mtime = file_path.stat().st_mtime + last_mtime = self.file_mtimes.get(file_type, 0) + + if current_mtime > last_mtime: + updated[file_type] = True + self.file_mtimes[file_type] = current_mtime + else: + updated[file_type] = False + except Exception as e: + logger.error(f"Failed to check file update ({file_type}): {e}") + updated[file_type] = False + + return updated + + def load_internal_state(self) -> Dict[str, Any]: + """ + Load internal state from file + + Returns: + Internal state dictionary with default values + """ + default_state = { + "baseline_state": {"initialized": False, "initial_allocation": {}}, + "baseline_vw_state": { + "initialized": False, + "initial_allocation": {}, + }, + "momentum_state": { + "positions": {}, + "cash": 0.0, + "initialized": False, + }, + "equity_history": [], + "baseline_history": [], + "baseline_vw_history": [], + "momentum_history": [], + "price_history": {}, + "portfolio_state": { + "cash": self.initial_cash, + "positions": {}, + "margin_used": 0.0, + }, + "all_trades": [], + "daily_position_history": {}, + } + + if not self.internal_state_file.exists(): + return default_state + + try: + with open(self.internal_state_file, "r", encoding="utf-8") as f: + data = json.load(f) + + # Merge with defaults + for key, value in default_state.items(): + data.setdefault(key, value) + + logger.info("Loaded internal state from file") + return data + + except Exception as e: + logger.warning( + f"Failed to load internal state, using defaults: {e}", + ) + return default_state + + def save_internal_state(self, state: Dict[str, Any]): + """ + Save internal state to file + + Args: + state: Internal state dictionary + """ + if not state: + return + + try: + with open(self.internal_state_file, "w", encoding="utf-8") as f: + json.dump(state, f, indent=2, ensure_ascii=False) + except Exception as e: + logger.error(f"Failed to save internal state: {e}") + + def load_portfolio_state(self) -> Dict[str, Any]: + """ + Load portfolio state from internal state + + Returns: + Portfolio state dictionary: {cash, positions, margin_used} + """ + internal_state = self.load_internal_state() + portfolio_state = internal_state.get("portfolio_state", {}) + + return { + "cash": portfolio_state.get("cash", self.initial_cash), + "positions": portfolio_state.get("positions", {}), + "margin_used": portfolio_state.get("margin_used", 0.0), + "margin_requirement": 0.25, # Default 25% margin requirement + } + + def save_portfolio_state(self, portfolio: Dict[str, Any]): + """ + Save portfolio state to internal state + + Args: + portfolio: Portfolio dictionary + """ + internal_state = self.load_internal_state() + internal_state["portfolio_state"] = { + "cash": portfolio.get("cash", 0.0), + "positions": portfolio.get("positions", {}), + "margin_used": portfolio.get("margin_used", 0.0), + } + self.save_internal_state(internal_state) + + def initialize_empty_dashboard(self): + """Initialize empty dashboard files with default values""" + # Summary + self.save_file( + "summary", + { + "totalAssetValue": self.initial_cash, + "totalReturn": 0.0, + "cashPosition": self.initial_cash, + "tickerWeights": {}, + "totalTrades": 0, + "pnlPct": 0.0, + "balance": self.initial_cash, + "equity": [], + "baseline": [], + "baseline_vw": [], + "momentum": [], + }, + ) + + # Holdings + self.save_file("holdings", []) + + # Stats + self.save_file( + "stats", + { + "totalAssetValue": self.initial_cash, + "totalReturn": 0.0, + "cashPosition": self.initial_cash, + "tickerWeights": {}, + "totalTrades": 0, + "winRate": 0.0, + "bullBear": { + "bull": {"n": 0, "win": 0}, + "bear": {"n": 0, "win": 0}, + }, + }, + ) + + # Trades + self.save_file("trades", []) + + # Leaderboard with model info + self.generate_leaderboard() + + logger.info("Initialized empty dashboard") + + def generate_leaderboard(self): + """Generate leaderboard with agent model info from environment""" + from ..config.constants import AGENT_CONFIG + from ..llm.models import get_agent_model_info + + leaderboard = [] + ranking_entries = [] + team_entries = [] + + for agent_id, config in AGENT_CONFIG.items(): + model_name, model_provider = get_agent_model_info(agent_id) + + entry = { + "agentId": agent_id, + "name": config["name"], + "role": config["role"], + "avatar": config["avatar"], + "rank": None if config["is_team_role"] else 0, + "winRate": None, + "bull": {"n": 0, "win": 0, "unknown": 0}, + "bear": {"n": 0, "win": 0, "unknown": 0}, + "logs": [], + "signals": [], + "modelName": model_name, + "modelProvider": model_provider, + } + + if config["is_team_role"]: + team_entries.append(entry) + else: + ranking_entries.append(entry) + + leaderboard = team_entries + ranking_entries + self.save_file("leaderboard", leaderboard) + logger.info("Leaderboard generated with model info") + + def update_leaderboard_model_info(self): + """ + Update model info in existing leaderboard (preserves performance data) + """ + from ..config.constants import AGENT_CONFIG + from ..llm.models import get_agent_model_info + + existing = self.load_file("leaderboard") or [] + + if not existing: + self.generate_leaderboard() + return + + for entry in existing: + agent_id = entry.get("agentId") + if agent_id and agent_id in AGENT_CONFIG: + model_name, model_provider = get_agent_model_info(agent_id) + entry["modelName"] = model_name + entry["modelProvider"] = model_provider + + self.save_file("leaderboard", existing) + logger.info("Leaderboard model info updated") + + def get_current_timestamp_ms(self, date: str = None) -> int: + """ + Get timestamp in milliseconds from date string or current time + + Args: + date: Optional date string (YYYY-MM-DD) for backtest compatibility. + Uses market close time (16:00) for the timestamp. + + Returns: + Timestamp in milliseconds + """ + if date: + # Parse date and use market close time (16:00) for backtest + dt = datetime.strptime(f"{date} 16:00:00", "%Y-%m-%d %H:%M:%S") + return int(dt.timestamp() * 1000) + return int(datetime.now().timestamp() * 1000) + + def calculate_portfolio_value( + self, + portfolio: Dict[str, Any], + prices: Dict[str, float], + ) -> float: + """ + Calculate total portfolio value (net asset value) + + Args: + portfolio: Portfolio state with cash and positions + prices: Current prices for each ticker + + Returns: + Total portfolio value + """ + cash = portfolio.get("cash", 0.0) + margin_used = portfolio.get("margin_used", 0.0) + total = cash + margin_used + + positions = portfolio.get("positions", {}) + for ticker, position in positions.items(): + price = prices.get(ticker, 0) + if price > 0: + long_qty = position.get("long", 0) + short_qty = position.get("short", 0) + total += long_qty * price + total -= short_qty * price + + return total + + def update_dashboard_after_cycle( + self, + portfolio: Dict[str, Any], + prices: Dict[str, float], + date: str, + executed_trades: List[Dict[str, Any]] = None, + baseline_values: Optional[Dict[str, float]] = None, + ): + """ + Update all dashboard files after a trading cycle + + Args: + portfolio: Current portfolio state + prices: Current prices for each ticker + date: Trading date (YYYY-MM-DD) + executed_trades: List of executed trades + [{ticker, action, quantity, price}] + baseline_values: Optional baseline portfolio values from settlement + {equal_weight, market_cap_weighted, momentum} + """ + # Use provided date for timestamp (backtest compatible) + timestamp_ms = self.get_current_timestamp_ms(date) + + net_value = self.calculate_portfolio_value(portfolio, prices) + + state = self.load_internal_state() + + # Initialize all histories + for key in [ + "equity_history", + "baseline_history", + "baseline_vw_history", + "momentum_history", + ]: + if key not in state: + state[key] = [] + + # Add initial points if empty (all histories should start together) + if len(state["equity_history"]) == 0: + initial_point = { + "t": timestamp_ms - 86400000, + "v": round(self.initial_cash, 2), + } + state["equity_history"].append(initial_point) + state["baseline_history"].append(initial_point.copy()) + state["baseline_vw_history"].append(initial_point.copy()) + state["momentum_history"].append(initial_point.copy()) + + # Add current data points - all histories updated together + state["equity_history"].append( + { + "t": timestamp_ms, + "v": round(net_value, 2), + }, + ) + + # If baseline_values provided, use them; + # otherwise forward-fill from last value + if baseline_values: + state["baseline_history"].append( + { + "t": timestamp_ms, + "v": round( + baseline_values.get("equal_weight", self.initial_cash), + 2, + ), + }, + ) + state["baseline_vw_history"].append( + { + "t": timestamp_ms, + "v": round( + baseline_values.get( + "market_cap_weighted", + self.initial_cash, + ), + 2, + ), + }, + ) + state["momentum_history"].append( + { + "t": timestamp_ms, + "v": round( + baseline_values.get("momentum", self.initial_cash), + 2, + ), + }, + ) + else: + # Forward-fill: use last known value + last_baseline = ( + state["baseline_history"][-1]["v"] + if state["baseline_history"] + else self.initial_cash + ) + last_baseline_vw = ( + state["baseline_vw_history"][-1]["v"] + if state["baseline_vw_history"] + else self.initial_cash + ) + last_momentum = ( + state["momentum_history"][-1]["v"] + if state["momentum_history"] + else self.initial_cash + ) + + state["baseline_history"].append( + {"t": timestamp_ms, "v": last_baseline}, + ) + state["baseline_vw_history"].append( + {"t": timestamp_ms, "v": last_baseline_vw}, + ) + state["momentum_history"].append( + {"t": timestamp_ms, "v": last_momentum}, + ) + + state["portfolio_state"] = { + "cash": portfolio.get("cash", 0.0), + "positions": portfolio.get("positions", {}), + "margin_used": portfolio.get("margin_used", 0.0), + } + + # Update trades with structured data + if executed_trades: + if "all_trades" not in state: + state["all_trades"] = [] + + for i, trade in enumerate(executed_trades): + action = trade.get("action", "hold") + side = ( + "LONG" + if action == "long" + else "SHORT" + if action == "short" + else "HOLD" + ) + + trade_id = ( + f"t_{date.replace('-', '')}_{trade.get('ticker', '')}_{i}" + ) + state["all_trades"].append( + { + "id": trade_id, + "ts": timestamp_ms, + "trading_date": date, + "side": side, + "ticker": trade.get("ticker", ""), + "qty": trade.get("quantity", 0), + "price": round(trade.get("price", 0), 2), + }, + ) + + state["last_update_date"] = date + + self.save_internal_state(state) + + self._generate_summary(state, net_value, prices) + self._generate_holdings(state, prices) + self._generate_stats(state, net_value) + self._generate_trades(state) + + logger.info(f"Dashboard updated: net_value=${net_value:,.2f}") + + def _generate_summary( + self, + state: Dict[str, Any], + net_value: float, + prices: Dict[str, float], + ): + """Generate summary.json""" + portfolio_state = state.get("portfolio_state", {}) + cash = portfolio_state.get("cash", self.initial_cash) + + # Calculate ticker weights + positions = portfolio_state.get("positions", {}) + ticker_weights = {} + + for ticker, position in positions.items(): + price = prices.get(ticker, 0) + if price > 0 and net_value > 0: + long_qty = position.get("long", 0) + short_qty = position.get("short", 0) + position_value = (long_qty - short_qty) * price + ticker_weights[ticker] = round(position_value / net_value, 4) + + # Calculate return + total_return = ( + (net_value - self.initial_cash) / self.initial_cash + ) * 100 + + summary = { + "totalAssetValue": round(net_value, 2), + "totalReturn": round(total_return, 2), + "cashPosition": round(cash, 2), + "tickerWeights": ticker_weights, + "totalTrades": len(state.get("all_trades", [])), + "pnlPct": round(total_return, 2), + "balance": round(net_value, 2), + "equity": state.get("equity_history", []), + "baseline": state.get("baseline_history", []), + "baseline_vw": state.get("baseline_vw_history", []), + "momentum": state.get("momentum_history", []), + } + + self.save_file("summary", summary) + + def _generate_holdings( + self, + state: Dict[str, Any], + prices: Dict[str, float], + ): + """Generate holdings.json""" + portfolio_state = state.get("portfolio_state", {}) + positions = portfolio_state.get("positions", {}) + cash = portfolio_state.get("cash", self.initial_cash) + margin_used = portfolio_state.get("margin_used", 0.0) + + # Calculate total value + total_value = cash + margin_used + for ticker, position in positions.items(): + price = prices.get(ticker, 0) + if price > 0: + long_qty = position.get("long", 0) + short_qty = position.get("short", 0) + total_value += (long_qty - short_qty) * price + + holdings = [] + + # Add stock positions + for ticker, position in positions.items(): + price = prices.get(ticker, 0) + long_qty = position.get("long", 0) + short_qty = position.get("short", 0) + net_qty = long_qty - short_qty + + if net_qty != 0 and price > 0: + market_value = net_qty * price + weight = ( + abs(market_value) / total_value if total_value > 0 else 0 + ) + + holdings.append( + { + "ticker": ticker, + "quantity": net_qty, + "currentPrice": round(price, 2), + "marketValue": round(market_value, 2), + "weight": round(weight, 4), + }, + ) + + # Add cash + cash_weight = cash / total_value if total_value > 0 else 0 + holdings.append( + { + "ticker": "CASH", + "quantity": 1, + "currentPrice": round(cash, 2), + "marketValue": round(cash, 2), + "weight": round(cash_weight, 4), + }, + ) + + # Sort by weight + holdings.sort(key=lambda x: abs(x["weight"]), reverse=True) + + self.save_file("holdings", holdings) + + def _generate_stats(self, state: Dict[str, Any], net_value: float): + """Generate stats.json""" + portfolio_state = state.get("portfolio_state", {}) + cash = portfolio_state.get("cash", self.initial_cash) + total_return = ( + (net_value - self.initial_cash) / self.initial_cash + ) * 100 + + stats = { + "totalAssetValue": round(net_value, 2), + "totalReturn": round(total_return, 2), + "cashPosition": round(cash, 2), + "tickerWeights": {}, + "totalTrades": len(state.get("all_trades", [])), + "winRate": 0.0, + "bullBear": { + "bull": {"n": 0, "win": 0}, + "bear": {"n": 0, "win": 0}, + }, + } + + self.save_file("stats", stats) + + def _generate_trades(self, state: Dict[str, Any]): + """Generate trades.json""" + all_trades = state.get("all_trades", []) + + sorted_trades = sorted( + all_trades, + key=lambda x: x.get("ts", 0), + reverse=True, + ) + + trades = [] + for trade in sorted_trades[:100]: + trades.append( + { + "id": trade.get("id"), + "timestamp": trade.get("ts"), + "trading_date": trade.get("trading_date"), + "side": trade.get("side", ""), + "ticker": trade.get("ticker", ""), + "qty": trade.get("qty", 0), + "price": trade.get("price", 0), + }, + ) + + self.save_file("trades", trades) + + # Server State Management Methods + + def load_server_state(self) -> Dict[str, Any]: + """ + Load server state from file + + Returns: + Server state dictionary with feed_history and other data + """ + default_state = { + "status": "initializing", + "current_date": None, + "portfolio": { + "total_value": self.initial_cash, + "cash": self.initial_cash, + "pnl_percent": 0.0, + "equity": [], + "baseline": [], + "baseline_vw": [], + "momentum": [], + "strategies": [], + }, + "holdings": [], + "trades": [], + "stats": self._get_default_stats(), + "leaderboard": [], + "realtime_prices": {}, + "system_started": datetime.now().isoformat(), + "feed_history": [], + "last_day_history": [], + "trading_days_total": 0, + "trading_days_completed": 0, + } + + if not self.server_state_file.exists(): + return default_state + + with open(self.server_state_file, "r", encoding="utf-8") as f: + saved_state = json.load(f) + + # Merge with defaults to ensure all fields exist + for key, value in default_state.items(): + saved_state.setdefault(key, value) + + logger.info(f"Server state loaded from: {self.server_state_file}") + logger.info( + f"Feed history: {len(saved_state.get('feed_history', []))} messages", # noqa: E501 + ) + logger.info( + f"Holdings: {len(saved_state.get('holdings', []))} items", + ) + logger.info(f"Trades: {len(saved_state.get('trades', []))} records") + + return saved_state + + def save_server_state(self, state: Dict[str, Any]): + """ + Save server state to file + + Args: + state: Server state dictionary + """ + state_to_save = { + **state, + "last_saved": datetime.now().isoformat(), + } + + # Limit feed_history size + if "feed_history" in state_to_save: + state_to_save["feed_history"] = state_to_save["feed_history"][ + : self.max_feed_history + ] + + # Limit trades + if "trades" in state_to_save: + state_to_save["trades"] = state_to_save["trades"][:100] + + with open(self.server_state_file, "w", encoding="utf-8") as f: + json.dump( + state_to_save, + f, + ensure_ascii=False, + indent=2, + default=str, + ) + + logger.debug(f"Server state saved to: {self.server_state_file}") + + def add_feed_message( + self, + state: Dict[str, Any], + event: Dict[str, Any], + ) -> bool: + """ + Add a message to feed history + + Args: + state: Server state dictionary to update + event: Event dictionary with type, content, etc. + + Returns: + True if message was added, False if filtered out + """ + event_type = event.get("type", "") + + # Types to save in feed history + save_types = { + "system", + "agent_message", + "day_start", + "day_complete", + "day_error", + "team_summary", + "conference_start", + "conference_message", + "conference_end", + "memory", + } + + if event_type not in save_types: + return False + + # Ensure timestamp exists + if "timestamp" not in event: + event["timestamp"] = datetime.now().isoformat() + + # Store event directly (flat structure, no metadata wrapper) + feed_msg = dict(event) + + # Insert at beginning (newest first) + if "feed_history" not in state: + state["feed_history"] = [] + + state["feed_history"].insert(0, feed_msg) + + # Trim to max size + if len(state["feed_history"]) > self.max_feed_history: + state["feed_history"] = state["feed_history"][ + : self.max_feed_history + ] + + return True + + def _get_default_stats(self) -> Dict[str, Any]: + """Get default stats structure""" + return { + "totalAssetValue": 0.0, + "totalReturn": -100.0, + "cashPosition": 0.0, + "tickerWeights": {}, + "totalTrades": 0, + "winRate": 0.0, + "bullBear": { + "bull": {"n": 0, "win": 0}, + "bear": {"n": 0, "win": 0}, + }, + } + + def update_server_state_from_dashboard(self, state: Dict[str, Any]): + """ + Update server state with current dashboard data + + Args: + state: Server state dictionary to update + """ + # Load dashboard data + summary = self.load_file("summary") or {} + holdings = self.load_file("holdings") or [] + stats = self.load_file("stats") or self._get_default_stats() + trades = self.load_file("trades") or [] + leaderboard = self.load_file("leaderboard") or [] + + # Update state + state["portfolio"] = { + "total_value": summary.get("totalAssetValue", self.initial_cash), + "cash": summary.get("cashPosition", self.initial_cash), + "pnl_percent": summary.get("pnlPct", 0.0), + "equity": summary.get("equity", []), + "baseline": summary.get("baseline", []), + "baseline_vw": summary.get("baseline_vw", []), + "momentum": summary.get("momentum", []), + "strategies": [], + # Live returns (will be populated when session is active) + "equity_return": [], + "baseline_return": [], + "baseline_vw_return": [], + "momentum_return": [], + } + state["holdings"] = holdings + state["stats"] = stats + state["trades"] = trades + state["leaderboard"] = leaderboard + + # ========== Live Returns Tracking ========== + + def start_live_session(self): + """ + Start tracking live returns for current trading session. + Captures current values as session start baseline. + """ + summary = self.load_file("summary") or {} + state = self.load_internal_state() + + # Capture current values as session start + equity_history = state.get("equity_history", []) + baseline_history = state.get("baseline_history", []) + baseline_vw_history = state.get("baseline_vw_history", []) + momentum_history = state.get("momentum_history", []) + + self._session_start_equity = ( + equity_history[-1]["v"] + if equity_history + else summary.get("totalAssetValue", self.initial_cash) + ) + self._session_start_baseline = ( + baseline_history[-1]["v"] + if baseline_history + else self.initial_cash + ) + self._session_start_baseline_vw = ( + baseline_vw_history[-1]["v"] + if baseline_vw_history + else self.initial_cash + ) + self._session_start_momentum = ( + momentum_history[-1]["v"] + if momentum_history + else self.initial_cash + ) + + # Clear live return history + self._live_return_history = [] + + # Add starting point at 0% + timestamp = int(datetime.now().timestamp() * 1000) + self._live_return_history.append( + { + "t": timestamp, + "equity": 0.0, + "baseline": 0.0, + "baseline_vw": 0.0, + "momentum": 0.0, + }, + ) + + logger.info( + "Live session started: " + f"equity=${self._session_start_equity:,.2f}, " + f"baseline=${self._session_start_baseline:,.2f}", + ) + + def end_live_session(self): + """End live returns tracking session""" + self._session_start_equity = None + self._session_start_baseline = None + self._session_start_baseline_vw = None + self._session_start_momentum = None + self._live_return_history = [] + logger.info("Live session ended") + + def update_live_returns( + self, + current_equity: Optional[float] = None, + current_baseline: Optional[float] = None, + current_baseline_vw: Optional[float] = None, + current_momentum: Optional[float] = None, + ) -> Optional[Dict[str, Any]]: + """ + Update live returns with current values. + + Args: + current_equity: Current portfolio value + current_baseline: Current EW baseline value + current_baseline_vw: Current VW baseline value + current_momentum: Current momentum strategy value + + Returns: + Dict with live return data point or None if session not active + """ + if ( + self._session_start_equity is None + or self._session_start_baseline is None + or self._session_start_baseline_vw is None + or self._session_start_momentum is None + ): + return None + + timestamp = int(datetime.now().timestamp() * 1000) + point = {"t": timestamp} + + # Calculate returns (only if we have valid values) + if current_equity is not None and self._session_start_equity > 0: + ret = ( + (current_equity - self._session_start_equity) + / self._session_start_equity + ) * 100 + point["equity"] = round(ret, 4) + + if current_baseline is not None and self._session_start_baseline > 0: + ret = ( + (current_baseline - self._session_start_baseline) + / self._session_start_baseline + ) * 100 + point["baseline"] = round(ret, 4) + + if ( + current_baseline_vw is not None + and self._session_start_baseline_vw > 0 + ): + ret = ( + (current_baseline_vw - self._session_start_baseline_vw) + / self._session_start_baseline_vw + ) * 100 + point["baseline_vw"] = round(ret, 4) + + if current_momentum is not None and self._session_start_momentum > 0: + ret = ( + (current_momentum - self._session_start_momentum) + / self._session_start_momentum + ) * 100 + point["momentum"] = round(ret, 4) + + # Only add point if we have at least one return value + if any(k != "t" for k in point): + self._live_return_history.append(point) + # Limit history size + if len(self._live_return_history) > 500: + self._live_return_history = self._live_return_history[-500:] + return point + + return None + + def get_live_returns(self) -> Dict[str, List[Dict[str, Any]]]: + """ + Get live return curves for the current session. + + Returns: + Dict with separate arrays for each strategy: + - equity_return: [{t, v}, ...] + - baseline_return: [{t, v}, ...] + - baseline_vw_return: [{t, v}, ...] + - momentum_return: [{t, v}, ...] + """ + if not self._live_return_history: + return { + "equity_return": [], + "baseline_return": [], + "baseline_vw_return": [], + "momentum_return": [], + } + + # Convert combined history to separate arrays + equity_return = [] + baseline_return = [] + baseline_vw_return = [] + momentum_return = [] + + for point in self._live_return_history: + t = point["t"] + if "equity" in point: + equity_return.append({"t": t, "v": point["equity"]}) + if "baseline" in point: + baseline_return.append({"t": t, "v": point["baseline"]}) + if "baseline_vw" in point: + baseline_vw_return.append({"t": t, "v": point["baseline_vw"]}) + if "momentum" in point: + momentum_return.append({"t": t, "v": point["momentum"]}) + + return { + "equity_return": equity_return, + "baseline_return": baseline_return, + "baseline_vw_return": baseline_vw_return, + "momentum_return": momentum_return, + } + + @property + def is_live_session_active(self) -> bool: + """Check if live session is active""" + return self._session_start_equity is not None diff --git a/evotraders/backend/tests/__init__.py b/evotraders/backend/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/evotraders/backend/tests/test_agents.py b/evotraders/backend/tests/test_agents.py new file mode 100644 index 0000000..a426fec --- /dev/null +++ b/evotraders/backend/tests/test_agents.py @@ -0,0 +1,580 @@ +# -*- coding: utf-8 -*- +# pylint: disable=W0212 +import json +import tempfile +from pathlib import Path +from unittest.mock import MagicMock + +import pytest +from agentscope.message import Msg + + +class TestAnalystAgent: + def test_init_valid_analyst_type(self): + from backend.agents.analyst import AnalystAgent + + mock_toolkit = MagicMock() + mock_model = MagicMock() + mock_formatter = MagicMock() + + agent = AnalystAgent( + analyst_type="technical_analyst", + toolkit=mock_toolkit, + model=mock_model, + formatter=mock_formatter, + ) + + assert agent.analyst_type_key == "technical_analyst" + assert agent.name == "technical_analyst_analyst" + assert agent.analyst_persona == "Technical Analyst" + + def test_init_invalid_analyst_type(self): + from backend.agents.analyst import AnalystAgent + + mock_toolkit = MagicMock() + mock_model = MagicMock() + mock_formatter = MagicMock() + + with pytest.raises(ValueError) as excinfo: + AnalystAgent( + analyst_type="invalid_type", + toolkit=mock_toolkit, + model=mock_model, + formatter=mock_formatter, + ) + + assert "Unknown analyst type" in str(excinfo.value) + + def test_init_custom_agent_id(self): + from backend.agents.analyst import AnalystAgent + + mock_toolkit = MagicMock() + mock_model = MagicMock() + mock_formatter = MagicMock() + + agent = AnalystAgent( + analyst_type="fundamentals_analyst", + toolkit=mock_toolkit, + model=mock_model, + formatter=mock_formatter, + agent_id="custom_analyst_id", + ) + + assert agent.name == "custom_analyst_id" + + def test_load_system_prompt(self): + from backend.agents.analyst import AnalystAgent + + mock_toolkit = MagicMock() + mock_model = MagicMock() + mock_formatter = MagicMock() + + agent = AnalystAgent( + analyst_type="sentiment_analyst", + toolkit=mock_toolkit, + model=mock_model, + formatter=mock_formatter, + ) + + prompt = agent._load_system_prompt() + assert isinstance(prompt, str) + assert len(prompt) > 0 + + +class TestPMAgent: + def test_init_default(self): + from backend.agents.portfolio_manager import PMAgent + + mock_model = MagicMock() + mock_formatter = MagicMock() + + agent = PMAgent( + model=mock_model, + formatter=mock_formatter, + ) + + assert agent.name == "portfolio_manager" + assert agent.portfolio["cash"] == 100000.0 + assert agent.portfolio["positions"] == {} + assert agent.portfolio["margin_requirement"] == 0.25 + + def test_init_custom_cash(self): + from backend.agents.portfolio_manager import PMAgent + + mock_model = MagicMock() + mock_formatter = MagicMock() + + agent = PMAgent( + model=mock_model, + formatter=mock_formatter, + initial_cash=50000.0, + margin_requirement=0.5, + ) + + assert agent.portfolio["cash"] == 50000.0 + assert agent.portfolio["margin_requirement"] == 0.5 + + def test_get_portfolio_state(self): + from backend.agents.portfolio_manager import PMAgent + + mock_model = MagicMock() + mock_formatter = MagicMock() + + agent = PMAgent( + model=mock_model, + formatter=mock_formatter, + initial_cash=75000.0, + ) + + state = agent.get_portfolio_state() + + assert state["cash"] == 75000.0 + assert state is not agent.portfolio # Should be a copy + + def test_load_portfolio_state(self): + from backend.agents.portfolio_manager import PMAgent + + mock_model = MagicMock() + mock_formatter = MagicMock() + + agent = PMAgent( + model=mock_model, + formatter=mock_formatter, + ) + + new_portfolio = { + "cash": 50000.0, + "positions": { + "AAPL": {"long": 100, "short": 0, "long_cost_basis": 150.0}, + }, + "margin_used": 1000.0, + } + + agent.load_portfolio_state(new_portfolio) + + assert agent.portfolio["cash"] == 50000.0 + assert "AAPL" in agent.portfolio["positions"] + + def test_update_portfolio(self): + from backend.agents.portfolio_manager import PMAgent + + mock_model = MagicMock() + mock_formatter = MagicMock() + + agent = PMAgent( + model=mock_model, + formatter=mock_formatter, + ) + + agent.update_portfolio({"cash": 80000.0}) + assert agent.portfolio["cash"] == 80000.0 + + def _get_text_from_tool_response(self, result): + """Helper to extract text from ToolResponse content""" + content = result.content[0] + if hasattr(content, "text"): + return content.text + elif isinstance(content, dict): + return content.get("text", "") + return str(content) + + def test_make_decision_long(self): + from backend.agents.portfolio_manager import PMAgent + + mock_model = MagicMock() + mock_formatter = MagicMock() + + agent = PMAgent( + model=mock_model, + formatter=mock_formatter, + ) + + result = agent._make_decision( + ticker="AAPL", + action="long", + quantity=100, + confidence=80, + reasoning="Strong fundamentals", + ) + + text = self._get_text_from_tool_response(result) + assert "Decision recorded" in text + assert agent._decisions["AAPL"]["action"] == "long" + assert agent._decisions["AAPL"]["quantity"] == 100 + + def test_make_decision_hold(self): + from backend.agents.portfolio_manager import PMAgent + + mock_model = MagicMock() + mock_formatter = MagicMock() + + agent = PMAgent( + model=mock_model, + formatter=mock_formatter, + ) + + result = agent._make_decision( + ticker="GOOGL", + action="hold", + quantity=0, + confidence=50, + reasoning="Neutral outlook", + ) + + text = self._get_text_from_tool_response(result) + assert "Decision recorded" in text + assert agent._decisions["GOOGL"]["action"] == "hold" + assert agent._decisions["GOOGL"]["quantity"] == 0 + + def test_make_decision_invalid_action(self): + from backend.agents.portfolio_manager import PMAgent + + mock_model = MagicMock() + mock_formatter = MagicMock() + + agent = PMAgent( + model=mock_model, + formatter=mock_formatter, + ) + + result = agent._make_decision( + ticker="AAPL", + action="invalid", + quantity=10, + ) + + text = self._get_text_from_tool_response(result) + assert "Invalid action" in text + + def test_get_decisions(self): + from backend.agents.portfolio_manager import PMAgent + + mock_model = MagicMock() + mock_formatter = MagicMock() + + agent = PMAgent( + model=mock_model, + formatter=mock_formatter, + ) + + agent._make_decision("AAPL", "long", 100) + agent._make_decision("GOOGL", "short", 50) + + decisions = agent.get_decisions() + assert len(decisions) == 2 + assert decisions["AAPL"]["action"] == "long" + assert decisions["GOOGL"]["action"] == "short" + + +class TestRiskAgent: + def test_init_default(self): + from backend.agents.risk_manager import RiskAgent + + mock_model = MagicMock() + mock_formatter = MagicMock() + + agent = RiskAgent( + model=mock_model, + formatter=mock_formatter, + ) + + assert agent.name == "risk_manager" + + def test_init_custom_name(self): + from backend.agents.risk_manager import RiskAgent + + mock_model = MagicMock() + mock_formatter = MagicMock() + + agent = RiskAgent( + model=mock_model, + formatter=mock_formatter, + name="custom_risk_manager", + ) + + assert agent.name == "custom_risk_manager" + + def test_load_system_prompt(self): + from backend.agents.risk_manager import RiskAgent + + mock_model = MagicMock() + mock_formatter = MagicMock() + + agent = RiskAgent( + model=mock_model, + formatter=mock_formatter, + ) + + prompt = agent._load_system_prompt() + assert isinstance(prompt, str) + assert len(prompt) > 0 + + +class TestStorageService: + def test_calculate_portfolio_value_cash_only(self): + from backend.services.storage import StorageService + + with tempfile.TemporaryDirectory() as tmpdir: + storage = StorageService( + dashboard_dir=Path(tmpdir), + initial_cash=100000.0, + ) + + portfolio = {"cash": 100000.0, "positions": {}, "margin_used": 0.0} + prices = {} + + value = storage.calculate_portfolio_value(portfolio, prices) + assert value == 100000.0 + + def test_calculate_portfolio_value_with_positions(self): + from backend.services.storage import StorageService + + with tempfile.TemporaryDirectory() as tmpdir: + storage = StorageService( + dashboard_dir=Path(tmpdir), + initial_cash=100000.0, + ) + + portfolio = { + "cash": 50000.0, + "positions": { + "AAPL": {"long": 100, "short": 0}, + "GOOGL": {"long": 0, "short": 10}, + }, + "margin_used": 5000.0, + } + prices = {"AAPL": 150.0, "GOOGL": 100.0} + + value = storage.calculate_portfolio_value(portfolio, prices) + assert value == 69000.0 + + def test_update_dashboard_after_cycle(self): + from backend.services.storage import StorageService + + with tempfile.TemporaryDirectory() as tmpdir: + storage = StorageService( + dashboard_dir=Path(tmpdir), + initial_cash=100000.0, + ) + + portfolio = { + "cash": 90000.0, + "positions": {"AAPL": {"long": 50, "short": 0}}, + "margin_used": 0.0, + } + prices = {"AAPL": 200.0} + + storage.update_dashboard_after_cycle( + portfolio=portfolio, + prices=prices, + date="2024-01-15", + executed_trades=[ + { + "ticker": "AAPL", + "action": "long", + "quantity": 50, + "price": 200.0, + }, + ], + ) + + summary = storage.load_file("summary") + assert summary is not None + assert summary["totalAssetValue"] == 100000.0 # 90000 + 50*200 + + holdings = storage.load_file("holdings") + assert holdings is not None + assert len(holdings) > 0 + + trades = storage.load_file("trades") + assert trades is not None + assert len(trades) == 1 + assert trades[0]["ticker"] == "AAPL" + assert trades[0]["qty"] == 50 + assert trades[0]["price"] == 200.0 + + def test_generate_summary(self): + from backend.services.storage import StorageService + + with tempfile.TemporaryDirectory() as tmpdir: + storage = StorageService( + dashboard_dir=Path(tmpdir), + initial_cash=100000.0, + ) + + state = { + "portfolio_state": { + "cash": 50000.0, + "positions": {"AAPL": {"long": 100, "short": 0}}, + "margin_used": 0.0, + }, + "equity_history": [{"t": 1000, "v": 100000}], + "all_trades": [], + } + prices = {"AAPL": 500.0} + + storage._generate_summary(state, 100000.0, prices) + + summary = storage.load_file("summary") + assert summary["totalAssetValue"] == 100000.0 + assert summary["totalReturn"] == 0.0 + + def test_generate_holdings(self): + from backend.services.storage import StorageService + + with tempfile.TemporaryDirectory() as tmpdir: + storage = StorageService( + dashboard_dir=Path(tmpdir), + initial_cash=100000.0, + ) + + state = { + "portfolio_state": { + "cash": 50000.0, + "positions": {"AAPL": {"long": 100, "short": 0}}, + "margin_used": 0.0, + }, + } + prices = {"AAPL": 500.0} + + storage._generate_holdings(state, prices) + + holdings = storage.load_file("holdings") + assert len(holdings) == 2 # AAPL + CASH + + aapl_holding = next( + (h for h in holdings if h["ticker"] == "AAPL"), + None, + ) + assert aapl_holding is not None + assert aapl_holding["quantity"] == 100 + assert aapl_holding["currentPrice"] == 500.0 + + +class TestTradeExecutor: + def test_execute_trade_long(self): + from backend.utils.trade_executor import PortfolioTradeExecutor + + executor = PortfolioTradeExecutor( + initial_portfolio={ + "cash": 100000.0, + "positions": {}, + "margin_requirement": 0.25, + "margin_used": 0.0, + }, + ) + + result = executor.execute_trade( + ticker="AAPL", + action="long", + quantity=10, + price=150.0, + ) + + assert result["status"] == "success" + assert executor.portfolio["positions"]["AAPL"]["long"] == 10 + assert executor.portfolio["cash"] == 98500.0 # 100000 - 10*150 + + def test_execute_trade_short(self): + from backend.utils.trade_executor import PortfolioTradeExecutor + + executor = PortfolioTradeExecutor( + initial_portfolio={ + "cash": 100000.0, + "positions": { + "AAPL": { + "long": 50, + "short": 0, + "long_cost_basis": 100.0, + "short_cost_basis": 0.0, + }, + }, + "margin_requirement": 0.25, + "margin_used": 0.0, + }, + ) + + result = executor.execute_trade( + ticker="AAPL", + action="short", + quantity=30, + price=150.0, + ) + + assert result["status"] == "success" + assert executor.portfolio["positions"]["AAPL"]["long"] == 20 # 50 - 30 + + def test_execute_trade_hold(self): + from backend.utils.trade_executor import PortfolioTradeExecutor + + executor = PortfolioTradeExecutor() + + result = executor.execute_trade( + ticker="AAPL", + action="hold", + quantity=0, + price=150.0, + ) + + assert result["status"] == "success" + assert result["message"] == "No trade needed" + + +class TestPipelineExecution: + def test_execute_decisions(self): + from backend.core.pipeline import TradingPipeline + from backend.agents.portfolio_manager import PMAgent + + mock_model = MagicMock() + mock_formatter = MagicMock() + + pm = PMAgent( + model=mock_model, + formatter=mock_formatter, + initial_cash=100000.0, + ) + + pipeline = TradingPipeline( + analysts=[], + risk_manager=MagicMock(), + portfolio_manager=pm, + max_comm_cycles=0, + ) + + decisions = { + "AAPL": {"action": "long", "quantity": 10}, + "GOOGL": {"action": "short", "quantity": 5}, + } + prices = {"AAPL": 150.0, "GOOGL": 100.0} + + result = pipeline._execute_decisions(decisions, prices, "2024-01-15") + + assert len(result["executed_trades"]) == 2 + assert result["executed_trades"][0]["ticker"] == "AAPL" + assert result["executed_trades"][0]["quantity"] == 10 + assert pm.portfolio["positions"]["AAPL"]["long"] == 10 + + +class TestMsgContentIsString: + def test_msg_content_string(self): + msg = Msg(name="test", content="simple string", role="user") + assert isinstance(msg.content, str) + + def test_msg_content_json_string(self): + data = {"key": "value", "nested": {"a": 1}} + msg = Msg(name="test", content=json.dumps(data), role="user") + assert isinstance(msg.content, str) + + parsed = json.loads(msg.content) + assert parsed["key"] == "value" + + def test_msg_content_should_not_be_dict(self): + data = {"key": "value"} + msg = Msg(name="test", content=json.dumps(data), role="assistant") + + assert not isinstance(msg.content, dict) + assert isinstance(msg.content, str) + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/evotraders/backend/tests/test_market_service.py b/evotraders/backend/tests/test_market_service.py new file mode 100644 index 0000000..4390049 --- /dev/null +++ b/evotraders/backend/tests/test_market_service.py @@ -0,0 +1,438 @@ +# -*- coding: utf-8 -*- +# pylint: disable=W0212 +import asyncio +import time +from unittest.mock import MagicMock, AsyncMock, patch +import pytest +from backend.services.market import MarketService +from backend.data.mock_price_manager import MockPriceManager +from backend.data.polling_price_manager import PollingPriceManager + + +class TestMockPriceManager: + def test_init_default(self): + manager = MockPriceManager() + + assert manager.poll_interval == 10 + assert manager.volatility == 0.5 + assert manager.running is False + assert len(manager.subscribed_symbols) == 0 + + def test_init_custom(self): + manager = MockPriceManager(poll_interval=5, volatility=1.0) + + assert manager.poll_interval == 5 + assert manager.volatility == 1.0 + + def test_subscribe(self): + manager = MockPriceManager() + manager.subscribe(["AAPL", "MSFT"]) + + assert "AAPL" in manager.subscribed_symbols + assert "MSFT" in manager.subscribed_symbols + assert manager.base_prices["AAPL"] == 237.50 # default price + assert manager.base_prices["MSFT"] == 425.30 # default price + + def test_subscribe_with_base_prices(self): + manager = MockPriceManager() + manager.subscribe(["AAPL"], base_prices={"AAPL": 100.0}) + + assert manager.base_prices["AAPL"] == 100.0 + assert manager.open_prices["AAPL"] == 100.0 + assert manager.latest_prices["AAPL"] == 100.0 + + def test_subscribe_unknown_symbol(self): + manager = MockPriceManager() + manager.subscribe(["UNKNOWN"]) + + assert "UNKNOWN" in manager.subscribed_symbols + assert manager.base_prices["UNKNOWN"] > 0 # random price generated + + def test_unsubscribe(self): + manager = MockPriceManager() + manager.subscribe(["AAPL", "MSFT"]) + manager.unsubscribe(["AAPL"]) + + assert "AAPL" not in manager.subscribed_symbols + assert "MSFT" in manager.subscribed_symbols + + def test_add_price_callback(self): + manager = MockPriceManager() + callback = MagicMock() + manager.add_price_callback(callback) + + assert callback in manager.price_callbacks + + def test_generate_price_update_within_bounds(self): + manager = MockPriceManager(volatility=0.5) + manager.subscribe(["AAPL"], base_prices={"AAPL": 100.0}) + + for _ in range(100): + new_price = manager._generate_price_update("AAPL") + # Should be within +/-10% of open + assert 90.0 <= new_price <= 110.0 + + def test_update_prices_triggers_callback(self): + manager = MockPriceManager() + manager.subscribe(["AAPL"], base_prices={"AAPL": 100.0}) + + callback = MagicMock() + manager.add_price_callback(callback) + + manager._update_prices() + + callback.assert_called_once() + call_args = callback.call_args[0][0] + assert call_args["symbol"] == "AAPL" + assert "price" in call_args + assert "timestamp" in call_args + + def test_start_stop(self): + manager = MockPriceManager(poll_interval=1) + manager.subscribe(["AAPL"], base_prices={"AAPL": 100.0}) + + manager.start() + assert manager.running is True + + time.sleep(0.1) # let thread start + + manager.stop() + assert manager.running is False + + def test_start_without_subscription(self): + manager = MockPriceManager() + manager.start() + + assert ( + manager.running is False + ) # should not start without subscriptions + + def test_get_latest_price(self): + manager = MockPriceManager() + manager.subscribe(["AAPL"], base_prices={"AAPL": 100.0}) + + price = manager.get_latest_price("AAPL") + assert price == 100.0 + + def test_get_latest_price_unknown(self): + manager = MockPriceManager() + price = manager.get_latest_price("UNKNOWN") + assert price is None + + def test_get_all_latest_prices(self): + manager = MockPriceManager() + manager.subscribe( + ["AAPL", "MSFT"], + base_prices={"AAPL": 100.0, "MSFT": 200.0}, + ) + + prices = manager.get_all_latest_prices() + assert prices["AAPL"] == 100.0 + assert prices["MSFT"] == 200.0 + + def test_reset_open_prices(self): + manager = MockPriceManager() + manager.subscribe(["AAPL"], base_prices={"AAPL": 100.0}) + manager.latest_prices["AAPL"] = 105.0 + + manager.reset_open_prices() + + # Open price should change (based on latest with small gap) + assert manager.open_prices["AAPL"] != 100.0 + + def test_set_base_price(self): + manager = MockPriceManager() + manager.subscribe(["AAPL"], base_prices={"AAPL": 100.0}) + + manager.set_base_price("AAPL", 150.0) + + assert manager.base_prices["AAPL"] == 150.0 + assert manager.open_prices["AAPL"] == 150.0 + assert manager.latest_prices["AAPL"] == 150.0 + + +class TestPollingPriceManager: + def test_init(self): + manager = PollingPriceManager(api_key="test_key", poll_interval=30) + + assert manager.api_key == "test_key" + assert manager.poll_interval == 30 + assert manager.running is False + + def test_subscribe(self): + manager = PollingPriceManager(api_key="test_key") + manager.subscribe(["AAPL", "MSFT"]) + + assert "AAPL" in manager.subscribed_symbols + assert "MSFT" in manager.subscribed_symbols + + def test_unsubscribe(self): + manager = PollingPriceManager(api_key="test_key") + manager.subscribe(["AAPL", "MSFT"]) + manager.unsubscribe(["AAPL"]) + + assert "AAPL" not in manager.subscribed_symbols + assert "MSFT" in manager.subscribed_symbols + + def test_add_price_callback(self): + manager = PollingPriceManager(api_key="test_key") + callback = MagicMock() + manager.add_price_callback(callback) + + assert callback in manager.price_callbacks + + @patch.object(PollingPriceManager, "_fetch_prices") + def test_start_stop(self): + manager = PollingPriceManager(api_key="test_key", poll_interval=1) + manager.subscribe(["AAPL"]) + + manager.start() + assert manager.running is True + + time.sleep(0.1) + + manager.stop() + assert manager.running is False + + def test_start_without_subscription(self): + manager = PollingPriceManager(api_key="test_key") + manager.start() + + assert manager.running is False + + def test_get_latest_price(self): + manager = PollingPriceManager(api_key="test_key") + manager.latest_prices["AAPL"] = 150.0 + + price = manager.get_latest_price("AAPL") + assert price == 150.0 + + def test_get_open_price(self): + manager = PollingPriceManager(api_key="test_key") + manager.open_prices["AAPL"] = 148.0 + + price = manager.get_open_price("AAPL") + assert price == 148.0 + + def test_reset_open_prices(self): + manager = PollingPriceManager(api_key="test_key") + manager.open_prices["AAPL"] = 150.0 + + manager.reset_open_prices() + + assert len(manager.open_prices) == 0 + + +class TestMarketService: + def test_init_mock_mode(self): + service = MarketService( + tickers=["AAPL", "MSFT"], + poll_interval=10, + mock_mode=True, + ) + + assert service.tickers == ["AAPL", "MSFT"] + assert service.poll_interval == 10 + assert service.mock_mode is True + assert service.running is False + + def test_init_real_mode(self): + service = MarketService( + tickers=["AAPL"], + mock_mode=False, + api_key="test_key", + ) + + assert service.mock_mode is False + assert service.api_key == "test_key" + + @pytest.mark.asyncio + async def test_start_mock_mode(self): + service = MarketService( + tickers=["AAPL"], + poll_interval=10, + mock_mode=True, + ) + + broadcast_func = AsyncMock() + + await service.start(broadcast_func) + + assert service.running is True + assert service._price_manager is not None + assert isinstance(service._price_manager, MockPriceManager) + + service.stop() + + @pytest.mark.asyncio + async def test_start_real_mode_without_api_key(self): + service = MarketService( + tickers=["AAPL"], + mock_mode=False, + api_key=None, + ) + + broadcast_func = AsyncMock() + + with pytest.raises(ValueError) as excinfo: + await service.start(broadcast_func) + + assert "API key required" in str(excinfo.value) + + @pytest.mark.asyncio + async def test_start_already_running(self): + service = MarketService( + tickers=["AAPL"], + mock_mode=True, + ) + + broadcast_func = AsyncMock() + + await service.start(broadcast_func) + assert service.running is True + + # Start again should not fail + await service.start(broadcast_func) + + service.stop() + + def test_stop(self): + service = MarketService( + tickers=["AAPL"], + mock_mode=True, + ) + service.running = True + service._price_manager = MagicMock() + + service.stop() + + assert service.running is False + assert service._price_manager is None + + def test_stop_when_not_running(self): + service = MarketService( + tickers=["AAPL"], + mock_mode=True, + ) + + # Should not raise + service.stop() + assert service.running is False + + def test_get_price_sync(self): + service = MarketService(tickers=["AAPL"], mock_mode=True) + service.cache["AAPL"] = {"price": 150.0, "open": 148.0} + + price = service.get_price_sync("AAPL") + assert price == 150.0 + + def test_get_price_sync_not_found(self): + service = MarketService(tickers=["AAPL"], mock_mode=True) + + price = service.get_price_sync("MSFT") + assert price is None + + def test_get_all_prices(self): + service = MarketService(tickers=["AAPL", "MSFT"], mock_mode=True) + service.cache["AAPL"] = {"price": 150.0} + service.cache["MSFT"] = {"price": 400.0} + + prices = service.get_all_prices() + + assert prices["AAPL"] == 150.0 + assert prices["MSFT"] == 400.0 + + @pytest.mark.asyncio + async def test_broadcast_price_update(self): + service = MarketService(tickers=["AAPL"], mock_mode=True) + service._broadcast_func = AsyncMock() + + price_data = { + "symbol": "AAPL", + "price": 150.0, + "open": 148.0, + "timestamp": 1234567890, + } + + await service._broadcast_price_update(price_data) + + service._broadcast_func.assert_called_once() + call_args = service._broadcast_func.call_args[0][0] + assert call_args["type"] == "price_update" + assert call_args["symbol"] == "AAPL" + assert call_args["price"] == 150.0 + + @pytest.mark.asyncio + async def test_broadcast_price_update_no_func(self): + service = MarketService(tickers=["AAPL"], mock_mode=True) + service._broadcast_func = None + + price_data = {"symbol": "AAPL", "price": 150.0, "open": 148.0} + + # Should not raise + await service._broadcast_price_update(price_data) + + @pytest.mark.asyncio + async def test_price_callback_thread_safety(self): + service = MarketService( + tickers=["AAPL"], + poll_interval=1, + mock_mode=True, + ) + + received_prices = [] + + async def capture_broadcast(msg): + received_prices.append(msg) + + await service.start(capture_broadcast) + + # Wait for at least one price update + await asyncio.sleep(1.5) + + service.stop() + + # Should have received at least one price update + assert len(received_prices) >= 1 + assert received_prices[0]["type"] == "price_update" + + +class TestMarketServiceIntegration: + @pytest.mark.asyncio + async def test_full_mock_cycle(self): + service = MarketService( + tickers=["AAPL", "MSFT"], + poll_interval=1, + mock_mode=True, + ) + + messages = [] + + async def collect_messages(msg): + messages.append(msg) + + await service.start(collect_messages) + + # Wait for price updates + await asyncio.sleep(2.5) + + service.stop() + + # Should have received multiple price updates + assert len(messages) >= 2 + + # Check message structure + symbols_seen = set() + for msg in messages: + assert msg["type"] == "price_update" + assert "symbol" in msg + assert "price" in msg + assert "ret" in msg + symbols_seen.add(msg["symbol"]) + + # Should have prices for both tickers + assert "AAPL" in symbols_seen or "MSFT" in symbols_seen + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/evotraders/backend/tests/test_settlement.py b/evotraders/backend/tests/test_settlement.py new file mode 100644 index 0000000..e8e17c9 --- /dev/null +++ b/evotraders/backend/tests/test_settlement.py @@ -0,0 +1,195 @@ +# -*- coding: utf-8 -*- +""" +Test Settlement Coordinator and Baseline Calculations +""" + +from backend.utils.baselines import ( + BaselineCalculator, + calculate_momentum_scores, +) +from backend.utils.analyst_tracker import ( + AnalystPerformanceTracker, + update_leaderboard_with_evaluations, +) + + +def test_baseline_equal_weight(): + """Test equal weight baseline calculation""" + calculator = BaselineCalculator(initial_capital=100000.0) + + tickers = ["AAPL", "MSFT", "GOOGL"] + prices = {"AAPL": 150.0, "MSFT": 300.0, "GOOGL": 120.0} + + value = calculator.calculate_equal_weight_value(tickers, prices) + + assert value > 0 + assert calculator.equal_weight_initialized is True + + +def test_baseline_market_cap_weighted(): + """Test market cap weighted baseline calculation""" + calculator = BaselineCalculator(initial_capital=100000.0) + + tickers = ["AAPL", "MSFT", "GOOGL"] + prices = {"AAPL": 150.0, "MSFT": 300.0, "GOOGL": 120.0} + market_caps = {"AAPL": 3e12, "MSFT": 2e12, "GOOGL": 1.5e12} + + value = calculator.calculate_market_cap_weighted_value( + tickers, + prices, + market_caps, + ) + + assert value > 0 + assert calculator.market_cap_initialized is True + + +def test_momentum_scores(): + """Test momentum score calculation""" + tickers = ["AAPL", "MSFT"] + prices_history = { + "AAPL": [ + ("2024-01-01", 100.0), + ("2024-01-02", 105.0), + ("2024-01-03", 110.0), + ], + "MSFT": [ + ("2024-01-01", 200.0), + ("2024-01-02", 195.0), + ("2024-01-03", 190.0), + ], + } + + scores = calculate_momentum_scores( + tickers, + prices_history, + lookback_days=2, + ) + + assert scores["AAPL"] > 0 + assert scores["MSFT"] < 0 + + +def test_analyst_tracker_predictions(): + """Test analyst prediction recording with structured format""" + tracker = AnalystPerformanceTracker() + + final_predictions = [ + { + "agent": "technical_analyst", + "predictions": [ + {"ticker": "AAPL", "direction": "up", "confidence": 0.8}, + {"ticker": "MSFT", "direction": "down", "confidence": 0.7}, + {"ticker": "GOOGL", "direction": "neutral", "confidence": 0.5}, + ], + }, + { + "agent": "fundamentals_analyst", + "predictions": [ + {"ticker": "AAPL", "direction": "up", "confidence": 0.9}, + {"ticker": "MSFT", "direction": "up", "confidence": 0.6}, + {"ticker": "GOOGL", "direction": "down", "confidence": 0.75}, + ], + }, + ] + + tracker.record_analyst_predictions(final_predictions) + + assert "technical_analyst" in tracker.daily_predictions + assert "fundamentals_analyst" in tracker.daily_predictions + assert tracker.daily_predictions["technical_analyst"]["AAPL"] == "long" + assert tracker.daily_predictions["technical_analyst"]["MSFT"] == "short" + assert tracker.daily_predictions["technical_analyst"]["GOOGL"] == "hold" + + +def test_analyst_evaluation(): + """Test analyst prediction evaluation""" + tracker = AnalystPerformanceTracker() + + tracker.daily_predictions = { + "technical_analyst": { + "AAPL": "long", + "MSFT": "short", + }, + } + + open_prices = {"AAPL": 100.0, "MSFT": 200.0} + close_prices = {"AAPL": 105.0, "MSFT": 195.0} + + evaluations = tracker.evaluate_predictions( + open_prices, + close_prices, + "2024-01-15", + ) + + assert "technical_analyst" in evaluations + eval_result = evaluations["technical_analyst"] + assert eval_result["correct_predictions"] == 2 + assert eval_result["win_rate"] == 1.0 + + # Verify individual signals format + assert "signals" in eval_result + assert len(eval_result["signals"]) == 2 + for signal in eval_result["signals"]: + assert "ticker" in signal + assert "signal" in signal + assert "date" in signal + assert "is_correct" in signal + assert signal["date"] == "2024-01-15" + + +def test_leaderboard_update(): + """Test leaderboard update with evaluations""" + leaderboard = [ + { + "agentId": "technical_analyst", + "name": "Technical Analyst", + "rank": 0, + "winRate": None, + "bull": {"n": 0, "win": 0, "unknown": 0}, + "bear": {"n": 0, "win": 0, "unknown": 0}, + "signals": [], + }, + ] + + evaluations = { + "technical_analyst": { + "total_predictions": 2, + "correct_predictions": 1, + "win_rate": 0.5, + "bull": {"n": 1, "win": 1, "unknown": 0}, + "bear": {"n": 1, "win": 0, "unknown": 0}, + "hold": 0, + "signals": [ + { + "ticker": "AAPL", + "signal": "bull", + "date": "2024-01-01", + "is_correct": True, + }, + { + "ticker": "MSFT", + "signal": "bear", + "date": "2024-01-01", + "is_correct": False, + }, + ], + }, + } + + updated = update_leaderboard_with_evaluations( + leaderboard, + evaluations, + ) + + assert updated[0]["bull"]["n"] == 1 + assert updated[0]["bull"]["win"] == 1 + assert updated[0]["winRate"] == 0.5 + assert len(updated[0]["signals"]) == 2 + + # Verify signal format matches frontend expectations + for signal in updated[0]["signals"]: + assert "ticker" in signal + assert "signal" in signal + assert "date" in signal + assert "is_correct" in signal diff --git a/evotraders/backend/tools/__init__.py b/evotraders/backend/tools/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/evotraders/backend/tools/analysis_tools.py b/evotraders/backend/tools/analysis_tools.py new file mode 100644 index 0000000..c2f4c39 --- /dev/null +++ b/evotraders/backend/tools/analysis_tools.py @@ -0,0 +1,1240 @@ +# -*- coding: utf-8 -*- +""" +Analysis tools for fundamental, technical, sentiment, and valuation analysis. + +All tools accept tickers as List[str] with default from analysis context. +Returns human-readable text format for easy LLM consumption. +""" +# flake8: noqa: E501 +# pylint: disable=C0301,W0613 +import logging +import traceback +from datetime import datetime, timedelta +from functools import wraps +from statistics import median +from typing import List, Optional + +import numpy as np +import pandas as pd +from agentscope.message import TextBlock +from agentscope.tool import ToolResponse + +from backend.tools.data_tools import ( + get_company_news, + get_financial_metrics, + get_insider_trades, + get_market_cap, + get_prices, + prices_to_df, + search_line_items, +) + +logger = logging.getLogger(__name__) + + +def _to_text_response(text: str) -> ToolResponse: + """Convert text string to ToolResponse.""" + return ToolResponse(content=[TextBlock(type="text", text=text)]) + + +def _safe_float(value, default=0.0) -> float: + """Safely convert to float.""" + try: + if pd.isna(value) or np.isnan(value): + return default + return float(value) + except (ValueError, TypeError, OverflowError): + return default + + +def safe(func): + """Decorator to catch exceptions in tool functions.""" + + @wraps(func) + def wrapper(*args, **kwargs): + try: + return func(*args, **kwargs) + except Exception as e: + error_msg = f"Error in {func.__name__}: {str(e)}" + logger.error(f"{error_msg}\n{traceback.format_exc()}") + return _to_text_response(f"[ERROR] {error_msg}") + + return wrapper + + +def _fmt(val, fmt=".2f", suffix="") -> str: + """Format value with handling for None.""" + if val is None: + return "N/A" + try: + return f"{val:{fmt}}{suffix}" + except (ValueError, TypeError): + return str(val) + + +def _resolved_date(current_date: Optional[str]) -> str: + """Ensure we always return a concrete date string.""" + return current_date or datetime.today().strftime("%Y-%m-%d") + + +# ==================== Fundamental Analysis Tools ==================== + + +@safe +def analyze_efficiency_ratios( + tickers: Optional[List[str]] = None, + current_date: Optional[str] = None, +) -> ToolResponse: + """ + Analyze asset utilization efficiency ratios for stocks. + + Evaluates how efficiently companies use assets to generate revenue. + Higher ratios generally indicate better operational efficiency. + + Args: + tickers: List of stock tickers. If None, uses all tickers from context. + current_date: Analysis date (YYYY-MM-DD). If None, uses date from context. + + Returns: + Text summary of efficiency metrics for all tickers. + """ + + current_date = _resolved_date(current_date) + lines = [f"=== Efficiency Ratios Analysis ({current_date}) ===\n"] + + for ticker in tickers: + metrics = get_financial_metrics(ticker=ticker, end_date=current_date) + if not metrics: + lines.append(f"{ticker}: No data available\n") + continue + + m = metrics[0] + lines.append(f"{ticker}:") + lines.append(f" Asset Turnover: {_fmt(m.asset_turnover)}") + lines.append(f" Inventory Turnover: {_fmt(m.inventory_turnover)}") + lines.append(f" Receivables Turnover: {_fmt(m.receivables_turnover)}") + lines.append( + f" Working Capital Turnover: {_fmt(m.working_capital_turnover)}", + ) + lines.append("") + + return _to_text_response("\n".join(lines)) + + +@safe +def analyze_profitability( + tickers: Optional[List[str]] = None, + current_date: Optional[str] = None, +) -> ToolResponse: + """ + Analyze profitability metrics for stocks. + + Assesses how effectively companies generate profit from operations and equity. + Higher margins indicate stronger profitability and better cost management. + + Args: + tickers: List of stock tickers. If None, uses all tickers from context. + current_date: Analysis date (YYYY-MM-DD). If None, uses date from context. + + Returns: + Text summary of profitability metrics for all tickers. + """ + + current_date = _resolved_date(current_date) + lines = [f"=== Profitability Analysis ({current_date}) ===\n"] + + for ticker in tickers: + metrics = get_financial_metrics(ticker=ticker, end_date=current_date) + if not metrics: + lines.append(f"{ticker}: No data available\n") + continue + + m = metrics[0] + roe = _safe_float(m.return_on_equity) + net_margin = _safe_float(m.net_margin) + op_margin = _safe_float(m.operating_margin) + lines.append(f"{ticker}:") + lines.append(f" Return on Equity (ROE): {_fmt(roe/100, '.1%')}") + lines.append(f" Net Margin: {_fmt(net_margin/100, '.1%')}") + lines.append(f" Operating Margin: {_fmt(op_margin/100, '.1%')}") + lines.append("") + + return _to_text_response("\n".join(lines)) + + +@safe +def analyze_growth( + tickers: Optional[List[str]] = None, + current_date: Optional[str] = None, +) -> ToolResponse: + """ + Analyze growth metrics for stocks. + + Evaluates company growth trajectory across key financial dimensions. + Higher growth rates may indicate strong business momentum. + + Args: + tickers: List of stock tickers. If None, uses all tickers from context. + current_date: Analysis date (YYYY-MM-DD). If None, uses date from context. + + Returns: + Text summary of growth metrics for all tickers. + """ + + current_date = _resolved_date(current_date) + lines = [f"=== Growth Analysis ({current_date}) ===\n"] + + for ticker in tickers: + metrics = get_financial_metrics(ticker=ticker, end_date=current_date) + if not metrics: + lines.append(f"{ticker}: No data available\n") + continue + + m = metrics[0] + lines.append(f"{ticker}:") + lines.append(f" Revenue Growth: {_fmt(m.revenue_growth, '.1%')}") + lines.append(f" Earnings Growth: {_fmt(m.earnings_growth, '.1%')}") + lines.append( + f" Book Value Growth: {_fmt(m.book_value_growth, '.1%')}", + ) + lines.append("") + + return _to_text_response("\n".join(lines)) + + +@safe +def analyze_financial_health( + tickers: Optional[List[str]] = None, + current_date: Optional[str] = None, +) -> ToolResponse: + """ + Analyze financial health metrics for stocks. + + Assesses financial stability and ability to meet obligations. + Strong financial health suggests lower bankruptcy risk. + + Args: + tickers: List of stock tickers. If None, uses all tickers from context. + current_date: Analysis date (YYYY-MM-DD). If None, uses date from context. + + Returns: + Text summary of financial health metrics for all tickers. + """ + + current_date = _resolved_date(current_date) + lines = [f"=== Financial Health Analysis ({current_date}) ===\n"] + + for ticker in tickers: + metrics = get_financial_metrics(ticker=ticker, end_date=current_date) + if not metrics: + lines.append(f"{ticker}: No data available\n") + continue + + m = metrics[0] + lines.append(f"{ticker}:") + lines.append( + f" Current Ratio: {_fmt(m.current_ratio)} (>1 is healthy)", + ) + lines.append(f" Debt to Equity: {_fmt(m.debt_to_equity)}") + lines.append( + f" Free Cash Flow/Share: ${_fmt(m.free_cash_flow_per_share)}", + ) + lines.append(f" EPS: ${_fmt(m.earnings_per_share)}") + lines.append("") + + return _to_text_response("\n".join(lines)) + + +@safe +def analyze_valuation_ratios( + tickers: Optional[List[str]] = None, + current_date: Optional[str] = None, +) -> ToolResponse: + """ + Analyze valuation ratios for stocks. + + Evaluates whether stocks are overvalued or undervalued using common multiples. + Lower ratios may indicate undervaluation but compare with industry peers. + + Args: + tickers: List of stock tickers. If None, uses all tickers from context. + current_date: Analysis date (YYYY-MM-DD). If None, uses date from context. + + Returns: + Text summary of valuation ratios for all tickers. + """ + + current_date = _resolved_date(current_date) + lines = [f"=== Valuation Ratios Analysis ({current_date}) ===\n"] + + for ticker in tickers: + metrics = get_financial_metrics(ticker=ticker, end_date=current_date) + if not metrics: + lines.append(f"{ticker}: No data available\n") + continue + + m = metrics[0] + lines.append(f"{ticker}:") + lines.append(f" P/E Ratio: {_fmt(m.price_to_earnings_ratio)}") + lines.append(f" P/B Ratio: {_fmt(m.price_to_book_ratio)}") + lines.append(f" P/S Ratio: {_fmt(m.price_to_sales_ratio)}") + lines.append("") + + return _to_text_response("\n".join(lines)) + + +@safe +def get_financial_metrics_tool( + tickers: Optional[List[str]] = None, + current_date: Optional[str] = None, + period: str = "ttm", +) -> ToolResponse: + """ + Get comprehensive financial metrics for stocks. + + Retrieves complete set of financial metrics for fundamental analysis. + + Args: + tickers: List of stock tickers. If None, uses all tickers from context. + current_date: Analysis date (YYYY-MM-DD). If None, uses date from context. + period: Time period - 'ttm', 'quarterly', or 'annual'. Default 'ttm'. + + Returns: + Text summary of all available financial metrics for all tickers. + """ + + current_date = _resolved_date(current_date) + lines = [ + f"=== Comprehensive Financial Metrics ({current_date}, {period}) ===\n", + ] + + for ticker in tickers: + metrics = get_financial_metrics( + ticker=ticker, + end_date=current_date, + period=period, + ) + if not metrics: + lines.append(f"{ticker}: No data available\n") + continue + + m = metrics[0] + lines.append(f"{ticker}:") + lines.append(f" Market Cap: ${_fmt(m.market_cap, ',.0f')}") + lines.append( + f" P/E: {_fmt(m.price_to_earnings_ratio)} | P/B: {_fmt(m.price_to_book_ratio)} | P/S: {_fmt(m.price_to_sales_ratio)}", + ) + lines.append( + f" ROE: {_fmt(m.return_on_equity, '.1%')} | Net Margin: {_fmt(m.net_margin, '.1%')}", + ) + lines.append( + f" Revenue Growth: {_fmt(m.revenue_growth, '.1%')} | Earnings Growth: {_fmt(m.earnings_growth, '.1%')}", + ) + lines.append( + f" Current Ratio: {_fmt(m.current_ratio)} | D/E: {_fmt(m.debt_to_equity)}", + ) + lines.append( + f" EPS: ${_fmt(m.earnings_per_share)} | FCF/Share: ${_fmt(m.free_cash_flow_per_share)}", + ) + lines.append("") + + return _to_text_response("\n".join(lines)) + + +# ==================== Technical Analysis Tools ==================== + + +@safe +def analyze_trend_following( + tickers: Optional[List[str]] = None, + current_date: Optional[str] = None, +) -> ToolResponse: + """ + Trend following analysis using moving averages and MACD. + + Identifies market trends using SMA (20/50/200) and MACD indicators. + Helps determine if stocks are in uptrend, downtrend, or consolidation. + + Args: + tickers: List of stock tickers. If None, uses all tickers from context. + current_date: Analysis date (YYYY-MM-DD). If None, uses date from context. + + Returns: + Text summary of trend analysis for all tickers. + """ + + current_date = _resolved_date(current_date) + lines = [f"=== Trend Following Analysis ({current_date}) ===\n"] + + end_dt = datetime.strptime(current_date, "%Y-%m-%d") + extended_start = (end_dt - timedelta(days=250)).strftime("%Y-%m-%d") + + for ticker in tickers: + prices = get_prices( + ticker=ticker, + start_date=extended_start, + end_date=current_date, + ) + if not prices or len(prices) < 10: + lines.append(f"{ticker}: Insufficient price data\n") + continue + + df = prices_to_df(prices) + n = len(df) + + # Calculate moving averages + sma_20_win = min(20, n // 2) + sma_50_win = min(50, n - 5) if n > 25 else min(25, n - 5) + sma_200_win = min(200, n - 10) if n > 200 else None + + df["SMA_20"] = df["close"].rolling(window=sma_20_win).mean() + df["SMA_50"] = df["close"].rolling(window=sma_50_win).mean() + if sma_200_win: + df["SMA_200"] = df["close"].rolling(window=sma_200_win).mean() + + df["EMA_12"] = df["close"].ewm(span=min(12, n // 3)).mean() + df["EMA_26"] = df["close"].ewm(span=min(26, n // 2)).mean() + df["MACD"] = df["EMA_12"] - df["EMA_26"] + df["MACD_signal"] = df["MACD"].ewm(span=9).mean() + + current_price = _safe_float(df["close"].iloc[-1]) + sma_20 = _safe_float(df["SMA_20"].iloc[-1]) + sma_50 = _safe_float(df["SMA_50"].iloc[-1]) + sma_200 = ( + _safe_float(df["SMA_200"].iloc[-1]) + if "SMA_200" in df.columns + else None + ) + macd = _safe_float(df["MACD"].iloc[-1]) + macd_signal = _safe_float(df["MACD_signal"].iloc[-1]) + + # Determine trend + if sma_200: + trend = "BULLISH" if current_price > sma_200 else "BEARISH" + distance_200ma = ((current_price - sma_200) / sma_200) * 100 + else: + trend = "UNKNOWN" + distance_200ma = None + + macd_signal_str = "BUY" if macd > macd_signal else "SELL" + + lines.append(f"{ticker}: ${current_price:.2f}") + lines.append( + f" SMA20: ${sma_20:.2f} | SMA50: ${sma_50:.2f} | SMA200: {f'${sma_200:.2f}' if sma_200 else 'N/A'}", + ) + lines.append( + f" MACD: {macd:.3f} | Signal: {macd_signal:.3f} -> {macd_signal_str}", + ) + lines.append( + f" Long-term Trend: {trend}" + + ( + f" ({distance_200ma:+.1f}% from 200MA)" + if distance_200ma + else "" + ), + ) + lines.append("") + + return _to_text_response("\n".join(lines)) + + +@safe +def analyze_mean_reversion( + tickers: Optional[List[str]] = None, + current_date: Optional[str] = None, +) -> ToolResponse: + """ + Mean reversion analysis using Bollinger Bands and RSI. + + Identifies overbought/oversold conditions. + RSI >70 = overbought, <30 = oversold. + Price near bands may signal reversal. + + Args: + tickers: List of stock tickers. If None, uses all tickers from context. + current_date: Analysis date (YYYY-MM-DD). If None, uses date from context. + + Returns: + Text summary of mean reversion signals for all tickers. + """ + + current_date = _resolved_date(current_date) + lines = [f"=== Mean Reversion Analysis ({current_date}) ===\n"] + + end_dt = datetime.strptime(current_date, "%Y-%m-%d") + extended_start = (end_dt - timedelta(days=60)).strftime("%Y-%m-%d") + + for ticker in tickers: + prices = get_prices( + ticker=ticker, + start_date=extended_start, + end_date=current_date, + ) + if not prices or len(prices) < 5: + lines.append(f"{ticker}: Insufficient price data\n") + continue + + df = prices_to_df(prices) + n = len(df) + + # Bollinger Bands + window = min(20, n - 2) + df["SMA"] = df["close"].rolling(window=window).mean() + df["STD"] = df["close"].rolling(window=window).std() + df["Upper_Band"] = df["SMA"] + (2 * df["STD"]) + df["Lower_Band"] = df["SMA"] - (2 * df["STD"]) + + # RSI + delta = df["close"].diff() + gain = (delta.where(delta > 0, 0)).rolling(window=14).mean() + loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean() + rs = gain / loss + df["RSI"] = 100 - (100 / (1 + rs)) + + current_price = _safe_float(df["close"].iloc[-1]) + sma = _safe_float(df["SMA"].iloc[-1]) + upper = _safe_float(df["Upper_Band"].iloc[-1]) + lower = _safe_float(df["Lower_Band"].iloc[-1]) + rsi = _safe_float(df["RSI"].iloc[-1]) + deviation = (current_price - sma) / sma * 100 + + # Signal interpretation + if rsi > 70: + rsi_signal = "OVERBOUGHT" + elif rsi < 30: + rsi_signal = "OVERSOLD" + else: + rsi_signal = "NEUTRAL" + + if current_price > upper: + bb_signal = "ABOVE UPPER BAND (potential sell)" + elif current_price < lower: + bb_signal = "BELOW LOWER BAND (potential buy)" + else: + bb_signal = "WITHIN BANDS" + + lines.append(f"{ticker}: ${current_price:.2f}") + lines.append( + f" Bollinger: Lower ${lower:.2f} | SMA ${sma:.2f} | Upper ${upper:.2f}", + ) + lines.append(f" Position: {bb_signal}") + lines.append(f" RSI: {rsi:.1f} -> {rsi_signal}") + lines.append(f" Price Deviation from SMA: {deviation:+.1f}%") + lines.append("") + + return _to_text_response("\n".join(lines)) + + +@safe +def analyze_momentum( + tickers: Optional[List[str]] = None, + current_date: Optional[str] = None, +) -> ToolResponse: + """ + Momentum analysis for different time periods. + + Measures price momentum over 5, 10, and 20 day periods. + Positive momentum indicates upward price pressure. + + Args: + tickers: List of stock tickers. If None, uses all tickers from context. + current_date: Analysis date (YYYY-MM-DD). If None, uses date from context. + + Returns: + Text summary of momentum indicators for all tickers. + """ + + current_date = _resolved_date(current_date) + lines = [f"=== Momentum Analysis ({current_date}) ===\n"] + + end_dt = datetime.strptime(current_date, "%Y-%m-%d") + extended_start = (end_dt - timedelta(days=45)).strftime("%Y-%m-%d") + + for ticker in tickers: + prices = get_prices( + ticker=ticker, + start_date=extended_start, + end_date=current_date, + ) + if not prices or len(prices) < 5: + lines.append(f"{ticker}: Insufficient price data\n") + continue + + df = prices_to_df(prices) + n = len(df) + df["returns"] = df["close"].pct_change() + + # Adaptive periods + short_p = min(5, n // 3) + med_p = min(10, n // 2) + long_p = min(20, n - 2) + + current_price = _safe_float(df["close"].iloc[-1]) + mom_5 = ( + _safe_float( + (df["close"].iloc[-1] / df["close"].iloc[-short_p - 1] - 1) + * 100, + ) + if n > short_p + else 0 + ) + mom_10 = ( + _safe_float( + (df["close"].iloc[-1] / df["close"].iloc[-med_p - 1] - 1) + * 100, + ) + if n > med_p + else 0 + ) + mom_20 = ( + _safe_float( + (df["close"].iloc[-1] / df["close"].iloc[-long_p - 1] - 1) + * 100, + ) + if n > long_p + else 0 + ) + volatility = _safe_float( + df["returns"].tail(20).std() * np.sqrt(252) * 100, + ) + + # Overall momentum signal + avg_mom = (mom_5 + mom_10 + mom_20) / 3 + if avg_mom > 2: + signal = "STRONG BULLISH" + elif avg_mom > 0: + signal = "BULLISH" + elif avg_mom > -2: + signal = "BEARISH" + else: + signal = "STRONG BEARISH" + + lines.append(f"{ticker}: ${current_price:.2f}") + lines.append( + f" 5-day: {mom_5:+.1f}% | 10-day: {mom_10:+.1f}% | 20-day: {mom_20:+.1f}%", + ) + lines.append(f" Volatility (annualized): {volatility:.1f}%") + lines.append(f" Overall: {signal}") + lines.append("") + + return _to_text_response("\n".join(lines)) + + +@safe +def analyze_volatility( + tickers: Optional[List[str]] = None, + current_date: Optional[str] = None, +) -> ToolResponse: + """ + Volatility analysis for different time windows. + + Measures price volatility over 10, 20, and 60 day periods. + Higher volatility indicates higher risk but potentially higher returns. + + Args: + tickers: List of stock tickers. If None, uses all tickers from context. + current_date: Analysis date (YYYY-MM-DD). If None, uses date from context. + + Returns: + Text summary of volatility metrics for all tickers. + """ + + current_date = _resolved_date(current_date) + lines = [f"=== Volatility Analysis ({current_date}) ===\n"] + + end_dt = datetime.strptime(current_date, "%Y-%m-%d") + extended_start = (end_dt - timedelta(days=90)).strftime("%Y-%m-%d") + + for ticker in tickers: + prices = get_prices( + ticker=ticker, + start_date=extended_start, + end_date=current_date, + ) + if not prices or len(prices) < 5: + lines.append(f"{ticker}: Insufficient price data\n") + continue + + df = prices_to_df(prices) + n = len(df) + df["returns"] = df["close"].pct_change() + + # Adaptive windows + short_w = min(10, n // 2) + med_w = min(20, n - 2) + long_w = min(60, n - 1) if n > 30 else med_w + + current_price = _safe_float(df["close"].iloc[-1]) + vol_10 = _safe_float( + df["returns"].tail(short_w).std() * np.sqrt(252) * 100, + ) + vol_20 = _safe_float( + df["returns"].tail(med_w).std() * np.sqrt(252) * 100, + ) + vol_60 = _safe_float( + df["returns"].tail(long_w).std() * np.sqrt(252) * 100, + ) + + # Risk assessment + if vol_20 > 50: + risk = "HIGH RISK" + elif vol_20 > 25: + risk = "MODERATE RISK" + else: + risk = "LOW RISK" + + lines.append(f"{ticker}: ${current_price:.2f}") + lines.append( + f" 10-day Vol: {vol_10:.1f}% | 20-day Vol: {vol_20:.1f}% | 60-day Vol: {vol_60:.1f}%", + ) + lines.append(f" Risk Level: {risk}") + lines.append("") + + return _to_text_response("\n".join(lines)) + + +# ==================== Sentiment Analysis Tools ==================== + + +@safe +def analyze_insider_trading( + tickers: Optional[List[str]] = None, + current_date: Optional[str] = None, + start_date: Optional[str] = None, +) -> ToolResponse: + """ + Analyze insider trading activity. + + Tracks buying/selling by company insiders (executives, directors). + Insider buying can signal confidence; selling may indicate concerns. + + Args: + tickers: List of stock tickers. If None, uses all tickers from context. + current_date: Analysis date. If None, uses date from context. + start_date: Optional start date for lookback period. + + Returns: + Text summary of insider trading activity for all tickers. + """ + + current_date = _resolved_date(current_date) + lines = [f"=== Insider Trading Analysis ({current_date}) ===\n"] + + for ticker in tickers: + trades = get_insider_trades( + ticker=ticker, + end_date=current_date, + start_date=start_date, + limit=1000, + ) + + if not trades: + lines.append(f"{ticker}: No insider trading data\n") + continue + + shares = pd.Series([t.transaction_shares for t in trades]).dropna() + + if len(shares) == 0: + lines.append(f"{ticker}: {len(trades)} trades but no share data\n") + continue + + buy_count = int((shares > 0).sum()) + sell_count = int((shares < 0).sum()) + buy_vol = float(shares[shares > 0].sum()) + sell_vol = float(abs(shares[shares < 0].sum())) + + # Sentiment interpretation + if buy_count > sell_count * 2: + sentiment = "STRONG INSIDER BUYING" + elif buy_count > sell_count: + sentiment = "NET INSIDER BUYING" + elif sell_count > buy_count * 2: + sentiment = "STRONG INSIDER SELLING" + elif sell_count > buy_count: + sentiment = "NET INSIDER SELLING" + else: + sentiment = "MIXED INSIDER ACTIVITY" + + lines.append(f"{ticker}:") + lines.append(f" Buys: {buy_count} trades ({buy_vol:,.0f} shares)") + lines.append(f" Sells: {sell_count} trades ({sell_vol:,.0f} shares)") + lines.append(f" Signal: {sentiment}") + lines.append("") + + return _to_text_response("\n".join(lines)) + + +@safe +def analyze_news_sentiment( + tickers: Optional[List[str]] = None, + current_date: Optional[str] = None, + start_date: Optional[str] = None, +) -> ToolResponse: + """ + Analyze recent news for stocks. + + Retrieves and summarizes recent news articles. + Use this to understand recent events and market sentiment. + + Args: + tickers: List of stock tickers. If None, uses all tickers from context. + current_date: Analysis date. If None, uses date from context. + start_date: Optional start date for lookback period. + + Returns: + Text summary of recent news for all tickers. + """ + + current_date = _resolved_date(current_date) + lines = [f"=== News Analysis ({current_date}) ===\n"] + + for ticker in tickers: + news = get_company_news( + ticker=ticker, + end_date=current_date, + start_date=start_date, + limit=10, + ) + + if not news: + lines.append(f"{ticker}: No recent news\n") + continue + + lines.append(f"{ticker} - {len(news)} recent articles:") + for i, n in enumerate(news[:5], 1): + date_str = n.date[:10] if n.date else "N/A" + lines.append(f" {i}. [{date_str}] {n.title[:80]}...") + lines.append(f" Source: {n.source}") + if len(news) > 5: + lines.append(f" ... and {len(news) - 5} more articles") + lines.append("") + + return _to_text_response("\n".join(lines)) + + +# ==================== Valuation Analysis Tools ==================== + + +@safe +def dcf_valuation_analysis( + tickers: Optional[List[str]] = None, + current_date: Optional[str] = None, +) -> ToolResponse: + """ + Discounted Cash Flow (DCF) valuation analysis. + + Estimates intrinsic value by projecting future free cash flows. + Positive value_gap indicates potential undervaluation. + + Args: + tickers: List of stock tickers. If None, uses all tickers from context. + current_date: Analysis date. If None, uses date from context. + + Returns: + Text summary of DCF valuation for all tickers. + """ + + current_date = _resolved_date(current_date) + lines = [f"=== DCF Valuation Analysis ({current_date}) ===\n"] + + for ticker in tickers: + metrics = get_financial_metrics( + ticker=ticker, + end_date=current_date, + limit=8, + ) + if not metrics: + lines.append(f"{ticker}: No financial metrics\n") + continue + + line_items = search_line_items( + ticker=ticker, + line_items=["free_cash_flow"], + end_date=current_date, + period="ttm", + limit=2, + ) + if ( + not line_items + or not line_items[0].free_cash_flow + or line_items[0].free_cash_flow <= 0 + ): + lines.append(f"{ticker}: Invalid free cash flow data\n") + continue + + market_cap = get_market_cap(ticker, current_date) + if not market_cap: + lines.append(f"{ticker}: Market cap unavailable\n") + continue + + m = metrics[0] + current_fcf = line_items[0].free_cash_flow + growth_rate = m.earnings_growth or 0.05 + discount_rate = 0.10 + terminal_growth = 0.03 + num_years = 5 + + # DCF calculation + pv_fcf = sum( + current_fcf + * (1 + growth_rate) ** year + / (1 + discount_rate) ** year + for year in range(1, num_years + 1) + ) + terminal_fcf = ( + current_fcf + * (1 + growth_rate) ** num_years + * (1 + terminal_growth) + ) + terminal_value = terminal_fcf / (discount_rate - terminal_growth) + pv_terminal = terminal_value / (1 + discount_rate) ** num_years + enterprise_value = pv_fcf + pv_terminal + value_gap = (enterprise_value - market_cap) / market_cap * 100 + + # Assessment + if value_gap > 20: + assessment = "SIGNIFICANTLY UNDERVALUED" + elif value_gap > 0: + assessment = "POTENTIALLY UNDERVALUED" + elif value_gap > -20: + assessment = "POTENTIALLY OVERVALUED" + else: + assessment = "SIGNIFICANTLY OVERVALUED" + + lines.append(f"{ticker}:") + lines.append(f" Current FCF: ${current_fcf:,.0f}") + lines.append(f" DCF Enterprise Value: ${enterprise_value:,.0f}") + lines.append(f" Market Cap: ${market_cap:,.0f}") + lines.append(f" Value Gap: {value_gap:+.1f}% -> {assessment}") + lines.append("") + + return _to_text_response("\n".join(lines)) + + +@safe +def owner_earnings_valuation_analysis( + tickers: Optional[List[str]] = None, + current_date: Optional[str] = None, +) -> ToolResponse: + """ + Buffett-style owner earnings valuation analysis. + + Owner earnings = Net Income + D&A - CapEx - Working Capital Changes. + Represents true cash owners could extract from the business. + + Args: + tickers: List of stock tickers. If None, uses all tickers from context. + current_date: Analysis date. If None, uses date from context. + + Returns: + Text summary of owner earnings valuation for all tickers. + """ + + current_date = _resolved_date(current_date) + lines = [f"=== Owner Earnings Valuation ({current_date}) ===\n"] + + for ticker in tickers: + metrics = get_financial_metrics( + ticker=ticker, + end_date=current_date, + limit=8, + ) + if not metrics: + lines.append(f"{ticker}: No financial metrics\n") + continue + + line_items = search_line_items( + ticker=ticker, + line_items=[ + "net_income", + "depreciation_and_amortization", + "capital_expenditure", + "working_capital", + ], + end_date=current_date, + period="ttm", + limit=2, + ) + if len(line_items) < 2: + lines.append(f"{ticker}: Insufficient financial data\n") + continue + + market_cap = get_market_cap(ticker, current_date) + if not market_cap: + lines.append(f"{ticker}: Market cap unavailable\n") + continue + + m = metrics[0] + current, previous = line_items[0], line_items[1] + + net_income = current.net_income or 0 + depreciation = current.depreciation_and_amortization or 0 + capex = current.capital_expenditure or 0 + wc_change = (current.working_capital or 0) - ( + previous.working_capital or 0 + ) + + owner_earnings = net_income + depreciation - capex - wc_change + if owner_earnings <= 0: + lines.append( + f"{ticker}: Negative owner earnings (${owner_earnings:,.0f})\n", + ) + continue + + # Valuation + growth_rate = m.earnings_growth or 0.05 + required_return = 0.15 + margin_of_safety = 0.25 + num_years = 5 + + pv_earnings = sum( + owner_earnings + * (1 + growth_rate) ** year + / (1 + required_return) ** year + for year in range(1, num_years + 1) + ) + terminal_growth = min(growth_rate, 0.03) + terminal_earnings = ( + owner_earnings + * (1 + growth_rate) ** num_years + * (1 + terminal_growth) + ) + terminal_value = terminal_earnings / ( + required_return - terminal_growth + ) + pv_terminal = terminal_value / (1 + required_return) ** num_years + + intrinsic_value = (pv_earnings + pv_terminal) * (1 - margin_of_safety) + value_gap = (intrinsic_value - market_cap) / market_cap * 100 + + # Assessment + if value_gap > 20: + assessment = "SIGNIFICANTLY UNDERVALUED" + elif value_gap > 0: + assessment = "POTENTIALLY UNDERVALUED" + elif value_gap > -20: + assessment = "POTENTIALLY OVERVALUED" + else: + assessment = "SIGNIFICANTLY OVERVALUED" + + lines.append(f"{ticker}:") + lines.append(f" Owner Earnings: ${owner_earnings:,.0f}") + lines.append( + f" Intrinsic Value (w/ 25% MoS): ${intrinsic_value:,.0f}", + ) + lines.append(f" Market Cap: ${market_cap:,.0f}") + lines.append(f" Value Gap: {value_gap:+.1f}% -> {assessment}") + lines.append("") + + return _to_text_response("\n".join(lines)) + + +@safe +def ev_ebitda_valuation_analysis( + tickers: Optional[List[str]] = None, + current_date: Optional[str] = None, +) -> ToolResponse: + """ + EV/EBITDA multiple valuation analysis. + + Compares current EV/EBITDA to historical median. + Lower multiples relative to history may indicate undervaluation. + + Args: + tickers: List of stock tickers. If None, uses all tickers from context. + current_date: Analysis date. If None, uses date from context. + + Returns: + Text summary of EV/EBITDA valuation for all tickers. + """ + + current_date = _resolved_date(current_date) + lines = [f"=== EV/EBITDA Valuation ({current_date}) ===\n"] + + for ticker in tickers: + metrics = get_financial_metrics( + ticker=ticker, + end_date=current_date, + limit=8, + ) + if not metrics: + lines.append(f"{ticker}: No financial metrics\n") + continue + + m = metrics[0] + if ( + not m.enterprise_value + or not m.enterprise_value_to_ebitda_ratio + or m.enterprise_value_to_ebitda_ratio <= 0 + ): + lines.append(f"{ticker}: Missing EV/EBITDA data\n") + continue + + market_cap = get_market_cap(ticker, current_date) + if not market_cap: + lines.append(f"{ticker}: Market cap unavailable\n") + continue + + current_ebitda = ( + m.enterprise_value / m.enterprise_value_to_ebitda_ratio + ) + + valid_multiples = [ + x.enterprise_value_to_ebitda_ratio + for x in metrics + if x.enterprise_value_to_ebitda_ratio + and x.enterprise_value_to_ebitda_ratio > 0 + ] + if len(valid_multiples) < 3: + lines.append(f"{ticker}: Insufficient historical data\n") + continue + + median_multiple = median(valid_multiples) + current_multiple = m.enterprise_value_to_ebitda_ratio + + implied_ev = median_multiple * current_ebitda + net_debt = m.enterprise_value - market_cap + implied_equity = max(implied_ev - net_debt, 0) + + value_gap = ( + (implied_equity - market_cap) / market_cap * 100 + if market_cap > 0 + else 0 + ) + multiple_discount = ( + (median_multiple - current_multiple) / median_multiple * 100 + ) + + # Assessment + if multiple_discount > 10: + assessment = "TRADING BELOW HISTORICAL MULTIPLE" + elif multiple_discount > -10: + assessment = "NEAR HISTORICAL AVERAGE" + else: + assessment = "TRADING ABOVE HISTORICAL MULTIPLE" + + lines.append(f"{ticker}:") + lines.append(f" Current EV/EBITDA: {current_multiple:.1f}x") + lines.append(f" Historical Median: {median_multiple:.1f}x") + lines.append(f" Multiple vs History: {multiple_discount:+.1f}%") + lines.append(f" Implied Equity Value: ${implied_equity:,.0f}") + lines.append(f" Value Gap: {value_gap:+.1f}% -> {assessment}") + lines.append("") + + return _to_text_response("\n".join(lines)) + + +@safe +def residual_income_valuation_analysis( + tickers: Optional[List[str]] = None, + current_date: Optional[str] = None, +) -> ToolResponse: + """ + Residual Income Model (RIM) valuation analysis. + + Values company based on book value plus PV of future residual income. + Residual income = Net Income - (Cost of Equity x Book Value). + + Args: + tickers: List of stock tickers. If None, uses all tickers from context. + current_date: Analysis date. If None, uses date from context. + + Returns: + Text summary of residual income valuation for all tickers. + """ + + current_date = _resolved_date(current_date) + lines = [f"=== Residual Income Valuation ({current_date}) ===\n"] + + for ticker in tickers: + metrics = get_financial_metrics( + ticker=ticker, + end_date=current_date, + limit=8, + ) + if not metrics: + lines.append(f"{ticker}: No financial metrics\n") + continue + + line_items = search_line_items( + ticker=ticker, + line_items=["net_income"], + end_date=current_date, + period="ttm", + limit=1, + ) + if not line_items or not line_items[0].net_income: + lines.append(f"{ticker}: No net income data\n") + continue + + market_cap = get_market_cap(ticker, current_date) + if not market_cap: + lines.append(f"{ticker}: Market cap unavailable\n") + continue + + m = metrics[0] + if not m.price_to_book_ratio or m.price_to_book_ratio <= 0: + lines.append(f"{ticker}: Invalid P/B ratio\n") + continue + + net_income = line_items[0].net_income + pb_ratio = m.price_to_book_ratio + book_value = market_cap / pb_ratio + + # Model parameters + cost_of_equity = 0.10 + bv_growth = m.book_value_growth or 0.03 + terminal_growth = 0.03 + num_years = 5 + margin_of_safety = 0.20 + + initial_ri = net_income - cost_of_equity * book_value + if initial_ri <= 0: + lines.append(f"{ticker}: Negative residual income\n") + continue + + # PV calculation + pv_ri = sum( + initial_ri * (1 + bv_growth) ** year / (1 + cost_of_equity) ** year + for year in range(1, num_years + 1) + ) + terminal_ri = initial_ri * (1 + bv_growth) ** (num_years + 1) + terminal_value = terminal_ri / (cost_of_equity - terminal_growth) + pv_terminal = terminal_value / (1 + cost_of_equity) ** num_years + + intrinsic_value = (book_value + pv_ri + pv_terminal) * ( + 1 - margin_of_safety + ) + value_gap = (intrinsic_value - market_cap) / market_cap * 100 + + lines.append(f"{ticker}:") + lines.append(f" Book Value: ${book_value:,.0f}") + lines.append(f" Residual Income: ${initial_ri:,.0f}") + lines.append( + f" Intrinsic Value (w/ 20% MoS): ${intrinsic_value:,.0f}", + ) + lines.append(f" Value Gap: {value_gap:+.1f}%") + lines.append("") + + return _to_text_response("\n".join(lines)) + + +# Tool Registry for dynamic toolkit creation +TOOL_REGISTRY = { + "analyze_efficiency_ratios": analyze_efficiency_ratios, + "analyze_profitability": analyze_profitability, + "analyze_growth": analyze_growth, + "analyze_financial_health": analyze_financial_health, + "analyze_valuation_ratios": analyze_valuation_ratios, + "get_financial_metrics_tool": get_financial_metrics_tool, + "analyze_trend_following": analyze_trend_following, + "analyze_mean_reversion": analyze_mean_reversion, + "analyze_momentum": analyze_momentum, + "analyze_volatility": analyze_volatility, + "analyze_insider_trading": analyze_insider_trading, + "analyze_news_sentiment": analyze_news_sentiment, + "dcf_valuation_analysis": dcf_valuation_analysis, + "owner_earnings_valuation_analysis": owner_earnings_valuation_analysis, + "ev_ebitda_valuation_analysis": ev_ebitda_valuation_analysis, + "residual_income_valuation_analysis": residual_income_valuation_analysis, +} diff --git a/evotraders/backend/tools/data_tools.py b/evotraders/backend/tools/data_tools.py new file mode 100644 index 0000000..498441d --- /dev/null +++ b/evotraders/backend/tools/data_tools.py @@ -0,0 +1,720 @@ +# -*- coding: utf-8 -*- +# flake8: noqa: E501 +# pylint: disable=C0301 +""" +Data fetching tools for financial data. + +All functions use centralized data source configuration from data_config.py. +The data source is automatically determined based on available API keys: +- Priority: FINNHUB_API_KEY > FINANCIAL_DATASETS_API_KEY +""" +import datetime +import time + +import finnhub +import pandas as pd +import pandas_market_calendars as mcal +import requests + +from backend.config.data_config import ( + get_config, + get_api_key, +) +from backend.data.cache import get_cache +from backend.data.schema import ( + CompanyFactsResponse, + CompanyNews, + CompanyNewsResponse, + FinancialMetrics, + FinancialMetricsResponse, + InsiderTrade, + InsiderTradeResponse, + LineItem, + LineItemResponse, + Price, + PriceResponse, +) + +# Global cache instance +_cache = get_cache() + + +def get_last_tradeday(date: str) -> str: + """ + Get the previous trading day for the specified date + + Args: + date: Date string (YYYY-MM-DD) + + Returns: + Previous trading day date string (YYYY-MM-DD) + """ + current_date = datetime.datetime.strptime(date, "%Y-%m-%d") + _NYSE_CALENDAR = mcal.get_calendar("NYSE") + + if _NYSE_CALENDAR is not None: + # Get trading days before current date + # Go back 90 days from current date to get all trading days + start_search = current_date - datetime.timedelta(days=90) + + if hasattr(_NYSE_CALENDAR, "valid_days"): + # pandas_market_calendars + trading_dates = _NYSE_CALENDAR.valid_days( + start_date=start_search.strftime("%Y-%m-%d"), + end_date=current_date.strftime("%Y-%m-%d"), + ) + else: + # exchange_calendars + trading_dates = _NYSE_CALENDAR.sessions_in_range( + start_search.strftime("%Y-%m-%d"), + current_date.strftime("%Y-%m-%d"), + ) + + # Convert to date list + trading_dates_list = [ + pd.Timestamp(d).strftime("%Y-%m-%d") for d in trading_dates + ] + + # Find current date position in the list + if date in trading_dates_list: + # If current date is a trading day, return previous trading day + idx = trading_dates_list.index(date) + if idx > 0: + return trading_dates_list[idx - 1] + else: + # If it's the first trading day, go back further + prev_date = current_date - datetime.timedelta(days=1) + return get_last_tradeday(prev_date.strftime("%Y-%m-%d")) + else: + # If current date is not a trading day, return the nearest trading day + if trading_dates_list: + return trading_dates_list[-1] + + return prev_date.strftime("%Y-%m-%d") + + +def _make_api_request( + url: str, + headers: dict, + method: str = "GET", + json_data: dict = None, + max_retries: int = 3, +) -> requests.Response: + """ + Make an API request with rate limiting handling and moderate backoff. + + Args: + url: The URL to request + headers: Headers to include in the request + method: HTTP method (GET or POST) + json_data: JSON data for POST requests + max_retries: Maximum number of retries (default: 3) + + Returns: + requests.Response: The response object + + Raises: + Exception: If the request fails with a non-429 error + """ + for attempt in range(max_retries + 1): # +1 for initial attempt + if method.upper() == "POST": + response = requests.post(url, headers=headers, json=json_data) + else: + response = requests.get(url, headers=headers) + + if response.status_code == 429 and attempt < max_retries: + # Linear backoff: 60s, 90s, 120s, 150s... + delay = 60 + (30 * attempt) + print( + f"Rate limited (429). Attempt {attempt + 1}/{max_retries + 1}. Waiting {delay}s before retrying...", + ) + time.sleep(delay) + continue + + # Return the response (whether success, other errors, or final 429) + return response + + +def get_prices( + ticker: str, + start_date: str, + end_date: str, +) -> list[Price]: + """ + Fetch price data from cache or API. + + Uses centralized data source configuration (FINNHUB_API_KEY prioritized). + + Args: + ticker: Stock ticker symbol + start_date: Start date (YYYY-MM-DD) + end_date: End date (YYYY-MM-DD) + + Returns: + list[Price]: List of Price objects + """ + config = get_config() + data_source = config.source + api_key = config.api_key + + # Create a cache key that includes all parameters to ensure exact matches + cache_key = f"{ticker}_{start_date}_{end_date}_{data_source}" + + # Check cache first - simple exact match + if cached_data := _cache.get_prices(cache_key): + return [Price(**price) for price in cached_data] + + prices = [] + + if data_source == "finnhub": + # Use Finnhub API + client = finnhub.Client(api_key=api_key) + + # Convert dates to timestamps + start_timestamp = int( + datetime.datetime.strptime(start_date, "%Y-%m-%d").timestamp(), + ) + end_timestamp = int( + ( + datetime.datetime.strptime(end_date, "%Y-%m-%d") + + datetime.timedelta(days=1) + ).timestamp(), + ) + + # Fetch candle data from Finnhub + candles = client.stock_candles( + ticker, + "D", + start_timestamp, + end_timestamp, + ) + + # Convert to Price objects + for i in range(len(candles["t"])): + price = Price( + open=candles["o"][i], + close=candles["c"][i], + high=candles["h"][i], + low=candles["l"][i], + volume=int(candles["v"][i]), + time=datetime.datetime.fromtimestamp(candles["t"][i]).strftime( + "%Y-%m-%d", + ), + ) + prices.append(price) + + else: # financial_datasets + # Use Financial Datasets API + headers = {"X-API-KEY": api_key} + + url = f"https://api.financialdatasets.ai/prices/?ticker={ticker}&interval=day&interval_multiplier=1&start_date={start_date}&end_date={end_date}" + response = _make_api_request(url, headers) + if response.status_code != 200: + raise ValueError( + f"Error fetching data: {ticker} - {response.status_code} - {response.text}", + ) + + # Parse response with Pydantic model + price_response = PriceResponse(**response.json()) + prices = price_response.prices + + if not prices: + return [] + + # Cache the results using the comprehensive cache key + _cache.set_prices(cache_key, [p.model_dump() for p in prices]) + return prices + + +def get_financial_metrics( + ticker: str, + end_date: str, + period: str = "ttm", + limit: int = 10, +) -> list[FinancialMetrics]: + """ + Fetch financial metrics from cache or API. + + Uses centralized data source configuration (FINNHUB_API_KEY prioritized). + + Args: + ticker: Stock ticker symbol + end_date: End date (YYYY-MM-DD) + period: Period type (default: "ttm") + limit: Number of records to fetch + + Returns: + list[FinancialMetrics]: List of financial metrics + """ + config = get_config() + data_source = config.source + api_key = config.api_key + + # Create a cache key that includes all parameters to ensure exact matches + cache_key = f"{ticker}_{period}_{end_date}_{limit}_{data_source}" + + # Check cache first - simple exact match + if cached_data := _cache.get_financial_metrics(cache_key): + return [FinancialMetrics(**metric) for metric in cached_data] + + financial_metrics = [] + + if data_source == "finnhub": + # Use Finnhub API - Basic Financials + client = finnhub.Client(api_key=api_key) + + # Fetch basic financials from Finnhub + # metric='all' returns all available metrics + financials = client.company_basic_financials(ticker, "all") + + if not financials or "metric" not in financials: + return [] + + # Finnhub returns {series: {...