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<h1 class="title is-1 publication-title">TradingAgents: Multi-Agents LLM Financial Trading Framework</h1>
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<div class="is-size-5 publication-authors">
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<span class="author-block">Yijia Xiao<sup>1</sup>,</span>
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<span class="author-block">Edward Sun<sup>1</sup>,</span>
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<span class="author-block">Di Luo<sup>2</sup>,</span>
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<span class="author-block">Wei Wang<sup>1</sup></span>
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<span class="author-block"><sup>1</sup>University of California, Los Angeles,</span>
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<span class="author-block"><sup>2</sup>Massachusetts Institute of Technology</span>
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<span class="link-block"><a href="https://arxiv.org/abs/XXXX.XXXXX" class="external-link button is-normal is-rounded is-dark"><span class="icon"><i class="fas fa-file-pdf"></i></span><span>Paper</span></a></span>
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<h2 class="title is-3">Abstract</h2>
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<p>Societies of LLM-powered agents have advanced automated problem-solving, particularly in finance. Yet, most frameworks don’t replicate the collaborative workflows of real trading firms. <strong>TradingAgents</strong> addresses this gap by assigning specialized LLM-powered agents—analysts, researchers, traders, and risk managers—to simulate a dynamic, team-based environment. These agents collaborate through debates, structured outputs, and risk checks. Experiments show that <strong>TradingAgents</strong> significantly improves key performance metrics over baseline models, highlighting the promise of multi-agent LLM frameworks in financial trading.</p>
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<h2 class="title is-3">Introduction</h2>
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<div class="content has-text-justified">
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<p>Autonomous agents equipped with Large Language Models (LLMs) can mimic human problem-solving in finance—an intricate domain shaped by fundamentals, market sentiment, and macro factors. While deep learning models have long struggled with explainability, LLM-based systems show promise by pairing structured reasoning with interpretability. However, current solutions often lack organizational realism and rely on purely conversational interfaces susceptible to context loss.</p>
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<p><strong>TradingAgents</strong> fills these gaps by emulating the multi-agent decision-making processes of trading firms. The framework includes fundamental, sentiment, news, and technical analysts, along with bullish and bearish researchers, traders, and a risk management team. They coordinate using structured documents and concise dialogues. Our architecture leverages specialized LLM roles, combining clarity with deeper debates. Through extensive evaluations, <strong>TradingAgents</strong> delivers robust performance across multiple assets, validating the importance of multi-agent collaboration for real-world trading systems.</p>
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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">Related Work</h2>
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<h3 class="title is-4">LLMs as Financial Assistants</h3>
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<div class="content has-text-justified">
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<p>Specialized LLMs in finance have improved domain understanding via fine-tuning or from-scratch training on financial corpora (e.g., FinGPT, BloombergGPT). These models often excel at classification tasks but face challenges in generative quality compared to powerful general-purpose models like GPT-4.</p>
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<strong>Fine-Tuned LLMs for Finance</strong>
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<p>Fine-tuning boosts performance on tasks such as financial sentiment analysis. Examples include PIXIU (FinMA) and Instruct-FinGPT. They outperform generic open-source LLMs but still lag behind top-tier proprietary models in some generative tasks.</p>
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<strong>Finance LLMs Trained from Scratch</strong>
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<p>Models like BloombergGPT and XuanYuan 2.0 blend general corpora with specialized financial data, delivering strong domain-specific results. While they may not match larger closed-source models, they remain competitive among open-source counterparts.</p>
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<figure class="image">
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<img src="./static/images/schema.png" alt="TradingAgents Overall Framework Organization">
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<figcaption class="has-text-centered"><strong>Figure 1:</strong> TradingAgents Overall Framework Organization. <em>I. Analysts Team</em>: Four analysts concurrently gather relevant market information. <em>II. Research Team</em>: The team discusses data. <em>III. Trader</em>: Makes final decisions using debates and history. <em>IV. Risk Management Team</em>: Monitors risk. <em>V. Fund Manager</em>: Approves and executes trades.</figcaption>
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</figure>
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</div>
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<h3 class="title is-4">LLMs as Traders</h3>
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<div class="content has-text-justified">
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<p>LLMs directly executing trades often rely on news-driven or reasoning-driven prompts, sometimes enhanced by reinforcement learning. Debate and reflection modules help overcome hallucinations and bolster factual accuracy.</p>
