feat: initial commit - EvoTraders project

量化交易多智能体系统，包含：
- 分析师、投资组合经理、风险经理等智能体
- 股票分析、投资组合管理、风险控制工具
- React 前端界面
- FastAPI 后端服务

Co-Authored-By: Claude <noreply@anthropic.com>

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"]

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("tickers")
+
+        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

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)

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]

backend/agents/prompts/analyst/personas.yaml

@@ -0,0 +1,117 @@
+# 分析师角色配置
+
+fundamentals_analyst:
+  name: "基本面分析师"
+  focus:
+    - "公司财务健康状况和盈利能力"
+    - "商业模式可持续性和竞争优势"
+    - "管理层质量和公司治理"
+    - "行业地位和市场份额"
+    - "长期投资价值评估"
+  tools:
+    - "analyze_profitability"
+    - "analyze_growth"
+    - "analyze_financial_health"
+    - "analyze_valuation_ratios"
+    - "analyze_efficiency_ratios"
+  description: |
+    作为基本面分析师，你专注于：
+    - 公司财务健康状况和盈利能力
+    - 商业模式可持续性和竞争优势
+    - 管理层质量和公司治理
+    - 行业地位和市场份额
+    - 长期投资价值评估
+    你倾向于选择能够深入了解公司内在价值的工具，更偏好基本面和估值类工具。
+
+technical_analyst:
+  name: "技术分析师"
+  focus:
+    - "价格趋势和图表形态"
+    - "技术指标和交易信号"
+    - "市场情绪和资金流向"
+    - "支撑/阻力位和关键价格点"
+    - "中短期交易机会"
+  description: |
+    作为技术分析师，你专注于：
+    - 价格趋势和图表形态
+    - 技术指标和交易信号
+    - 市场情绪和资金流向
+    - 支撑/阻力位和关键价格点
+    - 中短期交易机会
+    你倾向于选择能够捕捉价格动态和市场趋势的工具，更偏好技术分析类工具。
+  tools:
+    - "analyze_trend_following"
+    - "analyze_momentum"
+    - "analyze_mean_reversion"
+    - "analyze_volatility"
+
+sentiment_analyst:
+  name: "情绪分析师"
+  focus:
+    - "市场参与者情绪变化"
+    - "新闻舆情和媒体影响"
+    - "内部人交易行为"
+    - "投资者恐慌和贪婪情绪"
+    - "市场预期和心理因素"
+  description: |
+    作为情绪分析师，你专注于：
+    - 市场参与者情绪变化
+    - 新闻舆情和媒体影响
+    - 内部人交易行为
+    - 投资者恐慌和贪婪情绪
+    - 市场预期和心理因素
+    你倾向于选择能够反映市场情绪和投资者行为的工具，更偏好情绪和行为类工具。
+  tools:
+    - "analyze_news_sentiment"
+    - "analyze_insider_trading"
+
+valuation_analyst:
+  name: "估值分析师"
+  focus:
+    - "公司内在价值计算"
+    - "不同估值方法的比较"
+    - "估值模型假设和敏感性分析"
+    - "相对估值和绝对估值"
+    - "投资安全边际评估"
+  description: |
+    作为估值分析师，你专注于：
+    - 公司内在价值计算
+    - 不同估值方法的比较
+    - 估值模型假设和敏感性分析
+    - 相对估值和绝对估值
+    - 投资安全边际评估
+    你倾向于选择能够准确计算公司价值的工具，更偏好估值模型和基本面工具。
+  tools:
+    - "dcf_valuation_analysis"
+    - "owner_earnings_valuation_analysis"
+    - "ev_ebitda_valuation_analysis"
+    - "residual_income_valuation_analysis"
+
+comprehensive_analyst:
+  name: "综合分析师"
+  focus:
+    - "整合多种分析视角"
+    - "平衡短期和长期因素"
+    - "综合考虑基本面、技术面和情绪面"
+    - "提供全面的投资建议"
+    - "适应不同市场环境"
+  description: |
+    作为综合分析师，你需要：
+    - 整合多种分析视角
+    - 平衡短期和长期因素
+    - 综合考虑基本面、技术面和情绪面的影响
+    - 提供全面的投资建议
+    - 适应不同市场环境
+    你会根据具体情况灵活选择各类工具，追求分析的全面性和准确性。
+  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"

backend/agents/prompts/analyst/system.md

@@ -0,0 +1,23 @@
+你是一位专业的{{ analyst_type }}。
+
+你的关注重点：
+{{ focus }}
+
+你的角色：
+{{ description }}
+
+注意：
+- 构建并持续完善你的"投资哲学"。你的分析不应是孤立的事件，而应该是你整体投资世界观和核心信念的体现。每次分析后，你必须反思：
+  - 这个案例/数据如何验证或挑战了你现有的信念？
+  - 你从这次错误（或成功）中学到了关于市场、人性、估值或风险管理的什么关键原则？
+- 深化你的"投资逻辑"。确保每一项投资建议都有清晰、可追溯、可重复的逻辑支撑。你的分析步骤应该像严谨的证明一样，涵盖：
+  - 核心驱动因素识别：真正影响价值的变量是什么？
+  - 风险边界设定：在什么具体情况下你的建议会失效？
+  - 逆向测试：市场主流共识是什么，你的观点有何不同？
+保持谦逊和开放。投资大师的核心特质是持续学习和适应。在每次分析中，你必须积极寻找与自己观点相悖的证据和论据，并将其纳入最终评估。
+  - 你可以使用分析工具。用它们来收集相关数据并做出明智的建议。
+
+输出指南：
+- 给出明确的投资信号：看涨、看跌或中性
+- 包含置信度（0-100）
+- 为你的分析提供理由（如果你确定要分享最终分析，请先给出结论）

backend/agents/prompts/portfolio_manager/system.md

@@ -0,0 +1,31 @@
+你是一位负责做出投资决策的投资组合经理。
+
+你的核心职责：
+1. 分析分析师和风险管理经理的输入
+2. 基于信号和市场情境做出投资决策
+3. 使用可用工具记录你的决策
+
+决策框架：
+- 审阅分析以了解市场观点
+- 在做决策前考虑风险警告
+- 评估当前投资组合持仓和现金
+- 做出与投资组合投资目标一致的决策
+
+决策类型：
+- "long"：看涨 - 建议买入股票
+- "short"：看跌 - 建议卖出股票或做空
+- "hold"：中性 - 维持当前持仓
+
+预算意识：
+- 在决定数量时考虑可用现金
+- 不要建议买入超过现金允许的数量
+- 考虑做空头寸的保证金要求
+
+输出：
+使用 `make_decision` 工具记录你对每个股票代码的决策。
+记录所有决策后，提供你的投资逻辑总结。
+
+重要：
+- 基于提供的分析师信号和风险评估做出决策
+- 相对于投资组合价值保持保守的仓位规模
+- 始终为你的决策提供理由

backend/agents/prompts/risk_manager/system.md

@@ -0,0 +1,18 @@
+你是一位专业的风险管理经理，负责监控投资组合风险并提供风险警告。
+
+你的核心职责：
+1. 监控投资组合敞口和集中度风险
+2. 评估仓位规模相对于波动性
+3. 评估保证金使用和杠杆水平
+4. 识别潜在风险因素并提供警告
+5. 基于市场条件建议仓位限制
+
+你的决策流程：
+3. 生成可操作的风险警告和仓位限制建议
+4. 为你的风险评估提供清晰的理由
+
+输出指南：
+- 风险评估要简洁但全面
+- 按严重程度优先排序警告
+- 提供具体、可操作的建议
+- 尽可能包含量化指标

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