}, metric: {...}, metricType: ..., symbol: ...} + # We need to create a FinancialMetrics object from this + metric_data = financials.get("metric", {}) + + # Create a FinancialMetrics object with available data + metric = _map_finnhub_metrics(ticker, end_date, period, metric_data) + + financial_metrics = [metric] + + else: # financial_datasets + # Use Financial Datasets API + headers = {"X-API-KEY": api_key} + + url = f"https://api.financialdatasets.ai/financial-metrics/?ticker={ticker}&report_period_lte={end_date}&limit={limit}&period={period}" + response = _make_api_request(url, headers) + if response.status_code != 200: + raise ValueError( + f"Error fetching data: {ticker} - {response.status_code} - {response.text}", + ) + + # Parse response with Pydantic model + metrics_response = FinancialMetricsResponse(**response.json()) + financial_metrics = metrics_response.financial_metrics + + if not financial_metrics: + return [] + + # Cache the results as dicts using the comprehensive cache key + _cache.set_financial_metrics( + cache_key, + [m.model_dump() for m in financial_metrics], + ) + return financial_metrics + + +def _map_finnhub_metrics( + ticker: str, + end_date: str, + period: str, + metric_data: dict, +) -> FinancialMetrics: + """Map Finnhub metric data to FinancialMetrics model.""" + return FinancialMetrics( + ticker=ticker, + report_period=end_date, + period=period, + currency="USD", + market_cap=metric_data.get("marketCapitalization"), + enterprise_value=None, + price_to_earnings_ratio=metric_data.get("peBasicExclExtraTTM"), + price_to_book_ratio=metric_data.get("pbAnnual"), + price_to_sales_ratio=metric_data.get("psAnnual"), + enterprise_value_to_ebitda_ratio=None, + enterprise_value_to_revenue_ratio=None, + free_cash_flow_yield=None, + peg_ratio=None, + gross_margin=metric_data.get("grossMarginTTM"), + operating_margin=metric_data.get("operatingMarginTTM"), + net_margin=metric_data.get("netProfitMarginTTM"), + return_on_equity=metric_data.get("roeTTM"), + return_on_assets=metric_data.get("roaTTM"), + return_on_invested_capital=metric_data.get("roicTTM"), + asset_turnover=metric_data.get("assetTurnoverTTM"), + inventory_turnover=metric_data.get("inventoryTurnoverTTM"), + receivables_turnover=metric_data.get("receivablesTurnoverTTM"), + days_sales_outstanding=None, + operating_cycle=None, + working_capital_turnover=None, + current_ratio=metric_data.get("currentRatioAnnual"), + quick_ratio=metric_data.get("quickRatioAnnual"), + cash_ratio=None, + operating_cash_flow_ratio=None, + debt_to_equity=metric_data.get("totalDebt/totalEquityAnnual"), + debt_to_assets=None, + interest_coverage=None, + revenue_growth=metric_data.get("revenueGrowthTTMYoy"), + earnings_growth=None, + book_value_growth=None, + earnings_per_share_growth=metric_data.get("epsGrowthTTMYoy"), + free_cash_flow_growth=None, + operating_income_growth=None, + ebitda_growth=None, + payout_ratio=metric_data.get("payoutRatioAnnual"), + earnings_per_share=metric_data.get("epsBasicExclExtraItemsTTM"), + book_value_per_share=metric_data.get("bookValuePerShareAnnual"), + free_cash_flow_per_share=None, + ) + + +def search_line_items( + ticker: str, + line_items: list[str], + end_date: str, + period: str = "ttm", + limit: int = 10, +) -> list[LineItem]: + """Fetch line items from Financial Datasets API (only supported source).""" + api_key = get_api_key() + headers = {"X-API-KEY": api_key} + + url = "https://api.financialdatasets.ai/financials/search/line-items" + body = { + "tickers": [ticker], + "line_items": line_items, + "end_date": end_date, + "period": period, + "limit": limit, + } + response = _make_api_request(url, headers, method="POST", json_data=body) + if response.status_code != 200: + raise ValueError( + f"Error fetching data: {ticker} - {response.status_code} - {response.text}", + ) + data = response.json() + response_model = LineItemResponse(**data) + search_results = response_model.search_results + if not search_results: + return [] + + return search_results[:limit] + + +def _fetch_finnhub_insider_trades( + ticker: str, + start_date: str | None, + end_date: str, + limit: int, + api_key: str, +) -> list[InsiderTrade]: + """Fetch insider trades from Finnhub API.""" + client = finnhub.Client(api_key=api_key) + + from_date = start_date or ( + datetime.datetime.strptime(end_date, "%Y-%m-%d") + - datetime.timedelta(days=365) + ).strftime("%Y-%m-%d") + + insider_data = client.stock_insider_transactions( + ticker, + from_date, + end_date, + ) + + if not insider_data or "data" not in insider_data: + return [] + + return [ + _convert_finnhub_insider_trade(ticker, trade) + for trade in insider_data["data"][:limit] + ] + + +def _fetch_fd_insider_trades( + ticker: str, + start_date: str | None, + end_date: str, + limit: int, + api_key: str, +) -> list[InsiderTrade]: + """Fetch insider trades from Financial Datasets API.""" + headers = {"X-API-KEY": api_key} + all_trades = [] + current_end_date = end_date + + while True: + url = f"https://api.financialdatasets.ai/insider-trades/?ticker={ticker}&filing_date_lte={current_end_date}" + if start_date: + url += f"&filing_date_gte={start_date}" + url += f"&limit={limit}" + + response = _make_api_request(url, headers) + if response.status_code != 200: + raise ValueError( + f"Error fetching data: {ticker} - {response.status_code} - {response.text}", + ) + + data = response.json() + response_model = InsiderTradeResponse(**data) + insider_trades = response_model.insider_trades + + if not insider_trades: + break + + all_trades.extend(insider_trades) + + if not start_date or len(insider_trades) < limit: + break + + current_end_date = min( + trade.filing_date for trade in insider_trades + ).split("T")[0] + + if current_end_date <= start_date: + break + + return all_trades + + +def get_insider_trades( + ticker: str, + end_date: str, + start_date: str | None = None, + limit: int = 1000, +) -> list[InsiderTrade]: + """Fetch insider trades from cache or API.""" + config = get_config() + data_source = config.source + api_key = config.api_key + + cache_key = ( + f"{ticker}_{start_date or 'none'}_{end_date}_{limit}_{data_source}" + ) + + if cached_data := _cache.get_insider_trades(cache_key): + return [InsiderTrade(**trade) for trade in cached_data] + + if data_source == "finnhub": + all_trades = _fetch_finnhub_insider_trades( + ticker, + start_date, + end_date, + limit, + api_key, + ) + else: + all_trades = _fetch_fd_insider_trades( + ticker, + start_date, + end_date, + limit, + api_key, + ) + + if not all_trades: + return [] + + _cache.set_insider_trades( + cache_key, + [trade.model_dump() for trade in all_trades], + ) + return all_trades + + +def _fetch_finnhub_company_news( + ticker: str, + start_date: str | None, + end_date: str, + limit: int, + api_key: str, +) -> list[CompanyNews]: + """Fetch company news from Finnhub API.""" + client = finnhub.Client(api_key=api_key) + + from_date = start_date or ( + datetime.datetime.strptime(end_date, "%Y-%m-%d") + - datetime.timedelta(days=30) + ).strftime("%Y-%m-%d") + + news_data = client.company_news(ticker, _from=from_date, to=end_date) + + if not news_data: + return [] + + all_news = [] + for news_item in news_data[:limit]: + company_news = CompanyNews( + ticker=ticker, + title=news_item.get("headline", ""), + related=news_item.get("related", ""), + source=news_item.get("source", ""), + date=( + datetime.datetime.fromtimestamp( + news_item.get("datetime", 0), + datetime.timezone.utc, + ).strftime("%Y-%m-%d") + if news_item.get("datetime") + else None + ), + url=news_item.get("url", ""), + summary=news_item.get("summary", ""), + category=news_item.get("category", ""), + ) + all_news.append(company_news) + return all_news + + +def _fetch_fd_company_news( + ticker: str, + start_date: str | None, + end_date: str, + limit: int, + api_key: str, +) -> list[CompanyNews]: + """Fetch company news from Financial Datasets API.""" + headers = {"X-API-KEY": api_key} + all_news = [] + current_end_date = end_date + + while True: + url = f"https://api.financialdatasets.ai/news/?ticker={ticker}&end_date={current_end_date}" + if start_date: + url += f"&start_date={start_date}" + url += f"&limit={limit}" + + response = _make_api_request(url, headers) + if response.status_code != 200: + raise ValueError( + f"Error fetching data: {ticker} - {response.status_code} - {response.text}", + ) + + data = response.json() + response_model = CompanyNewsResponse(**data) + company_news = response_model.news + + if not company_news: + break + + all_news.extend(company_news) + + if not start_date or len(company_news) < limit: + break + + current_end_date = min( + news.date for news in company_news if news.date is not None + ).split("T")[0] + + if current_end_date <= start_date: + break + + return all_news + + +def get_company_news( + ticker: str, + end_date: str, + start_date: str | None = None, + limit: int = 1000, +) -> list[CompanyNews]: + """Fetch company news from cache or API.""" + config = get_config() + data_source = config.source + api_key = config.api_key + + cache_key = ( + f"{ticker}_{start_date or 'none'}_{end_date}_{limit}_{data_source}" + ) + + if cached_data := _cache.get_company_news(cache_key): + return [CompanyNews(**news) for news in cached_data] + + if data_source == "finnhub": + all_news = _fetch_finnhub_company_news( + ticker, + start_date, + end_date, + limit, + api_key, + ) + else: + all_news = _fetch_fd_company_news( + ticker, + start_date, + end_date, + limit, + api_key, + ) + + if not all_news: + return [] + + _cache.set_company_news( + cache_key, + [news.model_dump() for news in all_news], + ) + return all_news + + +def _convert_finnhub_insider_trade(ticker: str, trade: dict) -> InsiderTrade: + """Convert Finnhub insider trade format to InsiderTrade model.""" + shares_after = trade.get("share", 0) + change = trade.get("change", 0) + + return InsiderTrade( + ticker=ticker, + issuer=None, + name=trade.get("name", ""), + title=None, + is_board_director=None, + transaction_date=trade.get("transactionDate", ""), + transaction_shares=abs(change), + transaction_price_per_share=trade.get("transactionPrice", 0.0), + transaction_value=abs(change) * trade.get("transactionPrice", 0.0), + shares_owned_before_transaction=( + shares_after - change if shares_after and change else None + ), + shares_owned_after_transaction=float(shares_after) + if shares_after + else None, + security_title=None, + filing_date=trade.get("filingDate", ""), + ) + + +def get_market_cap(ticker: str, end_date: str) -> float | None: + """Fetch market cap from the API. Finnhub values are converted from millions.""" + config = get_config() + data_source = config.source + api_key = config.api_key + + # For today's date, use company facts API + if end_date == datetime.datetime.now().strftime("%Y-%m-%d"): + headers = {"X-API-KEY": api_key} + url = ( + f"https://api.financialdatasets.ai/company/facts/?ticker={ticker}" + ) + response = _make_api_request(url, headers) + if response.status_code != 200: + return None + + data = response.json() + response_model = CompanyFactsResponse(**data) + return response_model.company_facts.market_cap + + financial_metrics = get_financial_metrics(ticker, end_date) + if not financial_metrics: + return None + + market_cap = financial_metrics[0].market_cap + if not market_cap: + return None + + # Finnhub returns market cap in millions + if data_source == "finnhub": + market_cap = market_cap * 1_000_000 + + return market_cap + + +def prices_to_df(prices: list[Price]) -> pd.DataFrame: + """Convert prices to a DataFrame.""" + df = pd.DataFrame([p.model_dump() for p in prices]) + df["Date"] = pd.to_datetime(df["time"]) + df.set_index("Date", inplace=True) + numeric_cols = ["open", "close", "high", "low", "volume"] + for col in numeric_cols: + df[col] = pd.to_numeric(df[col], errors="coerce") + df.sort_index(inplace=True) + return df diff --git a/evotraders/backend/utils/__init__.py b/evotraders/backend/utils/__init__.py new file mode 100644 index 0000000..1201dd1 --- /dev/null +++ b/evotraders/backend/utils/__init__.py @@ -0,0 +1,4 @@ +# -*- coding: utf-8 -*- +# This file can be empty + +"""Utility modules for the application.""" diff --git a/evotraders/backend/utils/analyst_tracker.py b/evotraders/backend/utils/analyst_tracker.py new file mode 100644 index 0000000..6942afd --- /dev/null +++ b/evotraders/backend/utils/analyst_tracker.py @@ -0,0 +1,277 @@ +# -*- coding: utf-8 -*- +""" +Analyst Performance Tracker +Tracks analyst predictions and calculates win rates for leaderboard +""" + +import logging +from typing import Any, Dict, List, Optional + +logger = logging.getLogger(__name__) + + +class AnalystPerformanceTracker: + """ + Tracks analyst predictions and evaluates accuracy + + Workflow: + 1. Record analyst predictions for each ticker before market close + 2. After market close, evaluate predictions against actual returns + 3. Update leaderboard with win rates and statistics + """ + + def __init__(self): + self.daily_predictions = {} + + def record_analyst_predictions( + self, + final_predictions: List[Dict[str, Any]], + ): + """ + Record predictions from analysts for the current trading day + + Args: + final_predictions: List of structured prediction results + Format: [ + { + 'agent': 'analyst_name', + 'predictions': [ + {'ticker': 'AAPL', ' + direction': 'up', + 'confidence': 0.75}, + ... + ] + }, + ... + ] + tickers: List of tickers being analyzed + """ + self.daily_predictions = {} + + direction_mapping = { + "up": "long", + "down": "short", + "neutral": "hold", + } + + for result in final_predictions: + analyst_id = result.get("agent") + if not analyst_id: + continue + + predictions = result.get("predictions", []) + + self.daily_predictions[analyst_id] = {} + + for pred in predictions: + ticker = pred.get("ticker") + direction = pred.get("direction", "neutral") + + if ticker: + signal = direction_mapping.get(direction, "hold") + self.daily_predictions[analyst_id][ticker] = signal + + def evaluate_predictions( + self, + open_prices: Optional[Dict[str, float]], + close_prices: Dict[str, float], + date: str, + ) -> Dict[str, Dict[str, Any]]: + """ + Evaluate analyst predictions against actual market moves + + Args: + open_prices: Opening prices for each ticker + close_prices: Closing prices for each ticker + date: Trading date string (YYYY-MM-DD) + + Returns: + Dict mapping analyst_id to evaluation results + """ + evaluation_results = {} + + # Map internal signal types to frontend display names + signal_display_map = { + "long": "bull", + "short": "bear", + "hold": "neutral", + } + + for analyst_id, predictions in self.daily_predictions.items(): + correct_long = 0 + correct_short = 0 + incorrect_long = 0 + incorrect_short = 0 + unknown_long = 0 + unknown_short = 0 + hold_count = 0 + + # Individual signal records for frontend display + individual_signals: List[Dict[str, Any]] = [] + + for ticker, prediction in predictions.items(): + open_price = open_prices.get(ticker, 0) + close_price = close_prices.get(ticker, 0) + + signal_type = signal_display_map.get(prediction, "neutral") + + # Cannot evaluate if prices are missing + if open_price <= 0 or close_price <= 0: + if prediction == "long": + unknown_long += 1 + elif prediction == "short": + unknown_short += 1 + + individual_signals.append( + { + "ticker": ticker, + "signal": signal_type, + "date": date, + "is_correct": "unknown", + }, + ) + continue + + actual_return = (close_price - open_price) / open_price + + if prediction == "long": + is_correct = actual_return > 0 + if is_correct: + correct_long += 1 + else: + incorrect_long += 1 + + individual_signals.append( + { + "ticker": ticker, + "signal": signal_type, + "date": date, + "is_correct": is_correct, + }, + ) + + elif prediction == "short": + is_correct = actual_return < 0 + if is_correct: + correct_short += 1 + else: + incorrect_short += 1 + + individual_signals.append( + { + "ticker": ticker, + "signal": signal_type, + "date": date, + "is_correct": is_correct, + }, + ) + + elif prediction == "hold": + hold_count += 1 + individual_signals.append( + { + "ticker": ticker, + "signal": signal_type, + "date": date, + "is_correct": None, + }, + ) + + total_long = correct_long + incorrect_long + unknown_long + total_short = correct_short + incorrect_short + unknown_short + evaluated_long = correct_long + incorrect_long + evaluated_short = correct_short + incorrect_short + total_evaluated = evaluated_long + evaluated_short + correct_predictions = correct_long + correct_short + + win_rate = ( + correct_predictions / total_evaluated + if total_evaluated > 0 + else None + ) + + evaluation_results[analyst_id] = { + "total_predictions": total_evaluated, + "correct_predictions": correct_predictions, + "win_rate": win_rate, + "bull": { + "n": total_long, + "win": correct_long, + "unknown": unknown_long, + }, + "bear": { + "n": total_short, + "win": correct_short, + "unknown": unknown_short, + }, + "hold": hold_count, + "signals": individual_signals, + } + + return evaluation_results + + def clear_daily_predictions(self): + """Clear predictions after evaluation""" + self.daily_predictions = {} + + +def update_leaderboard_with_evaluations( + leaderboard: List[Dict[str, Any]], + evaluations: Dict[str, Dict[str, Any]], +) -> List[Dict[str, Any]]: + """ + Update leaderboard with new evaluation results + + Args: + leaderboard: Current leaderboard data + evaluations: Evaluation results for the day + + Returns: + Updated leaderboard + """ + for entry in leaderboard: + agent_id = entry.get("agentId") + if not agent_id or agent_id not in evaluations: + continue + + eval_result = evaluations[agent_id] + + # Update aggregate stats + entry["bull"]["n"] += eval_result["bull"]["n"] + entry["bull"]["win"] += eval_result["bull"]["win"] + entry["bull"]["unknown"] = ( + entry["bull"].get("unknown", 0) + eval_result["bull"]["unknown"] + ) + entry["bear"]["n"] += eval_result["bear"]["n"] + entry["bear"]["win"] += eval_result["bear"]["win"] + entry["bear"]["unknown"] = ( + entry["bear"].get("unknown", 0) + eval_result["bear"]["unknown"] + ) + + # Calculate win rate based on evaluated signals only + # evaluated = total - unknown + evaluated_bull = entry["bull"]["n"] - entry["bull"]["unknown"] + evaluated_bear = entry["bear"]["n"] - entry["bear"]["unknown"] + total_evaluated = evaluated_bull + evaluated_bear + total_wins = entry["bull"]["win"] + entry["bear"]["win"] + + if total_evaluated > 0: + entry["winRate"] = round(total_wins / total_evaluated, 4) + + # Add individual signal records + if "signals" not in entry: + entry["signals"] = [] + + for signal in eval_result.get("signals", []): + entry["signals"].append(signal) + + # Keep only recent signals (e.g., last 100 individual signals) + entry["signals"] = entry["signals"][-100:] + + # Re-rank analysts by win rate (rank starts from 1) + analyst_entries = [e for e in leaderboard if e.get("rank") is not None] + analyst_entries.sort(key=lambda e: e.get("winRate", 0), reverse=True) + for idx, entry in enumerate(analyst_entries): + entry["rank"] = idx + 1 # Rank 1 = highest win rate (gold medal) + + return leaderboard diff --git a/evotraders/backend/utils/baselines.py b/evotraders/backend/utils/baselines.py new file mode 100644 index 0000000..424039c --- /dev/null +++ b/evotraders/backend/utils/baselines.py @@ -0,0 +1,356 @@ +# -*- coding: utf-8 -*- +""" +Baseline Strategy Calculators +Tracks performance of simple baseline strategies for comparison +""" +import logging +from datetime import datetime +from typing import Any, Dict, List, Tuple, TypedDict + +logger = logging.getLogger(__name__) + + +class Portfolio(TypedDict): + cash: float + positions: Dict[str, float] + + +class BaselineCalculator: + """ + Calculates baseline strategy returns for comparison + + Strategies: + 1. Equal-weight: Allocate equal weight to all tickers + 2. Market-cap-weighted: Allocate proportional to market cap + 3. Simple momentum: Monthly rebalance, + long top 50% momentum, short bottom 50% + """ + + def __init__(self, initial_capital: float = 100000.0): + self.initial_capital = initial_capital + + self.equal_weight_portfolio: Portfolio = {"cash": 0.0, "positions": {}} + self.market_cap_portfolio: Portfolio = {"cash": 0.0, "positions": {}} + self.momentum_portfolio: Portfolio = { + "cash": initial_capital, + "positions": {}, + } + + self.equal_weight_initialized = False + self.market_cap_initialized = False + self.momentum_last_rebalance_date = None + + def calculate_equal_weight_value( + self, + tickers: List[str], + prices: Dict[str, float], + ) -> float: + """ + Calculate equal-weight portfolio value + + On first call, initialize positions with equal allocation + Subsequently, mark-to-market existing positions + """ + if not self.equal_weight_initialized: + allocation_per_ticker = self.initial_capital / len(tickers) + self.equal_weight_portfolio["cash"] = 0.0 + for ticker in tickers: + price = prices.get(ticker, 0) + if price > 0: + shares = allocation_per_ticker / price + self.equal_weight_portfolio["positions"][ticker] = shares + self.equal_weight_initialized = True + + total_value = self.equal_weight_portfolio["cash"] + for ticker, shares in self.equal_weight_portfolio["positions"].items(): + price = prices.get(ticker, 0) + total_value += shares * price + + return total_value + + def calculate_market_cap_weighted_value( + self, + tickers: List[str], + prices: Dict[str, float], + market_caps: Dict[str, float], + ) -> float: + """ + Calculate market-cap-weighted portfolio value + + On first call, initialize positions weighted by market cap + Subsequently, mark-to-market existing positions + """ + if not self.market_cap_initialized: + total_market_cap = sum(market_caps.get(t, 0) for t in tickers) + if total_market_cap <= 0: + logger.warning("No market cap data, using equal weight") + return self.calculate_equal_weight_value(tickers, prices) + + self.market_cap_portfolio["cash"] = 0.0 + for ticker in tickers: + market_cap = market_caps.get(ticker, 0) + price = prices.get(ticker, 0) + if market_cap > 0 and price > 0: + weight = market_cap / total_market_cap + allocation = self.initial_capital * weight + shares = allocation / price + self.market_cap_portfolio["positions"][ticker] = shares + self.market_cap_initialized = True + + total_value = self.market_cap_portfolio["cash"] + for ticker, shares in self.market_cap_portfolio["positions"].items(): + price = prices.get(ticker, 0) + total_value += shares * price + + return total_value + + def calculate_momentum_value( + self, + tickers: List[str], + prices: Dict[str, float], + momentum_scores: Dict[str, float], + date: str, + rebalance: bool = False, + ) -> float: + """ + Calculate momentum strategy portfolio value + + Strategy: Monthly rebalance + - Long top 50% momentum stocks + - Short bottom 50% momentum stocks (if shorting enabled) + - Equal weight within each group + + Args: + tickers: List of tickers + prices: Current prices + momentum_scores: Momentum scores for each ticker + date: Current date (YYYY-MM-DD) + rebalance: Force rebalance if True + """ + should_rebalance = rebalance + if self.momentum_last_rebalance_date is None: + should_rebalance = True + elif not rebalance: + last_date = datetime.strptime( + self.momentum_last_rebalance_date, + "%Y-%m-%d", + ) + current_date = datetime.strptime(date, "%Y-%m-%d") + if (current_date.year, current_date.month) != ( + last_date.year, + last_date.month, + ): + should_rebalance = True + + if should_rebalance: + self._rebalance_momentum_portfolio( + tickers, + prices, + momentum_scores, + ) + self.momentum_last_rebalance_date = date + + total_value = self.momentum_portfolio["cash"] + for ticker, shares in self.momentum_portfolio["positions"].items(): + price = prices.get(ticker, 0) + total_value += shares * price + + return total_value + + def _rebalance_momentum_portfolio( + self, + tickers: List[str], + prices: Dict[str, float], + momentum_scores: Dict[str, float], + ): + """Rebalance momentum portfolio based on current momentum scores""" + current_value = self.momentum_portfolio["cash"] + for ticker, shares in self.momentum_portfolio["positions"].items(): + price = prices.get(ticker, 0) + current_value += shares * price + + self.momentum_portfolio["positions"] = {} + + sorted_tickers = sorted( + tickers, + key=lambda t: momentum_scores.get(t, 0), + reverse=True, + ) + + mid_point = len(sorted_tickers) // 2 + long_tickers = ( + sorted_tickers[:mid_point] if mid_point > 0 else sorted_tickers + ) + + if len(long_tickers) == 0: + self.momentum_portfolio["cash"] = current_value + return + + allocation_per_ticker = current_value / len(long_tickers) + used_capital = 0.0 + + for ticker in long_tickers: + price = prices.get(ticker, 0) + if price > 0: + shares = allocation_per_ticker / price + self.momentum_portfolio["positions"][ticker] = shares + used_capital += allocation_per_ticker + + self.momentum_portfolio["cash"] = current_value - used_capital + + def get_all_baseline_values( + self, + tickers: List[str], + prices: Dict[str, float], + market_caps: Dict[str, float], + momentum_scores: Dict[str, float], + date: str, + rebalance_momentum: bool = False, + ) -> Dict[str, float]: + """ + Get all baseline portfolio values in one call + + Returns: + Dict with keys: equal_weight, market_cap_weighted, momentum + """ + equal_weight_value = self.calculate_equal_weight_value(tickers, prices) + market_cap_value = self.calculate_market_cap_weighted_value( + tickers, + prices, + market_caps, + ) + momentum_value = self.calculate_momentum_value( + tickers, + prices, + momentum_scores, + date, + rebalance_momentum, + ) + + return { + "equal_weight": equal_weight_value, + "market_cap_weighted": market_cap_value, + "momentum": momentum_value, + } + + def export_state(self) -> Dict[str, Any]: + """ + Export calculator state for persistence + + Returns: + Dictionary containing all portfolio states for serialization + """ + return { + "baseline_state": { + "initialized": self.equal_weight_initialized, + "initial_allocation": dict( + self.equal_weight_portfolio["positions"], + ), + }, + "baseline_vw_state": { + "initialized": self.market_cap_initialized, + "initial_allocation": dict( + self.market_cap_portfolio["positions"], + ), + }, + "momentum_state": { + "positions": dict(self.momentum_portfolio["positions"]), + "cash": self.momentum_portfolio["cash"], + "initialized": self.momentum_last_rebalance_date is not None, + "last_rebalance_date": self.momentum_last_rebalance_date, + }, + } + + def load_state(self, state: Dict[str, Any]): + """ + Load calculator state from persistence + + Args: + state: Dictionary containing baseline_state, baseline_vw_state, + momentum_state from storage + """ + # Load equal-weight state + baseline_state = state.get("baseline_state", {}) + if baseline_state.get("initialized", False): + self.equal_weight_initialized = True + self.equal_weight_portfolio["positions"] = dict( + baseline_state.get("initial_allocation", {}), + ) + self.equal_weight_portfolio["cash"] = 0.0 + logger.info( + f"Restored equal-weight portfolio with " + f"{len(self.equal_weight_portfolio['positions'])} positions", + ) + + # Load market-cap-weighted state + baseline_vw_state = state.get("baseline_vw_state", {}) + if baseline_vw_state.get("initialized", False): + self.market_cap_initialized = True + self.market_cap_portfolio["positions"] = dict( + baseline_vw_state.get("initial_allocation", {}), + ) + self.market_cap_portfolio["cash"] = 0.0 + logger.info( + f"Restored market-cap portfolio with " + f"{len(self.market_cap_portfolio['positions'])} positions", + ) + + # Load momentum state + momentum_state = state.get("momentum_state", {}) + if momentum_state.get("initialized", False): + self.momentum_portfolio["positions"] = dict( + momentum_state.get("positions", {}), + ) + self.momentum_portfolio["cash"] = momentum_state.get( + "cash", + self.initial_capital, + ) + self.momentum_last_rebalance_date = momentum_state.get( + "last_rebalance_date", + ) + logger.info( + f"Restored momentum portfolio with " + f"{len(self.momentum_portfolio['positions'])} positions, " + f"last rebalance: {self.momentum_last_rebalance_date}", + ) + + +def calculate_momentum_scores( + tickers: List[str], + prices_history: Dict[str, List[Tuple[str, float]]], + lookback_days: int = 20, +) -> Dict[str, float]: + """ + Calculate momentum scores for tickers + + Args: + tickers: List of tickers + prices_history: Dict mapping ticker to list of (date, price) tuples + lookback_days: Number of days to calculate momentum + + Returns: + Dict mapping ticker to momentum score (percentage return) + """ + momentum_scores = {} + + for ticker in tickers: + history = prices_history.get(ticker, []) + if len(history) < 2: + momentum_scores[ticker] = 0.0 + continue + + sorted_history = sorted(history, key=lambda x: x[0]) + + if len(sorted_history) < lookback_days: + start_price = sorted_history[0][1] + end_price = sorted_history[-1][1] + else: + start_price = sorted_history[-lookback_days][1] + end_price = sorted_history[-1][1] + + if start_price > 0: + momentum_scores[ticker] = (end_price - start_price) / start_price + else: + momentum_scores[ticker] = 0.0 + + return momentum_scores diff --git a/evotraders/backend/utils/msg_adapter.py b/evotraders/backend/utils/msg_adapter.py new file mode 100644 index 0000000..af524d4 --- /dev/null +++ b/evotraders/backend/utils/msg_adapter.py @@ -0,0 +1,321 @@ +# -*- coding: utf-8 -*- +""" +Message Adapter - Converts AgentScope Msg to frontend JSON format +Ensures compatibility with existing frontend without modifications +""" +import json +import logging +from datetime import datetime +from typing import Any, Dict, List, Optional + +from agentscope.message import Msg + +logger = logging.getLogger(__name__) + + +class FrontendAdapter: + """ + Adapter to convert AgentScope messages to frontend-compatible format + + Frontend expects specific message types: + - agent: Agent thinking/analysis messages + - team_summary: Portfolio summary with equity curves + - team_holdings: Current portfolio holdings + - team_stats: Portfolio statistics + - team_trades: Trade history + - team_leaderboard: Agent performance rankings + - price_update: Real-time price updates + - system: System notifications + """ + + @staticmethod + def parse(msg: Msg) -> Optional[Dict[str, Any]]: + """ + Parse AgentScope Msg to frontend format + + Args: + msg: AgentScope Msg object + + Returns: + Dictionary in frontend format, or None if message should be skipped + """ + if msg is None: + return None + + # Determine message type based on metadata or content + msg_type = FrontendAdapter._determine_type(msg) + + if msg_type == "agent": + return FrontendAdapter._format_agent_msg(msg) + elif msg_type == "portfolio_update": + return FrontendAdapter._format_portfolio_msg(msg) + elif msg_type == "system": + return FrontendAdapter._format_system_msg(msg) + else: + # Default: treat as agent message + return FrontendAdapter._format_agent_msg(msg) + + @staticmethod + def _determine_type(msg: Msg) -> str: + """Determine frontend message type from Msg""" + # Check metadata for explicit type + if hasattr(msg, "metadata") and msg.metadata: + if "type" in msg.metadata: + return msg.metadata["type"] + + # Check if message contains portfolio update + if "portfolio" in msg.metadata: + return "portfolio_update" + + # Check message name/role + if msg.name == "system": + return "system" + + # Default to agent message + return "agent" + + @staticmethod + def _format_agent_msg(msg: object) -> Dict[str, Any]: + """ + Format agent message for frontend + + Args: + msg: Either AgentScope Msg or dict from pipeline results + + Frontend expects: + { + "type": "agent", + "role_key": "analyst_id", + "content": "message text", + "timestamp": "ISO timestamp" + } + """ + # Handle dict from pipeline results + if isinstance(msg, dict): + name = msg.get("agent", "unknown") + content = msg.get("content", "") + else: + # Handle Msg object + name = msg.name + content = msg.content + + return { + "type": "agent", + "role_key": name, + "content": content + if isinstance(content, str) + else json.dumps(content), + "timestamp": datetime.now().isoformat(), + } + + @staticmethod + def _format_portfolio_msg(msg: Msg) -> Dict[str, Any]: + """ + Format portfolio update message + + This typically generates multiple frontend messages: + - team_summary + - team_holdings + - team_stats + - team_trades (if trades were executed) + """ + metadata = msg.metadata or {} + portfolio = metadata.get("portfolio", {}) + + messages: List[Dict[str, Any]] = [] + + # Generate holdings message + holdings = FrontendAdapter.build_holdings(portfolio) + if holdings: + messages.append( + { + "type": "team_holdings", + "data": holdings, + "timestamp": datetime.now().isoformat(), + }, + ) + + # Generate stats message + stats = FrontendAdapter.build_stats(portfolio) + if stats: + messages.append( + { + "type": "team_stats", + "data": stats, + "timestamp": datetime.now().isoformat(), + }, + ) + + # Generate trades message if execution logs exist + execution_logs = metadata.get("execution_logs", []) + if execution_logs: + trades = FrontendAdapter.build_trades(execution_logs) + messages.append( + { + "type": "team_trades", + "mode": "incremental", + "data": trades, + "timestamp": datetime.now().isoformat(), + }, + ) + + # Return composite message + return { + "type": "composite", + "messages": messages, + } + + @staticmethod + def _format_system_msg(msg: Msg) -> Dict[str, Any]: + """Format system message""" + return { + "type": "system", + "content": msg.content + if isinstance(msg.content, str) + else json.dumps(msg.content), + "timestamp": datetime.now().isoformat(), + } + + @staticmethod + def build_holdings( + portfolio: Dict[str, Any], + prices: Dict[str, float] = None, + ) -> List[Dict[str, Any]]: + """Build holdings array from portfolio state""" + holdings = [] + prices = prices or {} + + positions = portfolio.get("positions", {}) + cash = portfolio.get("cash", 0.0) + + # Calculate total value using current prices + total_value = cash + for ticker, position in positions.items(): + long_shares = position.get("long", 0) + short_shares = position.get("short", 0) + price = prices.get(ticker) or position.get("avg_price", 0) + total_value += (long_shares - short_shares) * price + + # Build holdings for each position + for ticker, position in positions.items(): + long_shares = position.get("long", 0) + short_shares = position.get("short", 0) + avg_price = position.get("avg_price", 0) + current_price = prices.get(ticker) or avg_price + + net_shares = long_shares - short_shares + if net_shares == 0: + continue + + market_value = net_shares * current_price + weight = market_value / total_value if total_value > 0 else 0 + + holdings.append( + { + "ticker": ticker, + "quantity": net_shares, + "avg": avg_price, + "currentPrice": current_price, + "marketValue": market_value, + "weight": weight, + }, + ) + + # Add cash as a holding + if cash > 0: + holdings.append( + { + "ticker": "CASH", + "quantity": 1, + "avg": cash, + "currentPrice": cash, + "marketValue": cash, + "weight": cash / total_value if total_value > 0 else 0, + }, + ) + + return holdings + + @staticmethod + def build_stats( + portfolio: Dict[str, Any], + prices: Dict[str, float] = None, + ) -> Dict[str, Any]: + """Build stats dictionary from portfolio""" + prices = prices or {} + positions = portfolio.get("positions", {}) + cash = portfolio.get("cash", 0.0) + margin_used = portfolio.get("margin_used", 0.0) + + # Calculate total value using current prices + total_value = cash + for ticker, position in positions.items(): + long_shares = position.get("long", 0) + short_shares = position.get("short", 0) + price = prices.get(ticker) or position.get("avg_price", 0) + total_value += (long_shares - short_shares) * price + + # Calculate ticker weights + ticker_weights = {} + for ticker, position in positions.items(): + long_shares = position.get("long", 0) + short_shares = position.get("short", 0) + price = prices.get(ticker) or position.get("avg_price", 0) + + market_value = (long_shares - short_shares) * price + if market_value != 0: + ticker_weights[ticker] = ( + market_value / total_value if total_value > 0 else 0 + ) + + # Calculate total return + initial_cash = portfolio.get("initial_cash", 100000.0) + total_return = ( + ((total_value - initial_cash) / initial_cash * 100) + if initial_cash > 0 + else 0.0 + ) + + return { + "totalAssetValue": round(total_value, 2), + "totalReturn": round(total_return, 2), + "cashPosition": round(cash, 2), + "tickerWeights": ticker_weights, + "marginUsed": round(margin_used, 2), + } + + @staticmethod + def build_trades(execution_logs: List[str]) -> List[Dict[str, Any]]: + """ + Build trades array from execution logs + + Frontend expects: + [{ + "ts": 1234567890, + "ticker": "AAPL", + "side": "LONG", + "qty": 100, + "price": 150.0, + "reason": "Buy signal" + }, ...] + """ + trades = [] + timestamp = int(datetime.now().timestamp() * 1000) + + for log in execution_logs: + # Parse execution log (simplified - should use structured data) + if "Executed" in log: + # Extract trade details from log string + # in real implementation, pass structured data + trades.append( + { + "ts": timestamp, + "ticker": "UNKNOWN", # Should parse from log + "side": "LONG", # Should parse from log + "qty": 0, # Should parse from log + "price": 0.0, # Should parse from log + "reason": log, + }, + ) + + return trades diff --git a/evotraders/backend/utils/progress.py b/evotraders/backend/utils/progress.py new file mode 100644 index 0000000..c904cd1 --- /dev/null +++ b/evotraders/backend/utils/progress.py @@ -0,0 +1,140 @@ +# -*- coding: utf-8 -*- +from datetime import datetime, timezone +from typing import Callable, Optional + +from rich.console import Console +from rich.live import Live +from rich.style import Style +from rich.table import Table +from rich.text import Text + +console = Console() + + +class AgentProgress: + """Manages progress tracking for multiple agents.""" + + def __init__(self): + self.agent_status = {} + self.table = Table(show_header=False, box=None, padding=(0, 1)) + self.live = Live(self.table, console=console, refresh_per_second=4) + self.started = False + self.update_handlers = [] + + def register_handler( + self, + handler: Callable[[str, Optional[str], str], None], + ): + """Register a handler to be called when agent status updates.""" + self.update_handlers.append(handler) + return handler # Return handler to support use as decorator + + def unregister_handler( + self, + handler: Callable[[str, Optional[str], str], None], + ): + """Unregister a previously registered handler.""" + if handler in self.update_handlers: + self.update_handlers.remove(handler) + + def start(self): + """Start the progress display.""" + if not self.started: + self.live.start() + self.started = True + + def stop(self): + """Stop the progress display.""" + if self.started: + self.live.stop() + self.started = False + + def update_status( + self, + agent_name: str, + ticker: Optional[str] = None, + status: str = "", + analysis: Optional[str] = None, + ): + """Update the status of an agent.""" + if agent_name not in self.agent_status: + self.agent_status[agent_name] = {"status": "", "ticker": None} + + if ticker: + self.agent_status[agent_name]["ticker"] = ticker + if status: + self.agent_status[agent_name]["status"] = status + if analysis: + self.agent_status[agent_name]["analysis"] = analysis + + # Set the timestamp as UTC datetime + timestamp = datetime.now(timezone.utc).isoformat() + self.agent_status[agent_name]["timestamp"] = timestamp + + # Notify all registered handlers + for handler in self.update_handlers: + handler(agent_name, ticker, status, analysis, timestamp) + + self._refresh_display() + + def get_all_status(self): + """Get the current status of all agents as a dictionary.""" + return { + agent_name: { + "ticker": info["ticker"], + "status": info["status"], + "display_name": self._get_display_name(agent_name), + } + for agent_name, info in self.agent_status.items() + } + + def _get_display_name(self, agent_name: str) -> str: + """Convert agent_name to a display-friendly format.""" + return agent_name.replace("_agent", "").replace("_", " ").title() + + def _refresh_display(self): + """Refresh the progress display.""" + self.table.columns.clear() + self.table.add_column(width=100) + + # Sort Risk Management and Portfolio Management at the bottom + def sort_key(item): + agent_name = item[0] + if "risk_manager" in agent_name: + return (2, agent_name) + elif "portfolio_manager" in agent_name: + return (3, agent_name) + else: + return (1, agent_name) + + for agent_name, info in sorted( + self.agent_status.items(), + key=sort_key, + ): + status = info["status"] + ticker = info["ticker"] + # Create the status text with appropriate styling + if status.lower() == "done": + style = Style(color="green", bold=True) + symbol = "✓" + elif