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<strong>News-Driven Agents</strong>
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<p>These agents use market news to gauge sentiment. Both closed-source (GPT-4) and open-source (Qwen) models show promising gains via simple sentiment-driven strategies.</p>
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<strong>Reasoning-Driven Agents</strong>
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<p>Frameworks like FinMem and TradingGPT integrate multi-round reasoning, reflection, and debates between agents with different stances, enabling more robust trading signals.</p>
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<strong>Reinforcement Learning-Driven Agents</strong>
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<p>RL aligns LLM outputs with backtest rewards, often leveraging memorized states and technical signals to refine decision-making.</p>
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</div>
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<h3 class="title is-4">LLMs as Alpha Miners</h3>
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<div class="content has-text-justified">
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<p>Some frameworks focus on generating alpha factors rather than final trades. Systems like QuantAgent and AlphaGPT iteratively refine alpha scripts through feedback from an LLM-based judge and real-market performance, accelerating systematic strategy development.</p>
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</div>
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</div>
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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">TradingAgents: Role Specialization</h2>
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<div class="content has-text-justified">
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<p><strong>TradingAgents</strong> assigns each LLM agent a clear role. This mirrors how real trading firms split responsibilities—e.g., fundamental, sentiment, news, and technical analysts gather data, while researchers balance bullish and bearish arguments. A trader synthesizes these inputs, and risk managers ensure exposures stay within safe limits. This structured approach fosters comprehensive coverage of market signals.</p>
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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 (Figure 2) covers fundamental, sentiment, news, and technical aspects. Each member focuses on different market signals, providing the basis for research and trading decisions.</p>
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<figure class="image">
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<img src="./static/images/Analyst.png" alt="TradingAgents Analyst Team" style="width: 65%;">
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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 Analysts</strong>: Evaluate intrinsic value via earnings, balance sheets, etc.</li>
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<li><strong>Sentiment Analysts</strong>: Analyze social media and public sentiment data.</li>
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<li><strong>News Analysts</strong>: Track macro events, economic indicators, and other critical news.</li>
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<li><strong>Technical Analysts</strong>: Calculate metrics like MACD/RSI to identify trends and patterns.</li>
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</ul>
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</div>
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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>(Figure 3) Bullish and bearish researchers debate the analysts’ findings, challenging each other’s viewpoints to produce a balanced outcome.</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</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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<figcaption class="has-text-centered"><strong>Figure 4:</strong> TradingAgents Trader Decision-Making Process</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/RiskMGMT.png" alt="TradingAgents Risk Management Team and Fund Manager Approval Workflow">
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<figcaption class="has-text-centered"><strong>Figure 5:</strong> TradingAgents Risk Management and Fund Manager Workflow</figcaption>
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</figure>
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</div>
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</div>
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<ul>
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<li><strong>Bullish Researchers</strong>: Highlight favorable signals and positive growth opportunities.</li>
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<li><strong>Bearish Researchers</strong>: Emphasize caution, identifying risks or negative signals.</li>
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</ul>
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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>(Figure 4) Trader agents synthesize all insights to form buy/sell decisions, weighing returns against potential downside.</p>
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<ul>
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<li>Review data from analysts and researchers.</li>
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<li>Determine optimal trade timing and size.</li>
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<li>Execute orders and manage portfolios.</li>
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</ul>
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</div>
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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>(Figure 5) Risk managers ensure safety by evaluating volatility, liquidity, and other exposures. They enforce stop-loss measures and signal portfolio rebalancing when necessary.</p>
