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index.html
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<h3 class="title is-4">Analyst Team</h3>
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<div class="content has-text-justified">
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<p>The Analyst Team is composed of specialized agents responsible for gathering and analyzing various types of market data to inform trading decisions. Each agent focuses on a specific aspect of market analysis, bringing together a comprehensive view of the market's conditions.</p>
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<p>The Analyst Team (Figure 2) is composed of specialized agents responsible for gathering and analyzing various types of market data to inform trading decisions. Each agent focuses on a specific aspect of market analysis, bringing together a comprehensive view of the market's conditions.</p>
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<div class="columns">
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<div class="column">
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<figure class="image">
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<img src="./static/images/Analyst.png" alt="TradingAgents Analyst Team">
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<figcaption class="has-text-centered"><strong>Figure 2:</strong> TradingAgents Analyst Team</figcaption>
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</figure>
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</div>
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<div class="column">
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<figure class="image">
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<img src="./static/images/Researcher.png" alt="TradingAgents Researcher Team">
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<figcaption class="has-text-centered"><strong>Figure 3:</strong> TradingAgents Researcher Team</figcaption>
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</figure>
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</div>
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</div>
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<figure class="image">
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<img src="./static/images/Analyst.png" alt="TradingAgents Analyst Team">
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<figcaption class="has-text-centered"><strong>Figure 2:</strong> TradingAgents Analyst Team</figcaption>
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</figure>
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<ul>
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<li><strong>Fundamental Analyst Agents</strong>: These agents evaluate company fundamentals by analyzing financial statements, earnings reports, insider transactions, and other pertinent data. They assess a company's intrinsic value to identify undervalued or overvalued stocks, providing insights into long-term investment potential.</li>
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<h3 class="title is-4">Researcher Team</h3>
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<div class="content has-text-justified">
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<p>The Researcher Team is responsible for critically evaluating the information provided by the Analyst Team. Comprised of agents adopting both bullish and bearish perspectives, they engage in multiple rounds of debate to assess the potential risks and benefits of investment decisions.</p>
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<ul>
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<li><strong>Bullish Researchers</strong>: These agents advocate for investment opportunities by highlighting positive indicators, growth potential, and favorable market conditions. They construct arguments supporting the initiation or continuation of positions in certain assets.</li>
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<li><strong>Bearish Researchers</strong>: Conversely, these agents focus on potential downsides, risks, and unfavorable market signals. They provide cautionary insights, questioning the viability of investment strategies and highlighting possible negative outcomes.</li>
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</ul>
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<p>Through this dialectical process, the Researcher Team aims to reach a balanced understanding of the market situation. Their thorough analysis helps in identifying the most promising investment strategies while anticipating possible challenges, thus aiding the Trader Agents in making informed decisions.</p>
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</div>
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<h3 class="title is-4">Trader Agents</h3>
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<div class="content has-text-justified">
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<p>Trader Agents are responsible for executing trading decisions based on the comprehensive analysis provided by the Analyst Team and the nuanced perspectives from the Researcher Team. They assess the synthesized information, considering both quantitative data and qualitative insights, to determine optimal trading actions.</p>
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<p>The Researcher Team (Figure 3) is responsible for critically evaluating the information provided by the Analyst Team. Comprised of agents adopting both bullish and bearish perspectives, they engage in multiple rounds of debate to assess the potential risks and benefits of investment decisions.</p>
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<div class="columns">
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<div class="column">
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<figure class="image">
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<img src="./static/images/Researcher.png" alt="TradingAgents Researcher Team">
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<figcaption class="has-text-centered"><strong>Figure 3:</strong> TradingAgents Researcher Team: Bullish Perspectives and Bearish Perspectives</figcaption>
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</figure>
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</div>
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<div class="column">
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<figure class="image">
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<img src="./static/images/Trader.png" alt="TradingAgents Trader Decision-Making Process">
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</div>
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</div>
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<ul>
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<li><strong>Bullish Researchers</strong>: These agents advocate for investment opportunities by highlighting positive indicators, growth potential, and favorable market conditions. They construct arguments supporting the initiation or continuation of positions in certain assets.</li>