status.lower() == "error": + style = Style(color="red", bold=True) + symbol = "✗" + else: + style = Style(color="yellow") + symbol = "⋯" + + agent_display = self._get_display_name(agent_name) + status_text = Text() + status_text.append(f"{symbol} ", style=style) + status_text.append(f"{agent_display:<20}", style=Style(bold=True)) + + if ticker: + status_text.append(f"[{ticker}] ", style=Style(color="cyan")) + status_text.append(status, style=style) + + self.table.add_row(status_text) + + +# Create a global instance +progress = AgentProgress() diff --git a/evotraders/backend/utils/settlement.py b/evotraders/backend/utils/settlement.py new file mode 100644 index 0000000..a091302 --- /dev/null +++ b/evotraders/backend/utils/settlement.py @@ -0,0 +1,338 @@ +# -*- coding: utf-8 -*- +""" +Settlement Coordinator +Unified daily settlement logic for agent portfolio, baselines, and analyst tracking +""" +# flake8: noqa: E501 +import logging +from datetime import datetime +from typing import Any, Dict, List, Optional + +from backend.services.storage import StorageService +from backend.utils.analyst_tracker import ( + AnalystPerformanceTracker, + update_leaderboard_with_evaluations, +) +from backend.utils.baselines import ( + BaselineCalculator, + calculate_momentum_scores, +) + +logger = logging.getLogger(__name__) + + +class SettlementCoordinator: + """ + Coordinates daily settlement after market close + + Responsibilities: + 1. Calculate agent portfolio P&L + 2. Update baseline portfolios (equal-weight, market-cap, momentum) + 3. Evaluate analyst predictions and update leaderboard + 4. Update summary.json with all portfolio values + 5. Persist state to storage + """ + + def __init__( + self, + storage: "StorageService", + initial_capital: float = 100000.0, + ): + self.storage = storage + self.initial_capital = initial_capital + self.baseline_calculator = BaselineCalculator(initial_capital) + self.analyst_tracker = AnalystPerformanceTracker() + + self.price_history: Dict[str, List[tuple]] = {} + + # Load persisted state from storage + self._load_persisted_state() + + def _load_persisted_state(self): + """ + Load persisted baseline and price history state from storage + + This restores the baseline calculator state so that backtest/live mode + can resume from where it left off. + """ + internal_state = self.storage.load_internal_state() + + # Load baseline calculator state + baseline_state = { + "baseline_state": internal_state.get("baseline_state", {}), + "baseline_vw_state": internal_state.get("baseline_vw_state", {}), + "momentum_state": internal_state.get("momentum_state", {}), + } + self.baseline_calculator.load_state(baseline_state) + + # Load price history for momentum calculation + saved_price_history = internal_state.get("price_history", {}) + if saved_price_history: + # Convert saved format back to list of tuples + for ticker, history in saved_price_history.items(): + self.price_history[ticker] = [ + (entry["date"], entry["price"]) + if isinstance(entry, dict) + else tuple(entry) + for entry in history + ] + logger.info( + f"Restored price history for {len(self.price_history)} tickers", + ) + + def _save_persisted_state(self): + """ + Save baseline and price history state to storage + + This persists the baseline calculator state so that backtest/live mode + can resume from where it left off after restart. + """ + internal_state = self.storage.load_internal_state() + + # Export baseline calculator state + baseline_state = self.baseline_calculator.export_state() + internal_state["baseline_state"] = baseline_state["baseline_state"] + internal_state["baseline_vw_state"] = baseline_state[ + "baseline_vw_state" + ] + internal_state["momentum_state"] = baseline_state["momentum_state"] + + # Save price history (convert tuples to dicts for JSON serialization) + price_history_serializable = {} + for ticker, history in self.price_history.items(): + price_history_serializable[ticker] = [ + {"date": date, "price": price} for date, price in history + ] + internal_state["price_history"] = price_history_serializable + + self.storage.save_internal_state(internal_state) + logger.info("Persisted baseline calculator and price history state") + + def record_analyst_predictions( + self, + final_predictions: List[Dict[str, Any]], + ): + """ + Record structured analyst predictions before market close + + Args: + final_predictions: Structured prediction results from analysts + Format: [ + { + 'agent': 'analyst_name', + 'predictions': [ + {'ticker': 'AAPL', 'direction': 'up', 'confidence': 0.75}, + ... + ] + }, + ... + ] + tickers: List of tickers being analyzed + """ + self.analyst_tracker.record_analyst_predictions(final_predictions) + + def update_price_history( + self, + date: str, + prices: Dict[str, float], + ): + """ + Update price history for momentum calculation + + Args: + date: Trading date (YYYY-MM-DD) + prices: Current prices for each ticker + """ + for ticker, price in prices.items(): + if ticker not in self.price_history: + self.price_history[ticker] = [] + self.price_history[ticker].append((date, price)) + + self.price_history[ticker] = self.price_history[ticker][-60:] + + def run_daily_settlement( + self, + date: str, + tickers: List[str], + open_prices: Optional[Dict[str, float]], + close_prices: Dict[str, float], + market_caps: Dict[str, float], + agent_portfolio: Dict[str, Any], + analyst_results: List[Dict[str, Any]], # pylint: disable=W0613 + ) -> Dict[str, Any]: + """ + Run complete daily settlement + + Args: + date: Trading date (YYYY-MM-DD) + tickers: List of tickers + open_prices: Opening prices + close_prices: Closing prices + market_caps: Market caps for each ticker + agent_portfolio: Current agent portfolio state + analyst_results: Analyst analysis results + + Returns: + Settlement results including all portfolio values and evaluations + """ + logger.info(f"Running daily settlement for {date}") + + self.update_price_history(date, close_prices) + + momentum_scores = calculate_momentum_scores( + tickers, + self.price_history, + lookback_days=20, + ) + + rebalance_momentum = self._should_rebalance_momentum(date) + + baseline_values = self.baseline_calculator.get_all_baseline_values( + tickers=tickers, + prices=close_prices, + market_caps=market_caps, + momentum_scores=momentum_scores, + date=date, + rebalance_momentum=rebalance_momentum, + ) + + agent_value = self.storage.calculate_portfolio_value( + agent_portfolio, + close_prices, + ) + + analyst_evaluations = self.analyst_tracker.evaluate_predictions( + open_prices, + close_prices, + date, + ) + + leaderboard = self.storage.load_file("leaderboard") or [] + updated_leaderboard = update_leaderboard_with_evaluations( + leaderboard, + analyst_evaluations, + ) + self.storage.save_file("leaderboard", updated_leaderboard) + + self._update_summary_with_baselines( + date, + agent_value, + baseline_values, + ) + + self.analyst_tracker.clear_daily_predictions() + + # Persist baseline calculator and price history state + self._save_persisted_state() + + return { + "date": date, + "agent_portfolio_value": agent_value, + "baseline_values": baseline_values, + "analyst_evaluations": analyst_evaluations, + "baselines_updated": True, + "leaderboard_updated": True, + } + + def _should_rebalance_momentum(self, date: str) -> bool: + """ + Check if momentum portfolio should rebalance + + Returns True if it's a new month + """ + last_rebalance = self.baseline_calculator.momentum_last_rebalance_date + if last_rebalance is None: + return True + + last_date = datetime.strptime(last_rebalance, "%Y-%m-%d") + current_date = datetime.strptime(date, "%Y-%m-%d") + + return (current_date.year, current_date.month) != ( + last_date.year, + last_date.month, + ) + + def _update_summary_with_baselines( + self, + date: str, + agent_value: float, + baseline_values: Dict[str, float], + ): + """ + Update summary.json with agent and baseline portfolio values + + NOTE: History updates are now handled centrally by storage.update_dashboard_after_cycle() + to ensure all histories (equity, baseline, baseline_vw, momentum) stay synchronized. + baseline_values are returned in run_daily_settlement() and passed to storage. + + Args: + date: Trading date (used for backtest-compatible timestamps) + agent_value: Agent portfolio value + baseline_values: Baseline portfolio values + """ + # History updates are now handled by storage.update_dashboard_after_cycle() + # which receives baseline_values from settlement_result and updates all histories together. + # This ensures equity and baseline data points are always synchronized. + + def update_intraday_values( + self, + tickers: List[str], + current_prices: Dict[str, float], + market_caps: Dict[str, float], + agent_portfolio: Dict[str, Any], + ) -> Dict[str, float]: + """ + Update portfolio values with current prices (for live mode intraday updates) + + Args: + tickers: List of tickers + current_prices: Current prices + market_caps: Market caps + agent_portfolio: Current agent portfolio + + Returns: + Dict with current portfolio values + """ + agent_value = self.storage.calculate_portfolio_value( + agent_portfolio, + current_prices, + ) + + equal_weight = self.baseline_calculator.calculate_equal_weight_value( + tickers, + current_prices, + ) + market_cap = ( + self.baseline_calculator.calculate_market_cap_weighted_value( + tickers, + current_prices, + market_caps, + ) + ) + + momentum_scores = calculate_momentum_scores( + tickers, + self.price_history, + lookback_days=20, + ) + + last_date = ( + list(self.price_history.values())[0][-1][0] + if self.price_history + else "" + ) + + momentum = self.baseline_calculator.calculate_momentum_value( + tickers, + current_prices, + momentum_scores, + date=last_date, + rebalance=False, + ) + + return { + "agent": agent_value, + "equal_weight": equal_weight, + "market_cap_weighted": market_cap, + "momentum": momentum, + } diff --git a/evotraders/backend/utils/terminal_dashboard.py b/evotraders/backend/utils/terminal_dashboard.py new file mode 100644 index 0000000..3be0418 --- /dev/null +++ b/evotraders/backend/utils/terminal_dashboard.py @@ -0,0 +1,348 @@ +# -*- coding: utf-8 -*- +""" +Terminal Dashboard - Persistent unified panel using Rich Live +""" +# pylint: disable=R0915,R0912 +import logging +import threading +import time +from typing import Any, Dict, List, Optional + +from rich.console import Console +from rich.live import Live +from rich.panel import Panel +from rich.table import Table + +logger = logging.getLogger(__name__) + + +class TerminalDashboard: + """Unified persistent terminal dashboard""" + + def __init__(self, console: Console = None): + self.console = console or Console() + self.live: Optional[Live] = None + + # Config state + self.mode = "live" + self.config_name = "" + self.host = "0.0.0.0" + self.port = 8765 + self.poll_interval = 10 + self.trigger_time = "now" + self.mock = False + self.enable_memory = False + self.local_time = "" + self.nyse_time = "" + self.start_date = "" + self.end_date = "" + self.tickers: List[str] = [] + self.initial_cash = 100000.0 + + # Trading state + self.current_date = "-" + self.status = "Initializing" + self.total_value = 0.0 + self.cash = 0.0 + self.pnl_pct = 0.0 + self.holdings: List[Dict] = [] + self.trades: List[Dict] = [] + self.days_completed = 0 + self.days_total = 0 + + # Progress message (last line) + self.progress = "" + self._dots_index = 0 + self._animator_running = False + self._animator_thread: Optional[threading.Thread] = None + + def set_config( + self, + mode: str, + config_name: str, + host: str, + port: int, + poll_interval: int, + trigger_time: str = "now", + mock: bool = False, + enable_memory: bool = False, + local_time: str = "", + nyse_time: str = "", + start_date: str = "", + end_date: str = "", + tickers: List[str] = None, + initial_cash: float = 100000.0, + ): + """Set configuration state""" + self.mode = mode + self.config_name = config_name + self.host = host + self.port = port + self.poll_interval = poll_interval + self.trigger_time = trigger_time + self.mock = mock + self.enable_memory = enable_memory + self.local_time = local_time + self.nyse_time = nyse_time + self.start_date = start_date + self.end_date = end_date + self.tickers = tickers or [] + self.initial_cash = initial_cash + self.total_value = initial_cash + self.cash = initial_cash + + def _build_panel(self) -> Panel: + """Build the unified dashboard panel""" + # Main grid + main_table = Table.grid(padding=(0, 2)) + main_table.add_column(width=28) + main_table.add_column(width=22) + main_table.add_column(width=22) + + # Left: Config + Status + left = Table.grid(padding=(0, 0)) + left.add_column() + + # Mode line + if self.mode == "backtest": + mode_str = "[cyan]Backtest[/cyan]" + elif self.mock: + mode_str = "[yellow]MOCK[/yellow]" + else: + mode_str = "[green]LIVE[/green]" + + left.add_row(f"[bold]Mode:[/bold] {mode_str}") + left.add_row(f"[dim]Config:[/dim] {self.config_name}") + left.add_row(f"[dim]Server:[/dim] {self.host}:{self.port}") + + if self.mode == "live" and self.nyse_time: + left.add_row(f"[dim]NYSE:[/dim] {self.nyse_time[:19]}") + trigger_display = ( + "[green]NOW[/green]" + if self.trigger_time == "now" + else self.trigger_time + ) + left.add_row(f"[dim]Trigger:[/dim] {trigger_display}") + + # Status + left.add_row("") + status_style = "green" if self.status == "Running" else "yellow" + left.add_row( + "[bold]Status:[/bold] " + f"[{status_style}]{self.status}[/{status_style}]", + ) + if self.mode == "backtest": + left.add_row( + f"[dim]Backtesting Period:[/dim] {self.days_total} days\n" + f" {self.start_date} -> {self.end_date}", + ) + left.add_row(f"[dim]Current Date:[/dim] {self.current_date}") + + # Middle: Portfolio + mid = Table.grid(padding=(0, 0)) + mid.add_column() + + pnl_style = "green" if self.pnl_pct >= 0 else "red" + mid.add_row("[bold]Portfolio[/bold]") + mid.add_row(f"NAV: [bold]${self.total_value:,.0f}[/bold]") + mid.add_row(f"Cash: ${self.cash:,.0f}") + mid.add_row(f"P&L: [{pnl_style}]{self.pnl_pct:+.2f}%[/{pnl_style}]") + + # Positions + mid.add_row("") + mid.add_row("[bold]Positions[/bold]") + stock_holdings = [ + h for h in self.holdings if h.get("ticker") != "CASH" + ] + if stock_holdings: + for h in stock_holdings[:7]: + qty = h.get("quantity", 0) + ticker = h.get("ticker", "")[:5] + val = h.get("marketValue", 0) + qty_str = f"{qty:+d}" if qty != 0 else "0" + mid.add_row( + f"[cyan]{ticker:<5}[/cyan] {qty_str:>5} ${val:>7,.0f}", + ) + if len(stock_holdings) > 7: + mid.add_row(f"[dim]+{len(stock_holdings) - 7} more[/dim]") + else: + mid.add_row("[dim]No positions[/dim]") + + # Right: Recent Trades + right = Table.grid(padding=(0, 0)) + right.add_column() + + right.add_row("[bold]Recent Trades[/bold]") + if self.trades: + for t in self.trades[:10]: + side = t.get("side", "") + ticker = t.get("ticker", "")[:5] + qty = t.get("qty", 0) + if side == "LONG": + side_str = "[green]L[/green]" + elif side == "SHORT": + side_str = "[red]S[/red]" + else: + side_str = "[dim]H[/dim]" + right.add_row(f"{side_str} [cyan]{ticker:<5}[/cyan] {qty:>4}") + if len(self.trades) > 10: + right.add_row(f"[dim]+{len(self.trades) - 10} more[/dim]") + else: + right.add_row("[dim]No trades[/dim]") + + main_table.add_row(left, mid, right) + + # Outer table to add progress line at bottom + outer = Table.grid(padding=(0, 0)) + outer.add_column() + outer.add_row(main_table) + + # Progress line (last row) with animated dots + if self.progress: + DOTS_FRAMES = [" ", ". ", ".. ", "..."] + dots = DOTS_FRAMES[self._dots_index % len(DOTS_FRAMES)] + outer.add_row("") + outer.add_row(f"[dim]> {self.progress}{dots}[/dim]") + + # Build panel + title = "[bold cyan]EvoTraders[/bold cyan]" + if self.mode == "backtest": + title += " [dim]Backtest[/dim]" + elif self.mock: + title += " [dim]Mock[/dim]" + else: + title += " [dim]Live[/dim]" + + return Panel( + outer, + title=title, + border_style="cyan", + padding=(0, 1), + ) + + def _run_animator(self): + """Background thread to animate the dots""" + while self._animator_running: + time.sleep(0.3) + if self.progress and self.live: + self._dots_index += 1 + self.live.update(self._build_panel()) + + def start(self): + """Start the live dashboard display""" + self.live = Live( + self._build_panel(), + console=self.console, + refresh_per_second=4, + vertical_overflow="visible", + ) + self.live.start() + + # Start animator thread + self._animator_running = True + self._animator_thread = threading.Thread( + target=self._run_animator, + daemon=True, + ) + self._animator_thread.start() + + def stop(self): + """Stop the live dashboard""" + self._animator_running = False + if self._animator_thread: + self._animator_thread.join(timeout=0.5) + self._animator_thread = None + if self.live: + self.live.stop() + self.live = None + + def update( + self, + date: str = None, + status: str = None, + portfolio: Dict[str, Any] = None, + holdings: List[Dict] = None, + trades: List[Dict] = None, + days_completed: int = None, + days_total: int = None, + ): + """Update dashboard state and refresh display""" + if date: + self.current_date = date + if status: + self.status = status + if days_completed is not None: + self.days_completed = days_completed + if days_total is not None: + self.days_total = days_total + + if portfolio: + self.total_value = portfolio.get( + "totalAssetValue", + 0, + ) or portfolio.get( + "total_value", + self.initial_cash, + ) + self.cash = portfolio.get("cashPosition", 0) or portfolio.get( + "cash", + self.initial_cash, + ) + if self.total_value > 0 and self.initial_cash > 0: + self.pnl_pct = ( + (self.total_value - self.initial_cash) / self.initial_cash + ) * 100 + + if holdings is not None: + self.holdings = holdings + if trades is not None: + self.trades = trades + + if self.live: + self.live.update(self._build_panel()) + + def log(self, msg: str, also_log: bool = True): + """ + Update progress message and refresh panel + + Args: + msg: Progress message to display + also_log: Whether to also write to logger (default True) + """ + self.progress = msg + if also_log: + logger.info(msg) + if self.live: + self.live.update(self._build_panel()) + + def print_final_summary(self): + """Print final summary when dashboard stops""" + pnl_style = "green" if self.pnl_pct >= 0 else "red" + + if self.mode == "backtest": + msg = ( + f"[bold]Backtest Complete[/bold] | " + f"Days: {self.days_completed} | " + f"NAV: ${self.total_value:,.0f} | " + f"Return: [{pnl_style}]{self.pnl_pct:+.2f}%[/{pnl_style}]" + ) + else: + msg = ( + f"[bold]Session End[/bold] | " + f"NAV: ${self.total_value:,.0f} | " + f"P&L: [{pnl_style}]{self.pnl_pct:+.2f}%[/{pnl_style}]" + ) + + self.console.print(Panel(msg, border_style="green")) + + +# Global instance +_dashboard: Optional[TerminalDashboard] = None + + +def get_dashboard() -> TerminalDashboard: + """Get or create global dashboard instance""" + global _dashboard + if _dashboard is None: + _dashboard = TerminalDashboard() + return _dashboard diff --git a/evotraders/backend/utils/trade_executor.py b/evotraders/backend/utils/trade_executor.py new file mode 100644 index 0000000..50b1ee7 --- /dev/null +++ b/evotraders/backend/utils/trade_executor.py @@ -0,0 +1,772 @@ +# -*- coding: utf-8 -*- +""" +Trading Execution Engine - Supports Two Modes +1. Signal mode: Only records directional signal decisions +2. Portfolio mode: Executes specific trades and tracks positions +""" +# flake8: noqa: E501 +from copy import deepcopy +from datetime import datetime +from typing import Any, Dict, List, Optional + + +class DirectionSignalRecorder: + """Direction signal recorder, records daily investment direction decisions""" + + def __init__(self): + """Initialize direction signal recorder""" + self.signal_log = [] # Record all directional signal history + + def record_direction_signals( + self, + decisions: Dict[str, Dict[str, Any]], + current_date: str = None, + ) -> Dict[str, Any]: + """ + Record Portfolio Manager's directional signal decisions + + Args: + decisions: PM's direction decisions {ticker: {action, confidence, reasoning}} + current_date: Current date (used for backtest compatibility) + + Returns: + Signal recording report + """ + if current_date is None: + current_date = datetime.now().strftime("%Y-%m-%d") + + # Use provided date for timestamp (backtest compatible) + timestamp = f"{current_date}T09:30:00" + + signal_report: Dict[str, Any] = { + "recorded_signals": {}, + "date": current_date, + "timestamp": timestamp, + "total_signals": len(decisions), + } + + print( + f"\n📊 Recording directional signal decisions for {current_date}...", + ) + + # Record directional signal for each ticker + for ticker, decision in decisions.items(): + action = decision.get("action", "hold") + confidence = decision.get("confidence", 0) + reasoning = decision.get("reasoning", "") + + # Record signal + signal_record = { + "ticker": ticker, + "action": action, + "confidence": confidence, + "reasoning": reasoning, + "date": current_date, + "timestamp": timestamp, + } + + self.signal_log.append(signal_record) + signal_report["recorded_signals"][ticker] = { + "action": action, + "confidence": confidence, + } + + # Display signal + action_emoji = {"long": "📈", "short": "📉", "hold": "➖"} + emoji = action_emoji.get(action, "❓") + print( + f" {emoji} {ticker}: {action.upper()} (Confidence: {confidence}%) - {reasoning}", + ) + + print(f"\n✅ Recorded directional signals for {len(decisions)} stocks") + + return signal_report + + def get_signal_summary(self) -> Dict[str, Any]: + """Get signal recording summary""" + return { + "total_signals": len(self.signal_log), + "signal_log": self.signal_log, + } + + +def parse_pm_decisions(pm_output: Dict[str, Any]) -> Dict[str, Dict[str, Any]]: + """ + Parse Portfolio Manager output format + + Args: + pm_output: PM's raw output + + Returns: + Standardized decision format + """ + if isinstance(pm_output, dict) and "decisions" in pm_output: + return pm_output["decisions"] + elif isinstance(pm_output, dict): + # If directly a decision dictionary + return pm_output + else: + print(f"Warning: Unable to parse PM output format: {type(pm_output)}") + return {} + + +class PortfolioTradeExecutor: + """Portfolio mode trade executor, executes specific trades and tracks positions""" + + portfolio: Dict[str, Any] + trade_history: List[Dict[str, Any]] + portfolio_history: List[Dict[str, Any]] + + def __init__(self, initial_portfolio: Optional[Dict[str, Any]] = None): + """ + Initialize Portfolio trade executor + + Args: + initial_portfolio: Initial portfolio state + """ + + if initial_portfolio is None: + self.portfolio = { + "cash": 100000.0, + "positions": {}, + # Default 0.0 (short selling disabled) + "margin_requirement": 0.0, + "margin_used": 0.0, + } + else: + self.portfolio = deepcopy(initial_portfolio) + + self.trade_history = [] # Trade history + self.portfolio_history = [] # Portfolio history + + def execute_trade( + self, + ticker: str, + action: str, + quantity: int, + price: float, + current_date: str = None, + ) -> Dict[str, Any]: + """ + Execute a single trade + + Args: + ticker: Stock ticker + action: Trade action (long/short/hold) + quantity: Number of shares + price: Current price + current_date: Trade date + + Returns: + Trade result dictionary + """ + if current_date is None: + current_date = datetime.now().strftime("%Y-%m-%d") + + if action == "hold" or quantity == 0: + return {"status": "success", "message": "No trade needed"} + + if price <= 0: + return {"status": "failed", "reason": "Invalid price"} + + result = self._execute_single_trade( + ticker=ticker, + action=action, + target_quantity=quantity, + price=price, + date=current_date, + ) + + return result + + def execute_trades( + self, + decisions: Dict[str, Dict[str, Any]], + current_prices: Dict[str, float], + current_date: str = None, + ) -> Dict[str, Any]: + """ + Execute trading decisions and update positions + + Args: + decisions: {ticker: {action, quantity, confidence, reasoning}} + current_prices: {ticker: current_price} + current_date: Current date (used for backtest compatibility) + + Returns: + Trade execution report + """ + if current_date is None: + current_date = datetime.now().strftime("%Y-%m-%d") + + # Use provided date for timestamp (backtest compatible) + timestamp = f"{current_date}T09:30:00" + + execution_report: Dict[str, Any] = { + "date": current_date, + "timestamp": timestamp, + "executed_trades": [], + "failed_trades": [], + "portfolio_before": deepcopy(self.portfolio), + "portfolio_after": None, + } + + print(f"\n💼 Executing Portfolio trades for {current_date}...") + + # Execute trades for each ticker + for ticker, decision in decisions.items(): + action = decision.get("action", "hold") + quantity = decision.get("quantity", 0) + + if action == "hold" or quantity == 0: + continue + + price = current_prices.get(ticker, 0) + if price <= 0: + execution_report["failed_trades"].append( + { + "ticker": ticker, + "action": action, + "quantity": quantity, + "reason": "No valid price data", + }, + ) + print( + f" ❌ {ticker}: Unable to execute {action} - No valid price", + ) + continue + + # Execute trade + trade_result = self._execute_single_trade( + ticker, + action, + quantity, + price, + current_date, + ) + if trade_result["status"] == "success": + execution_report["executed_trades"].append(trade_result) + + trades_info = ", ".join(trade_result.get("trades", [])) + print( + f" ✔ {ticker}: {action} Target {quantity} shares " + f"({trades_info}) @ ${price:.2f}", + ) + else: + execution_report["failed_trades"].append(trade_result) + print( + f" ✗ {ticker}: Unable to execute {action} - {trade_result['reason']}", + ) + + # Record final portfolio state + execution_report["portfolio_after"] = deepcopy(self.portfolio) + self.portfolio_history.append( + { + "date": current_date, + "portfolio": deepcopy(self.portfolio), + }, + ) + + # Calculate portfolio value + portfolio_value = self._calculate_portfolio_value(current_prices) + execution_report["portfolio_value"] = portfolio_value + + print("\n✔ Trade execution completed:") + print(f" Success: {len(execution_report['executed_trades'])} trades") + print(f" Failed: {len(execution_report['failed_trades'])} trades") + print(f" Portfolio value: ${portfolio_value:,.2f}") + print(f" Cash balance: ${self.portfolio['cash']:,.2f}") + + return execution_report + + def _execute_single_trade( + self, + ticker: str, + action: str, + target_quantity: int, + price: float, + date: str, + ) -> Dict[str, Any]: + """ + Execute single trade - Incremental mode + + Args: + ticker: Stock ticker + action: long(add position)/short(reduce position)/hold + target_quantity: Incremental quantity (long=buy shares, short=sell shares) + price: Current price + date: Trade date + """ + + # Ensure position exists + if ticker not in self.portfolio["positions"]: + self.portfolio["positions"][ticker] = { + "long": 0, + "short": 0, + "long_cost_basis": 0.0, + "short_cost_basis": 0.0, + } + + position = self.portfolio["positions"][ticker] + current_long = position["long"] + current_short = position["short"] + + trades_executed = [] # Record actually executed trade steps + + if action == "long": + result = self._execute_long_action( + ticker, + target_quantity, + price, + date, + current_long, + current_short, + trades_executed, + ) + if result["status"] == "failed": + return result + + elif action == "short": + result = self._execute_short_action( + ticker, + target_quantity, + price, + date, + current_long, + current_short, + trades_executed, + ) + if result["status"] == "failed": + return result + + elif action == "hold": + print(f"\n⏸️ {ticker} Position unchanged: {current_long} shares") + + # Record trade with backtest-compatible timestamp + trade_record = { + "status": "success", + "ticker": ticker, + "action": action, + "target_quantity": target_quantity, + "price": price, + "trades": trades_executed, + "date": date, + "timestamp": f"{date}T09:30:00", + } + + self.trade_history.append(trade_record) + + return trade_record + + def _execute_long_action( + self, + ticker: str, + target_quantity: int, + price: float, + date: str, + current_long: int, + current_short: int, + trades_executed: list, + ) -> Dict[str, Any]: + """Execute long action: Buy shares or cover shorts first""" + print( + f"\n📈 {ticker} Long operation: Current Long {current_long}, " + f"Short {current_short} → Target quantity {target_quantity}", + ) + + if target_quantity <= 0: + print(" ⏸️ Quantity is 0, no trade needed") + return {"status": "success"} + + remaining = target_quantity + + # If has short position, cover first + if current_short > 0: + cover_qty = min(remaining, current_short) + print(f" 1️⃣ Cover short: {cover_qty} shares") + cover_result = self._cover_short_position( + ticker, + cover_qty, + price, + date, + ) + if cover_result["status"] == "failed": + return cover_result + trades_executed.append(f"Cover {cover_qty} shares") + remaining -= cover_qty + + # If still has remaining quantity, buy long + if remaining > 0: + print(f" 2️⃣ Buy long: {remaining} shares") + buy_result = self._buy_long_position( + ticker, + remaining, + price, + date, + ) + if buy_result["status"] == "failed": + return buy_result + trades_executed.append(f"Buy {remaining} shares") + + # Display final result + final_long = self.portfolio["positions"][ticker]["long"] + final_short = self.portfolio["positions"][ticker]["short"] + print( + f" ✅ Final state: Long {final_long} shares, Short {final_short} shares", + ) + + return {"status": "success"} + + def _execute_short_action( + self, + ticker: str, + target_quantity: int, + price: float, + date: str, + current_long: int, + current_short: int, + trades_executed: list, + ) -> Dict[str, Any]: + """Execute short action: Sell long positions first, then short if needed""" + print( + f"\n📉 {ticker} Short operation (quantity={target_quantity} shares):", + ) + print( + f" Current state: Long {current_long} shares, Short {current_short} shares", + ) + + if target_quantity <= 0: + print(" ⏸️ Quantity is 0, no trade needed") + return {"status": "success"} + + remaining_quantity = target_quantity + + # Step 1: If there are long positions, sell first + if current_long > 0: + sell_quantity = min(remaining_quantity, current_long) + print(f" 1️⃣ Sell long: {sell_quantity} shares") + sell_result = self._sell_long_position( + ticker, + sell_quantity, + price, + date, + ) + if sell_result["status"] == "failed": + return sell_result + trades_executed.append(f"Sell {sell_quantity} shares") + remaining_quantity -= sell_quantity + + # Step 2: If there's remaining quantity, establish or increase short position + if remaining_quantity > 0: + print(f" 2️⃣ Short: {remaining_quantity} shares") + short_result = self._open_short_position( + ticker, + remaining_quantity, + price, + date, + ) + if short_result["status"] == "failed": + return short_result + trades_executed.append(f"Short {remaining_quantity} shares") + + # Display final result + final_long = self.portfolio["positions"][ticker]["long"] + final_short = self.portfolio["positions"][ticker]["short"] + print( + f" ✅ Final state: Long {final_long} shares, Short {final_short} shares", + ) + + return {"status": "success"} + + def _buy_long_position( + self, + ticker: str, + quantity: int, + price: float, + _date: str, + ) -> Dict[str, Any]: + """Buy long position""" + position = self.portfolio["positions"][ticker] + trade_value = quantity * price + + if self.portfolio["cash"] < trade_value: + return { + "status": "failed", + "ticker": ticker, + "action": "buy", + "quantity": quantity, + "price": price, + "reason": f"Insufficient cash (needed: ${trade_value:.2f}, available: " + f"${self.portfolio['cash']:.2f})", + } + + # Update position cost basis + old_long = position["long"] + old_cost_basis = position["long_cost_basis"] + new_long = old_long + quantity + + # 🐛 Debug info + print(f" 🔍 Buy {ticker}:") + print(f" Old position: {old_long} shares @ ${old_cost_basis:.2f}") + print(f" Buy: {quantity} shares @ ${price:.2f}") + print(f" New position: {new_long} shares") + + if new_long > 0: + new_cost_basis = ( + (old_long * old_cost_basis) + (quantity * price) + ) / new_long + print( + f" New cost: ${new_cost_basis:.2f} = " + f"(({old_long} × ${old_cost_basis:.2f}) + " + f"({quantity} × ${price:.2f})) / {new_long}", + ) + position["long_cost_basis"] = new_cost_basis + position["long"] = new_long + + # Deduct cash + self.portfolio["cash"] -= trade_value + + return {"status": "success"} + + def _sell_long_position( + self, + ticker: str, + quantity: int, + price: float, + _date: str, + ) -> Dict[str, Any]: + """Sell long position""" + position = self.portfolio["positions"][ticker] + + if position["long"] < quantity: + return { + "status": "failed", + "ticker": ticker, + "action": "sell", + "quantity": quantity, + "price": price, + "reason": f"Insufficient long position (holding: {position['long']}," + f" trying to sell: {quantity})", + } + + # Reduce position + position["long"] -= quantity + if position["long"] == 0: + position["long_cost_basis"] = 0.0 + + # Increase cash + trade_value = quantity * price + self.portfolio["cash"] += trade_value + + return {"status": "success"} + + def _open_short_position( + self, + ticker: str, + quantity: int, + price: float, + _date: str, + ) -> Dict[str, Any]: + """Open short position""" + position = self.portfolio["positions"][ticker] + trade_value = quantity * price + margin_needed = trade_value * self.portfolio["margin_requirement"] + + if self.portfolio["cash"] < margin_needed: + return { + "status": "failed", + "ticker": ticker, + "action": "short", + "quantity": quantity, + "price": price, + "reason": f"Insufficient margin (needed: ${margin_needed:.2f}, " + f"available: ${self.portfolio['cash']:.2f})", + } + + # Update position cost basis + old_short = position["short"] + old_cost_basis = position["short_cost_basis"] + new_short = old_short + quantity + if new_short > 0: + position["short_cost_basis"] = ( + (old_short * old_cost_basis) + (quantity * price) + ) / new_short + position["short"] = new_short + + # Increase cash (short sale proceeds) and margin used + self.portfolio["cash"] += trade_value - margin_needed + self.portfolio["margin_used"] += margin_needed + + return {"status": "success"} + + def _cover_short_position( + self, + ticker: str, + quantity: int, + price: float, + _date: str, + ) -> Dict[str, Any]: + """Cover short position""" + position = self.portfolio["positions"][ticker] + + if position["short"] < quantity: + return { + "status": "failed", + "ticker": ticker, + "action": "cover", + "quantity": quantity, + "price": price, + "reason": f"Insufficient short position (holding: {position['short']}, " + f"trying to cover: {quantity})", + } + + # Calculate released margin - 🔧 FIX: Use cost_basis instead of current price + trade_value = quantity * price + cost_basis = position["short_cost_basis"] + margin_released = ( + quantity * cost_basis * self.portfolio["margin_requirement"] + ) + + # Reduce position + position["short"] -= quantity + if position["short"] == 0: + position["short_cost_basis"] = 0.0 + + # Deduct cash (buy to cover) and release margin + self.portfolio["cash"] -= trade_value + self.portfolio["cash"] += margin_released + self.portfolio["margin_used"] -= margin_released + + return {"status": "success"} + + def _calculate_portfolio_value( + self, + current_prices: Dict[str, float], + ) -> float: + """Calculate total portfolio value (net liquidation value)""" + # Add margin_used back because it's frozen cash, not lost money + total_value = self.portfolio["cash"] + self.portfolio["margin_used"] + + for ticker, position in self.portfolio["positions"].items(): + if ticker in current_prices: + price = current_prices[ticker] + # Add long position value + total_value += position["long"] * price + # Subtract short position value (liability) + total_value -= position["short"] * price + + return total_value + + def get_portfolio_summary( + self, + current_prices: Dict[str, float], + ) -> Dict[str, Any]: + """Get portfolio summary""" + portfolio_value = self._calculate_portfolio_value(current_prices) + + positions_summary = [] + for ticker, position in self.portfolio["positions"].items(): + if position["long"] > 0 or position["short"] > 0: + price = current_prices.get(ticker, 0) + long_value = position["long"] * price + short_value = position["short"] * price + + positions_summary.append( + { + "ticker": ticker, + "long_shares": position["long"], + "short_shares": position["short"], + "long_value": long_value, + "short_value": short_value, + "long_cost_basis": position["long_cost_basis"], + "short_cost_basis": position["short_cost_basis"], + "long_pnl": ( + long_value + - (position["long"] * position["long_cost_basis"]) + if position["long"] > 0 + else 0 + ), + "short_pnl": ( + (position["short"] * position["short_cost_basis"]) + - short_value + if position["short"] > 0 + else 0 + ), + }, + ) + + return { + "portfolio_value": portfolio_value, + "cash": self.portfolio["cash"], + "margin_used": self.portfolio["margin_used"], + "positions": positions_summary, + "total_trades": len(self.trade_history), + } + + +def execute_trading_decisions( + pm_decisions: Dict[str, Any], + current_date: str = None, +) -> Dict[str, Any]: + """ + Convenience function to record directional signal decisions (Signal mode) + + Args: + pm_decisions: PM's direction decisions + current_date: Current date (optional) + + Returns: + Signal recording report + """ + # Parse PM decisions + decisions = parse_pm_decisions(pm_decisions) + + # Create direction signal recorder + recorder = DirectionSignalRecorder() + + # Record directional signals + signal_report = recorder.record_direction_signals(decisions, current_date) + + return signal_report + + +def execute_portfolio_trades( + pm_decisions: Dict[str, Any], + current_prices: Dict[str, float], + portfolio: Dict[str, Any], + current_date: str = None, +) -> Dict[str, Any]: + """ + Execute Portfolio mode trading decisions + + Args: + pm_decisions: PM's trading decisions + current_prices: Current prices + portfolio: Current portfolio state + current_date: Current date (optional) + + Returns: + Trade execution report and updated portfolio + """ + # Parse PM decisions + decisions = parse_pm_decisions(pm_decisions) + + # Create Portfolio trade executor + executor = PortfolioTradeExecutor(initial_portfolio=portfolio) + + # Execute trades + execution_report = executor.execute_trades( + decisions, + current_prices, + current_date, + ) + + # Add portfolio summary + execution_report["portfolio_summary"] = executor.get_portfolio_summary( + current_prices, + ) + + # Return updated portfolio + execution_report["updated_portfolio"] = executor.portfolio + + return execution_report diff --git a/evotraders/docs/assets/dashboard.jpg b/evotraders/docs/assets/dashboard.jpg new file mode 100644 index 0000000000000000000000000000000000000000..6709d534c4adf6e33697fc040b94e134908438e3 GIT binary patch literal 184735 zcmeFZcQ~9)_c(qdLL`J}i5AhVAbJ;x-s|cm(XAT2MWhg25X7p{?XsdQSv5rof+(v+ zj}k=hf_!hu^W;h1_mlVg`Tq60uHW^p%Wce=d*;k(bI#0}{WkP%5+GGkP*wnNZ~y=Y z`~kjA;|wXw%35gYXeubH$^QVn1HjLzYXIQv;_0rVc$3l4$e58}^2Zw|u-jH1ZYO{L 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API +# At least FINANCIAL_DATASETS_API_KEY is required, corresponding to FIN_DATA_SOURCE=financial_datasets; It's recommended to add FINNHUB_API_KEY, corresponding to FIN_DATA_SOURCE=finnhub; FINNHUB_API_KEY is mandatory for live mode +# 至少需要FINANCIAL_DATASETS_API_KEY，对应FIN_DATA_SOURCE=financial_datasets；推荐添加FINNHUB_API_KEY，对应FIN_DATA_SOURCE=finnhub；如果使用live模式必须添加FINNHUB_API_KEY + +# finnhub: https://finnhub.io/register +# financial datasets: https://www.financialdatasets.ai/ + +FIN_DATA_SOURCE = #finnhub or financial_datasets | finnhub 或 financial_datasets +FINANCIAL_DATASETS_API_KEY= #required | 必填 +FINNHUB_API_KEY= #optional | 可选 + +# Model API +OPENAI_API_KEY= +OPENAI_BASE_URL= +MODEL_NAME=qwen3-max-preview + +#记忆模块（Embedding and llm calls for Reme memory) +# default to use aliyun dashscope url, more details: https://help.aliyun.com/zh/model-studio/what-is-model-studio +MEMORY_API_KEY= + + +# ================== Agent-Specific Model Configuration | Agent特定模型配置 ================== +# Configure different base models for different roles | 为不同角色配置不同的基座模型 +# If not configured, global MODEL_NAME and MODEL_PROVIDER will be used | 如果未配置，将使用全局MODEL_NAME和MODEL_PROVIDER +# +# Role List | 角色列表： +# - SENTIMENT_ANALYST: Sentiment Analyst | 情绪分析师 +# - TECHNICAL_ANALYST: Technical Analyst | 技术分析师 +# - FUNDAMENTALS_ANALYST: Fundamentals Analyst | 基本面分析师 +# - VALUATION_ANALYST: Valuation Analyst | 估值分析师 +# - PORTFOLIO_MANAGER: Portfolio Manager | 投资组合经理 +# - RISK_MANAGER: Risk Manager | 风险管理经理 + +AGENT_SENTIMENT_ANALYST_MODEL_NAME=deepseek-v3.2-exp +AGENT_TECHNICAL_ANALYST_MODEL_NAME=glm-4.6 +AGENT_FUNDAMENTALS_ANALYST_MODEL_NAME=qwen3-max-preview +AGENT_VALUATION_ANALYST_MODEL_NAME=Moonshot-Kimi-K2-Instruct +AGENT_RISK_MANAGER_MODEL_NAME=qwen3-max-preview +AGENT_PORTFOLIO_MANAGER_MODEL_NAME=qwen3-max-preview + + +# ================== Advanced Configuration | 高阶配置 ================== + +# Maximum conference discussion cycles (default: 2) | 最大会议讨论轮数（默认：2） +MAX_COMM_CYCLES=2 + +# Margin Requirement | 保证金比例 +MARGIN_REQUIREMENT=0.5 +# 0.5 = Standard margin (recommended) | 标准保证金（推荐） +# 0.25 = Maintenance margin (aggressive) | 维持保证金（激进） + +# Historical data start date +DATA_START_DATE=2022-01-01 +# Auto update data on startup (true/false) +AUTO_UPDATE_DATA=true \ No newline at end of file diff --git a/evotraders/frontend/.gitignore b/evotraders/frontend/.gitignore new file mode 100644 index 0000000..fa8bfd1 --- /dev/null +++ b/evotraders/frontend/.gitignore @@ -0,0 +1,33 @@ +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* +.env.local + +# Dependencies +node_modules + +# Build output +dist +dist-ssr +*.local + +# Lock files +package-lock.json +yarn.lock +pnpm-lock.yaml + +# Editor directories and files +.vscode/* +!.vscode/extensions.json +.idea +.DS_Store +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? diff --git a/evotraders/frontend/README.md b/evotraders/frontend/README.md new file mode 100644 index 0000000..b5b523f --- /dev/null +++ b/evotraders/frontend/README.md @@ -0,0 +1,7 @@ + +## QuickStart +```bash +cd frontend +npm install +npm run dev +``` \ No newline at end of file diff --git a/evotraders/frontend/components.json b/evotraders/frontend/components.json new file mode 100644 index 0000000..41716e3 --- /dev/null +++ b/evotraders/frontend/components.json @@ -0,0 +1,22 @@ +{ + "$schema": "https://ui.shadcn.com/schema.json", + "style": "new-york", + "rsc": false, + "tsx": true, + "tailwind": { + "config": "tailwind.config.js", + "css": "index.css", + "baseColor": "neutral", + "cssVariables": true, + "prefix": "" + }, + "iconLibrary": "lucide", + "aliases": { + "components": "@/components", + "utils": "@/lib/utils", + "ui": "@/components/ui", + "lib": "@/lib", + "hooks": "@/hooks" + }, + "registries": {} +} diff --git a/evotraders/frontend/env.template b/evotraders/frontend/env.template new file mode 100644 index 0000000..0499b80 --- /dev/null +++ b/evotraders/frontend/env.template @@ -0,0 +1,10 @@ +# Frontend Environment Variables Template +# 复制此文件为 .env 并修改配置 + +# WebSocket服务器地址 +# 本地开发 +VITE_WS_URL=ws://localhost:8765 + +# 生产环境（替换为你的实际服务器地址） +# VITE_WS_URL=wss://your-server.com:8765 + diff --git a/evotraders/frontend/eslint.config.js b/evotraders/frontend/eslint.config.js new file mode 100644 index 0000000..777fe18 --- /dev/null +++ b/evotraders/frontend/eslint.config.js @@ -0,0 +1,29 @@ +import js from "@eslint/js"; +import globals from "globals"; +import reactHooks from "eslint-plugin-react-hooks"; +import reactRefresh from "eslint-plugin-react-refresh"; +import { defineConfig, globalIgnores } from "eslint/config"; + +export default defineConfig([ + globalIgnores(["dist"]), + { + files: ["**/*.{js,jsx}"], + extends: [ + js.configs.recommended, + reactHooks.configs["recommended-latest"], + reactRefresh.configs.vite, + ], + languageOptions: { + ecmaVersion: 2020, + globals: globals.browser, + parserOptions: { + ecmaVersion: "latest", + ecmaFeatures: { jsx: true }, + sourceType: "module", + }, + }, + rules: { + "no-unused-vars": ["error", { varsIgnorePattern: "^[A-Z_]" }], + }, + }, +]); diff --git a/evotraders/frontend/index.css b/evotraders/frontend/index.css new file mode 100644 index 0000000..fcfdfb9 --- /dev/null +++ b/evotraders/frontend/index.css @@ -0,0 +1,68 @@ +@tailwind base; +@tailwind components; +@tailwind utilities; + +@layer base { + :root { + --background: 0 0% 100%; + --foreground: 0 0% 3.9%; + --card: 0 0% 100%; + --card-foreground: 0 0% 3.9%; + --popover: 0 0% 100%; + --popover-foreground: 0 0% 3.9%; + --primary: 0 0% 9%; + --primary-foreground: 0 0% 98%; + --secondary: 0 0% 96.1%; + --secondary-foreground: 0 0% 9%; + --muted: 0 0% 96.1%; + --muted-foreground: 0 0% 45.1%; + --accent: 0 0% 96.1%; + --accent-foreground: 0 0% 9%; + --destructive: 0 84.2% 60.2%; + --destructive-foreground: 0 0% 98%; + --border: 0 0% 89.8%; + --input: 0 0% 89.8%; + --ring: 0 0% 3.9%; + --chart-1: 12 76% 61%; + --chart-2: 173 58% 39%; + --chart-3: 197 37% 24%; + --chart-4: 43 74% 66%; + --chart-5: 27 87% 67%; + --radius: 0.5rem + } + .dark { + --background: 0 0% 3.9%; + --foreground: 0 0% 98%; + --card: 0 0% 3.9%; + --card-foreground: 0 0% 98%; + --popover: 0 0% 3.9%; + --popover-foreground: 0 0% 98%; + --primary: 0 0% 98%; + --primary-foreground: 0 0% 9%; + --secondary: 0 0% 14.9%; + --secondary-foreground: 0 0% 98%; + --muted: 0 0% 14.9%; + --muted-foreground: 0 0% 63.9%; + --accent: 0 0% 14.9%; + --accent-foreground: 0 0% 98%; + --destructive: 0 62.8% 30.6%; + --destructive-foreground: 0 0% 98%; + --border: 0 0% 14.9%; + --input: 0 0% 14.9%; + --ring: 0 0% 83.1%; + --chart-1: 220 70% 50%; + --chart-2: 160 60% 45%; + --chart-3: 30 80% 55%; + --chart-4: 280 65% 60%; + --chart-5: 340 75% 55% + } +} + +@layer base { + * { + @apply border-border; + } + body { + @apply bg-background text-foreground; + } +} \ No newline at end of file diff --git a/evotraders/frontend/index.html b/evotraders/frontend/index.html new file mode 100644 index 0000000..aa0c766 --- /dev/null +++ b/evotraders/frontend/index.html @@ -0,0 +1,14 @@ + + + + + + + + EvoTraders + + +