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<ul>
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<li>Monitor market risk factors.</li>
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<li>Adjust trading strategies to stay within risk limits.</li>
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<li>Collaborate with traders to manage drawdowns.</li>
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</ul>
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<p>All agents follow a ReAct-style prompting framework. Their actions—like research, debate, or trade execution—are tracked in a shared environment, creating a cohesive multi-agent ecosystem reminiscent of real trading firms.</p>
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</div>
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</div>
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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">TradingAgents: Agent Workflow</h2>
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<div class="content has-text-justified">
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<h3 class="title is-4">Communication Protocol</h3>
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<p>Relying solely on natural language can lead to “telephone effect” issues for complex, long-horizon tasks. <strong>TradingAgents</strong> introduces structured reports to preserve key details and reduce message distortion, drawing inspiration from frameworks like MetaGPT. Each agent produces or queries structured entries—concise and focused—to streamline interactions.</p>
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<h3 class="title is-4">Types of Agent Interactions</h3>
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<p>Instead of lengthy dialogues, <strong>TradingAgents</strong> agents exchange structured documents containing critical data. Short natural language debates occur when merging contrasting opinions (e.g., bullish vs. bearish). Key communication types include:</p>
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<ul>
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<li><strong>Analyst Team</strong>: Each analyst produces specialized reports (fundamentals, sentiment, etc.).</li>
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<li><strong>Traders</strong>: Combine analyst reports into a decision signal with accompanying rationale.</li>
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</ul>
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<p>Debates among researchers or risk managers occur in natural language but are recorded as structured entries. This approach maintains clarity while enabling multi-round reasoning.</p>
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<h3 class="title is-4">Backbone LLMs</h3>
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<p>We employ both “quick-thinking” and “deep-thinking” LLMs, choosing models based on complexity and speed requirements. Analysts and traders use robust reasoning models for decision-making, while simpler tasks (e.g., data retrieval) rely on faster LLMs. This modular design, requiring no GPUs, allows easy swapping of different local or API-based models and ensures future scalability.</p>
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</div>
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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">Experiments</h2>
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<div class="content has-text-justified">
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<p>We evaluate our framework on multi-asset data spanning a realistic time period, combining historical prices, news, social sentiment, insider transactions, and more. Baselines include traditional strategies like Buy-and-Hold, MACD, and SMA, ensuring a fair comparison.</p>
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<h3 class="title is-4">Back Trading</h3>
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<p>Our dataset includes stocks like Apple and Google, daily news, social media sentiment, and technical indicators. Agents process only the data available up to each trading day, avoiding look-ahead bias.</p>
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<h3 class="title is-4">Simulation Setup</h3>
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<p>The simulation runs from June 19, 2024, to November 19, 2024. <strong>TradingAgents</strong> autonomously generates buy, sell, or hold signals, then records performance metrics. This daily cycle repeats for each asset under study.</p>
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<h3 class="title is-4">Baseline Models</h3>
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<p>We benchmark against several baselines:</p>
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<ul>
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<li><strong>Buy and Hold</strong></li>
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<li><strong>MACD</strong></li>
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<li><strong>KDJ and RSI</strong></li>
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<li><strong>ZMR</strong></li>
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<li><strong>SMA</strong></li>
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</ul>
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<h3 class="title is-4">Evaluation Metrics</h3>
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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/CumulativeReturns_AAPL.png" alt="Cumulative Returns on AAPL">
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<figcaption class="has-text-centered"><strong>(a)</strong> Cumulative Returns on AAPL</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/TradingAgents_Transactions_AAPL.png" alt="TradingAgents Transactions for AAPL">
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<figcaption class="has-text-centered">
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<strong>(b)</strong> TradingAgents Transactions for AAPL.<br>
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Green / Red Arrows for Long / Short Positions.