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<li><strong>Bearish Researchers</strong>: Conversely, these agents focus on potential downsides, risks, and unfavorable market signals. They provide cautionary insights, questioning the viability of investment strategies and highlighting possible negative outcomes.</li>
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</ul>
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<p>Through this dialectical process, the Researcher Team aims to reach a balanced understanding of the market situation. Their thorough analysis helps in identifying the most promising investment strategies while anticipating possible challenges, thus aiding the Trader Agents in making informed decisions.</p>
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</div>
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<h3 class="title is-4">Trader Agents</h3>
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<div class="content has-text-justified">
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<p>Trader Agents (Figure 4) are responsible for executing trading decisions based on the comprehensive analysis provided by the Analyst Team and the nuanced perspectives from the Researcher Team. They assess the synthesized information, considering both quantitative data and qualitative insights, to determine optimal trading actions.</p>
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<ul>
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<li>Evaluating recommendations and insights from analysts and researchers.</li>
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<li>Deciding on the timing and size of trades to maximize trading returns.</li>
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<h3 class="title is-4">Risk Management Team</h3>
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<div class="content has-text-justified">
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<p>The Risk Management Team monitors and controls the firm's exposure to various market risks. These agents continuously evaluate the portfolio's risk profile, ensuring that trading activities remain within predefined risk parameters and comply with regulatory requirements.</p>
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<p>The Risk Management Team (Figure 5) monitors and controls the firm's exposure to various market risks. These agents continuously evaluate the portfolio's risk profile, ensuring that trading activities remain within predefined risk parameters and comply with regulatory requirements.</p>
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<ul>
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<li>Assessing factors such as market volatility, liquidity, and counterparty risks.</li>
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</tr>
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</tbody>
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</table>
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<p class="has-text-centered"><strong>Table 1:</strong> TradingAgents (<strong>AIS</strong>): Comparison of RNA Sequence (left), Modality Fusion (middle), and TradingAgents (right). Embedding base models are BERT, PubMedBERT, and OpenAI's GPT text-embedding-3-large.</p>
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<p class="has-text-centered"><strong>Table 1:</strong> TradingAgents: Comparison of Performance Metrics across AAPL, GOOGL, and AMZN.</p>
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<h3 class="title is-4">Sharpe Ratio</h3>
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<p>The Sharpe Ratio performance highlights <strong>TradingAgents</strong>' exceptional ability to deliver superior risk-adjusted returns, consistently outperforming all baseline models across AAPL, GOOGL, and AMZN with Sharpe Ratios of at least 5.60—surpassing the next best models by a significant margin of at least 2.07 points. This result underscores <strong>TradingAgents</strong>' effectiveness in balancing returns against risk, a critical metric for sustainable and predictable investment growth. By excelling over market benchmarks like Buy-and-Hold and advanced strategies such as KDJRSI, SMA, MACD, and ZMR, <strong>TradingAgents</strong> demonstrates its adaptability and robustness in diverse market conditions. Its ability to maximize returns while maintaining controlled risk exposure establishes a solid foundation for multi-agent and debate-based automated trading algorithms.</p>
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</div>
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</section>
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<section class="section">
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<div class="container is-max-desktop">
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<div class="columns is-centered">
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<div class="column is-full-width">
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<h2 class="title is-3">Conclusion</h2>
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<div class="content has-text-justified">
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<p>In this paper, we introduced <strong>TradingAgents</strong>, an LLM-agent-powered stock trading framework that simulates a realistic trading firm environment with multiple specialized agents engaging in agentic debates and conversations. Leveraging the capabilities of LLMs to process and analyze diverse data sources, the framework enables informed trading decisions while utilizing multi-agent interactions to enhance performance through comprehensive reasoning and debate before acting. By integrating agents with distinct roles and risk profiles, along with a reflective agent and a dedicated risk management team, <strong>TradingAgents</strong> significantly improves trading outcomes and risk management compared to baseline models. Additionally, the collaborative nature of these agents ensures adaptability to varying market conditions. Extensive experiments demonstrate that <strong>TradingAgents</strong> outperforms traditional trading strategies and baselines in cumulative return, Sharpe ratio, and other critical metrics. Future work will focus on deploying the framework in a live trading environment, expanding agent roles, and incorporating real-time data processing to enhance performance further.</p>
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</div>
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</div>
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</div>
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</div>
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</section>
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<footer class="footer">
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<div class="container">
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