+ + + diff --git a/evotraders/frontend/package.json b/evotraders/frontend/package.json new file mode 100644 index 0000000..3efd982 --- /dev/null +++ b/evotraders/frontend/package.json @@ -0,0 +1,55 @@ +{ + "name": "live-trading-demo", + "private": true, + "version": "0.0.0", + "type": "module", + "scripts": { + "dev": "vite", + "build": "vite build", + "lint": "eslint .", + "preview": "vite preview", + "preview:host": "vite preview --host" + }, + "dependencies": { + "@radix-ui/react-dialog": "^1.1.15", + "@radix-ui/react-dropdown-menu": "^2.1.16", + "@radix-ui/react-label": "^2.1.7", + "@radix-ui/react-slider": "^1.3.6", + "@radix-ui/react-slot": "^1.2.3", + "@radix-ui/react-switch": "^1.2.6", + "@radix-ui/react-tabs": "^1.1.13", + "@radix-ui/react-tooltip": "^1.2.8", + "@react-three/drei": "^10.7.6", + "@react-three/fiber": "^9.3.0", + "@tailwindcss/vite": "^4.1.13", + "class-variance-authority": "^0.7.1", + "clsx": "^2.1.1", + "framer-motion": "^12.23.13", + "lucide-react": "^0.544.0", + "react": "^19.1.1", + "react-dom": "^19.1.1", + "react-markdown": "^10.1.0", + "recharts": "^3.2.1", + "remark-gfm": "^4.0.1", + "tailwind-merge": "^3.3.1", + "three": "^0.180.0", + "zustand": "^5.0.8" + }, + "devDependencies": { + "@eslint/js": "^9.33.0", + "@types/react": "^19.1.13", + "@types/react-dom": "^19.1.9", + "@vitejs/plugin-react": "^5.0.0", + "autoprefixer": "^10.4.21", + "eslint": "^9.33.0", + "eslint-plugin-react-hooks": "^5.2.0", + "eslint-plugin-react-refresh": "^0.4.20", + "globals": "^16.3.0", + "postcss": "^8.5.6", + "tailwindcss": "^3.4.17", + "tailwindcss-animate": "^1.0.7", + "typescript": "^5.9.2", + "vite": "^7.1.2", + "vite-tsconfig-paths": "^5.1.4" + } +} diff --git a/evotraders/frontend/postcss.config.js b/evotraders/frontend/postcss.config.js new file mode 100644 index 0000000..2aa7205 --- /dev/null +++ b/evotraders/frontend/postcss.config.js @@ -0,0 +1,6 @@ +export default { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +}; diff 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zhAxM5Reod%Td)882DB86KA}z@dH(MeAz!5A_!3v-)C@yY`_B)joP&1s@1_3xss4Lb z|MO75Q1$O)^>4BIx32!%t^RGS|BfsFPFDY!82_DD{~fLW1uXw!tA7#ezu@{mgadFM o|6fuT|DDwN%cazIo;u)eWTjv;;^X<}1pFuaSm{yD1EW{}3pTH5Gynhq literal 0 HcmV?d00001 diff --git a/evotraders/frontend/src/App.css b/evotraders/frontend/src/App.css new file mode 100644 index 0000000..b9d355d --- /dev/null +++ b/evotraders/frontend/src/App.css @@ -0,0 +1,42 @@ +#root { + max-width: 1280px; + margin: 0 auto; + padding: 2rem; + text-align: center; +} + +.logo { + height: 6em; + padding: 1.5em; + will-change: filter; + transition: filter 300ms; +} +.logo:hover { + filter: drop-shadow(0 0 2em #646cffaa); +} +.logo.react:hover { + filter: drop-shadow(0 0 2em #61dafbaa); +} + +@keyframes logo-spin { + from { + transform: rotate(0deg); + } + to { + transform: rotate(360deg); + } +} + +@media (prefers-reduced-motion: no-preference) { + a:nth-of-type(2) .logo { + animation: logo-spin infinite 20s linear; + } +} + +.card { + padding: 2em; +} + +.read-the-docs { + color: #888; +} diff --git a/evotraders/frontend/src/App.jsx b/evotraders/frontend/src/App.jsx new file mode 100644 index 0000000..bf2f071 --- /dev/null +++ b/evotraders/frontend/src/App.jsx @@ -0,0 +1,1031 @@ +import React, { useEffect, useMemo, useRef, useState, useCallback } from "react"; + +// Configuration and constants +import { AGENTS, INITIAL_TICKERS } from './config/constants'; + +// Services +import { ReadOnlyClient } from './services/websocket'; + +// Hooks +import { useFeedProcessor } from './hooks/useFeedProcessor'; + +// Styles +import GlobalStyles from './styles/GlobalStyles'; + +// Components +import RoomView from './components/RoomView'; +import NetValueChart from './components/NetValueChart'; +import AgentFeed from './components/AgentFeed'; +import StockLogo from './components/StockLogo'; +import StatisticsView from './components/StatisticsView'; +import PerformanceView from './components/PerformanceView'; +import AboutModal from './components/AboutModal'; +import RulesView from './components/RulesView'; +import Header from './components/Header.jsx'; + +// Utils +import { formatNumber, formatTickerPrice } from './utils/formatters'; + +/** + * Live Trading Intelligence Platform - Read-Only Dashboard + * Geek Style - Terminal-inspired, minimal, monochrome + * + */ + +export default function LiveTradingApp() { + const [isConnected, setIsConnected] = useState(false); + const [connectionStatus, setConnectionStatus] = useState('connecting'); // 'connecting' | 'connected' | 'disconnected' + const [systemStatus, setSystemStatus] = useState('initializing'); // 'initializing' | 'running' | 'completed' + const [currentDate, setCurrentDate] = useState(null); + const [progress, setProgress] = useState({ current: 0, total: 0 }); + const [now, setNow] = useState(() => new Date()); + const [showAboutModal, setShowAboutModal] = useState(false); + + // View toggle: 'rules' | 'room' | 'chart' | 'statistics' + const [currentView, setCurrentView] = useState('chart'); // Start with chart, then animate to room + const [isInitialAnimating, setIsInitialAnimating] = useState(true); + const [lastUpdate, setLastUpdate] = useState(new Date()); + const [isUpdating, setIsUpdating] = useState(false); + + // Chart data + const [chartTab, setChartTab] = useState('all'); + const [portfolioData, setPortfolioData] = useState({ + netValue: 10000, + pnl: 0, + equity: [], + baseline: [], // Baseline strategy (Buy & Hold - Equal Weight) + baseline_vw: [], // Baseline strategy (Buy & Hold - Value Weighted) + momentum: [], // Momentum strategy + strategies: [] // Other strategies + }); + + // Feed data (using hook for simplified processing) + const { feed, processHistoricalFeed, processFeedEvent, addSystemMessage } = useFeedProcessor(); + + // Statistics data + const [holdings, setHoldings] = useState([]); + const [trades, setTrades] = useState([]); + const [stats, setStats] = useState(null); + const [leaderboard, setLeaderboard] = useState([]); + + // Ticker prices (now from real-time data) + const [tickers, setTickers] = useState(INITIAL_TICKERS); + const [rollingTickers, setRollingTickers] = useState({}); + + // Room bubbles + const [bubbles, setBubbles] = useState({}); + + // Resizable panels + const [leftWidth, setLeftWidth] = useState(70); // percentage + const [isResizing, setIsResizing] = useState(false); + + // Market status + const [serverMode, setServerMode] = useState(null); // 'live' | 'backtest' | null + const [marketStatus, setMarketStatus] = useState(null); // { status, status_text, ... } + const [virtualTime, setVirtualTime] = useState(null); // Virtual time from server (for mock mode) + + const clientRef = useRef(null); + const containerRef = useRef(null); + const agentFeedRef = useRef(null); + + // Track last virtual time update to calculate increment + const lastVirtualTimeRef = useRef(null); + const virtualTimeOffsetRef = useRef(0); + + // Last day history for replay + const [lastDayHistory, setLastDayHistory] = useState([]); + + // Determine if LIVE tab should be enabled + const isLiveEnabled = useMemo(() => { + if (!marketStatus) return false; + return marketStatus.status === 'open'; + }, [marketStatus]); + + // Switch away from LIVE tab when market closes + useEffect(() => { + if (!isLiveEnabled && chartTab === 'live') { + setChartTab('all'); + } + }, [isLiveEnabled, chartTab]); + + // Clock - use virtual time if available (for mock mode) + useEffect(() => { + if (virtualTime) { + // In mock mode, calculate offset from real time + const virtualTimeMs = new Date(virtualTime).getTime(); + const realTimeMs = Date.now(); + virtualTimeOffsetRef.current = virtualTimeMs - realTimeMs; + lastVirtualTimeRef.current = virtualTimeMs; + setNow(new Date(virtualTime)); + + // Update clock every second based on offset + const id = setInterval(() => { + const currentRealTime = Date.now(); + const currentVirtualTime = currentRealTime + virtualTimeOffsetRef.current; + setNow(new Date(currentVirtualTime)); + }, 1000); + + return () => clearInterval(id); + } else { + // In live mode, use real time + const id = setInterval(() => setNow(new Date()), 1000); + return () => clearInterval(id); + } + }, [virtualTime]); + + // Update clock when virtual time changes (recalculate offset) + useEffect(() => { + if (virtualTime) { + const virtualTimeMs = new Date(virtualTime).getTime(); + const realTimeMs = Date.now(); + virtualTimeOffsetRef.current = virtualTimeMs - realTimeMs; + lastVirtualTimeRef.current = virtualTimeMs; + setNow(new Date(virtualTime)); + } + }, [virtualTime]); + + // Track updates with visual feedback + useEffect(() => { + setLastUpdate(new Date()); + setIsUpdating(true); + const timer = setTimeout(() => setIsUpdating(false), 500); + return () => clearTimeout(timer); + }, [holdings, stats, trades, portfolioData.netValue]); + + // Initial animation: show room drawer sliding in + useEffect(() => { + // Wait a bit after mount, then trigger slide to room + const slideTimer = setTimeout(() => { + setCurrentView('room'); + }, 1200); // Wait 1200ms before starting animation (2x slower) + + // Disable animation flag after animation completes + const completeTimer = setTimeout(() => { + setIsInitialAnimating(false); + }, 5000); // 1200ms delay + 1600ms animation duration + 400ms buffer + + return () => { + clearTimeout(slideTimer); + clearTimeout(completeTimer); + }; + }, []); + + // Helper to check if bubble should still be visible + // Bubbles persist until replaced by ANY new message (cross-role) + // When any agent sends a new message, all previous bubbles are cleared + // Can search by agentId or agentName + const bubbleFor = (idOrName) => { + // First try direct lookup by id + let b = bubbles[idOrName]; + if (b) { + return b; + } + + // If not found, search by agentName + const agent = AGENTS.find(a => a.name === idOrName || a.id === idOrName); + if (agent) { + b = bubbles[agent.id]; + if (b) { + return b; + } + } + + return null; + }; + + // Handle jump to message in feed + const handleJumpToMessage = useCallback((bubble) => { + // Switch to room tab (if not already there) for better context + // Then scroll AgentFeed to the message + if (agentFeedRef.current && agentFeedRef.current.scrollToMessage) { + agentFeedRef.current.scrollToMessage(bubble); + } + }, []); + + // Auto-connect to server on mount + useEffect(() => { + // Define pushEvent inside useEffect to avoid dependency issues + const handlePushEvent = (evt) => { + if (!evt) return; + + try { + handleEventInternal(evt); + } catch (error) { + console.error('[Event Handler] Error:', error); + } + }; + + const handleEventInternal = (evt) => { + // Helper: Update tickers from realtime prices + const updateTickersFromPrices = (realtimePrices) => { + try { + setTickers(prevTickers => { + return prevTickers.map(ticker => { + const realtimeData = realtimePrices[ticker.symbol]; + if (realtimeData && realtimeData.price !== null && realtimeData.price !== undefined) { + // Use 'ret' from realtime data (relative to open price) if available + const newChange = (realtimeData.ret !== null && realtimeData.ret !== undefined) + ? realtimeData.ret + : (ticker.change !== null && ticker.change !== undefined ? ticker.change : 0); + + return { + ...ticker, + price: realtimeData.price, + change: newChange, + open: realtimeData.open || ticker.open + }; + } + return ticker; + }); + }); + } catch (error) { + console.error('Error updating tickers from prices:', error); + } + }; + + const handlers = { + // Error response (for fast forward errors) + error: (e) => { + console.error('[Error]', e.message); + + // Handle fast forward errors + if (e.message && e.message.includes('fast forward')) { + console.warn(`⚠️ ${e.message}`); + handlePushEvent({ + type: 'system', + content: `⚠️ ${e.message}`, + timestamp: Date.now() + }); + } + }, + + // Connection events + system: (e) => { + console.log('[System]', e.content); + if (e.content.includes('Connected')) { + setConnectionStatus('connected'); + setIsConnected(true); + } else if (e.content.includes('Disconnected')) { + setConnectionStatus('disconnected'); + setIsConnected(false); + } + processFeedEvent(e); + }, + + // Pong response from server + pong: (e) => { + console.log('[Heartbeat] Pong received'); + }, + + // Initial state from server + initial_state: (e) => { + try { + const state = e.state; + if (!state) return; + + setSystemStatus(state.status || 'initializing'); + setCurrentDate(state.current_date); + + // 设置服务器模式和市场状态 + if (state.server_mode) { + setServerMode(state.server_mode); + } + // 检查是否是mock模式 + const isMockMode = state.is_mock_mode === true; + if (state.market_status) { + setMarketStatus(state.market_status); + // 只在Mock模式下，如果市场状态包含虚拟时间，才保存它 + if (isMockMode && state.market_status.current_time) { + try { + const virtualTimeDate = new Date(state.market_status.current_time); + setVirtualTime(virtualTimeDate); + } catch (error) { + console.error('Error parsing virtual time from market_status:', error); + } + } else { + // 非Mock模式下清除virtualTime + setVirtualTime(null); + } + } + + if (state.trading_days_total) { + setProgress({ + current: state.trading_days_completed || 0, + total: state.trading_days_total + }); + } + + if (state.portfolio) { + setPortfolioData(prev => ({ + ...prev, + netValue: state.portfolio.total_value || prev.netValue, + pnl: state.portfolio.pnl_percent || 0, + equity: state.portfolio.equity || prev.equity, + baseline: state.portfolio.baseline || prev.baseline, + baseline_vw: state.portfolio.baseline_vw || prev.baseline_vw, + momentum: state.portfolio.momentum || prev.momentum, + strategies: state.portfolio.strategies || prev.strategies, + equity_return: state.portfolio.equity_return || prev.equity_return, + baseline_return: state.portfolio.baseline_return || prev.baseline_return, + baseline_vw_return: state.portfolio.baseline_vw_return || prev.baseline_vw_return, + momentum_return: state.portfolio.momentum_return || prev.momentum_return + })); + } + + if (state.holdings) setHoldings(state.holdings); + if (state.trades) setTrades(state.trades); + if (state.stats) setStats(state.stats); + if (state.leaderboard) setLeaderboard(state.leaderboard); + if (state.realtime_prices) updateTickersFromPrices(state.realtime_prices); + + // Load and process historical feed data + if (state.feed_history && Array.isArray(state.feed_history)) { + console.log(`✅ Loading ${state.feed_history.length} historical events`); + processHistoricalFeed(state.feed_history); + } + + // Load last day history for replay + if (state.last_day_history && Array.isArray(state.last_day_history)) { + setLastDayHistory(state.last_day_history); + console.log(`✅ Loaded ${state.last_day_history.length} last day events for replay`); + } + + console.log('Initial state loaded'); + } catch (error) { + console.error('Error loading initial state:', error); + } + }, + + // Market status update + market_status_update: (e) => { + if (e.market_status) { + setMarketStatus(e.market_status); + } + }, + + // Real-time price updates + price_update: (e) => { + try { + const { symbol, price, ret, open, portfolio, realtime_prices } = e; + + if (!symbol || !price) { + console.warn('[Price Update] Missing symbol or price:', e); + return; + } + + console.log(`[Price Update] ${symbol}: $${price} (ret: ${ret !== undefined ? ret.toFixed(2) : 'N/A'}%)`); + + // Update ticker price with animation + setTickers(prevTickers => { + return prevTickers.map(ticker => { + if (ticker.symbol === symbol) { + const oldPrice = ticker.price; + + // Use 'ret' from server (relative to open price) if available + // Otherwise fallback to calculating change from previous price + let newChange = ticker.change; + if (ret !== null && ret !== undefined) { + // Use server-provided ret (relative to open price) + newChange = ret; + } else if (oldPrice !== null && oldPrice !== undefined && isFinite(oldPrice)) { + // Fallback: calculate change from previous price + const priceChange = ((price - oldPrice) / oldPrice) * 100; + newChange = (newChange !== null && newChange !== undefined) + ? newChange + priceChange + : priceChange; + } else { + // First price received, set change to 0 + newChange = 0; + } + + // Trigger rolling animation only if price actually changed + if (oldPrice !== price) { + setRollingTickers(prev => ({ ...prev, [symbol]: true })); + setTimeout(() => { + setRollingTickers(prev => ({ ...prev, [symbol]: false })); + }, 500); + } + + return { + ...ticker, + price: price, + change: newChange, + open: open || ticker.open // Store open price + }; + } + return ticker; + }); + }); + + // Update all tickers from realtime_prices if provided + if (realtime_prices) { + updateTickersFromPrices(realtime_prices); + } + + // Update portfolio value if provided + if (portfolio && portfolio.total_value) { + setPortfolioData(prev => ({ + ...prev, + netValue: portfolio.total_value, + pnl: portfolio.pnl_percent || 0, + equity: portfolio.equity || prev.equity // Update equity curve + })); + } + } catch (error) { + console.error('[Price Update] Error:', error); + } + }, + + // Day progress events + day_start: (e) => { + setCurrentDate(e.date); + if (e.progress !== undefined) { + setProgress(prev => ({ + ...prev, + current: Math.floor(e.progress * (prev.total || 1)) + })); + } + setSystemStatus('running'); + processFeedEvent(e); + }, + + day_complete: (e) => { + // Update from day result + const result = e.result; + if (result && typeof result === 'object') { + // Update portfolio equity if available + if (result.portfolio_summary) { + const summary = result.portfolio_summary; + setPortfolioData(prev => { + const newEquity = [...prev.equity]; + // Add new data point + const dateObj = new Date(e.date); + newEquity.push({ + t: dateObj.getTime(), + v: summary.total_value || summary.cash || prev.netValue + }); + + return { + ...prev, + netValue: summary.total_value || summary.cash || prev.netValue, + pnl: summary.pnl_percent || 0, + equity: newEquity + }; + }); + } + } + processFeedEvent(e); + }, + + day_error: (e) => { + console.error('Day error:', e.date, e.error); + processFeedEvent(e); + }, + + conference_start: (e) => { + processFeedEvent(e); + }, + + conference_end: (e) => { + processFeedEvent(e); + }, + + agent_message: (e) => { + const agent = AGENTS.find(a => a.id === e.agentId); + + // Update bubbles for room view + setBubbles({ + [e.agentId]: { + text: e.content, + ts: Date.now(), + agentName: agent?.name || e.agentName || e.agentId + } + }); + + processFeedEvent(e); + }, + + conference_message: (e) => { + const agent = AGENTS.find(a => a.id === e.agentId); + + // Update bubbles for room view + setBubbles({ + [e.agentId]: { + text: e.content, + ts: Date.now(), + agentName: agent?.name || e.agentName || e.agentId + } + }); + + processFeedEvent(e); + }, + + memory: (e) => { + processFeedEvent(e); + }, + + team_summary: (e) => { + // Update portfolio data silently without creating feed messages + setPortfolioData(prev => ({ + ...prev, + netValue: e.balance || prev.netValue, + pnl: e.pnlPct || 0, + equity: e.equity || prev.equity, + baseline: e.baseline || prev.baseline, + baseline_vw: e.baseline_vw || prev.baseline_vw, + momentum: e.momentum || prev.momentum, + equity_return: e.equity_return || prev.equity_return, + baseline_return: e.baseline_return || prev.baseline_return, + baseline_vw_return: e.baseline_vw_return || prev.baseline_vw_return, + momentum_return: e.momentum_return || prev.momentum_return + })); + + // Portfolio updates are shown in the ticker bar, no need for feed messages + }, + + team_portfolio: (e) => { + if (e.holdings) setHoldings(e.holdings); + }, + + // ✅ 监听 holdings 更新（服务器广播的事件名） + team_holdings: (e) => { + if (e.data && Array.isArray(e.data)) { + setHoldings(e.data); + console.log(`✅ Holdings updated: ${e.data.length} positions`); + } + }, + + team_trades: (e) => { + // 支持两种格式：完整列表或单笔交易 + if (e.mode === 'full' && e.data && Array.isArray(e.data)) { + setTrades(e.data); + console.log(`✅ Trades updated (full): ${e.data.length} trades`); + } else if (Array.isArray(e.trades)) { + setTrades(e.trades); + } else if (e.trade) { + setTrades(prev => [e.trade, ...prev].slice(0, 100)); + } + }, + + team_stats: (e) => { + if (e.data) { + setStats(e.data); + console.log('✅ Stats updated'); + } else if (e.stats) { + setStats(e.stats); + } + }, + + team_leaderboard: (e) => { + // 服务器发送的格式: { type: 'team_leaderboard', data: [...], timestamp: ... } + if (Array.isArray(e.data)) { + setLeaderboard(e.data); + console.log('✅ Leaderboard updated:', e.data.length, 'agents'); + } else if (Array.isArray(e.rows)) { + setLeaderboard(e.rows); + } else if (Array.isArray(e.leaderboard)) { + setLeaderboard(e.leaderboard); + } + }, + + // 虚拟时间更新（Mock模式下的时间广播） + time_update: (e) => { + if (e.beijing_time_str) { + const statusEmoji = { + 'market_open': '📊', + 'off_market': '⏸️', + 'non_trading_day': '📅', + 'trade_execution': '💼' + }; + + const emoji = statusEmoji[e.status] || '⏰'; + const isMockMode = e.is_mock_mode === true; + let logMessage = `${emoji} ${isMockMode ? '虚拟时间' : '时间'}: ${e.beijing_time_str} | 状态: ${e.status}`; + + if (e.hours_to_open !== undefined) { + logMessage += ` | 距离开盘: ${e.hours_to_open}小时`; + } + if (e.hours_to_trade !== undefined) { + logMessage += ` | 距离交易: ${e.hours_to_trade}小时`; + } + if (e.trading_date) { + logMessage += ` | 交易日: ${e.trading_date}`; + } + + console.log(logMessage); + + // 只在Mock模式下保存虚拟时间（用于图表过滤和UI显示） + if (isMockMode && e.beijing_time) { + try { + const virtualTimeDate = new Date(e.beijing_time); + setVirtualTime(virtualTimeDate); + } catch (error) { + console.error('Error parsing virtual time:', error); + } + } else { + // 非Mock模式下清除virtualTime + setVirtualTime(null); + } + } + + // 更新市场状态（如果包含在time_update中） + if (e.market_status) { + setMarketStatus(e.market_status); + } + }, + + // 时间快进事件（Mock模式） + time_fast_forwarded: (e) => { + console.log(`⏩ 时间已快进 ${e.minutes} 分钟: ${e.old_time_str} → ${e.new_time_str}`); + + // 更新虚拟时间 + if (e.new_time) { + try { + const virtualTimeDate = new Date(e.new_time); + setVirtualTime(virtualTimeDate); + + // 添加到feed显示 + handlePushEvent({ + type: 'system', + content: `⏩ 时间快进 ${e.minutes} 分钟: ${e.old_time_str} → ${e.new_time_str}`, + timestamp: Date.now() + }); + } catch (error) { + console.error('Error parsing fast forwarded time:', error); + } + } + }, + + // 快进成功响应 + fast_forward_success: (e) => { + console.log(`✅ ${e.message}`); + }, + }; + + // Call handler or do nothing + try { + const handler = handlers[evt.type]; + if (handler) { + handler(evt); + } else { + console.log('[handleEvent] Unknown event type:', evt.type); + } + } catch (error) { + console.error('[handleEvent] Error handling event:', evt.type, error); + } + }; + + // Create and connect WebSocket client + const client = new ReadOnlyClient(handlePushEvent); + clientRef.current = client; + client.connect(); + setConnectionStatus('connecting'); + + return () => { + // Cleanup on unmount + if (clientRef.current) { + clientRef.current.disconnect(); + } + }; + }, []); // Empty dependency array - only run once on mount + + // Resizing handlers + const handleMouseDown = (e) => { + e.preventDefault(); + setIsResizing(true); + }; + + useEffect(() => { + if (!isResizing) return; + + const handleMouseMove = (e) => { + if (!containerRef.current) return; + const containerRect = containerRef.current.getBoundingClientRect(); + const newLeftWidth = ((e.clientX - containerRect.left) / containerRect.width) * 100; + + // Limit between 30% and 85% + if (newLeftWidth >= 30 && newLeftWidth <= 85) { + setLeftWidth(newLeftWidth); + } + }; + + const handleMouseUp = () => { + setIsResizing(false); + }; + + document.addEventListener('mousemove', handleMouseMove); + document.addEventListener('mouseup', handleMouseUp); + + return () => { + document.removeEventListener('mousemove', handleMouseMove); + document.removeEventListener('mouseup', handleMouseUp); + }; + }, [isResizing]); + + return ( +
+ + + {/* Header */} +
+
setShowAboutModal(true)} + evoTradersLinkStyle="default" + /> + +
+ {/* Mock Mode Indicator */} + {virtualTime && ( +
+ + + LIVE MOCK MODE + +
+ )} + + + {/* Clock Display (only in Mock mode) */} + {virtualTime && ( +
+
+ + VIRTUAL TIME + + + {now.toLocaleTimeString('en-US', { hour: '2-digit', minute: '2-digit', second: '2-digit', hour12: false })} + + + {now.toLocaleDateString('en-US', { month: 'short', day: 'numeric', year: 'numeric' })} + +
+ + {/* Fast Forward Button (only in Mock mode) */} + +
+ )} + + {/* Unified Status Indicator */} +
+ + + {isConnected ? (isUpdating ? 'SYNCING' : 'LIVE') : 'OFFLINE'} + + {marketStatus && ( + <> + · + + {marketStatus.status_text || (marketStatus.status === 'open' ? 'OPEN' : 'CLOSED')} + + + )} + · + {lastUpdate.toLocaleTimeString('en-US', { hour: '2-digit', minute: '2-digit', second: '2-digit', hour12: false })} +
+
+
+ + {/* Main Content */} + <> + {/* Ticker Bar */} +
+
+ {[0, 1].map((groupIdx) => ( +
+ {tickers.map(ticker => ( +
+ + {ticker.symbol} + + + {ticker.price !== null && ticker.price !== undefined + ? `$${formatTickerPrice(ticker.price)}` + : '-'} + + + = 0 ? 'positive' : 'negative' + }`}> + {ticker.change !== null && ticker.change !== undefined + ? `${ticker.change >= 0 ? '+' : ''}${ticker.change.toFixed(2)}%` + : '-'} + +
+ ))} +
+ ))} +
+
+ PORTFOLIO + ${formatNumber(portfolioData.netValue)} +
+
+ +
+ {/* Left Panel: Three-View Toggle (Room/Chart/Statistics) */} +
+
+
+
+ + + + + + + +
+ + {/* Slider container with four views */} +
+ {/* Rules View Panel */} +
+ +
+ + {/* Room View Panel */} +
+ +
+ + {/* Chart View Panel */} +
+
+ {/* Floating Timeframe Tabs */} +
+ + {/* */} +
+ + +
+
+ + {/* Statistics View Panel */} +
+ +
+
+
+
+
+ + {/* Resizer */} +
+ + {/* Right Panel: Agent Feed */} +
+ +
+
+ + + {/* About Modal */} + {showAboutModal && setShowAboutModal(false)} />} +
+ ); +} diff --git a/evotraders/frontend/src/components/AboutModal.jsx b/evotraders/frontend/src/components/AboutModal.jsx new file mode 100644 index 0000000..c192708 --- /dev/null +++ b/evotraders/frontend/src/components/AboutModal.jsx @@ -0,0 +1,358 @@ +import React, { useState } from 'react'; +import Header from './Header.jsx'; + +export default function AboutModal({ onClose }) { + const [isClosing, setIsClosing] = useState(false); + const [language, setLanguage] = useState('en'); // 'en' or 'zh' + + const handleClose = () => { + setIsClosing(true); + // Wait for animation to complete before actually closing + setTimeout(() => { + onClose(); + }, 600); // Match animation duration + }; + + const overlayStyle = { + position: 'fixed', + top: 0, + left: 0, + right: 0, + bottom: 0, + background: '#ffffff', + zIndex: 9999, + animation: isClosing + ? 'collapseUp 0.6s cubic-bezier(0.4, 0, 0.2, 1) forwards' + : 'expandDown 0.6s cubic-bezier(0.4, 0, 0.2, 1)', + transformOrigin: 'top center', + overflowY: 'auto' + }; + + const contentStyle = { + maxWidth: '900px', + width: '90%', + margin: '0 auto', + textAlign: 'left', + fontFamily: "'IBM Plex Mono', monospace", + color: '#000000', + lineHeight: 1.8, + fontSize: '14px', + letterSpacing: '0.01em', + padding: '60px 20px 80px', + animation: isClosing + ? 'fadeOutContent 0.4s ease forwards' + : 'fadeInContent 0.8s ease 0.3s backwards' + }; + + const highlight = { + color: '#615CED', + fontWeight: 600 + }; + + const linkStyle = { + color: '#615CED', + textDecoration: 'none', + borderBottom: '1px solid #615CED', + transition: 'all 0.2s' + }; + + const closeHintStyle = { + marginTop: '50px', + fontSize: '11px', + color: '#999', + cursor: 'pointer', + textAlign: 'center' + }; + + const languageSwitchStyle = { + display: 'flex', + justifyContent: 'center', + alignItems: 'center', + marginBottom: '25px', + marginTop: '10px', + gap: '0px', + fontSize: '11px', + fontFamily: "'IBM Plex Mono', monospace" + }; + + const getLangStyle = (isActive) => ({ + padding: '3px 8px', + cursor: 'pointer', + transition: 'all 0.2s', + background: isActive ? '#000' : '#fff', + color: isActive ? '#fff' : '#000', + border: 'none' + }); + + const content = { + en: { + + question: "What happens if AI models don't compete with each other, but instead trade like a ", + questionHighlight: "well-coordinated, high-performance team", + questionEnd: "?", + + intro: "Not arena, but TEAM. We Hope that AI is no longer entering the financial markets as isolated models—it is stepping in as ", + introHighlight1: "teams", + introContinue: ", collaborating in one of the most challenging and noise-filled ", + introHighlight2: "real-time environments", + introContinue2: ".", + + + point1Highlight: "✦ Complementary skills", + point1: " - across multiple agents—data analysis, strategy generation, risk management—working together like a real trading desk, exchanging information through notifications and meetings.", + + point2Highlight: "✦ An agent system that continually evolves", + point2: " — with memory modules that retain experience, learn from market feedback, reflect, and develop their own methodology over time.", + + point3Highlight: "✦ AI teams interacting with live markets", + point3: " — learning from real-time data and making immediate decisions, not just theoretical simulations." + }, + zh: { + intro: "如果不是让模型彼此竞争，而是像一支高效协作的团队一样进行实时交易，会发生什么？", + question: "这里不是竞技场，而是团队。我们希望Agents不再单打独斗，而是「组团」进入实时金融市场——这一十分困难且充满噪声的环境。", + + title1: "✦ 多智能体的技能互补", + point1: "不同模型、不同角色的智能体像真实的金融团队一样协作，各自承担数据分析、策略生成、风险控制等职责。", + + title2: "✦ 能够持续进化的智能体系统", + point2: "依托「记忆」模块，每个智能体都能跨回合保留经验，不断学习、反思与调整。我们希望能看到在长期实时交易中，Agent形成自己的独特方法论，而不是一次性偶然的推理。", + + title3: "✦ 实时参与市场的 AI Agents", + point3: "Agents从实时行情中学习，并给予即时决策；不是纸上谈兵，而是面对市场的真实波动。" + } + }; + + return ( + <> + + +
+ {/* Header */} +
e.stopPropagation()}> +
+
+ + {/* Content */} +
e.stopPropagation()}> + {/* Language Switch */} +
+ setLanguage('zh')} + > + 中文 + + ｜ + setLanguage('en')} + > + EN + +
+ + {language === 'en' ? ( + // English Content + <> + +
+ {content.en.question} + {content.en.questionHighlight} + {content.en.questionEnd} +
+ +
+ {content.en.intro} + {content.en.introHighlight1} + {content.en.introContinue} + {content.en.introHighlight2} + {content.en.introContinue2} +
+ +
+ {content.en.point1Highlight} + {content.en.point1} +
+ +
+ {content.en.point2Highlight} + {content.en.point2} +
+ +
+ {content.en.point3Highlight} + {content.en.point3} +
+ + + + ) : ( + // Chinese Content + <> +
+ {content.zh.intro} +
+ +
+ {content.zh.question} +
+ +
+ {content.zh.trying} +
+ +
+
+ {content.zh.title1} +
+
+ {content.zh.point1} +
+
+ {content.zh.point1Sub} +
+
+ +
+
+ {content.zh.title2} +
+
+ {content.zh.point2} +
+
+ {content.zh.point2Sub} +
+
+ +
+
+ {content.zh.title3} +
+
+ {content.zh.point3} +
+
+ +
+ 我们已经在github上开源。 +
+
+ EvoTraders 基于{' '} + + AgentScope + + {' '}搭建，并使用其中的{' '} + + ReMe + + {' '}作为记忆管理核心。 +
+ +
+ 你可以在此找到完整项目与示例： +
+ + )} + + + +
+ {language === 'en' ? 'Click here to close' : '点击此处关闭'} +
+
+
+ + ); +} + diff --git a/evotraders/frontend/src/components/AgentCard.jsx b/evotraders/frontend/src/components/AgentCard.jsx new file mode 100644 index 0000000..cbe3c69 --- /dev/null +++ b/evotraders/frontend/src/components/AgentCard.jsx @@ -0,0 +1,517 @@ +import React from 'react'; +import { ASSETS } from '../config/constants'; +import { getModelIcon, getShortModelName } from '../utils/modelIcons'; + +/** + * Get rank medal/trophy + */ +function getRankMedal(rank) { + if (rank === 1) return { emoji: '🏆', color: '#FFD700', label: 'Gold' }; + if (rank === 2) return { emoji: '🥈', color: '#C0C0C0', label: 'Silver' }; + if (rank === 3) return { emoji: '🥉', color: '#CD7F32', label: 'Bronze' }; + return { emoji: `#${rank}`, color: '#333333', label: `#${rank}` }; +} + +/** + * Agent Performance Card Component + * Horizontal dropdown panel displayed below the agent indicator bar + */ +export default function AgentCard({ agent, onClose, isClosing }) { + if (!agent) return null; + + const bullTotal = agent.bull?.n || 0; + const bullWins = agent.bull?.win || 0; + const bullUnknown = agent.bull?.unknown || 0; + const bearTotal = agent.bear?.n || 0; + const bearWins = agent.bear?.win || 0; + const bearUnknown = agent.bear?.unknown || 0; + const totalSignals = bullTotal + bearTotal; + const evaluatedBull = Math.max(bullTotal - bullUnknown, 0); + const evaluatedBear = Math.max(bearTotal - bearUnknown, 0); + const evaluatedTotal = evaluatedBull + evaluatedBear; + const bullWinRate = evaluatedBull > 0 ? (bullWins / evaluatedBull) : null; + const bearWinRate = evaluatedBear > 0 ? (bearWins / evaluatedBear) : null; + const overallWinRate = agent.winRate != null + ? agent.winRate + : (evaluatedTotal > 0 ? ((bullWins + bearWins) / evaluatedTotal) : null); + const overallColor = overallWinRate != null + ? (overallWinRate >= 0.5 ? '#00C853' : '#FF1744') + : '#555555'; + + const rankMedal = agent.rank ? getRankMedal(agent.rank) : null; + const isPortfolioManager = agent.id === 'portfolio_manager'; + const isRiskManager = agent.id === 'risk_manager'; + const displayName = isPortfolioManager ? 'Team' : agent.name; + + // Get model icon configuration + const modelInfo = getModelIcon(agent.modelName, agent.modelProvider); + const shortModelName = getShortModelName(agent.modelName); + + return ( +
+ {/* Horizontal scrollable content */} +
+ + +
+ {/* Agent Info with Rank */} +
+ {isPortfolioManager ? ( + Team + ) : agent.avatar ? ( + {agent.name} + ) : null} +
+
+ {displayName} +
+ {rankMedal && !isPortfolioManager && ( +
+ {rankMedal.emoji} Rank #{agent.rank} +
+ )} +
+
+ + {/* Risk Manager Note */} + {isRiskManager && ( +
+
+ ⓘ Risk Manager focuses on risk management and does not participate in prediction accuracy ranking. +
+
+ )} + + {/* Portfolio Manager Note */} + {isPortfolioManager && ( +
+
+ ⓘ Portfolio Manager provides the team's final signal(position), synthesizing all analyst recommendations, and does not participate in ranking. +
+
+ )} + + {/* Model Info Card */} + {agent.modelName && ( +
+
+ Model +
+
+ {modelInfo.logoPath ? ( + {modelInfo.provider} + ) : ( +
+ 🤖 +
+ )} +
+
+ {shortModelName} +
+
+ {modelInfo.provider} +
+
+ )} + + {/* Overall Win Rate */} + {!isRiskManager && !isPortfolioManager && ( +
+
+ Win Rate +
+
+ {overallWinRate != null ? `${(overallWinRate * 100).toFixed(1)}%` : 'N/A'} +
+
+ {bullWins + bearWins}Win / {evaluatedTotal}Eval +
+
+ Eval: total evaluated bull & bear signals.{'\n'}Win Rate = correct signals / total evaluated signals +
+
+ )} + + {/* Bull Stats */} + {!isRiskManager && !isPortfolioManager && ( +
+
+ Bull Win Rate +
+
= 0.5 ? '#00C853' : '#333333') : '#555555', + marginBottom: 2, + lineHeight: 1 + }}> + {bullWinRate != null ? `${(bullWinRate * 100).toFixed(1)}%` : 'N/A'} +
+
+ {bullWins}Win / {evaluatedBull}Eval + {bullUnknown > 0 && ` / ${bullUnknown}P`} +
+
+ )} + + {/* Bear Stats */} + {!isRiskManager && !isPortfolioManager && ( +
+
+ Bear Win Rate +
+
= 0.5 ? '#00C853' : '#333333') : '#555555', + marginBottom: 2, + lineHeight: 1 + }}> + {bearWinRate != null ? `${(bearWinRate * 100).toFixed(1)}%` : 'N/A'} +
+
+ {bearWins}Win / {evaluatedBear}Eval + {bearUnknown > 0 && ` / ${bearUnknown}P`} +
+
+ )} + + {/* Recent Signals - Horizontal scroll */} + {agent.signals && agent.signals.length > 0 && ( +
+ {[...agent.signals] + .filter(signal => signal && signal.signal) + .sort((a, b) => { + // Sort by date descending (newest first) + const dateA = a.date || ''; + const dateB = b.date || ''; + return dateB.localeCompare(dateA); + }) + .slice(0, 35) + .map((signal, idx) => { + const signalType = signal.signal.toLowerCase(); + const isBull = signalType.includes('bull') || signalType === 'long'; + const isBear = signalType.includes('bear') || signalType === 'short'; + const isNeutral = (!isBull && !isBear) || signalType.includes('neutral') || signalType === 'hold'; + const isCorrect = signal.is_correct === true; + const isUnknown = signal.is_correct === 'unknown' || signal.is_correct === null; + + // Determine result symbol/text and color: unknown has priority over neutral + let resultDisplay; + let resultColor = '#555555'; + let resultFontSize = 18; + + if (isUnknown) { + resultDisplay = '?'; + resultColor = '#FFA726'; // Orange for unknown + resultFontSize = 14; // Smaller font for text + } else if (isNeutral) { + resultDisplay = '-'; + resultColor = '#555555'; // Gray for neutral + } else { + resultDisplay = isCorrect ? '✓' : '✗'; + resultColor = isCorrect ? '#00C853' : '#FF1744'; // Green for correct, Red for wrong + } + + return ( +
+
+ {signal.ticker} +
+
+ {isBull ? 'bull' : isBear ? 'bear' : 'neutral'} +
+
+ {signal.date?.substring(5, 10) || 'N/A'} +
+
+ {resultDisplay} +
+
+ ); + })} + {/* Info card explaining signal display */} +
+
+ ⓘ Info +
+
+ Showing recent 5 trading days (1 week) signals only +
+
+
+ )} +
+
+ + +
+ ); +} + diff --git a/evotraders/frontend/src/components/AgentFeed.jsx b/evotraders/frontend/src/components/AgentFeed.jsx new file mode 100644 index 0000000..c2aed02 --- /dev/null +++ b/evotraders/frontend/src/components/AgentFeed.jsx @@ -0,0 +1,641 @@ +import React, { useState, useRef, useImperativeHandle, forwardRef } from 'react'; +import { formatTime } from '../utils/formatters'; +import { MESSAGE_COLORS, getAgentColors, AGENTS, ASSETS } from '../config/constants'; +import { getModelIcon } from '../utils/modelIcons'; +import MarkdownModal from './MarkdownModal'; + +const isAnalyst = (agentId, agentName) => { + if (agentId && agentId.includes('analyst')) return true; + if (agentName && agentName.toLowerCase().includes('analyst')) return true; + return false; +}; + +const isManager = (agentId, agentName) => { + if (agentId && agentId.includes('manager')) return true; + if (agentName && agentName.toLowerCase().includes('manager')) return true; + return false; +}; + +const stripMarkdown = (text) => { + return text + .replace(/[\s\S]*?<\/think>/gi, '') + .replace(/#{1,6}\s+/g, '') + .replace(/\*\*\*(.+?)\*\*\*/g, '$1') + .replace(/\*\*(.+?)\*\*/g, '$1') + .replace(/__(.+?)__/g, '$1') + .replace(/\*(.+?)\*/g, '$1') + .replace(/_(.+?)_/g, '$1') + .replace(/`(.+?)`/g, '$1') + .replace(/\[(.+?)\]\(.+?\)/g, '$1') + .replace(/!\[.*?\]\(.+?\)/g, '') + .replace(/^\s*[-*+]\s+/gm, '') + .replace(/^\s*\d+\.\s+/gm, '') + .replace(/^\s*>\s+/gm, '') + .replace(/\|/g, ' ') + .replace(/^[-=]+$/gm, ''); +}; + +const AgentFeed = forwardRef(({ feed, leaderboard }, ref) => { + const feedContentRef = useRef(null); + const [highlightedId, setHighlightedId] = useState(null); + const [selectedAgent, setSelectedAgent] = useState('all'); + const [dropdownOpen, setDropdownOpen] = useState(false); + + const getAgentModelInfo = (agentId) => { + if (!leaderboard || !agentId) return { modelName: null, modelProvider: null }; + const agentData = leaderboard.find(lb => lb.id === agentId || lb.agentId === agentId); + return { + modelName: agentData?.modelName, + modelProvider: agentData?.modelProvider + }; + }; + + // Get agent info by name + const getAgentInfoByName = (agentName) => { + if (!leaderboard || !agentName) return null; + const agentData = leaderboard.find(lb => lb.name === agentName || lb.agentName === agentName); + if (!agentData) return null; + return { + agentId: agentData.id || agentData.agentId, + modelName: agentData.modelName, + modelProvider: agentData.modelProvider + }; + }; + + // Get unique agent names from feed (only registered agents in AGENTS) + const getUniqueAgents = () => { + const agentNamesInFeed = new Set(); + + // Collect all agent names that appear in the feed + feed.forEach(item => { + if (item.type === 'message' && item.data?.agent) { + agentNamesInFeed.add(item.data.agent); + } else if (item.type === 'conference' && item.data?.messages) { + item.data.messages.forEach(msg => { + if (msg.agent) { + agentNamesInFeed.add(msg.agent); + } + }); + } + }); + + // Filter to only include registered agents and sort by AGENTS array order + const registeredAgentNames = AGENTS.map(a => a.name); + return registeredAgentNames.filter(name => agentNamesInFeed.has(name)); + }; + + // Filter feed based on selected agent + const filteredFeed = selectedAgent === 'all' + ? feed + : feed.filter(item => { + if (item.type === 'message') { + return item.data?.agent === selectedAgent; + } else if (item.type === 'conference') { + return item.data?.messages?.some(msg => msg.agent === selectedAgent); + } + return false; + }); + + useImperativeHandle(ref, () => ({ + scrollToMessage: (bubble) => { + if (!bubble || !feedContentRef.current) return; + + // Direct feedItemId match (used by replay mode) + if (bubble.feedItemId) { + const element = document.getElementById(`feed-item-${bubble.feedItemId}`); + if (element) { + element.scrollIntoView({ behavior: 'smooth', block: 'center' }); + setHighlightedId(bubble.feedItemId); + setTimeout(() => setHighlightedId(null), 2000); + return; + } + } + + const bubbleTimestamp = bubble.ts || bubble.timestamp; + + // Check if a message matches the bubble + const isMatch = (msg, checkTime = true) => { + const agentMatch = msg.agentId === bubble.agentId || msg.agent === bubble.agentName; + if (!agentMatch || !checkTime) return agentMatch; + return Math.abs(msg.timestamp - bubbleTimestamp) < 5000; + }; + + // Check if a feed item contains the target message + const itemContains = (item, checkTime = true) => { + if (item.type === 'message' && item.data) return isMatch(item.data, checkTime); + if (item.type === 'conference' && item.data?.messages) { + return item.data.messages.some(msg => isMatch(msg, checkTime)); + } + return false; + }; + + // Find exact match first, then fallback to agent match + const targetItem = feed.find(item => itemContains(item, true)) + || feed.find(item => itemContains(item, false)); + + if (targetItem) { + const element = document.getElementById(`feed-item-${targetItem.id}`); + if (element) { + element.scrollIntoView({ behavior: 'smooth', block: 'center' }); + setHighlightedId(targetItem.id); + setTimeout(() => setHighlightedId(null), 2000); + } + } + } + }), [feed]); + + const uniqueAgents = getUniqueAgents(); + + // Get current selection display info + const getCurrentSelectionInfo = () => { + if (selectedAgent === 'all') { + return { label: 'All Agents', modelInfo: null }; + } + const agentInfo = getAgentInfoByName(selectedAgent); + const modelInfo = agentInfo ? getModelIcon(agentInfo.modelName, agentInfo.modelProvider) : null; + return { label: selectedAgent, modelInfo }; + }; + + const currentSelection = getCurrentSelectionInfo(); + + return ( +
+
+