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</figcaption>
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</figure>
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</div>
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</div>
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<table class="table is-striped is-fullwidth is-centered">
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<thead>
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<tr>
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<th>Categories</th>
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<th>Models</th>
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<th colspan="4">AAPL</th>
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<th></th>
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<th colspan="4">GOOGL</th>
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<th></th>
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<th colspan="4">AMZN</th>
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</tr>
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<tr>
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<th></th>
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<th></th>
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<th>CR%↑</th>
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<th>ARR%↑</th>
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<th>SR↑</th>
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<th>MDD%↓</th>
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<th></th>
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<th>CR%↑</th>
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<th>ARR%↑</th>
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<th>SR↑</th>
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<th>MDD%↓</th>
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<th></th>
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<th>CR%↑</th>
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<th>ARR%↑</th>
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<th>SR↑</th>
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<th>MDD%↓</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>Market</td>
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<td>B&H</td>
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<td>-5.23</td><td>-5.09</td><td>-1.29</td><td>11.90</td>
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<td></td>
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<td>7.78</td><td>8.09</td><td>1.35</td><td>13.04</td>
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<td></td>
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<td>17.1</td><td>17.6</td><td>3.53</td><td>3.80</td>
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</tr>
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<tr>
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<td rowspan="4">Rule-based</td>
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<td>MACD</td>
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<td>-1.49</td><td>-1.48</td><td>-0.81</td><td>4.53</td>
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<td></td>
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<td>6.20</td><td>6.26</td><td>2.31</td><td>1.22</td>
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<td></td>
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<td>-</td><td>-</td><td>-</td><td>-</td>
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</tr>
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<tr>
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<td>KDJ&RSI</td>
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<td>2.05</td><td>2.07</td><td>1.64</td><td>1.09</td>
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<td></td>
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<td>0.4</td><td>0.4</td><td>0.02</td><td>1.58</td>
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<td></td>
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<td>-0.77</td><td>-0.76</td><td>-2.25</td><td>1.08</td>
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</tr>
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<tr>
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<td>ZMR</td>
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<td>0.57</td><td>0.57</td><td>0.17</td><td>0.86</td>
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<td></td>
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<td>-0.58</td><td>0.58</td><td>2.12</td><td>2.34</td>
|
||
<td></td>
|
||
<td>-0.77</td><td>-0.77</td><td>-2.45</td><td>0.82</td>
|
||
</tr>
|
||
<tr>
|
||
<td>SMA</td>
|
||
<td>-3.2</td><td>-2.97</td><td>-1.72</td><td>3.67</td>
|
||
<td></td>
|
||
<td>6.23</td><td>6.43</td><td>2.12</td><td>2.34</td>
|
||
<td></td>
|
||
<td>11.01</td><td>11.6</td><td>2.22</td><td>3.97</td>
|
||
</tr>
|
||
<tr>
|
||
<td rowspan="1">Ours</td>
|
||
<td><strong>TradingAgents</strong></td>
|
||
<td><strong style="color:green;">26.62</strong></td><td><strong style="color:green;">30.5</strong></td><td><strong style="color:green;">8.21</strong></td><td>0.91</td>
|
||
<td></td>
|
||
<td><strong style="color:green;">24.36</strong></td><td><strong style="color:green;">27.58</strong></td><td><strong style="color:green;">6.39</strong></td><td>1.69</td>
|
||
<td></td>
|
||
<td><strong style="color:green;">23.21</strong></td><td><strong style="color:green;">24.90</strong></td><td><strong style="color:green;">5.60</strong></td><td>2.11</td>
|
||
</tr>
|
||
<tr>
|
||
<td colspan="2">Improvement(%)</td>
|
||
<td>24.57</td><td>28.43</td><td>6.57</td><td>-</td>
|
||
<td></td>
|
||
<td>16.58</td><td>19.49</td><td>4.26</td><td>-</td>
|
||
<td></td>
|
||
<td>6.10</td><td>7.30</td><td>2.07</td><td>-</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<p class="has-text-centered"><strong>Table 1:</strong> TradingAgents: Comparison of Performance Metrics across AAPL, GOOGL, and AMZN.</p>
|
||
|
||
<h3 class="title is-4">Sharpe Ratio</h3>
|
||
<p><strong>TradingAgents</strong> consistently beats all baselines in risk-adjusted returns, showing Sharpe Ratios above 5.60 and surpassing the nearest competitors by at least 2.07 points. Its adaptability and robust debate mechanism enable high returns with controlled risk.</p>
|
||
|
||
<h3 class="title is-4">Maximum Drawdown</h3>
|
||
<p>Rule-based baselines limit downside but sacrifice overall returns. <strong>TradingAgents</strong> balances both, keeping maximum drawdown below 2% while generating superior returns, aided by dedicated risk-control agents.</p>
|
||
|
||
<h3 class="title is-4">Explainability</h3>
|
||
<p>Unlike dense deep-learning models, <strong>TradingAgents</strong> provides transparent logs of its ReAct-style reasoning for every trade decision. This approach greatly enhances human interpretability, facilitating debugging and fine-tuning in real markets.</p>
|
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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>We introduced <strong>TradingAgents</strong>, a multi-agent LLM trading framework inspired by professional trading firms. Its specialized analysts, researcher debates, and risk management teams create a rich decision-making ecosystem. By effectively combining structured reports and targeted dialogues, <strong>TradingAgents</strong> exceeds baseline performance across returns, Sharpe ratio, and drawdown metrics. Future work will explore live trading, expanded agent roles, and real-time data integration for even more refined trading outcomes.</p>
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