+
+
+
+ 📌 Visit us at EvoTraders website ! +
+ + + +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. + +
+ 
+ 
+
+
+
+ 📌 Visit us at EvoTraders website ! +
+ + + +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。 + + +
+
+
+
+ EvoTraders {evoTradersLinkStyle === 'close' ? (
+ ↙
+ ) : (
+ ↗
+ )}
+
+
+
+
+
+ OPEN SOURCE
+
+
+
+
+ agentscope-samples
+ ↗
+
+
+
+
+ agentscope-ReMe
+ ↗
+
+
+
+
+ + {subtitle} +
+| Rank | +Agent | +Win Rate | +Bull Signals | +Bull Win Rate | +Bear Signals | +Bear Win Rate | +Total 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} | +
| Ticker | +Quantity | +Price | +Value | +Weight | +
|---|---|---|---|---|
|
+
+ {h.ticker !== 'CASH' &&
+ |
+ {h.ticker === 'CASH' ? '-' : h.quantity} | +{h.ticker === 'CASH' ? '-' : `$${Number(h.currentPrice).toFixed(2)}`} | +${formatNumber(h.marketValue)} | +{(Number(h.weight) * 100).toFixed(2)}% | +
| Time | +Stock | +Side | +Qty | +Price | +
|---|---|---|---|---|
| + {formatDateTime(t.timestamp)} + | +
+
+
+ |
+ + + {t.side} + + | +{t.qty} | +${Number(t.price).toFixed(2)} | +