ACTIVITY FEED

+
+ +
+ + {dropdownOpen && ( +
+
{ + setSelectedAgent('all'); + setDropdownOpen(false); + }} + > + All Agents +
+ {uniqueAgents.map(agent => { + const agentInfo = getAgentInfoByName(agent); + const modelInfo = agentInfo ? getModelIcon(agentInfo.modelName, agentInfo.modelProvider) : null; + return ( +
{ + setSelectedAgent(agent); + setDropdownOpen(false); + }} + > + {modelInfo?.logoPath && ( + {modelInfo.provider} + )} + {agent} +
+ ); + })} +
+ )} +
+
+
+
+ {filteredFeed.length === 0 && ( +
+ {selectedAgent === 'all' + ? 'Waiting for system updates...' + : `No messages from ${selectedAgent}`} +
+ )} + + {filteredFeed.map(item => { + const isHighlighted = item.id === highlightedId; + if (item.type === 'conference') { + return ; + } else if (item.type === 'memory') { + return ; + } else if (item.data?.agent === 'System') { + return ; + } else { + return ; + } + })} +
+
+ ); +}); + +AgentFeed.displayName = 'AgentFeed'; + +export default AgentFeed; + +function SystemDivider({ message, itemId }) { + const content = String(message.content || '').substring(0, 100); + + return ( +
+
+ + {content} + +
+
+ ); +} + +function ConferenceItem({ conference, itemId, isHighlighted, getAgentModelInfo }) { + const colors = MESSAGE_COLORS.conference; + + return ( +
+
+ + CONFERENCE + + {conference.isLive && ● LIVE} + {formatTime(conference.startTime)} +
+ +
+ {conference.title} +
+ +
+ {conference.messages.map((msg, idx) => ( + + ))} +
+
+ ); +} + +function ConferenceMessage({ message, getAgentModelInfo }) { + const [expanded, setExpanded] = useState(false); + + const agentColors = message.agent === 'System' ? MESSAGE_COLORS.system : + message.agent === 'Memory' ? MESSAGE_COLORS.memory : + getAgentColors(message.agentId, message.agent); + + const agentModelData = message.agentId && getAgentModelInfo ? + getAgentModelInfo(message.agentId) : + { modelName: null, modelProvider: null }; + const modelInfo = getModelIcon(agentModelData.modelName, agentModelData.modelProvider); + + let content = message.content || ''; + if (typeof content === 'object') { + content = JSON.stringify(content, null, 2); + } else { + content = String(content); + } + + const needsTruncation = content.length > 200; + const MAX_EXPANDED_LENGTH = 10000; + + let displayContent = content; + if (!expanded && needsTruncation) { + displayContent = content.substring(0, 200) + '...'; + } else if (expanded && content.length > MAX_EXPANDED_LENGTH) { + displayContent = content.substring(0, MAX_EXPANDED_LENGTH) + '...'; + } + + return ( +
+
+ {modelInfo.logoPath && ( + {modelInfo.provider} + )} + {message.agent} +
+
+ {stripMarkdown(displayContent)} + {needsTruncation && ( + + )} +
+
+ ); +} + +function MemoryItem({ memory, itemId, isHighlighted }) { + const [expanded, setExpanded] = useState(false); + const [showTooltip, setShowTooltip] = useState(false); + const colors = MESSAGE_COLORS.memory; + + let content = memory.content || ''; + if (typeof content === 'object') { + content = JSON.stringify(content, null, 2); + } else { + content = String(content); + } + + const needsTruncation = content.length > 200; + const MAX_EXPANDED_LENGTH = 10000; + + let displayContent = content; + if (!expanded && needsTruncation) { + displayContent = content.substring(0, 200) + '...'; + } else if (expanded && content.length > MAX_EXPANDED_LENGTH) { + displayContent = content.substring(0, MAX_EXPANDED_LENGTH) + '...'; + } + + const agentLabel = memory.agent && memory.agent !== 'Memory' + ? `MEMORY · ${memory.agent}` + : 'MEMORY'; + + return ( +
+
+ +
setShowTooltip(true)} + onMouseLeave={() => setShowTooltip(false)} + > + + ReMe + + ↗ + + +
+ + {agentLabel} + + + {formatTime(memory.timestamp)} +
+ +
+
+ Memory powered by AgentScope-ReMe +
+
+ Not only retrieves historical memories but also generates suggestions and hints for the current task based on latest context. +
+
+ +
{stripMarkdown(displayContent)}
+ + {needsTruncation && ( + + )} +
+ ); +} + +function MessageItem({ message, itemId, isHighlighted, getAgentModelInfo }) { + const [expanded, setExpanded] = useState(false); + const [showModal, setShowModal] = useState(false); + const [isHovering, setIsHovering] = useState(false); + + const colors = message.agent === 'Memory' ? MESSAGE_COLORS.memory : + getAgentColors(message.agentId, message.agent); + const title = message.agent === 'Memory' ? 'MEMORY' : message.agent || 'AGENT'; + + const agentModelData = message.agentId && getAgentModelInfo ? + getAgentModelInfo(message.agentId) : + { modelName: null, modelProvider: null }; + const modelInfo = getModelIcon(agentModelData.modelName, agentModelData.modelProvider); + + const isAnalystAgent = isAnalyst(message.agentId, message.agent); + const isManagerAgent = isManager(message.agentId, message.agent); + const useModalView = isAnalystAgent || isManagerAgent; + + let content = message.content || ''; + if (typeof content === 'object') { + content = JSON.stringify(content, null, 2); + } else { + content = String(content); + } + + let displayContent = content; + let showExpandButton = false; + + if (useModalView) { + displayContent = content.length > 150 ? content.substring(0, 150) + '...' : content; + } else { + const needsTruncation = content.length > 200; + const MAX_EXPANDED_LENGTH = 8000; + + if (!expanded && needsTruncation) { + displayContent = content.substring(0, 200) + '...'; + showExpandButton = true; + } else if (expanded && content.length > MAX_EXPANDED_LENGTH) { + displayContent = content.substring(0, MAX_EXPANDED_LENGTH) + '...'; + showExpandButton = needsTruncation; + } else { + showExpandButton = needsTruncation; + } + } + + return ( + <> +
+
+ + {modelInfo.logoPath && message.agent !== 'Memory' && ( + {modelInfo.provider} + )} + {title} + + {formatTime(message.timestamp)} +
+ +
{stripMarkdown(displayContent)}
+ + {useModalView && ( + + )} + + {showExpandButton && ( + + )} +
+ {useModalView && ( + setShowModal(false)} + content={content} + agentName={message.agent} + reportType={isManagerAgent ? 'decision' : 'analysis'} + /> + )} + + ); +} diff --git a/evotraders/frontend/src/components/Header.jsx b/evotraders/frontend/src/components/Header.jsx new file mode 100644 index 0000000..7afb5e9 --- /dev/null +++ b/evotraders/frontend/src/components/Header.jsx @@ -0,0 +1,253 @@ +import React, { useState } from 'react'; + +/** + * Header Component + * Reusable header brand with EvoTraders logo, GitHub link, and Contact Us section + * + * @param {Function} onEvoTradersClick - Optional callback when EvoTraders is clicked + * @param {string} evoTradersLinkStyle - Optional style variant: 'default' | 'close' + */ +export default function Header({ + onEvoTradersClick = null, + evoTradersLinkStyle = 'default' // 'default' shows ↗, 'close' shows ↙ +}) { + const [activeContactCard, setActiveContactCard] = useState({ yue: false, jiaji: false }); + const [clickedContactCard, setClickedContactCard] = useState(null); + + const handleEvoTradersClick = () => { + if (onEvoTradersClick) { + onEvoTradersClick(); + } + }; + + return ( +
+ + EvoTraders Logo + EvoTraders {evoTradersLinkStyle === 'close' ? ( + ↙ + ) : ( + ↗ + )} + + + + + + OPEN SOURCE + + + + + + + agentscope-samples + ↗ + + + + + + + agentscope-ReMe + ↗ + + + + +
{ + const bothActive = activeContactCard.yue && activeContactCard.jiaji; + if (!bothActive) { + setActiveContactCard({ yue: true, jiaji: true }); + setClickedContactCard('both'); + } else { + setActiveContactCard({ yue: false, jiaji: false }); + setClickedContactCard(null); + } + }} + > + + Contact Us + + + {/* Two contact buttons */} +
+
{ + e.stopPropagation(); + if (activeContactCard.yue) { + setActiveContactCard(prev => ({ ...prev, yue: false })); + if (clickedContactCard === 'yue' || clickedContactCard === 'both') { + setClickedContactCard(null); + } + } else { + setActiveContactCard(prev => ({ ...prev, yue: true })); + setClickedContactCard('yue'); + } + }} + onMouseEnter={() => { + if (!clickedContactCard || clickedContactCard === 'yue' || clickedContactCard === 'both') { + setActiveContactCard(prev => ({ ...prev, yue: true })); + } + }} + onMouseLeave={() => { + if (clickedContactCard !== 'yue' && clickedContactCard !== 'both') { + setActiveContactCard(prev => ({ ...prev, yue: false })); + } + }} + style={{ + padding: '4px 8px', + background: activeContactCard.yue ? '#615CED' : '#f5f5f5', + color: activeContactCard.yue ? '#fff' : '#333', + border: '1px solid', + borderColor: activeContactCard.yue ? '#615CED' : '#e0e0e0', + borderRadius: '3px', + fontSize: '10px', + fontWeight: 700, + fontFamily: "'IBM Plex Mono', monospace", + cursor: 'pointer', + transition: 'all 0.2s', + letterSpacing: '0.5px', + whiteSpace: 'nowrap', + overflow: 'hidden', + maxWidth: activeContactCard.yue ? '80px' : '32px', + minWidth: activeContactCard.yue ? '80px' : '32px' + }} + > + {activeContactCard.yue ? ( + e.stopPropagation()} + > + Yue Wu ↗ + + ) : 'YW'} +
+ +
{ + e.stopPropagation(); + if (activeContactCard.jiaji) { + setActiveContactCard(prev => ({ ...prev, jiaji: false })); + if (clickedContactCard === 'jiaji' || clickedContactCard === 'both') { + setClickedContactCard(null); + } + } else { + setActiveContactCard(prev => ({ ...prev, jiaji: true })); + setClickedContactCard('jiaji'); + } + }} + onMouseEnter={() => { + if (!clickedContactCard || clickedContactCard === 'jiaji' || clickedContactCard === 'both') { + setActiveContactCard(prev => ({ ...prev, jiaji: true })); + } + }} + onMouseLeave={() => { + if (clickedContactCard !== 'jiaji' && clickedContactCard !== 'both') { + setActiveContactCard(prev => ({ ...prev, jiaji: false })); + } + }} + style={{ + padding: '4px 8px', + background: activeContactCard.jiaji ? '#615CED' : '#f5f5f5', + color: activeContactCard.jiaji ? '#fff' : '#333', + border: '1px solid', + borderColor: activeContactCard.jiaji ? '#615CED' : '#e0e0e0', + borderRadius: '3px', + fontSize: '10px', + fontWeight: 700, + fontFamily: "'IBM Plex Mono', monospace", + cursor: 'pointer', + transition: 'all 0.2s', + letterSpacing: '0.5px', + whiteSpace: 'nowrap', + overflow: 'hidden', + maxWidth: activeContactCard.jiaji ? '100px' : '32px', + minWidth: activeContactCard.jiaji ? '100px' : '32px' + }} + > + {activeContactCard.jiaji ? ( + e.stopPropagation()} + > + Jiaji Deng ↗ + + ) : 'JD'} +
+
+
+
+ ); +} + diff --git a/evotraders/frontend/src/components/MarkdownModal.jsx b/evotraders/frontend/src/components/MarkdownModal.jsx new file mode 100644 index 0000000..0e7015d --- /dev/null +++ b/evotraders/frontend/src/components/MarkdownModal.jsx @@ -0,0 +1,276 @@ +import React from 'react'; +import ReactMarkdown from 'react-markdown'; +import remarkGfm from 'remark-gfm'; + +function MarkdownModal({ isOpen, onClose, content, agentName, reportType = 'analysis' }) { + if (!isOpen) return null; + + const subtitle = reportType === 'decision' ? 'Decision Log' : 'Financial Analysis Report'; + + return ( +
+
e.stopPropagation()} + > + {/* Header */} +
+
+

+ {agentName} +

+

+ {subtitle} +

+
+ +
+ + {/* Content */} +
+ +
+ {content} +
+
+
+
+ ); +} + +export default MarkdownModal; + diff --git a/evotraders/frontend/src/components/NetValueChart.jsx b/evotraders/frontend/src/components/NetValueChart.jsx new file mode 100644 index 0000000..35bde68 --- /dev/null +++ b/evotraders/frontend/src/components/NetValueChart.jsx @@ -0,0 +1,831 @@ +import React, { useMemo, useState, useEffect } from 'react'; +import { LineChart, Line, XAxis, YAxis, CartesianGrid, Tooltip, ResponsiveContainer, Legend } from 'recharts'; +import { formatNumber, formatFullNumber } from '../utils/formatters'; + +/** + * Helper function to get the start time of the most recent trading session + * Trading session: 22:30 - next day 05:00 + * @param {Date|null} virtualTime - Virtual time from server (for mock mode), or null to use real time + */ +function getRecentTradingSessionStart(virtualTime = null) { + // Use virtual time if provided (for mock mode), otherwise use real time + let now; + if (virtualTime) { + // Ensure virtualTime is a valid Date object + if (virtualTime instanceof Date && !isNaN(virtualTime.getTime())) { + now = virtualTime; + } else if (typeof virtualTime === 'string') { + now = new Date(virtualTime); + if (isNaN(now.getTime())) { + console.warn('Invalid virtualTime string, using current time:', virtualTime); + now = new Date(); + } + } else { + console.warn('Invalid virtualTime type, using current time:', typeof virtualTime); + now = new Date(); + } + } else { + now = new Date(); + } + + const currentHour = now.getHours(); + const currentMinute = now.getMinutes(); + + // Check if currently in trading session + const isInTradingSession = (currentHour === 22 && currentMinute >= 30) || + currentHour >= 23 || + (currentHour >= 0 && currentHour < 5) || + (currentHour === 5 && currentMinute === 0); + + let sessionStartTime; + if (isInTradingSession) { + // Currently in trading session, find today's 22:30 + sessionStartTime = new Date(now); + sessionStartTime.setHours(22, 30, 0, 0); + // If current time is before 22:30, it means yesterday's 22:30 + if (now < sessionStartTime) { + sessionStartTime.setDate(sessionStartTime.getDate() - 1); + } + } else { + // Not in trading session, find previous session start (yesterday 22:30) + sessionStartTime = new Date(now); + sessionStartTime.setDate(sessionStartTime.getDate() - 1); + sessionStartTime.setHours(22, 30, 0, 0); + } + + return sessionStartTime; +} + +/** + * Helper function to filter strategy data for live view + * NOTE: Live mode returns are now pre-processed by the backend, restricted to the + * latest trading session and already starting at 0% at session start. This helper + * is kept for potential future use but is no longer used in live mode. + */ +function filterStrategyDataForLive(strategyData, equity, sessionStartTime) { + if (!strategyData || strategyData.length === 0 || !equity || equity.length === 0) return []; + + try { + if (!sessionStartTime || isNaN(sessionStartTime.getTime())) { + console.warn('Invalid sessionStartTime in filterStrategyDataForLive'); + return []; + } + + const sessionStartTimestamp = sessionStartTime.getTime(); + + // Find the last index before session + let lastDataBeforeSession = null; + for (let i = equity.length - 1; i >= 0; i--) { + if (equity[i] && typeof equity[i].t === 'number' && equity[i].t < sessionStartTimestamp) { + if (strategyData[i] && strategyData[i].v !== undefined && strategyData[i].v !== null) { + lastDataBeforeSession = strategyData[i]; + } + break; + } + } + + // Find data points in the session + const sessionData = []; + for (let i = 0; i < equity.length; i++) { + if (equity[i] && typeof equity[i].t === 'number' && + equity[i].t >= sessionStartTimestamp && + strategyData[i] && + strategyData[i].v !== undefined && strategyData[i].v !== null) { + sessionData.push(strategyData[i]); + } + } + + // If we have a value before session and session data, add the start point + // Create a start point with timestamp just before session start + if (lastDataBeforeSession && sessionData.length > 0) { + const startPoint = { + t: sessionStartTimestamp - 1, + v: lastDataBeforeSession.v + }; + return [startPoint, ...sessionData]; + } + + return sessionData; + } catch (error) { + console.error('Error in filterStrategyDataForLive:', error); + return []; + } +} + +/** + * Net Value Chart Component + * Displays portfolio value over time with multiple strategy comparisons + */ +export default function NetValueChart({ equity, baseline, baseline_vw, momentum, strategies, equity_return, baseline_return, baseline_vw_return, momentum_return, chartTab = 'all', virtualTime = null }) { + const [activePoint, setActivePoint] = useState(null); + const [stableYRange, setStableYRange] = useState(null); + const [legendTooltip, setLegendTooltip] = useState(null); + + // Legend descriptions + const legendDescriptions = { + 'EvoTraders': 'EvoTraders is our agents investment strategy', + 'Buy & Hold (EW)': 'Equal Weight: Can be viewed as an equal-weighted index of all invested stocks', + 'Buy & Hold (VW)': 'Value Weighted: Can be viewed as a market-cap weighted index of all invested stocks', + 'Momentum': 'Momentum Strategy: Buy stocks that have performed well in the past', + }; + + + // For live mode, use cumulative returns calculated by backend + // For all mode, use portfolio values directly + const dataSource = useMemo(() => { + if (chartTab === 'live') { + return { + equity: equity_return || equity, + baseline: baseline_return || baseline, + baseline_vw: baseline_vw_return || baseline_vw, + momentum: momentum_return || momentum + }; + } + return { + equity: equity, + baseline: baseline, + baseline_vw: baseline_vw, + momentum: momentum + }; + }, [chartTab, equity, baseline, baseline_vw, momentum, equity_return, baseline_return, baseline_vw_return, momentum_return]); + // Filter equity data based on chartTab + const filteredEquity = useMemo(() => { + if (chartTab === 'all') { + const sourceEquity = dataSource.equity; + if (!sourceEquity || sourceEquity.length === 0) return []; + + // ALL chart: Show only the last point per day + // Logic: Keep the last equity value before 22:30 each day (the last equity value before US next trading day opens) + // Data after 22:30 belongs to the next trading day's session and is not shown in this chart + // Time handling: timestamp(ms) -> UTC -> Asia/Shanghai timezone, then group and filter based on Asia/Shanghai time + const dailyData = {}; + + sourceEquity.forEach((d) => { + // Timestamp is in milliseconds, first create UTC time, then convert to Asia/Shanghai timezone + // Equivalent to: pd.to_datetime(timestamp, unit='ms', utc=True).dt.tz_convert('Asia/Shanghai') + const utcDate = new Date(d.t); // timestamp(ms) -> UTC time + + // Use Intl API to get date/time components in Asia/Shanghai timezone + const formatter = new Intl.DateTimeFormat('en-US', { + timeZone: 'Asia/Shanghai', + year: 'numeric', + month: '2-digit', + day: '2-digit', + hour: '2-digit', + minute: '2-digit', + hour12: false + }); + + const parts = formatter.formatToParts(utcDate); + const year = parts.find(p => p.type === 'year').value; + const month = parts.find(p => p.type === 'month').value; + const day = parts.find(p => p.type === 'day').value; + const hour = parseInt(parts.find(p => p.type === 'hour').value); + const minute = parseInt(parts.find(p => p.type === 'minute').value); + + // Check if before 22:30 (Asia/Shanghai timezone) + const isBefore2230 = hour < 22 || (hour === 22 && minute < 30); + + // Only process data before 22:30 + if (isBefore2230) { + // Use Asia/Shanghai timezone date as key + const dateKey = `${year}-${month}-${day}`; + + // Update if this day has no data yet, or if current data is later in time + if (!dailyData[dateKey] || new Date(d.t) > new Date(dailyData[dateKey].t)) { + dailyData[dateKey] = d; + } + } + }); + + // Convert to array and sort by time + return Object.values(dailyData).sort((a, b) => a.t - b.t); + } else if (chartTab === 'live') { + // LIVE chart: Show all updates from the most recent trading session (22:30-05:00) + // Live mode: Backend has already returned return curves for "current trading session + 0% starting point", frontend can use directly + const sourceEquity = dataSource.equity; + if (!sourceEquity || sourceEquity.length === 0) return []; + return sourceEquity; + } + return dataSource.equity || []; + }, [dataSource.equity, chartTab, virtualTime]); + // Helper function to get daily indices for 'all' view + const getDailyIndices = useMemo(() => { + if (!equity || equity.length === 0) return new Set(); + const dailyIndices = new Set(); + const dailyData = {}; + + const formatter = new Intl.DateTimeFormat('en-US', { + timeZone: 'Asia/Shanghai', + year: 'numeric', + month: '2-digit', + day: '2-digit', + hour: '2-digit', + minute: '2-digit', + hour12: false + }); + + equity.forEach((d, idx) => { + const utcDate = new Date(d.t); + const parts = formatter.formatToParts(utcDate); + const hour = parseInt(parts.find(p => p.type === 'hour').value); + const minute = parseInt(parts.find(p => p.type === 'minute').value); + + // Check if before 22:30 (Asia/Shanghai timezone) + const isBefore2230 = hour < 22 || (hour === 22 && minute < 30); + + // Only process data before 22:30 + if (isBefore2230) { + const year = parts.find(p => p.type === 'year').value; + const month = parts.find(p => p.type === 'month').value; + const day = parts.find(p => p.type === 'day').value; + const dateKey = `${year}-${month}-${day}`; + + if (!dailyData[dateKey] || new Date(d.t) > new Date(dailyData[dateKey].t)) { + dailyData[dateKey] = { data: d, index: idx }; + } + } + }); + + Object.values(dailyData).forEach(({ index }) => dailyIndices.add(index)); + return dailyIndices; + }, [equity]); + + // Filter baseline, baseline_vw, momentum, strategies to match filteredEquity indices + const filteredBaseline = useMemo(() => { + const sourceBaseline = dataSource.baseline; + if (!sourceBaseline || sourceBaseline.length === 0 || !equity || equity.length === 0) return []; + if (chartTab === 'all') { + return sourceBaseline.filter((_, idx) => getDailyIndices.has(idx)); + } else if (chartTab === 'live') { + // Live mode: Use backend pre-processed baseline return curves directly + return sourceBaseline; + } + return sourceBaseline; + }, [dataSource.baseline, equity, chartTab, getDailyIndices, virtualTime]); + const filteredBaselineVw = useMemo(() => { + const sourceBaselineVw = dataSource.baseline_vw; + if (!sourceBaselineVw || sourceBaselineVw.length === 0 || !equity || equity.length === 0) return []; + if (chartTab === 'all') { + return sourceBaselineVw.filter((_, idx) => getDailyIndices.has(idx)); + } else if (chartTab === 'live') { + // Live mode: Use backend pre-processed baseline return curves directly + return sourceBaselineVw; + } + return sourceBaselineVw; + }, [dataSource.baseline_vw, equity, chartTab, getDailyIndices, virtualTime]); + const filteredMomentum = useMemo(() => { + const sourceMomentum = dataSource.momentum; + if (!sourceMomentum || sourceMomentum.length === 0 || !equity || equity.length === 0) return []; + if (chartTab === 'all') { + return sourceMomentum.filter((_, idx) => getDailyIndices.has(idx)); + } else if (chartTab === 'live') { + // Live mode: Use backend pre-processed momentum return curves directly + return sourceMomentum; + } + return sourceMomentum; + }, [dataSource.momentum, equity, chartTab, getDailyIndices, virtualTime]); + const filteredStrategies = useMemo(() => { + if (!strategies || strategies.length === 0 || !equity || equity.length === 0) return []; + if (chartTab === 'all') { + return strategies.filter((_, idx) => getDailyIndices.has(idx)); + } else if (chartTab === 'live') { + const sessionStartTime = getRecentTradingSessionStart(virtualTime); + return filterStrategyDataForLive(strategies, equity, sessionStartTime); + } + return strategies; + }, [strategies, equity, chartTab, getDailyIndices, virtualTime]); + + const chartData = useMemo(() => { + if (!filteredEquity || filteredEquity.length === 0) return []; + + try { + // LIVE mode: Align all curves by timestamp with forward filling to ensure consistent point counts and aligned starting points + if (chartTab === 'live') { + // Build timestamp -> value mapping + const toMap = (arr) => { + const m = new Map(); + if (Array.isArray(arr)) { + arr.forEach((p) => { + if (p && typeof p.t === 'number' && typeof p.v === 'number') { + m.set(p.t, p.v); + } + }); + } + return m; + }; + + const portfolioMap = toMap(filteredEquity); + const baselineMap = toMap(filteredBaseline); + const baselineVwMap = toMap(filteredBaselineVw); + const momentumMap = toMap(filteredMomentum); + const strategyMap = toMap(filteredStrategies); + + // Collect all timestamps, sort by time + const timestampSet = new Set(); + [filteredEquity, filteredBaseline, filteredBaselineVw, filteredMomentum, filteredStrategies].forEach(arr => { + if (Array.isArray(arr)) { + arr.forEach(p => { + if (p && typeof p.t === 'number') timestampSet.add(p.t); + }); + } + }); + + const timestamps = Array.from(timestampSet).sort((a, b) => a - b); + if (timestamps.length === 0) return []; + + // Current values for forward filling, initialized to 0% to ensure starting point alignment + let currentPortfolio = 0; + let currentBaseline = 0; + let currentBaselineVw = 0; + let currentMomentum = 0; + let currentStrategy = 0; + + return timestamps.map((t, idx) => { + if (portfolioMap.has(t)) currentPortfolio = portfolioMap.get(t); + if (baselineMap.has(t)) currentBaseline = baselineMap.get(t); + if (baselineVwMap.has(t)) currentBaselineVw = baselineVwMap.get(t); + if (momentumMap.has(t)) currentMomentum = momentumMap.get(t); + if (strategyMap.has(t)) currentStrategy = strategyMap.get(t); + + const date = new Date(t); + if (isNaN(date.getTime())) { + console.warn('Invalid timestamp in live chart data:', t); + return null; + } + + return { + index: idx, + time: + date.toLocaleDateString('en-US', { + month: 'short', + day: 'numeric', + }) + + ' ' + + date.toLocaleTimeString('en-US', { + hour: '2-digit', + minute: '2-digit', + hour12: false, + }), + timestamp: t, + portfolio: currentPortfolio, + baseline: currentBaseline, + baseline_vw: currentBaselineVw, + momentum: currentMomentum, + strategy: currentStrategy, + }; + }).filter(item => item !== null); + } + + // ALL mode: Keep the original index-based alignment logic + return filteredEquity.map((d, idx) => { + if (!d || typeof d.t !== 'number' || typeof d.v !== 'number') { + console.warn('Invalid equity data point:', d); + return null; + } + + const date = new Date(d.t); + if (isNaN(date.getTime())) { + console.warn('Invalid timestamp:', d.t); + return null; + } + + const baselineVal = filteredBaseline?.[idx] + ? (typeof filteredBaseline[idx] === 'object' ? filteredBaseline[idx].v : filteredBaseline[idx]) + : null; + const baselineVwVal = filteredBaselineVw?.[idx] + ? (typeof filteredBaselineVw[idx] === 'object' ? filteredBaselineVw[idx].v : filteredBaselineVw[idx]) + : null; + const momentumVal = filteredMomentum?.[idx] + ? (typeof filteredMomentum[idx] === 'object' ? filteredMomentum[idx].v : filteredMomentum[idx]) + : null; + const strategyVal = filteredStrategies?.[idx] + ? (typeof filteredStrategies[idx] === 'object' ? filteredStrategies[idx].v : filteredStrategies[idx]) + : null; + + return { + index: idx, + time: + date.toLocaleDateString('en-US', { month: 'short', day: 'numeric' }) + + ' ' + + date.toLocaleTimeString('en-US', { + hour: '2-digit', + minute: '2-digit', + hour12: false, + }), + timestamp: d.t, + portfolio: d.v, + baseline: baselineVal || null, + baseline_vw: baselineVwVal || null, + momentum: momentumVal || null, + strategy: strategyVal || null, + }; + }).filter(item => item !== null); // Remove null entries + } catch (error) { + console.error('Error processing chart data:', error); + return []; + } + }, [filteredEquity, filteredBaseline, filteredBaselineVw, filteredMomentum, filteredStrategies, chartTab]); + + const { yMin, yMax, xTickIndices } = useMemo(() => { + if (chartData.length === 0) return { yMin: 0, yMax: 1, xTickIndices: [] }; + + // Calculate min and max from all series + const allValues = chartData.flatMap(d => + [d.portfolio, d.baseline, d.baseline_vw, d.momentum, d.strategy].filter(v => v !== null && isFinite(v)) + ); + + if (allValues.length === 0) { + return { yMin: 0, yMax: 1000000, xTickIndices: [] }; + } + + const dataMin = Math.min(...allValues); + const dataMax = Math.max(...allValues); + const range = dataMax - dataMin || 1; + + // For live mode (percentage data), use smaller padding and finer rounding + // For all mode (dollar amounts), use larger padding and coarser rounding + const isLiveMode = chartTab === 'live'; + + const paddingFactor = isLiveMode ? range * 0.15 : range * 0.03; + + let yMinCalc = dataMin - paddingFactor; + let yMaxCalc = dataMax + paddingFactor; + + // Smart rounding based on magnitude and mode + const magnitude = Math.max(Math.abs(yMinCalc), Math.abs(yMaxCalc)); + let roundTo; + + if (isLiveMode) { + // For percentage data, use much finer rounding + if (magnitude >= 100) { + roundTo = 10; + } else if (magnitude >= 10) { + roundTo = 1; + } else if (magnitude >= 1) { + roundTo = 0.1; + } else { + roundTo = 0.01; + } + } else { + // For dollar amounts, use coarser rounding + if (magnitude >= 1e6) { + roundTo = 10000; + } else if (magnitude >= 1e5) { + roundTo = 5000; + } else if (magnitude >= 1e4) { + roundTo = 1000; + } else { + roundTo = 100; + } + } + + yMinCalc = Math.floor(yMinCalc / roundTo) * roundTo; + yMaxCalc = Math.ceil(yMaxCalc / roundTo) * roundTo; + + // Stable range to prevent frequent updates + if (stableYRange) { + const { min: stableMin, max: stableMax } = stableYRange; + const stableRange = stableMax - stableMin; + const threshold = stableRange * 0.05; + + const needsUpdate = + dataMin < (stableMin + threshold) || + dataMax > (stableMax - threshold); + + if (!needsUpdate) { + yMinCalc = stableMin; + yMaxCalc = stableMax; + } + } + + // Calculate x-axis tick indices + const safeLength = Math.min(chartData.length, 10000); + const targetTicks = Math.min(8, Math.max(5, Math.floor(safeLength / 10))); + const step = Math.max(1, Math.floor(safeLength / (targetTicks - 1))); + + const indices = []; + for (let i = 0; i < safeLength && indices.length < 100; i += step) { + indices.push(i); + } + + if (safeLength > 0 && indices[indices.length - 1] !== safeLength - 1) { + indices.push(safeLength - 1); + } + + return { yMin: yMinCalc, yMax: yMaxCalc, xTickIndices: indices }; + }, [chartData, stableYRange]); + + // Update stableYRange in useEffect to avoid infinite re-renders + // Use functional update to avoid dependency on stableYRange + useEffect(() => { + if (yMin !== undefined && yMax !== undefined && yMin !== null && yMax !== null && isFinite(yMin) && isFinite(yMax)) { + setStableYRange(prevRange => { + if (!prevRange) { + // Initialize stable range + return { min: yMin, max: yMax }; + } else { + // Check if update is needed (5% threshold) + const stableRange = prevRange.max - prevRange.min; + const threshold = stableRange * 0.05; + const needsUpdate = + yMin < (prevRange.min + threshold) || + yMax > (prevRange.max - threshold); + + if (needsUpdate) { + return { min: yMin, max: yMax }; + } + // No update needed, return previous range + return prevRange; + } + }); + } + }, [yMin, yMax]); + + if (!equity || equity.length === 0) { + return ( +
+ NO DATA AVAILABLE +
+ ); + } + + const CustomTooltip = ({ active, payload }) => { + if (active && payload && payload.length) { + const isLiveMode = chartTab === 'live'; + return ( +
+
+ {payload[0].payload.time} +
+ {payload.map((entry, index) => ( +
+ {entry.name}: {isLiveMode ? `${entry.value.toFixed(2)}%` : `$${formatNumber(entry.value)}`}
+ ))} +
+ ); + } + return null; + }; + + const CustomDot = ({ dataKey, ...props }) => { + const { cx, cy, payload, index } = props; + const isActive = activePoint === index; + const isLastPoint = index === chartData.length - 1; + + // Only show dot for the last point + if (!isLastPoint) { + return null; + } + const colors = { + portfolio: '#00C853', + baseline: '#FF6B00', + baseline_vw: '#9C27B0', + momentum: '#2196F3', + strategy: '#795548' + }; + + return ( + setActivePoint(index)} + onMouseLeave={() => setActivePoint(null)} + onClick={() => console.log('Clicked point:', { dataKey, ...payload })} + /> + ); + }; + + const CustomXAxisTick = ({ x, y, payload }) => { + const shouldShow = xTickIndices.includes(payload.index); + if (!shouldShow) return null; + + return ( + + + {payload.value} + + + ); + }; + + const CustomLegend = ({ payload }) => { + if (!payload || payload.length === 0) return null; + + return ( +
+ {payload.map((entry, index) => { + const description = legendDescriptions[entry.value] || ''; + const isActive = legendTooltip === entry.value; + + return ( +
setLegendTooltip(entry.value)} + onMouseLeave={() => setLegendTooltip(null)} + onClick={(e) => { + e.stopPropagation(); + setLegendTooltip(isActive ? null : entry.value); + }} + > +
+ + {entry.value} + + {isActive && description && ( +
+ {description} +
+ )} +
+ ); + })} +
+ ); + }; + + return ( + + + } + interval={0} + /> + chartTab === 'live' ? `${value.toFixed(2)}%` : formatFullNumber(value)} + width={75} + /> + } /> + } + /> + + {/* Portfolio line */} + } + activeDot={{ r: 6, stroke: '#ffffff', strokeWidth: 2 }} + isAnimationActive={false} + /> + + {/* Baseline Equal Weight */} + {baseline && baseline.length > 0 && ( + } + activeDot={{ r: 6, stroke: '#ffffff', strokeWidth: 2 }} + isAnimationActive={false} + /> + )} + + {/* Baseline Value Weighted */} + {baseline_vw && baseline_vw.length > 0 && ( + } + activeDot={{ r: 6, stroke: '#ffffff', strokeWidth: 2 }} + isAnimationActive={false} + /> + )} + + {/* Momentum Strategy */} + {momentum && momentum.length > 0 && ( + } + activeDot={{ r: 6, stroke: '#ffffff', strokeWidth: 2 }} + isAnimationActive={false} + /> + )} + + {/* Other Strategies */} + {strategies && strategies.length > 0 && ( + } + activeDot={{ r: 6, stroke: '#ffffff', strokeWidth: 2 }} + isAnimationActive={false} + /> + )} + + + ); +} + diff --git a/evotraders/frontend/src/components/PerformanceView.jsx b/evotraders/frontend/src/components/PerformanceView.jsx new file mode 100644 index 0000000..8aea55e --- /dev/null +++ b/evotraders/frontend/src/components/PerformanceView.jsx @@ -0,0 +1,236 @@ +import React from 'react'; + +/** + * Performance View Component + * Displays agent performance leaderboard and signal history + */ +export default function PerformanceView({ leaderboard }) { + const rankedAgents = Array.isArray(leaderboard) + ? leaderboard.filter(agent => agent.agentId !== 'risk_manager') + : []; + return ( +
+ {/* Agent Performance Section */} +
+
+

Agent Performance - Signal Accuracy

+
+ + {rankedAgents.length === 0 ? ( +
No leaderboard data available
+ ) : ( +
+ + + + + + + + + + + + + + + {rankedAgents.map(agent => { + const bullTotal = agent.bull?.n || 0; + const bullWins = agent.bull?.win || 0; + const bullUnknown = agent.bull?.unknown || 0; + const bearTotal = agent.bear?.n || 0; + const bearWins = agent.bear?.win || 0; + const bearUnknown = agent.bear?.unknown || 0; + const totalSignals = bullTotal + bearTotal; + const evaluatedBull = Math.max(bullTotal - bullUnknown, 0); + const evaluatedBear = Math.max(bearTotal - bearUnknown, 0); + const evaluatedTotal = evaluatedBull + evaluatedBear; + const bullWinRate = evaluatedBull > 0 ? (bullWins / evaluatedBull) : null; + const bearWinRate = evaluatedBear > 0 ? (bearWins / evaluatedBear) : null; + const overallWinRate = agent.winRate != null + ? agent.winRate + : (evaluatedTotal > 0 ? ((bullWins + bearWins) / evaluatedTotal) : null); + const overallColor = overallWinRate != null + ? (overallWinRate >= 0.5 ? '#00C853' : '#FF1744') + : '#999999'; + + return ( + + + + + + + + + + + ); + })} + +
RankAgentWin RateBull SignalsBull Win RateBear SignalsBear Win RateTotal Signals
+ + {agent.rank === 1 ? '★ 1' : agent.rank} + + +
{agent.name}
+
{agent.role}
+ 		+ {overallWinRate != null ? `${(overallWinRate * 100).toFixed(1)}%` : 'N/A'} + +
{bullTotal} signals
+
{bullWins} wins
+ {bullUnknown > 0 && ( +
{bullUnknown} unknown
+ )} + 		= 0.5 ? '#00C853' : '#999999') : '#999999' }}> + {bullWinRate != null ? `${(bullWinRate * 100).toFixed(1)}%` : 'N/A'} + +
{bearTotal} signals
+
{bearWins} wins
+ {bearUnknown > 0 && ( +
{bearUnknown} unknown
+ )} + 		= 0.5 ? '#00C853' : '#999999') : '#999999' }}> + {bearWinRate != null ? `${(bearWinRate * 100).toFixed(1)}%` : 'N/A'} + {totalSignals}
+
+ )} +
+ + {/* Signal History with Dates */} + {rankedAgents.length > 0 && rankedAgents.some(agent => agent.signals && agent.signals.length > 0) && ( +
+
+

Signal History

+
+ +
+ {rankedAgents.map(agent => { + if (!agent.signals || agent.signals.length === 0) return null; + + // Sort by date descending (newest first) + const sortedSignals = [...agent.signals].sort((a, b) => + new Date(b.date).getTime() - new Date(a.date).getTime() + ); + + return ( +
+
+ {agent.name} +
+
+ {sortedSignals.map((signal, idx) => { + const signalType = signal.signal.toLowerCase(); + const isBull = signalType.includes('bull') || signalType === 'long'; + const isBear = signalType.includes('bear') || signalType === 'short'; + const isNeutral = signalType.includes('neutral') || signalType === 'hold'; + const resultStatus = signal.is_correct; + const isCorrect = resultStatus === true; + const isResultUnknown = resultStatus === 'unknown' || resultStatus === null || typeof resultStatus === 'undefined'; + const realReturnValue = signal.real_return; + const hasRealReturn = typeof realReturnValue === 'number' && Number.isFinite(realReturnValue); + const realReturnDisplay = hasRealReturn + ? `${realReturnValue >= 0 ? '+' : ''}${(realReturnValue * 100).toFixed(2)}%` + : 'Unknown'; + const realReturnColor = hasRealReturn + ? (realReturnValue >= 0 ? '#00C853' : '#FF1744') + : '#999999'; + const statusColor = isResultUnknown ? '#999999' : (isCorrect ? '#00C853' : '#FF1744'); + const statusSymbol = isResultUnknown ? '?' : (isCorrect ? '✓' : '✗'); + + return ( +
+
+ + {signal.date} + + + {signal.ticker} + + + {isBull ? 'Bull' : isBear ? 'Bear' : 'Neutral'} + + {!isNeutral && ( + + {realReturnDisplay} + + )} +
+ {!isNeutral && ( + + {statusSymbol} + + )} +
+ ); + })} +
+
+ Total: {sortedSignals.length} signals +
+
+ ); + })} +
+
+ )} +
+ ); +} + diff --git a/evotraders/frontend/src/components/RoomView.jsx b/evotraders/frontend/src/components/RoomView.jsx new file mode 100644 index 0000000..15a9f9e --- /dev/null +++ b/evotraders/frontend/src/components/RoomView.jsx @@ -0,0 +1,770 @@ +import React, { useEffect, useMemo, useRef, useState, useCallback } from 'react'; +import { ASSETS, SCENE_NATIVE, AGENT_SEATS, AGENTS } from '../config/constants'; +import AgentCard from './AgentCard'; +import { getModelIcon } from '../utils/modelIcons'; + +/** + * Custom hook to load an image + */ +function useImage(src) { + const [img, setImg] = useState(null); + useEffect(() => { + if (!src) { + setImg(null); + return; + } + // Reset image state when backend changes + setImg(null); + const image = new Image(); + image.src = src; + image.onload = () => setImg(image); + image.onerror = () => { + console.error(`Failed to load image: ${src}`); + setImg(null); + }; + // Cleanup: cancel loading if backend changes + return () => { + image.onload = null; + image.onerror = null; + }; + }, [src]); + return img; +} + +/** + * Get rank medal/trophy for display + */ +function getRankMedal(rank) { + if (rank === 1) return '🏆'; + if (rank === 2) return '🥈'; + if (rank === 3) return '🥉'; + return null; +} + +/** + * Room View Component + * Displays the conference room with agents, speech bubbles, and agent cards + * Supports click and hover (1.5s) to show agent performance cards + * Supports replay mode - completely independent from live mode + */ +export default function RoomView({ bubbles, bubbleFor, leaderboard, feed, onJumpToMessage }) { + const canvasRef = useRef(null); + const containerRef = useRef(null); + + // Agent selection and hover state + const [selectedAgent, setSelectedAgent] = useState(null); + const [hoveredAgent, setHoveredAgent] = useState(null); + const [isClosing, setIsClosing] = useState(false); + const hoverTimerRef = useRef(null); + const closeTimerRef = useRef(null); + + // Bubble expansion state + const [expandedBubbles, setExpandedBubbles] = useState({}); + + // Hidden bubbles (locally dismissed) + const [hiddenBubbles, setHiddenBubbles] = useState({}); + + // Handle bubble close + const handleCloseBubble = (agentId, bubbleKey, e) => { + e.stopPropagation(); + setHiddenBubbles(prev => ({ + ...prev, + [bubbleKey]: true + })); + }; + + // Replay state (must be defined before using in useMemo) + const [isReplaying, setIsReplaying] = useState(false); + const [replayBubbles, setReplayBubbles] = useState({}); + const [modeTransition, setModeTransition] = useState(null); // 'entering-replay' | 'exiting-replay' | null + const [isPaused, setIsPaused] = useState(false); + const replayTimerRef = useRef(null); + const replayTimeoutsRef = useRef([]); + const replayStateRef = useRef({ messages: [], currentIndex: 0 }); + + // Background image + const roomBgSrc = ASSETS.roomBg; + + const bgImg = useImage(roomBgSrc); + + // Calculate scale to fit canvas in container (80% of available space) + const [scale, setScale] = useState(0.8); + + useEffect(() => { + const updateScale = () => { + const container = containerRef.current; + if (!container) return; + + const { clientWidth, clientHeight } = container; + if (clientWidth <= 0 || clientHeight <= 0) return; + + const scaleX = clientWidth / SCENE_NATIVE.width; + const scaleY = clientHeight / SCENE_NATIVE.height; + const newScale = Math.min(scaleX, scaleY, 1.0) * 0.8; // Scale to 80% of original size + setScale(Math.max(0.3, newScale)); + }; + + updateScale(); + const resizeObserver = new ResizeObserver(updateScale); + if (containerRef.current) { + resizeObserver.observe(containerRef.current); + } + window.addEventListener('resize', updateScale); + + return () => { + resizeObserver.disconnect(); + window.removeEventListener('resize', updateScale); + }; + }, []); + + // Set canvas size + useEffect(() => { + const canvas = canvasRef.current; + if (!canvas) return; + + canvas.width = SCENE_NATIVE.width; + canvas.height = SCENE_NATIVE.height; + + const displayWidth = Math.round(SCENE_NATIVE.width * scale); + const displayHeight = Math.round(SCENE_NATIVE.height * scale); + canvas.style.width = `${displayWidth}px`; + canvas.style.height = `${displayHeight}px`; + }, [scale]); + + // Draw room background + useEffect(() => { + const canvas = canvasRef.current; + if (!canvas) return; + + const ctx = canvas.getContext('2d'); + ctx.imageSmoothingEnabled = false; + + // Clear canvas first + ctx.clearRect(0, 0, canvas.width, canvas.height); + + // Draw image if loaded + if (bgImg) { + ctx.drawImage(bgImg, 0, 0, SCENE_NATIVE.width, SCENE_NATIVE.height); + } + }, [bgImg, scale, roomBgSrc]); + + // Determine which agents are speaking + const speakingAgents = useMemo(() => { + const speaking = {}; + AGENTS.forEach(agent => { + const bubble = bubbleFor(agent.name); + speaking[agent.id] = !!bubble; + }); + return speaking; + }, [bubbles, bubbleFor]); + + // Find agent data from leaderboard + const getAgentData = (agentId) => { + const agent = AGENTS.find(a => a.id === agentId); + if (!agent) return null; + + // If no leaderboard data, return agent with default stats + if (!leaderboard || !Array.isArray(leaderboard)) { + return { + ...agent, + bull: { n: 0, win: 0, unknown: 0 }, + bear: { n: 0, win: 0, unknown: 0 }, + winRate: null, + signals: [], + rank: null + }; + } + + const leaderboardData = leaderboard.find(lb => lb.agentId === agentId); + + // If agent not in leaderboard, return agent with default stats + if (!leaderboardData) { + return { + ...agent, + bull: { n: 0, win: 0, unknown: 0 }, + bear: { n: 0, win: 0, unknown: 0 }, + winRate: null, + signals: [], + rank: null + }; + } + + // Merge data but preserve the correct avatar from AGENTS config + return { + ...agent, + ...leaderboardData, + avatar: agent.avatar // Always use the frontend's avatar URL + }; + }; + + // Get agent rank for display + const getAgentRank = (agentId) => { + const agentData = getAgentData(agentId); + return agentData?.rank || null; + }; + + // Handle agent click + const handleAgentClick = (agentId) => { + // Cancel any closing animation + if (closeTimerRef.current) { + clearTimeout(closeTimerRef.current); + closeTimerRef.current = null; + } + setIsClosing(false); + + const agentData = getAgentData(agentId); + if (agentData) { + setSelectedAgent(agentData); + } + }; + + // Handle agent hover + const handleAgentMouseEnter = (agentId) => { + setHoveredAgent(agentId); + // Clear any existing timer + if (hoverTimerRef.current) { + clearTimeout(hoverTimerRef.current); + hoverTimerRef.current = null; + } + // Cancel any closing animation + if (closeTimerRef.current) { + clearTimeout(closeTimerRef.current); + closeTimerRef.current = null; + } + setIsClosing(false); + + // If there's already a selected agent, switch immediately + // Otherwise, show after a short delay (0ms = immediate) + const agentData = getAgentData(agentId); + if (agentData) { + if (selectedAgent) { + // Already have a card open, switch immediately + setSelectedAgent(agentData); + } else { + // No card open, show after delay (currently 0ms = immediate) + hoverTimerRef.current = setTimeout(() => { + setSelectedAgent(agentData); + hoverTimerRef.current = null; + }, 0); + } + } + }; + + const handleAgentMouseLeave = () => { + setHoveredAgent(null); + // Clear timer if mouse leaves before 1.5 seconds + if (hoverTimerRef.current) { + clearTimeout(hoverTimerRef.current); + hoverTimerRef.current = null; + } + }; + + // Handle closing with animation + const handleClose = () => { + setIsClosing(true); + // Wait for animation to complete before removing + closeTimerRef.current = setTimeout(() => { + setSelectedAgent(null); + setIsClosing(false); + closeTimerRef.current = null; + }, 200); // Match the slideUp animation duration + }; + + // Cleanup timer on unmount + useEffect(() => { + return () => { + if (hoverTimerRef.current) { + clearTimeout(hoverTimerRef.current); + } + if (closeTimerRef.current) { + clearTimeout(closeTimerRef.current); + } + // Clean up replay timers + if (replayTimerRef.current) { + clearTimeout(replayTimerRef.current); + } + replayTimeoutsRef.current.forEach(timeoutId => clearTimeout(timeoutId)); + replayTimeoutsRef.current = []; + }; + }, []); + + // Show replay button when not in replay mode and has feed history + const showReplayButton = !isReplaying && feed && feed.length > 0; + + // Start replay with feed data + const handleReplayClick = useCallback(() => { + if (!feed || feed.length === 0) { + return; + } + startReplay(feed); + }, [feed]); + + // Extract agent messages from feed items + const extractAgentMessages = useCallback((feedItems) => { + const messages = []; + + feedItems.forEach((item, itemIndex) => { + if (item.type === 'message' && item.data) { + const msg = item.data; + // Skip system messages + if (msg.agent === 'System') return; + // Find matching agent + const agent = AGENTS.find(a => + a.id === msg.agentId || + a.name === msg.agent + ); + if (agent) { + messages.push({ + feedItemId: item.id, + agentId: agent.id, + agentName: agent.name, + content: msg.content, + timestamp: msg.timestamp + }); + } + } else if (item.type === 'conference' && item.data?.messages) { + item.data.messages.forEach((msg, msgIndex) => { + if (msg.agent === 'System') return; + const agent = AGENTS.find(a => + a.id === msg.agentId || + a.name === msg.agent + ); + if (agent) { + messages.push({ + feedItemId: item.id, + agentId: agent.id, + agentName: agent.name, + content: msg.content, + timestamp: msg.timestamp + }); + } + }); + } + }); + + return messages; + }, []); + + // Show next message in replay + const showNextMessage = useCallback(() => { + const { messages, currentIndex } = replayStateRef.current; + if (currentIndex >= messages.length) { + // End replay + setModeTransition('exiting-replay'); + setTimeout(() => { + setModeTransition(null); + setIsReplaying(false); + setIsPaused(false); + setReplayBubbles({}); + replayStateRef.current = { messages: [], currentIndex: 0 }; + }, 500); + return; + } + + const msg = messages[currentIndex]; + const bubbleId = `replay_${msg.agentId}_${currentIndex}`; + + setReplayBubbles(prev => ({ + ...prev, + [bubbleId]: { + id: bubbleId, + feedItemId: msg.feedItemId, + agentId: msg.agentId, + agentName: msg.agentName, + text: msg.content, + timestamp: msg.timestamp, + ts: msg.timestamp + } + })); + + // Remove bubble after 10 seconds (previously 5s) to keep replay text visible longer + const hideTimeout = setTimeout(() => { + setReplayBubbles(prev => { + const newBubbles = { ...prev }; + delete newBubbles[bubbleId]; + return newBubbles; + }); + }, 10000); + replayTimeoutsRef.current.push(hideTimeout); + + // Schedule next message + replayStateRef.current.currentIndex = currentIndex + 1; + // Wait longer before next bubble to match extended visibility (was 3s) + const nextTimeout = setTimeout(() => { + showNextMessage(); + }, 6000); + replayTimerRef.current = nextTimeout; + replayTimeoutsRef.current.push(nextTimeout); + }, []); + + // Start replay with feed data + const startReplay = useCallback((feedItems) => { + if (!feedItems || feedItems.length === 0) { + return; + } + + const agentMessages = extractAgentMessages(feedItems).reverse(); + if (agentMessages.length === 0) { + return; + } + + // Store messages for pause/resume + replayStateRef.current = { messages: agentMessages, currentIndex: 0 }; + + // Start transition animation + setModeTransition('entering-replay'); + setIsReplaying(true); + setIsPaused(false); + setReplayBubbles({}); + + // Clear any existing timeouts + replayTimeoutsRef.current.forEach(timeoutId => clearTimeout(timeoutId)); + replayTimeoutsRef.current = []; + + // Clear transition and start replay after animation completes + setTimeout(() => { + setModeTransition(null); + showNextMessage(); + }, 500); + }, [extractAgentMessages, showNextMessage]); + + // Pause replay + const pauseReplay = useCallback(() => { + if (replayTimerRef.current) { + clearTimeout(replayTimerRef.current); + replayTimerRef.current = null; + } + setIsPaused(true); + }, []); + + // Resume replay + const resumeReplay = useCallback(() => { + setIsPaused(false); + showNextMessage(); + }, [showNextMessage]); + + // Stop replay + const stopReplay = useCallback(() => { + // Clear all timeouts + replayTimeoutsRef.current.forEach(timeoutId => clearTimeout(timeoutId)); + replayTimeoutsRef.current = []; + + if (replayTimerRef.current) { + clearTimeout(replayTimerRef.current); + replayTimerRef.current = null; + } + + // Transition out of replay mode + setModeTransition('exiting-replay'); + // Clear transition and replay state after animation completes + setTimeout(() => { + setModeTransition(null); + setIsReplaying(false); + setIsPaused(false); + setReplayBubbles({}); + replayStateRef.current = { messages: [], currentIndex: 0 }; + }, 500); + }, []); + + // Get bubble for specific agent (supports both live and replay mode) + const getBubbleForAgent = useCallback((agentName) => { + if (isReplaying) { + // Find replay bubble for this agent + const bubble = Object.values(replayBubbles).find(b => { + const agent = AGENTS.find(a => a.id === b.agentId); + return agent && agent.name === agentName; + }); + return bubble || null; + } else { + // Use normal bubbleFor function + return bubbleFor(agentName); + } + }, [isReplaying, replayBubbles, bubbleFor]); + + return ( +
+ {/* Agents Indicator Bar */} +
+ {AGENTS.map((agent, index) => { + const rank = getAgentRank(agent.id); + const medal = rank ? getRankMedal(rank) : null; + const agentData = getAgentData(agent.id); + const modelInfo = getModelIcon(agentData?.modelName, agentData?.modelProvider); + + return ( + +
handleAgentClick(agent.id)} + onMouseEnter={() => handleAgentMouseEnter(agent.id)} + onMouseLeave={handleAgentMouseLeave} + > +
+ {agent.name} + + {medal && ( + + {medal} + + )} + {modelInfo.logoPath && ( + {modelInfo.provider} + )} +
+ {agent.name} +
+ {/* Divider after Risk Manager (index 1) */} + {index === 1 && ( +
+ )} + + ); + })} + + {/* Hint Text */} +
+ Click avatar to view details +
+
+ + {/* Room Canvas */} +
+
+
+ + + {/* Speech Bubbles */} + {AGENTS.map((agent, idx) => { + const bubble = getBubbleForAgent(agent.name); + if (!bubble) return null; + + const bubbleKey = `${agent.id}_${bubble.timestamp || bubble.id || bubble.ts}`; + + // Check if bubble is hidden + if (hiddenBubbles[bubbleKey]) return null; + + const pos = AGENT_SEATS[idx]; + const scaledWidth = SCENE_NATIVE.width * scale; + const scaledHeight = SCENE_NATIVE.height * scale; + + // Bubble left-bottom corner aligns to agent position + const left = Math.round(pos.x * scaledWidth); + const bottom = Math.round(pos.y * scaledHeight); + + // Get agent data for model info + const agentData = getAgentData(agent.id); + const modelInfo = getModelIcon(agentData?.modelName, agentData?.modelProvider); + + // Truncate long text - 200 collapsed, 500 expanded max + const maxLength = 200; + const maxExpandedLength = 500; + const isTruncated = bubble.text.length > maxLength; + const isExpanded = expandedBubbles[bubbleKey]; + const displayText = (!isExpanded && isTruncated) + ? bubble.text.substring(0, maxLength) + '...' + : (isExpanded && bubble.text.length > maxExpandedLength) + ? bubble.text.substring(0, maxExpandedLength) + '...' + : bubble.text; + + const toggleExpand = (e) => { + e.stopPropagation(); + setExpandedBubbles(prev => ({ + ...prev, + [bubbleKey]: !prev[bubbleKey] + })); + }; + + const handleJumpToFeed = (e) => { + e.stopPropagation(); + if (onJumpToMessage) { + onJumpToMessage(bubble); + } + }; + + return ( +
+ {/* Action buttons */} +
+ + +
+ + {/* Agent header with model icon */} +
+ {modelInfo.logoPath && ( + {modelInfo.provider} + )} +
{bubble.agentName || agent.name}
+
+ +
+ + {/* Message content */} +
+ {displayText} + {isTruncated && ( + + )} +
+
+ ); + })} +
+
+ + {/* Agent Card - Dropdown style below indicator bar */} + {selectedAgent && ( + <> + {/* Transparent overlay to close card */} +
+ + {/* Agent Card */} + + + )} + + {/* Mode Transition Overlay - sweeps in the dark gradient */} + {modeTransition === 'entering-replay' && ( +
+ )} + + {/* Mode Transition Overlay - sweeps out the dark gradient */} + {modeTransition === 'exiting-replay' && ( +
+ )} + + {/* Replay Button */} + {showReplayButton && ( +
+ +
+ )} + + {/* Replay Mode Background + Indicator */} + {isReplaying && !modeTransition && ( + <> +
+
+ {isPaused ? 'PAUSED' : 'REPLAY MODE'} + + +
+ + )} +
+
+ ); +} + diff --git a/evotraders/frontend/src/components/RulesView.jsx b/evotraders/frontend/src/components/RulesView.jsx new file mode 100644 index 0000000..8cdd583 --- /dev/null +++ b/evotraders/frontend/src/components/RulesView.jsx @@ -0,0 +1,483 @@ +import React, { useState, useEffect, useRef } from 'react'; +import { LLM_MODEL_LOGOS } from '../config/constants'; + +export default function RulesView() { + const [language, setLanguage] = useState('en'); // 'en' or 'zh' + const [scale, setScale] = useState(1); + const containerRef = useRef(null); + const contentRef = useRef(null); + + // Auto-scale content to fit container without scrolling + useEffect(() => { + const handleResize = () => { + if (containerRef.current && contentRef.current) { + const containerHeight = containerRef.current.clientHeight; + const contentHeight = contentRef.current.scrollHeight; + + if (contentHeight > containerHeight) { + const newScale = containerHeight / contentHeight; + setScale(Math.max(newScale * 0.95, 0.5)); // Min scale 0.5, with 95% of available space + } else { + setScale(1); + } + } + }; + + // Initial resize + handleResize(); + + // Listen to window resize + window.addEventListener('resize', handleResize); + + // Observe content changes + const observer = new ResizeObserver(handleResize); + if (contentRef.current) { + observer.observe(contentRef.current); + } + + return () => { + window.removeEventListener('resize', handleResize); + observer.disconnect(); + }; + }, [language]); + + const containerStyle = { + width: '100%', + height: '100%', + overflow: 'hidden', + display: 'flex', + alignItems: 'center', + justifyContent: 'center', + background: '#FFFFFF', + padding: '10px' + }; + + const contentWrapperStyle = { + transform: `scale(${scale})`, + transformOrigin: 'center center', + transition: 'transform 0.3s ease', + width: '100%', + maxWidth: '900px' + }; + + const innerContentStyle = { + color: '#000000', + fontFamily: "'IBM Plex Mono', monospace", + fontSize: '13px', + lineHeight: '1.6', + letterSpacing: '0.01em', + padding: '0 10px' + }; + + const highlight = { + color: '#000000', + fontWeight: 700 + }; + + const sectionTitleStyle = { + color: '#615CED', + fontSize: '16px', + fontWeight: 700, + marginBottom: '8px', + marginTop: '12px', + marginLeft: '-10px', + marginRight: '-10px', + width: 'calc(100% + 20px)', + padding: '8px 10px', + backgroundColor: '#FFFFFF', + letterSpacing: '0.5px', + boxSizing: 'border-box' + }; + + const subsectionStyle = { + marginBottom: '8px', + paddingLeft: '10px', + borderLeft: '2px solid #CCCCCC' + }; + + const linkStyle = { + color: '#615CED', + textDecoration: 'none', + borderBottom: '1px solid #615CED', + transition: 'all 0.2s' + }; + + const languageSwitchStyle = { + display: 'flex', + justifyContent: 'center', + alignItems: 'center', + marginBottom: '12px', + gap: '0px', + fontSize: '11px', + fontFamily: "'IBM Plex Mono', monospace" + }; + + const getLangStyle = (isActive) => ({ + padding: '4px 10px', + cursor: 'pointer', + transition: 'all 0.2s', + background: isActive ? '#000000' : 'transparent', + color: isActive ? '#FFFFFF' : '#666666', + border: 'none', + borderRadius: '2px' + }); + + const llmLogos = [ + { name: 'Alibaba', file: 'Alibaba.jpeg', label: 'Qwen', url: LLM_MODEL_LOGOS['Alibaba'] }, + { name: 'DeepSeek', file: 'DeepSeek.png', label: 'DeepSeek', url: LLM_MODEL_LOGOS['DeepSeek'] }, + { name: 'Moonshot', file: 'Moonshot.jpeg', label: 'Moonshot', url: LLM_MODEL_LOGOS['Moonshot'] }, + { name: 'Zhipu AI', file: 'Zhipu AI.png', label: 'Zhipu AI', url: LLM_MODEL_LOGOS['Zhipu AI'] } + ]; + + const content = { + en: { + section1Title: "Agent Setup", + pmRole: "Portfolio Manager", + pmDesc: "Makes final trading decisions and orchestrates team collaboration", + rmRole: "Risk Manager", + rmDesc: "Monitors portfolio risk and enforces risk limits", + analystsRole: "Analysts", + analystsDesc: "Conduct specialized research with different tools and AI models:", + analysts: [ + { name: "Valuation Analyst", model: "Moonshot", modelKey: "Moonshot" }, + { name: "Sentiment Analyst", model: "Qwen", modelKey: "Alibaba" }, + { name: "Fundamentals Analyst", model: "DeepSeek", modelKey: "DeepSeek" }, + { name: "Technical Analyst", model: "Zhipu AI", modelKey: "Zhipu AI" } + ], + + section2Title: "Agent Decision Mechanism", + + tradingProcess: "Daily Trading Process", + tradingDesc: "Agents trade on a daily frequency while continuously tracking portfolio performance. Before each day's final trading decision, agents go through three key phases:", + + analysisPhase: "• Analysis Phase", + analysisDesc: "All agents independently analyze information and form judgments based on their specialized tools.", + + communicationPhase: "• Communication Phase", + commIntro: "Multiple communication channels enable effective collaboration: 1v1 Private Chat / 1vN Notification / NvN Conference", + + decisionPhase: "• Decision Phase", + decisionDesc: "Portfolio Manager aggregates all information and makes the final team trading decision. The original trading signals from analysts are only used for individual-level ranking.", + + reflectionTitle: "Learning & Evolution", + reflectionDesc: "Agents reflect on daily investment performance, summarize insights, and store them in ", + remeLink: "ReMe", + reflectionDesc2: " memory framework for continuous improvement.", + + section3Title: "Performance Evaluation", + + chartTitle: "• Performance Chart", + chartDesc: "Track portfolio equity curve vs. benchmarks (equal-weight, value-weighted, momentum). Use this to assess overall strategy effectiveness.", + + rankingTitle: "• Analyst Rankings", + rankingDesc: "Click avatars in Trading Room to view analyst performance (Win Rate, Bull/Bear Win Rate). Use this to understand which analysts provide the most valuable insights.", + + statsTitle: "• Statistics", + statsDesc: "Detailed holdings and trade history. Use this for in-depth analysis of position management and execution quality.", + + callToAction: "Fork on ", + repoLink: "GitHub", + callToAction2: " to customize!" + }, + zh: { + section1Title: "Agent 设定", + pmRole: "Portfolio Manager", + pmDesc: "负责最终交易决策和团队协作", + rmRole: "Risk Manager", + rmDesc: "监控组合风险并执行风险限制", + analystsRole: "Analysts", + analystsDesc: "使用不同工具和 AI 模型进行专业研究：", + analysts: [ + { name: "Valuation Analyst", model: "Moonshot", modelKey: "Moonshot" }, + { name: "Sentiment Analyst", model: "Qwen", modelKey: "Alibaba" }, + { name: "Fundamentals Analyst", model: "DeepSeek", modelKey: "DeepSeek" }, + { name: "Technical Analyst", model: "Zhipu AI", modelKey: "Zhipu AI" } + ], + + section2Title: "Agent 决策机制", + + tradingProcess: "交易流程", + tradingDesc: "Agents 进行日频交易并持续跟踪组合净值。每天最终交易决策前，agents 经历三个关键阶段：", + + analysisPhase: "• 分析阶段", + analysisDesc: "所有 agents 根据各自的工具和信息独立分析并形成判断。", + + communicationPhase: "• 沟通阶段", + commIntro: "提供了多种沟通渠道：1v1 私聊 / 1vN 通知 / NvN 会议", + + decisionPhase: "• 决策阶段", + decisionDesc: "由 portfolio manager 汇总所有信息，并给出最终的团队交易。analysts 给出的原始交易信号仅做个人维度的排名。", + + reflectionTitle: "学习与进化", + reflectionDesc: "Agents 根据当日实际收益反思决策、总结经验，并存入 ", + remeLink: "ReMe", + reflectionDesc2: " 记忆框架以持续改进。", + + section3Title: "收益评估", + + chartTitle: "• 业绩图表", + chartDesc: "追踪组合收益曲线 vs. 基准策略（等权、市值加权、动量）。用于评估整体策略有效性。", + + rankingTitle: "• 分析师排名", + rankingDesc: "在 Trading Room 点击头像查看分析师表现（胜率、牛/熊市胜率）。用于了解哪些分析师提供最有价值的洞察。", + + statsTitle: "• 统计数据", + statsDesc: "详细的持仓和交易历史。用于深入分析仓位管理和执行质量。", + + callToAction: "在 ", + repoLink: "GitHub", + callToAction2: " 上 fork 并自定义！" + } + }; + + return ( +
+
+
+ {/* Language Switch */} +
+ setLanguage('zh')} + > + 中文 + + ｜ + setLanguage('en')} + > + EN + +
+ + {language === 'en' ? ( + // English Content + <> + {/* Section 1: Agent Setup */} +
{content.en.section1Title}
+ + {/* Roles */} +
+
+ {content.en.pmRole}: {content.en.pmDesc} +
+
+ {content.en.rmRole}: {content.en.rmDesc} +
+
+ {content.en.analystsRole}: {content.en.analystsDesc} +
+
+ + {/* Analysts with AI Models */} +
+ {content.en.analysts.map(analyst => { + const logo = llmLogos.find(l => l.name === analyst.modelKey); + return ( +
+ {logo && ( + {logo.label} + )} + {analyst.name} + - {analyst.model} +
+ ); + })} +
+ +
+ {content.en.callToAction} + + {content.en.repoLink} + + {content.en.callToAction2} +
+ + {/* Section 2: Agent Decision Mechanism */} +
{content.en.section2Title}
+ +
+
{content.en.tradingProcess}
+
{content.en.tradingDesc}
+ +
+
+ {content.en.analysisPhase.replace('• ', '')}: {content.en.analysisDesc} +
+ +
+ {content.en.communicationPhase.replace('• ', '')}: {content.en.commIntro} +
+ +
+ {content.en.decisionPhase.replace('• ', '')}: {content.en.decisionDesc} +
+
+
+ +
+
{content.en.reflectionTitle}
+
+ {content.en.reflectionDesc} + + {content.en.remeLink} + + {content.en.reflectionDesc2} +
+
+ + {/* Section 3: Performance Evaluation */} +
{content.en.section3Title}
+
+
+ {content.en.chartTitle.replace('• ', '')}: {content.en.chartDesc} +
+
+ {content.en.rankingTitle.replace('• ', '')}: {content.en.rankingDesc} +
+
+ {content.en.statsTitle.replace('• ', '')}: {content.en.statsDesc} +
+
+ + ) : ( + // Chinese Content + <> + {/* 第一部分：Agent 设定 */} +
{content.zh.section1Title}
+ + {/* 角色 */} +
+
+ {content.zh.pmRole}: {content.zh.pmDesc} +
+
+ {content.zh.rmRole}: {content.zh.rmDesc} +
+
+ {content.zh.analystsRole}: {content.zh.analystsDesc} +
+
+ + {/* Analysts 与 AI 模型 */} +
+ {content.zh.analysts.map(analyst => { + const logo = llmLogos.find(l => l.name === analyst.modelKey); + return ( +
+ {logo && ( + {logo.label} + )} + {analyst.name} + - {analyst.model} +
+ ); + })} +
+ +
+ {content.zh.callToAction} + + {content.zh.repoLink} + + {content.zh.callToAction2} +
+ + {/* 第二部分：Agent 决策机制 */} +
{content.zh.section2Title}
+ +
+
{content.zh.tradingProcess}
+
{content.zh.tradingDesc}
+ +
+
+ {content.zh.analysisPhase.replace('• ', '')}: {content.zh.analysisDesc} +
+ +
+ {content.zh.communicationPhase.replace('• ', '')}: {content.zh.commIntro} +
+ +
+ {content.zh.decisionPhase.replace('• ', '')}: {content.zh.decisionDesc} +
+
+
+ +
+
{content.zh.reflectionTitle}
+
+ {content.zh.reflectionDesc} + + {content.zh.remeLink} + + {content.zh.reflectionDesc2} +
+
+ + {/* 第三部分：收益评估 */} +
{content.zh.section3Title}
+
+
+ {content.zh.chartTitle.replace('• ', '')}: {content.zh.chartDesc} +
+
+ {content.zh.rankingTitle.replace('• ', '')}: {content.zh.rankingDesc} +
+
+ {content.zh.statsTitle.replace('• ', '')}: {content.zh.statsDesc} +
+
+ + )} +
+
+
+ ); +} diff --git a/evotraders/frontend/src/components/StatisticsView.jsx b/evotraders/frontend/src/components/StatisticsView.jsx new file mode 100644 index 0000000..cbe6dd0 --- /dev/null +++ b/evotraders/frontend/src/components/StatisticsView.jsx @@ -0,0 +1,593 @@ +import React, { useState, useEffect } from 'react'; +import StockLogo from './StockLogo'; +import { formatNumber, formatDateTime } from '../utils/formatters'; + +/** + * Statistics View Component + * Displays portfolio overview, holdings, and trade history in a side-by-side layout + * Left: Performance Overview (35%) | Right: Holdings + Trades (65%) + * No scrolling - content fits within viewport with pagination + */ +export default function StatisticsView({ trades, holdings, stats, baseline_vw, equity }) { + const [holdingsPage, setHoldingsPage] = useState(1); + const [tradesPage, setTradesPage] = useState(1); + const holdingsPerPage = 5; + const tradesPerPage = 8; + + // Calculate pagination for holdings + const totalHoldingsPages = Math.ceil(holdings.length / holdingsPerPage); + const holdingsStartIndex = (holdingsPage - 1) * holdingsPerPage; + const holdingsEndIndex = holdingsStartIndex + holdingsPerPage; + const currentHoldings = holdings.slice(holdingsStartIndex, holdingsEndIndex); + + // Calculate pagination for trades + const totalTradesPages = Math.ceil(trades.length / tradesPerPage); + const tradesStartIndex = (tradesPage - 1) * tradesPerPage; + const tradesEndIndex = tradesStartIndex + tradesPerPage; + const currentTrades = trades.slice(tradesStartIndex, tradesEndIndex); + + // Calculate excess return (Evatraders return - benchmark value-weighted return) + const calculateExcessReturn = () => { + if (!stats || !baseline_vw || baseline_vw.length === 0) { + return null; + } + + // Get Evatraders return from stats + const evatradersReturn = stats.totalReturn || 0; // Already in percentage + + // Calculate benchmark return from baseline_vw + // baseline_vw format: [{t: timestamp, v: value}, ...] or [value, ...] + let benchmarkInitialValue, benchmarkCurrentValue; + + if (baseline_vw.length > 0) { + const firstPoint = baseline_vw[0]; + const lastPoint = baseline_vw[baseline_vw.length - 1]; + + benchmarkInitialValue = typeof firstPoint === 'object' ? firstPoint.v : firstPoint; + benchmarkCurrentValue = typeof lastPoint === 'object' ? lastPoint.v : lastPoint; + + if (benchmarkInitialValue && benchmarkInitialValue > 0 && benchmarkCurrentValue) { + const benchmarkReturn = ((benchmarkCurrentValue - benchmarkInitialValue) / benchmarkInitialValue) * 100; + const excessReturn = evatradersReturn - benchmarkReturn; + return { + excessReturn: excessReturn, + benchmarkReturn: benchmarkReturn, + evatradersReturn: evatradersReturn + }; + } + } + + return null; + }; + + const excessReturnData = calculateExcessReturn(); + + // Reset to page 1 when data changes + useEffect(() => { + setHoldingsPage(1); + }, [holdings.length]); + + useEffect(() => { + setTradesPage(1); + }, [trades.length]); + + return ( +
+ {/* Left Panel: Performance Overview (35%) */} +
+ {stats ? ( +
+ {/* Header */} +
+

+ Performance +

+
+ + {/* Main Stats - Hierarchical Layout */} +
+ {/* Primary Metric - Total Asset Value */} +
+
+ Total Asset Value +
+
+ ${formatNumber(stats.totalAssetValue || 0)} +
+
+ + {/* Secondary Metrics - Grid: Excess Return, Win Rate, Absolute Return */} +
+ {/* 1. Excess Return */} + {excessReturnData ? ( +
+
+ Excess Return +
+
= 0 ? '#00C853' : '#FF1744', + fontFamily: '"Courier New", monospace' + }}> + {excessReturnData.excessReturn >= 0 ? '+' : ''}{excessReturnData.excessReturn.toFixed(2)}% +
+
+ vs. VW: {excessReturnData.benchmarkReturn >= 0 ? '+' : ''}{excessReturnData.benchmarkReturn.toFixed(2)}% +
+
+ ) : null} + + {/* 2. Win Rate */} +
+
+ Win Rate +
+
+ {Math.round(stats.winRate * 100)}% +
+
+ + {/* 3. Absolute Return */} +
+
+ Absolute Return +
+
= 0 ? '#00C853' : '#FF1744', + fontFamily: '"Courier New", monospace' + }}> + {(stats.totalReturn || 0) >= 0 ? '+' : ''}{(stats.totalReturn || 0).toFixed(2)}% +
+
+
+ + {/* Tertiary Metrics - Compact List */} +
+
+
+ Cash Position +
+
+ ${formatNumber(stats.cashPosition || 0)} +
+
+ +
+
+ Total Trades +
+
+ {stats.totalTrades || 0} +
+
+
+ + {/* Ticker Weights - Compact */} + {stats.tickerWeights && Object.keys(stats.tickerWeights).length > 0 && ( +
+
+ Portfolio Weights +
+
+ {Object.entries(stats.tickerWeights).map(([ticker, weight]) => ( +
+ {ticker} + {(weight * 100).toFixed(1)}% +
+ ))} +
+
+ )} +
+
+ ) : ( +
+ No statistics available +
+ )} +
+ + {/* Right Panel: Holdings + Trades (65%) */} +
+ {/* Portfolio Holdings - Top Half */} +
+
+

+ Portfolio Holdings +

+
+ +
+ {holdings.length === 0 ? ( +
+ No positions currently held +
+ ) : ( + <> +
+ + + + + + + + + + + + {currentHoldings.map(h => ( + + + + + + + + ))} + +
TickerQuantityPriceValueWeight
+
+ {h.ticker !== 'CASH' && } + {h.ticker} +
+ 		{h.ticker === 'CASH' ? '-' : h.quantity}{h.ticker === 'CASH' ? '-' : `$${Number(h.currentPrice).toFixed(2)}`}${formatNumber(h.marketValue)}{(Number(h.weight) * 100).toFixed(2)}%
+
+ + {totalHoldingsPages > 1 && ( +
+ + +
+ {holdingsPage} / {totalHoldingsPages} +
+ + +
+ )} + + )} +
+
+ + {/* Transaction History - Bottom Half */} +
+
+

+ Transaction History +

+ {trades.length > 0 && ( +
+ {trades.length} total +
+ )} +
+ +
+ {trades.length === 0 ? ( +
+ No trades recorded +
+ ) : ( + <> +
+ + + + + + + + + + + + {currentTrades.map((t, idx) => ( + + + + + + + + ))} + +
TimeStockSideQtyPrice
+ {formatDateTime(t.timestamp)} + +
+ + {t.ticker} +
+ 		+ + {t.side} + + {t.qty}${Number(t.price).toFixed(2)}
+
+ + {totalTradesPages > 1 && ( +
+ + +
+ {tradesPage} / {totalTradesPages} +
+ + +
+ )} + + )} +
+
+
+
+ ); +} diff --git a/evotraders/frontend/src/components/StockLogo.jsx b/evotraders/frontend/src/components/StockLogo.jsx new file mode 100644 index 0000000..b2f4d8b --- /dev/null +++ b/evotraders/frontend/src/components/StockLogo.jsx @@ -0,0 +1,28 @@ +import React from 'react'; +import { STOCK_LOGOS } from '../config/constants'; + +/** + * Stock Logo Component + * Displays company logo for a given ticker symbol + */ +export default function StockLogo({ ticker, size = 20 }) { + const logoUrl = STOCK_LOGOS[ticker]; + if (!logoUrl) return null; + + return ( + {ticker} { e.target.style.display = 'none'; }} + /> + ); +} + diff --git a/evotraders/frontend/src/config/constants.js b/evotraders/frontend/src/config/constants.js new file mode 100644 index 0000000..8e19c75 --- /dev/null +++ b/evotraders/frontend/src/config/constants.js @@ -0,0 +1,145 @@ +/** + * Application Configuration Constants + */ + +// Centralized CDN asset URLs +export const CDN_ASSETS = { + companyRoom: { + agent_1: "https://img.alicdn.com/imgextra/i4/O1CN01Lr7SOl1lSExV0tOwv_!!6000000004817-2-tps-370-320.png", + agent_2: "https://img.alicdn.com/imgextra/i3/O1CN017Kb8cY1VQNUmuK47o_!!6000000002647-2-tps-368-312.png", + agent_3: "https://img.alicdn.com/imgextra/i3/O1CN010Fp55w1YqtGpVjgsS_!!6000000003111-2-tps-370-320.png", + agent_4: "https://img.alicdn.com/imgextra/i3/O1CN01VnUsML1Dkq6fHw3ks_!!6000000000255-2-tps-366-316.png", + agent_5: "https://img.alicdn.com/imgextra/i4/O1CN01o0kCQw1kyvbulBSl7_!!6000000004753-2-tps-370-314.png", + agent_6: "https://img.alicdn.com/imgextra/i2/O1CN01cLV0zl1FI6ULAunTp_!!6000000000463-2-tps-368-320.png", + team_logo: "https://img.alicdn.com/imgextra/i2/O1CN01n2S8aV25hcZhhNH95_!!6000000007558-2-tps-616-700.png", + reme_logo: "https://img.alicdn.com/imgextra/i2/O1CN01FhncuT1Tqp8LfCaft_!!6000000002434-2-tps-915-250.png", + full_room_dark: "https://img.alicdn.com/imgextra/i2/O1CN014sOgzK28re5haGC3X_!!6000000007986-2-tps-1248-832.png", + full_room_with_roles_tech_style: "https://img.alicdn.com/imgextra/i1/O1CN01qhupIj1KU4vF3yoT2_!!6000000001166-2-tps-1248-832.png", + }, + llmModelLogos: { + "Zhipu AI": "https://img.alicdn.com/imgextra/i4/O1CN01PavE4h1SdFmbeUj6h_!!6000000002269-2-tps-92-92.png", + "Alibaba": "https://img.alicdn.com/imgextra/i4/O1CN01mTs8oZ1gsHOj0xy7O_!!6000000004197-0-tps-204-192.jpg", + "DeepSeek": "https://img.alicdn.com/imgextra/i3/O1CN01ocd9iO1D7S2qgEIXQ_!!6000000000169-2-tps-203-148.png", + "Moonshot": "https://img.alicdn.com/imgextra/i3/O1CN01rFzJg01wE0QFHNGLy_!!6000000006275-0-tps-182-148.jpg", + "Anthropic": "https://img.alicdn.com/imgextra/i4/O1CN01Sg8gbo1HKVnoU16rm_!!6000000000739-2-tps-148-148.png", + "Google": "https://img.alicdn.com/imgextra/i1/O1CN01fZwVYk1caBHdzh9qh_!!6000000003616-0-tps-148-148.jpg", + "OpenAI": "https://img.alicdn.com/imgextra/i3/O1CN01T1eaM8287qU0nZm91_!!6000000007886-2-tps-148-148.png", + "Groq": "https://img.alicdn.com/imgextra/i1/O1CN01WxASMc1QjXzhVl3eQ_!!6000000002012-2-tps-170-148.png", + "Ollama": "https://img.alicdn.com/imgextra/i1/O1CN01pN615e1i4vxLkQjVd_!!6000000004360-2-tps-204-192.png", + }, + stockLogos: { + "TSLA": "https://img.alicdn.com/imgextra/i4/O1CN01Pch4DD1DDrad8BQAQ_!!6000000000183-2-tps-128-128.png", + "AMZN": "https://img.alicdn.com/imgextra/i3/O1CN01KMsfnU25Wd4MGSgue_!!6000000007534-2-tps-128-128.png", + "NVDA": "https://img.alicdn.com/imgextra/i4/O1CN01Lq1eJr1mLeslgx6a0_!!6000000004938-2-tps-128-128.png", + "GOOGL": "https://img.alicdn.com/imgextra/i2/O1CN01kjJJbb25B6SESkOCn_!!6000000007487-2-tps-128-128.png", + "MSFT": "https://img.alicdn.com/imgextra/i4/O1CN01tdlNtQ1aFS7vHYfMG_!!6000000003300-2-tps-128-128.png", + "AAPL": "https://img.alicdn.com/imgextra/i4/O1CN01r0GH0q1diiHHOwxiO_!!6000000003770-2-tps-128-128.png", + "META": "https://img.alicdn.com/imgextra/i3/O1CN01pWAvHt1IkRqZoUG96_!!6000000000931-2-tps-130-96.png", + } +}; + +// Derived asset shortcuts +export const ASSETS = { + roomBg: CDN_ASSETS.companyRoom.full_room_with_roles_tech_style, + teamLogo: CDN_ASSETS.companyRoom.team_logo, + remeLogo: CDN_ASSETS.companyRoom.reme_logo, +}; + +// Stock logos mapping +export const STOCK_LOGOS = { ...CDN_ASSETS.stockLogos }; + +// Scene dimensions (actual image size) +export const SCENE_NATIVE = { width: 1248, height: 832 }; + +// Agent seat positions (percentage relative to image, origin at bottom-left) +// Format: { x: horizontal %, y: vertical % from bottom } +export const AGENT_SEATS = [ + { x: 0.44, y: 0.58 }, // portfolio_manager + { x: 0.55, y: 0.58 }, // risk_manager + { x: 0.33, y: 0.52 }, // valuation_analyst + { x: 0.42, y: 0.42 }, // sentiment_analyst + { x: 0.56, y: 0.42 }, // fundamentals_analyst + { x: 0.61, y: 0.49 }, // technical_analyst +]; + +// Agent definitions with subtle color schemes (very light backgrounds) +export const AGENTS = [ + { + id: "portfolio_manager", + name: "Portfolio Manager", + role: "Portfolio Manager", + avatar: CDN_ASSETS.companyRoom.agent_1, + colors: { bg: "#F9FDFF", text: "#1565C0", accent: "#1565C0" } + }, + { + id: "risk_manager", + name: "Risk Manager", + role: "Risk Manager", + avatar: CDN_ASSETS.companyRoom.agent_2, + colors: { bg: "#FFF8F8", text: "#C62828", accent: "#C62828" } + }, + { + id: "valuation_analyst", + name: "Valuation Analyst", + role: "Valuation Analyst", + avatar: CDN_ASSETS.companyRoom.agent_3, + colors: { bg: "#FAFFFA", text: "#2E7D32", accent: "#2E7D32" } + }, + { + id: "sentiment_analyst", + name: "Sentiment Analyst", + role: "Sentiment Analyst", + avatar: CDN_ASSETS.companyRoom.agent_4, + colors: { bg: "#FCFAFF", text: "#6A1B9A", accent: "#6A1B9A" } + }, + { + id: "fundamentals_analyst", + name: "Fundamentals Analyst", + role: "Fundamentals Analyst", + avatar: CDN_ASSETS.companyRoom.agent_5, + colors: { bg: "#FFFCF7", text: "#E65100", accent: "#E65100" } + }, + { + id: "technical_analyst", + name: "Technical Analyst", + role: "Technical Analyst", + avatar: CDN_ASSETS.companyRoom.agent_6, + colors: { bg: "#F9FEFF", text: "#00838F", accent: "#00838F" } + }, +]; + +// LLM logo URLs for reuse +export const LLM_MODEL_LOGOS = { ...CDN_ASSETS.llmModelLogos }; + +// Message type colors (very subtle backgrounds) +export const MESSAGE_COLORS = { + system: { bg: "#FAFAFA", text: "#424242", accent: "#424242" }, + memory: { bg: "#F2FDFF", text: "#00838F", accent: "#00838F" }, + conference: { bg: "#F1F4FF", text: "#3949AB", accent: "#3949AB" } +}; + +// Helper function to get agent colors by ID or name +export const getAgentColors = (agentId, agentName) => { + const agent = AGENTS.find(a => a.id === agentId || a.name === agentName); + return agent?.colors || MESSAGE_COLORS.system; +}; + +// UI timing constants +export const BUBBLE_LIFETIME_MS = 8000; +export const CHART_MARGIN = { left: 60, right: 20, top: 20, bottom: 40 }; +export const AXIS_TICKS = 5; + +// WebSocket configuration +export const WS_URL = import.meta.env.VITE_WS_URL || "ws://localhost:8765"; + +// Initial ticker symbols (MAG7 companies) +export const INITIAL_TICKERS = [ + { symbol: "AAPL", price: null, change: null }, + { symbol: "MSFT", price: null, change: null }, + { symbol: "GOOGL", price: null, change: null }, + { symbol: "AMZN", price: null, change: null }, + { symbol: "NVDA", price: null, change: null }, + { symbol: "META", price: null, change: null }, + { symbol: "TSLA", price: null, change: null } +]; + diff --git a/evotraders/frontend/src/hooks/useFeedProcessor.js b/evotraders/frontend/src/hooks/useFeedProcessor.js new file mode 100644 index 0000000..af8f8bd --- /dev/null +++ b/evotraders/frontend/src/hooks/useFeedProcessor.js @@ -0,0 +1,363 @@ +import { useState, useCallback, useRef } from "react"; +import { AGENTS } from "../config/constants"; + +const MAX_FEED_ITEMS = 200; + +/** + * Generate a unique ID for feed items + */ +const generateId = (prefix = "item") => `${prefix}-${Date.now()}-${Math.random().toString(36).substr(2, 9)}`; + +/** + * Convert raw event to a message object (for use within conferences or standalone) + */ +const eventToMessage = (evt) => { + if (!evt || !evt.type) { + return null; + } + + const agent = AGENTS.find(a => a.id === evt.agentId); + const timestamp = evt.timestamp || evt.ts || Date.now(); + + switch (evt.type) { + case "agent_message": + case "conference_message": + return { + id: generateId("msg"), + timestamp, + agentId: evt.agentId, + agent: agent?.name || evt.agentName || evt.agentId || "Agent", + role: agent?.role || evt.role || "Agent", + content: evt.content + }; + + case "memory": + return { + id: generateId("memory"), + timestamp, + agentId: evt.agentId, + agent: agent?.name || evt.agentId || "Memory", + role: "Memory", + content: evt.content || evt.text || "" + }; + + case "system": + case "day_start": + case "day_complete": + case "day_error": + return { + id: generateId("sys"), + timestamp, + agent: "System", + role: "System", + content: evt.content || `${evt.type}: ${evt.date || ""}` + }; + + default: + return null; + } +}; + +/** + * Convert raw event to a standalone feed item (non-conference) + */ +const eventToFeedItem = (evt) => { + if (!evt || !evt.type) { + return null; + } + + const message = eventToMessage(evt); + if (!message) { + return null; + } + + if (evt.type === "memory") { + return { + type: "memory", + id: message.id, + data: { + timestamp: message.timestamp, + agentId: message.agentId, + agent: message.agent, + content: message.content + } + }; + } + + return { + type: "message", + id: message.id, + data: message + }; +}; + +/** + * Custom hook for processing feed events with conference aggregation + */ +export function useFeedProcessor() { + const [feed, setFeed] = useState([]); + + // Active conference ref for real-time event handling + const activeConferenceRef = useRef(null); + + /** + * Process historical events from server + * Events come in reverse chronological order (newest first) + * So conference_end appears BEFORE conference_start in the array + */ + const processHistoricalFeed = useCallback((events) => { + if (!Array.isArray(events)) { + console.warn("processHistoricalFeed: expected array, got", typeof events); + return; + } + + console.log("📋 Processing historical events:", events.length); + + const feedItems = []; + let currentConference = null; + + // Process in chronological order (reverse the array) + const chronological = [...events].reverse(); + + for (const evt of chronological) { + if (!evt || !evt.type) { + continue; + } + + try { + if (evt.type === "conference_start") { + // Start a new conference + currentConference = { + id: evt.conferenceId || generateId("conf"), + title: evt.title || "Team Conference", + startTime: evt.timestamp || evt.ts || Date.now(), + endTime: null, + isLive: false, + participants: evt.participants || [], + messages: [] + }; + } else if (evt.type === "conference_end") { + // End current conference + if (currentConference) { + currentConference.endTime = evt.timestamp || evt.ts || Date.now(); + currentConference.isLive = false; + feedItems.push({ + type: "conference", + id: currentConference.id, + data: currentConference + }); + currentConference = null; + } + } else if (evt.type === "conference_message") { + // Add to current conference if exists + const message = eventToMessage(evt); + if (message && currentConference) { + currentConference.messages.push(message); + } else if (message) { + // Fallback: show as standalone message if no active conference + feedItems.push({ + type: "message", + id: message.id, + data: message + }); + } + } else { + // Non-conference events + const feedItem = eventToFeedItem(evt); + if (feedItem) { + if (currentConference) { + // Add to conference messages + currentConference.messages.push(feedItem.data); + } else { + feedItems.push(feedItem); + } + } + } + } catch (error) { + console.error("Error processing historical event:", evt.type, error); + } + } + + // If there's an unclosed conference, it's still live + if (currentConference) { + currentConference.isLive = true; + feedItems.push({ + type: "conference", + id: currentConference.id, + data: currentConference + }); + // Store as active for real-time updates + activeConferenceRef.current = currentConference; + console.log(`🔴 Restored active conference: ${currentConference.id} with ${currentConference.messages.length} messages`); + } + + // Reverse back to newest-first order + setFeed(feedItems.reverse()); + console.log(`✅ Processed ${feedItems.length} feed items from ${events.length} events`); + }, []); + + /** + * Process a single real-time event + * Handles conference aggregation for live events + */ + const processFeedEvent = useCallback((evt) => { + if (!evt || !evt.type) { + return null; + } + + // Handle conference start + if (evt.type === "conference_start") { + const conference = { + id: evt.conferenceId || generateId("conf"), + title: evt.title || "Team Conference", + startTime: evt.timestamp || evt.ts || Date.now(), + endTime: null, + isLive: true, + participants: evt.participants || [], + messages: [] + }; + activeConferenceRef.current = conference; + setFeed(prev => [{ type: "conference", id: conference.id, data: conference }, ...prev].slice(0, MAX_FEED_ITEMS)); + return conference; + } + + // Handle conference end + if (evt.type === "conference_end") { + const activeConf = activeConferenceRef.current; + activeConferenceRef.current = null; + + if (activeConf) { + const ended = { + ...activeConf, + endTime: evt.timestamp || evt.ts || Date.now(), + isLive: false + }; + setFeed(prev => prev.map(item => + item.type === "conference" && item.id === activeConf.id + ? { ...item, data: ended } + : item + )); + return ended; + } + return null; + } + + // Handle conference message + if (evt.type === "conference_message") { + const message = eventToMessage(evt); + if (!message) { + return null; + } + + const activeConf = activeConferenceRef.current; + if (activeConf) { + // Add to active conference + const updated = { + ...activeConf, + messages: [...activeConf.messages, message] + }; + activeConferenceRef.current = updated; + setFeed(prev => prev.map(item => + item.type === "conference" && item.id === activeConf.id + ? { ...item, data: updated } + : item + )); + return message; + } else { + // No active conference, show as standalone + const feedItem = { type: "message", id: message.id, data: message }; + setFeed(prev => [feedItem, ...prev].slice(0, MAX_FEED_ITEMS)); + return feedItem; + } + } + + // Handle other feed events (agent_message, memory, system, etc.) + const feedEventTypes = ["agent_message", "memory", "system", "day_start", "day_complete", "day_error"]; + if (!feedEventTypes.includes(evt.type)) { + return null; + } + + const feedItem = eventToFeedItem(evt); + if (!feedItem) { + return null; + } + + const activeConf = activeConferenceRef.current; + if (activeConf) { + // Add to active conference + const updated = { + ...activeConf, + messages: [...activeConf.messages, feedItem.data] + }; + activeConferenceRef.current = updated; + setFeed(prev => prev.map(item => + item.type === "conference" && item.id === activeConf.id + ? { ...item, data: updated } + : item + )); + return feedItem.data; + } else { + // No active conference, add as standalone + setFeed(prev => [feedItem, ...prev].slice(0, MAX_FEED_ITEMS)); + return feedItem; + } + }, []); + + /** + * Add a system message to the feed + */ + const addSystemMessage = useCallback((content) => { + const message = { + id: generateId("sys"), + timestamp: Date.now(), + agent: "System", + role: "System", + content + }; + + const activeConf = activeConferenceRef.current; + if (activeConf) { + const updated = { + ...activeConf, + messages: [...activeConf.messages, message] + }; + activeConferenceRef.current = updated; + setFeed(prev => prev.map(item => + item.type === "conference" && item.id === activeConf.id + ? { ...item, data: updated } + : item + )); + } else { + const feedItem = { type: "message", id: message.id, data: message }; + setFeed(prev => [feedItem, ...prev].slice(0, MAX_FEED_ITEMS)); + } + return message; + }, []); + + /** + * Clear all feed items and reset active conference + */ + const clearFeed = useCallback(() => { + setFeed([]); + activeConferenceRef.current = null; + }, []); + + /** + * Check if there's an active conference + */ + const hasActiveConference = useCallback(() => { + return activeConferenceRef.current !== null; + }, []); + + return { + feed, + setFeed, + processHistoricalFeed, + processFeedEvent, + addSystemMessage, + clearFeed, + hasActiveConference + }; +} + +export default useFeedProcessor; diff --git a/evotraders/frontend/src/index.css b/evotraders/frontend/src/index.css new file mode 100644 index 0000000..08a3ac9 --- /dev/null +++ b/evotraders/frontend/src/index.css @@ -0,0 +1,68 @@ +:root { + font-family: system-ui, Avenir, Helvetica, Arial, sans-serif; + line-height: 1.5; + font-weight: 400; + + color-scheme: light dark; + color: rgba(255, 255, 255, 0.87); + background-color: #242424; + + font-synthesis: none; + text-rendering: optimizeLegibility; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} + +a { + font-weight: 500; + color: #646cff; + text-decoration: inherit; +} +a:hover { + color: #535bf2; +} + +body { + margin: 0; + display: flex; + place-items: center; + min-width: 320px; + min-height: 100vh; +} + +h1 { + font-size: 3.2em; + line-height: 1.1; +} + +button { + border-radius: 8px; + border: 1px solid transparent; + padding: 0.6em 1.2em; + font-size: 1em; + font-weight: 500; + font-family: inherit; + background-color: #1a1a1a; + cursor: pointer; + transition: border-color 0.25s; +} +button:hover { + border-color: #646cff; +} +button:focus, +button:focus-visible { + outline: 4px auto -webkit-focus-ring-color; +} + +@media (prefers-color-scheme: light) { + :root { + color: #213547; + background-color: #ffffff; + } + a:hover { + color: #747bff; + } + button { + background-color: #f9f9f9; + } +} diff --git a/evotraders/frontend/src/main.jsx b/evotraders/frontend/src/main.jsx new file mode 100644 index 0000000..44931db --- /dev/null +++ b/evotraders/frontend/src/main.jsx @@ -0,0 +1,8 @@ +import React from 'react' +import ReactDOM from 'react-dom/client' +import App from './App.jsx' +import "./index.css"; + +ReactDOM.createRoot(document.getElementById('root')).render( + +) diff --git a/evotraders/frontend/src/services/websocket.js b/evotraders/frontend/src/services/websocket.js new file mode 100644 index 0000000..dc19fc1 --- /dev/null +++ b/evotraders/frontend/src/services/websocket.js @@ -0,0 +1,192 @@ +/** + * WebSocket Client for Read-Only Connection + * Handles connection, reconnection, and heartbeat + */ + +import { WS_URL } from "../config/constants"; + +export class ReadOnlyClient { + constructor(onEvent, { wsUrl = WS_URL, reconnectDelay = 3000, heartbeatInterval = 5000 } = {}) { + this.onEvent = onEvent; + this.wsUrl = wsUrl; + this.baseReconnectDelay = reconnectDelay; + this.reconnectDelay = reconnectDelay; + this.maxReconnectDelay = 30000; + this.heartbeatInterval = heartbeatInterval; + this.ws = null; + this.shouldReconnect = false; + this.reconnectTimer = null; + this.heartbeatTimer = null; + this.reconnectAttempts = 0; + this.lastPongTime = 0; + } + + connect() { + this.shouldReconnect = true; + this.reconnectAttempts = 0; + this.reconnectDelay = this.baseReconnectDelay; + this._connect(); + } + + _connect() { + if (!this.shouldReconnect) { + return; + } + + // Clear any existing connection + if (this.ws) { + this.ws.onopen = null; + this.ws.onmessage = null; + this.ws.onerror = null; + this.ws.onclose = null; + if (this.ws.readyState === WebSocket.OPEN || this.ws.readyState === WebSocket.CONNECTING) { + this.ws.close(); + } + this.ws = null; + } + + this.ws = new WebSocket(this.wsUrl); + + this.ws.onopen = () => { + this.reconnectAttempts = 0; + this.reconnectDelay = this.baseReconnectDelay; + this.lastPongTime = Date.now(); + this._safeEmit({ type: "system", content: "Connected to live server" }); + console.log("WebSocket connected"); + this._startHeartbeat(); + }; + + this.ws.onmessage = (ev) => { + try { + const msg = JSON.parse(ev.data); + + // Update pong time for any message (server is alive) + this.lastPongTime = Date.now(); + + if (msg.type === "pong") { + return; + } + + console.log("[WebSocket] Message received:", msg.type || "unknown"); + this._safeEmit(msg); + } catch (e) { + console.error("[WebSocket] Parse error:", e); + } + }; + + this.ws.onerror = (error) => { + console.error("WebSocket error:", error); + }; + + this.ws.onclose = (event) => { + const code = event.code || "Unknown"; + console.log(`[WebSocket] Connection closed: Code=${code}, WasClean=${event.wasClean}`); + + this._stopHeartbeat(); + this.ws = null; + + // Always attempt reconnect if shouldReconnect is true + if (this.shouldReconnect) { + this.reconnectAttempts++; + // Exponential backoff with cap + this.reconnectDelay = Math.min( + this.baseReconnectDelay * Math.pow(1.5, this.reconnectAttempts), + this.maxReconnectDelay + ); + + this._safeEmit({ + type: "system", + content: "Try to connect to data server..." + }); + + if (this.reconnectTimer) { + clearTimeout(this.reconnectTimer); + } + + this.reconnectTimer = setTimeout(() => { + console.log(`[WebSocket] Reconnect attempt ${this.reconnectAttempts}...`); + this._connect(); + }, this.reconnectDelay); + } + }; + } + + _safeEmit(msg) { + try { + this.onEvent(msg); + } catch (e) { + console.error("[WebSocket] Error in event handler:", e); + } + } + + _startHeartbeat() { + this._stopHeartbeat(); + this.lastPongTime = Date.now(); + + this.heartbeatTimer = setInterval(() => { + this._sendPing(); + + // Check for stale connection (no response in 60s) + const timeSinceLastPong = Date.now() - this.lastPongTime; + if (timeSinceLastPong > 60000 && this.ws) { + console.warn("[WebSocket] Connection appears stale, forcing reconnect"); + this.ws.close(); + } + }, this.heartbeatInterval); + } + + _sendPing() { + if (this.ws && this.ws.readyState === WebSocket.OPEN) { + try { + this.ws.send(JSON.stringify({ type: "ping" })); + } catch (e) { + console.error("Heartbeat send error:", e); + } + } + } + + _stopHeartbeat() { + if (this.heartbeatTimer) { + clearInterval(this.heartbeatTimer); + this.heartbeatTimer = null; + } + } + + send(message) { + if (this.ws && this.ws.readyState === WebSocket.OPEN) { + try { + const messageStr = typeof message === "string" ? message : JSON.stringify(message); + this.ws.send(messageStr); + return true; + } catch (e) { + console.error("Send error:", e); + return false; + } + } else { + console.warn("WebSocket is not connected, cannot send message"); + return false; + } + } + + disconnect() { + this.shouldReconnect = false; + this._stopHeartbeat(); + if (this.reconnectTimer) { + clearTimeout(this.reconnectTimer); + this.reconnectTimer = null; + } + if (this.ws) { + this.ws.onopen = null; + this.ws.onmessage = null; + this.ws.onerror = null; + this.ws.onclose = null; + try { + this.ws.close(); + } catch (e) { + console.error("Close error:", e); + } + } + this.ws = null; + } +} + diff --git a/evotraders/frontend/src/styles/GlobalStyles.jsx b/evotraders/frontend/src/styles/GlobalStyles.jsx new file mode 100644 index 0000000..df076b1 --- /dev/null +++ b/evotraders/frontend/src/styles/GlobalStyles.jsx @@ -0,0 +1,1876 @@ +import React from 'react'; + +/** + * Global CSS Styles for the EvoTraders Platform + * Terminal-inspired, minimal, monochrome design + */ +export default function GlobalStyles() { + return ( + + ); +} + diff --git a/evotraders/frontend/src/utils/formatters.js b/evotraders/frontend/src/utils/formatters.js new file mode 100644 index 0000000..78364e8 --- /dev/null +++ b/evotraders/frontend/src/utils/formatters.js @@ -0,0 +1,85 @@ +/** + * Formatting utility functions + */ + +/** + * Format time from timestamp + */ +export function formatTime(ts) { + try { + const d = new Date(ts); + return d.toLocaleString([], { + month: "2-digit", + day: "2-digit", + hour: "2-digit", + minute: "2-digit" + }); + } catch { + return ""; + } +} + +/** + * Format date and time from timestamp + */ +export function formatDateTime(ts) { + try { + const d = new Date(ts); + const date = d.toLocaleDateString("en-US", { month: "short", day: "numeric" }); + const time = d.toLocaleTimeString("en-US", { hour: "2-digit", minute: "2-digit", hour12: false }); + return `${date} ${time}`; + } catch { + return ""; + } +} + +/** + * Format number with commas (no decimals) + */ +export function formatNumber(num) { + if (!isFinite(num)) { + return "-"; + } + return Math.abs(num).toLocaleString(undefined, { maximumFractionDigits: 0 }); +} + +/** + * Format full number with commas for Y-axis + */ +export function formatFullNumber(num) { + if (!isFinite(num)) { + return "-"; + } + return num.toLocaleString(undefined, { maximumFractionDigits: 0 }); +} + +/** + * Format ticker price with appropriate decimal places + */ +export function formatTickerPrice(price) { + if (!isFinite(price)) { + return "-"; + } + if (price >= 1000) { + return price.toLocaleString(undefined, { minimumFractionDigits: 2, maximumFractionDigits: 2 }); + } else if (price >= 1) { + return price.toFixed(2); + } else { + return price.toFixed(4); + } +} + +/** + * Calculate duration between two timestamps + */ +export function calculateDuration(start, end) { + const diff = end - start; + const minutes = Math.floor(diff / 60000); + if (minutes < 60) { + return `${minutes}m`; + } + const hours = Math.floor(minutes / 60); + const mins = minutes % 60; + return `${hours}h ${mins}m`; +} + diff --git a/evotraders/frontend/src/utils/modelIcons.js b/evotraders/frontend/src/utils/modelIcons.js new file mode 100644 index 0000000..c8b056d --- /dev/null +++ b/evotraders/frontend/src/utils/modelIcons.js @@ -0,0 +1,396 @@ +/** + * Model Icons and Styling Utilities + * + * Provides icon and styling configuration for different LLM models + */ + +import { LLM_MODEL_LOGOS } from "../config/constants"; + +/** + * Get model icon and styling based on model name + * @param {string} modelName - The model name (e.g., "qwen-plus", "gpt-4o") + * @param {string} modelProvider - The model provider (e.g., "OPENAI", "ANTHROPIC") + * @returns {object} Icon configuration { logoPath, color, bgColor, label, provider } + */ +export function getModelIcon(modelName, modelProvider) { + if (!modelName) { + return { + logoPath: null, + color: "#666666", + bgColor: "#f5f5f5", + label: "Default", + provider: "Default" + }; + } + + const name = modelName.toLowerCase(); + const provider = (modelProvider || "").toUpperCase(); + + // ========== Priority 1: Model Name Based Detection (Highest Priority) ========== + // This ensures we infer the correct logo from model name even if provider is OPENAI + + // GLM Models (智谱AI) + if (name.includes("glm")) { + return { + logoPath: LLM_MODEL_LOGOS["Zhipu AI"], + color: "#4A90E2", + bgColor: "#E3F2FD", + label: "GLM-4.6", + provider: "Zhipu AI" + }; + } + + // Qwen Models (阿里云/通义千问) + if (name.includes("qwen")) { + return { + logoPath: LLM_MODEL_LOGOS["Alibaba"], + color: "#FF6A00", + bgColor: "#FFF3E0", + label: name.includes("max") ? "Qwen-Max" : name.includes("plus") ? "Qwen-Plus" : "Qwen", + provider: "Alibaba" + }; + } + + // DeepSeek Models + if (name.includes("deepseek")) { + return { + logoPath: LLM_MODEL_LOGOS["DeepSeek"], + color: "#1976D2", + bgColor: "#E3F2FD", + label: "DeepSeek-V3", + provider: "DeepSeek" + }; + } + + // Moonshot/Kimi Models (月之暗面) + if (name.includes("moonshot") || name.includes("kimi")) { + return { + logoPath: LLM_MODEL_LOGOS["Moonshot"], + color: "#7B68EE", + bgColor: "#F3E5F5", + label: "Kimi-K2", + provider: "Moonshot" + }; + } + + // Anthropic Claude Models (check model name first) + if (name.includes("claude")) { + return { + logoPath: LLM_MODEL_LOGOS["Anthropic"], + color: "#D97706", + bgColor: "#FEF3C7", + label: "Claude", + provider: "Anthropic" + }; + } + + // Google Gemini Models (check model name first) + if (name.includes("gemini")) { + return { + logoPath: LLM_MODEL_LOGOS["Google"], + color: "#4285F4", + bgColor: "#E8F0FE", + label: "Gemini", + provider: "Google" + }; + } + + // OpenAI GPT Models (check model name first) + if (name.includes("gpt") || name.includes("o1") || name.includes("o2") || name.includes("o3")) { + return { + logoPath: LLM_MODEL_LOGOS["OpenAI"], + color: "#10A37F", + bgColor: "#E8F5E9", + label: name.includes("4o") ? "GPT-4o" : name.includes("4.5") ? "GPT-4.5" : name.includes("4") ? "GPT-4" : name.includes("3.5") ? "GPT-3.5" : "OpenAI", + provider: "OpenAI" + }; + } + + // ========== Priority 2: Provider Based Detection (Fallback) ========== + // Only use provider if model name doesn't match any known patterns + + // Anthropic Claude Models (provider fallback) + if (provider === "ANTHROPIC") { + return { + logoPath: LLM_MODEL_LOGOS["Anthropic"], + color: "#D97706", + bgColor: "#FEF3C7", + label: "Claude", + provider: "Anthropic" + }; + } + + // Google Gemini Models (provider fallback) + if (provider === "GOOGLE") { + return { + logoPath: LLM_MODEL_LOGOS["Google"], + color: "#4285F4", + bgColor: "#E8F0FE", + label: "Gemini", + provider: "Google" + }; + } + + // OpenAI Models (provider fallback - only if model name doesn't match) + if (provider === "OPENAI") { + return { + logoPath: LLM_MODEL_LOGOS["OpenAI"], + color: "#10A37F", + bgColor: "#E8F5E9", + label: "OpenAI", + provider: "OpenAI" + }; + } + + // Groq Models + if (provider === "GROQ") { + return { + logoPath: LLM_MODEL_LOGOS["Groq"], + color: "#DC2626", + bgColor: "#FEE2E2", + label: "Groq", + provider: "Groq" + }; + } + + // Ollama Models + if (provider === "OLLAMA") { + return { + logoPath: LLM_MODEL_LOGOS["Ollama"], + color: "#000000", + bgColor: "#F5F5F5", + label: "Ollama", + provider: "Ollama" + }; + } + + // OpenRouter Models + if (provider === "OPENROUTER") { + return { + logoPath: null, + color: "#8B5CF6", + bgColor: "#F5F3FF", + label: "OpenRouter", + provider: "OpenRouter" + }; + } + + // GigaChat Models + if (provider === "GIGACHAT") { + return { + logoPath: null, + color: "#9333EA", + bgColor: "#FAF5FF", + label: "GigaChat", + provider: "GigaChat" + }; + } + + // Default fallback + return { + logoPath: null, + color: "#666666", + bgColor: "#f5f5f5", + label: modelName.substring(0, 15), + provider: provider || "Unknown" + }; +} + +/** + * Get short model name for display + * @param {string} modelName - The full model name + * @returns {string} Short version of the model name (preserves full version numbers and suffixes) + */ +export function getShortModelName(modelName) { + if (!modelName) { + return "N/A"; + } + + const name = modelName.toLowerCase(); + + // Helper function to capitalize first letter of each word + const capitalizeWords = (str) => { + return str.split(/[-_\s]/).map(word => + word.charAt(0).toUpperCase() + word.slice(1) + ).join("-"); + }; + + // GLM - preserve version numbers + if (name.includes("glm")) { + // Extract version number if present (e.g., glm-4.6, glm-4.5) + const versionMatch = name.match(/glm[_-]?(\d+\.\d+)/); + if (versionMatch) { + return `GLM-${versionMatch[1]}`; + } + return "GLM-4.6"; // Default + } + + // Qwen - preserve full version and suffixes + if (name.includes("qwen")) { + // Match patterns like: qwen3-max-preview, qwen-max, qwen-plus, qwen-flash + if (name.includes("qwen3-max")) { + // Extract suffix if present (e.g., -preview) + const fullMatch = name.match(/qwen3-max[_-]?([a-z0-9-]+)?/); + if (fullMatch && fullMatch[1]) { + return `Qwen3-Max-${capitalizeWords(fullMatch[1])}`; + } + return "Qwen3-Max"; + } + if (name.includes("qwen-max")) { + const fullMatch = name.match(/qwen-max[_-]?([a-z0-9-]+)?/); + if (fullMatch && fullMatch[1]) { + return `Qwen-Max-${capitalizeWords(fullMatch[1])}`; + } + return "Qwen-Max"; + } + if (name.includes("qwen-plus")) { + const fullMatch = name.match(/qwen-plus[_-]?([a-z0-9-]+)?/); + if (fullMatch && fullMatch[1]) { + return `Qwen-Plus-${capitalizeWords(fullMatch[1])}`; + } + return "Qwen-Plus"; + } + if (name.includes("qwen-flash")) { + const fullMatch = name.match(/qwen-flash[_-]?([a-z0-9-]+)?/); + if (fullMatch && fullMatch[1]) { + return `Qwen-Flash-${capitalizeWords(fullMatch[1])}`; + } + return "Qwen-Flash"; + } + // Generic qwen with version + const versionMatch = name.match(/qwen[_-]?(\d+[a-z0-9-]*)?/); + if (versionMatch && versionMatch[1]) { + return `Qwen-${capitalizeWords(versionMatch[1])}`; + } + return "Qwen"; + } + + // DeepSeek - preserve full version numbers and suffixes + if (name.includes("deepseek")) { + // Match patterns like: deepseek-v3.1, deepseek-v3.2-exp, deepseek-v3 + // First try to match with version and suffix + const fullMatch = name.match(/deepseek[_-]?v?(\d+\.\d+[a-z0-9]*)[_-]?([a-z0-9-]+)?/); + if (fullMatch) { + const version = fullMatch[1]; + const suffix = fullMatch[2]; + if (suffix) { + return `DeepSeek-V${version}-${capitalizeWords(suffix)}`; + } + return `DeepSeek-V${version}`; + } + // Try to match just version + const versionMatch = name.match(/deepseek[_-]?v?(\d+\.\d+)/); + if (versionMatch) { + return `DeepSeek-V${versionMatch[1]}`; + } + // Fallback to generic DeepSeek + return "DeepSeek"; + } + + // Moonshot/Kimi - preserve full model names + if (name.includes("moonshot") || name.includes("kimi")) { + // Match patterns like: moonshot-kimi-k2-instruct, kimi-k2-instruct + // First check if it contains k2 + if (name.includes("k2")) { + // Try to extract suffix after k2 (e.g., -instruct) + const k2Match = name.match(/k2[_-]?([a-z0-9-]+)?/); + if (k2Match && k2Match[1]) { + return `Moonshot-Kimi-K2-${capitalizeWords(k2Match[1])}`; + } + return "Moonshot-Kimi-K2"; + } + if (name.includes("kimi")) { + return "Kimi"; + } + return "Moonshot"; + } + + // OpenAI - preserve full version numbers + if (name.includes("gpt") || name.includes("o1") || name.includes("o2") || name.includes("o3")) { + // Match patterns like: gpt-4o, gpt-4.5, gpt-4, gpt-3.5-turbo + if (name.includes("gpt-4o")) { + const suffixMatch = name.match(/gpt-4o[_-]?([a-z0-9-]+)?/); + if (suffixMatch && suffixMatch[1]) { + return `GPT-4o-${capitalizeWords(suffixMatch[1])}`; + } + return "GPT-4o"; + } + if (name.includes("gpt-4.5")) { + const suffixMatch = name.match(/gpt-4\.5[_-]?([a-z0-9-]+)?/); + if (suffixMatch && suffixMatch[1]) { + return `GPT-4.5-${capitalizeWords(suffixMatch[1])}`; + } + return "GPT-4.5"; + } + if (name.includes("gpt-4")) { + const suffixMatch = name.match(/gpt-4[_-]?([a-z0-9-]+)?/); + if (suffixMatch && suffixMatch[1]) { + return `GPT-4-${capitalizeWords(suffixMatch[1])}`; + } + return "GPT-4"; + } + if (name.includes("gpt-3.5")) { + const suffixMatch = name.match(/gpt-3\.5[_-]?([a-z0-9-]+)?/); + if (suffixMatch && suffixMatch[1]) { + return `GPT-3.5-${capitalizeWords(suffixMatch[1])}`; + } + return "GPT-3.5"; + } + // O-series models + if (name.includes("o3")) { + return "O3"; + } + if (name.includes("o2")) { + return "O2"; + } + if (name.includes("o1")) { + return "O1"; + } + return "OpenAI"; + } + + // Claude - preserve full model names + if (name.includes("claude")) { + if (name.includes("claude-opus")) { + const versionMatch = name.match(/claude-opus[_-]?(\d+[a-z0-9-]*)?/); + if (versionMatch && versionMatch[1]) { + return `Claude-Opus-${capitalizeWords(versionMatch[1])}`; + } + return "Claude-Opus"; + } + if (name.includes("claude-sonnet")) { + const versionMatch = name.match(/claude-sonnet[_-]?(\d+[a-z0-9-]*)?/); + if (versionMatch && versionMatch[1]) { + return `Claude-Sonnet-${capitalizeWords(versionMatch[1])}`; + } + return "Claude-Sonnet"; + } + if (name.includes("claude-haiku")) { + const versionMatch = name.match(/claude-haiku[_-]?(\d+[a-z0-9-]*)?/); + if (versionMatch && versionMatch[1]) { + return `Claude-Haiku-${capitalizeWords(versionMatch[1])}`; + } + return "Claude-Haiku"; + } + return "Claude"; + } + + // Google Gemini + if (name.includes("gemini")) { + const versionMatch = name.match(/gemini[_-]?([a-z0-9.-]+)?/); + if (versionMatch && versionMatch[1]) { + return `Gemini-${capitalizeWords(versionMatch[1])}`; + } + return "Gemini"; + } + + // If no specific pattern matched, return formatted original name + // Truncate only if extremely long (over 30 chars) + if (modelName.length > 30) { + return capitalizeWords(modelName.substring(0, 27)) + "..."; + } + + // Return formatted original name + return capitalizeWords(modelName); +} + diff --git a/evotraders/frontend/tailwind.config.js b/evotraders/frontend/tailwind.config.js new file mode 100644 index 0000000..cc653b7 --- /dev/null +++ b/evotraders/frontend/tailwind.config.js @@ -0,0 +1,60 @@ +import tailwindcssAnimate from "tailwindcss-animate"; + +/** @type {import('tailwindcss').Config} */ +const config = { + darkMode: ["class"], + content: ["./index.html", "./src/**/*.{js,ts,jsx,tsx}"], + theme: { + extend: { + borderRadius: { + lg: "var(--radius)", + md: "calc(var(--radius) - 2px)", + sm: "calc(var(--radius) - 4px)" + }, + colors: { + background: "hsl(var(--background))", + foreground: "hsl(var(--foreground))", + card: { + DEFAULT: "hsl(var(--card))", + foreground: "hsl(var(--card-foreground))" + }, + popover: { + DEFAULT: "hsl(var(--popover))", + foreground: "hsl(var(--popover-foreground))" + }, + primary: { + DEFAULT: "hsl(var(--primary))", + foreground: "hsl(var(--primary-foreground))" + }, + secondary: { + DEFAULT: "hsl(var(--secondary))", + foreground: "hsl(var(--secondary-foreground))" + }, + muted: { + DEFAULT: "hsl(var(--muted))", + foreground: "hsl(var(--muted-foreground))" + }, + accent: { + DEFAULT: "hsl(var(--accent))", + foreground: "hsl(var(--accent-foreground))" + }, + destructive: { + DEFAULT: "hsl(var(--destructive))", + foreground: "hsl(var(--destructive-foreground))" + }, + border: "hsl(var(--border))", + input: "hsl(var(--input))", + ring: "hsl(var(--ring))", + chart: { + "1": "hsl(var(--chart-1))", + "2": "hsl(var(--chart-2))", + "3": "hsl(var(--chart-3))", + "4": "hsl(var(--chart-4))", + "5": "hsl(var(--chart-5))" + } + } + } + }, + plugins: [tailwindcssAnimate, require("tailwindcss-animate")], +}; +export default config; diff --git a/evotraders/frontend/tsconfig.json b/evotraders/frontend/tsconfig.json new file mode 100644 index 0000000..b945d5f --- /dev/null +++ b/evotraders/frontend/tsconfig.json @@ -0,0 +1,23 @@ +{ + "compilerOptions": { + "baseUrl": "src", + "paths": { + "@/*": ["*"] + }, + "module": "nodenext", + "target": "esnext", + "types": [], + "sourceMap": true, + "declaration": true, + "declarationMap": true, + "noUncheckedIndexedAccess": true, + "exactOptionalPropertyTypes": true, + "strict": true, + "jsx": "react-jsx", + "verbatimModuleSyntax": true, + "isolatedModules": true, + "noUncheckedSideEffectImports": true, + "moduleDetection": "force", + "skipLibCheck": true + } +} diff --git a/evotraders/frontend/vite.config.js b/evotraders/frontend/vite.config.js new file mode 100644 index 0000000..7391a96 --- /dev/null +++ b/evotraders/frontend/vite.config.js @@ -0,0 +1,16 @@ +import { defineConfig } from "vite"; +import react from "@vitejs/plugin-react"; +import tsconfigPaths from "vite-tsconfig-paths"; +import tailwindcss from "@tailwindcss/vite"; + +export default defineConfig({ + server: { + allowedHosts: ["localhost", "trading.evoagents.cn","www.evoagents.cn"] + }, + plugins: [react(), tsconfigPaths(),tailwindcss()], + preview: { + host: "0.0.0.0", + port: 4173 + }, +}); + diff --git a/evotraders/pyproject.toml b/evotraders/pyproject.toml new file mode 100644 index 0000000..bc48298 --- /dev/null +++ b/evotraders/pyproject.toml @@ -0,0 +1,80 @@ +[build-system] +requires = ["setuptools>=61.0", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "evotraders" +version = "0.1.0" +description = "EvoTraders: A self-evolving multi-agent trading system" +readme = "README.md" +requires-python = ">=3.9" +license = {text = "MIT"} +authors = [ + {name = "EvoTraders Team "} +] +keywords = ["trading", "ai", "multi-agent", "fintech", "algorithmic-trading"] +classifiers = [ + "Development Status :: 3 - Alpha", + "Intended Audience :: Developers", + "Intended Audience :: Financial and Insurance Industry", + "License :: OSI Approved :: MIT License", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Topic :: Office/Business :: Financial :: Investment", +] + +dependencies = [ + "agentscope>=1.0.8", + "reme-ai>=0.2.0.3", + "asyncio>=3.4.3", + "rich>=13.6.0", + "websockets>=12.0", + "websocket-client>=1.6.0", + "python-dotenv>=1.0.0", + "finnhub-python>=2.4.25", + "numpy>=1.24.0", + "pandas>=2.0.0", + "matplotlib>=3.7.0", + "seaborn>=0.12.0", + "pandas-market-calendars>=5.0.0", + "typer>=0.12.5", + "openai>=2.9.0", + "anthropic>=0.20.0", + "dotenv", + "typer", +] + + +[project.optional-dependencies] +dev = [ + "pytest>=8.3.3", + "ruff>=0.6.9", + "black>=25.0.0" +] + +[project.urls] +Homepage = "http://trading.evoagents.cn" +Repository = "https://github.com/agentscope-ai/agentscope-samples/evotraders" +Documentation = "https://github.com/agentscope-ai/agentscope-samples/evotraders/README.md" +"Bug Tracker" = "https://github.com/agentscope-ai/agentscope-samples/issues" + +[project.scripts] +evotraders = "backend.cli:app" + +[tool.setuptools] +packages = ["backend", "backend.agents", "backend.config", + "backend.data", "backend.llm", + "backend.tools", "backend.utils", "backend.services"] + +[tool.ruff] +line-length = 88 +target-version = "py39" + +[tool.pytest.ini_options] +testpaths = ["backend/tests"] +python_files = ["test_*.py"] +python_classes = ["Test*"] +python_functions = ["test_*"] +

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