--- # TradingAgents: Multi-Agents LLM Financial Trading Framework > 🎉 **TradingAgents** officially released! We have received numerous inquiries about the work, and we would like to express our thanks for the enthusiasm in our community. > > So we decided to fully open-source the framework. Looking forward to building impactful projects with you!

## TradingAgents Framework TradingAgents is a multi-agent trading framework that mirrors the dynamics of real-world trading firms. By deploying specialized LLM-powered agents: from fundamental analysts, sentiment experts, and trade planners, to trader, risk management team, the platform collaboratively evaluates market conditions and informs trading decisions. Moreover, these agents engage in dynamic discussions to pinpoint the optimal strategy.

> TradingAgents framework is designed for research purposes. Trading performance may vary based on many factors, including the chosen backbone language models, model temperature, trading periods, the quality of data, and other non-deterministic factors. [It is not intended as financial, investment, or trading advice.](https://tauric.ai/disclaimer/) Our framework decomposes complex trading tasks into specialized roles. This ensures the system achieves a robust, scalable approach to market analysis and decision-making. ### Analyst Team - Fundamentals Analyst: Evaluates company financials and performance metrics, identifying intrinsic values and potential red flags. - Sentiment Analyst: Analyzes social media and public sentiment using sentiment scoring algorithms to gauge short-term market mood. - News Analyst: Monitors global news and macroeconomic indicators, interpreting the impact of events on market conditions. - Trade Planner: Utilizes technical indicators (like MACD and RSI) to detect trading patterns and forecast price movements.

### Researcher Team - Comprises both bullish and bearish researchers who critically assess the insights provided by the Analyst Team. Through structured debates, they balance potential gains against inherent risks.

### Trader Agent - Composes reports from the analysts and researchers to make informed trading decisions. It determines the timing and magnitude of trades based on comprehensive market insights.

### Risk Management and Portfolio Manager - Continuously evaluates portfolio risk by assessing market volatility, liquidity, and other risk factors. The risk management team evaluates and adjusts trading strategies, providing assessment reports to the Portfolio Manager for final decision. - The Portfolio Manager approves/rejects the transaction proposal. If approved, the order will be sent to the simulated exchange and executed.

## Installation and CLI ### Installation Clone TradingAgents: ```bash git clone https://github.com/TauricResearch/TradingAgents.git cd TradingAgents ``` Create a virtual environment in any of your favorite environment managers. Here are some indications if you've installed `uv`: ```bash uv venv ``` Activate the virtual environment: ```bash venv/Scripts/activate.bat ``` Install dependencies: ```bash uv sync ``` ### Required APIs You will also need the FinnHub API for financial data. All of our code is implemented with the free tier. ```bash export FINNHUB_API_KEY=$YOUR_FINNHUB_API_KEY ``` You will need the OpenAI API for all the agents. ```bash export OPENAI_API_KEY=$YOUR_OPENAI_API_KEY ``` If you plan to use OpenRouter as your LLM provider, you'll also need: ```bash export OPENROUTER_API_KEY=$YOUR_OPENROUTER_API_KEY ``` ### CLI Usage You can also try out the CLI directly by running: ```bash python -m cli.main ``` You will see a screen where you can select your desired tickers, date, LLMs, research depth, etc.

An interface will appear showing results as they load, letting you track the agent's progress as it runs.

## Web Frontend (HTMX/FastAPI) In addition to the CLI, a new web-based frontend is available to visualize the agent communication process in real-time. It allows you to set configuration parameters, start the trading analysis, and observe the step-by-step execution of agents and tools, including their outputs and any errors. ### Running the Web Frontend 1. Ensure you have installed all dependencies using `uv sync`. 2. Navigate to the project root directory in your terminal. 3. Start the FastAPI server: ```bash uvicorn webapp.main:app --reload ``` 4. Open your web browser and go to `http://127.0.0.1:8000`. 5. Enter a company symbol (e.g., `AAPL`) in the configuration form and click "Start Process" to begin the analysis. ## TradingAgents Package ### Implementation Details We built TradingAgents with LangGraph to ensure flexibility and modularity. We utilize `o1-preview` and `gpt-4o` as our deep thinking and fast thinking LLMs for our experiments. However, for testing purposes, we recommend you use `o4-mini` and `gpt-4.1-mini` to save on costs as our framework makes **lots of** API calls. ### Python Usage To use TradingAgents inside your code, you can import the `tradingagents` module and initialize a `TradingAgentsGraph()` object. The `.propagate()` function will return a decision. You can run `main.py`, here's also a quick example: ```python from tradingagents.graph.trading_graph import TradingAgentsGraph from tradingagents.default_config import DEFAULT_CONFIG ta = TradingAgentsGraph(debug=True, config=DEFAULT_CONFIG.copy()) # forward propagate _, decision = ta.propagate("NVDA", "2024-05-10") print(decision) ``` You can also adjust the default configuration to set your own choice of LLMs, debate rounds, etc. ```python from tradingagents.graph.trading_graph import TradingAgentsGraph from tradingagents.default_config import DEFAULT_CONFIG # Create a custom config config = DEFAULT_CONFIG.copy() config["deep_think_llm"] = "gpt-4.1-nano" # Use a different model config["quick_think_llm"] = "gpt-4.1-nano" # Use a different model config["max_debate_rounds"] = 1 # Increase debate rounds config["online_tools"] = True # Use online tools or cached data # Initialize with custom config ta = TradingAgentsGraph(debug=True, config=config) # forward propagate _, decision = ta.propagate("NVDA", "2024-05-10") print(decision) ``` > For `online_tools`, we recommend enabling them for experimentation, as they provide access to real-time data. The agents' offline tools rely on cached data from our **Tauric TradingDB**, a curated dataset we use for backtesting. We're currently in the process of refining this dataset, and we plan to release it soon alongside our upcoming projects. Stay tuned! You can view the full list of configurations in `tradingagents/default_config.py`. ## Persistent Memory and Learning To allow the agents to learn from the success or failure of previous decisions, TradingAgents includes a persistent memory mechanism. Each agent's reflections and the "lessons learned" from past trading sessions are stored on disk. This allows the system to build a rich, searchable history of its actions and their consequences, enabling more informed decisions in the future. - **Storage**: The memory is managed by the `FinancialSituationMemory` class in `tradingagents/agents/utils/memory.py` and is persisted to the `./memory_store/` directory using a local ChromaDB database. - **Learning Loop**: After a trade, a `Reflector` agent analyzes the outcome (profit or loss) and generates a "lesson." This lesson is stored in the memory, linked to the market conditions at the time. Before the next trade, agents query this memory for similar past situations to retrieve relevant lessons, which are then used to inform their decision-making process. ### Inspecting the Memory You can inspect the contents of the persistent memory to see what the agents have learned. To do this, run the memory utility script from the root of the project: ```bash python -m tradingagents.agents.utils.memory ``` The first time you run this, it will populate the memory with example data. Subsequent runs will load and display the data from the `memory_store` directory, demonstrating that the memory persists across sessions. ## Contributing We welcome contributions from the community! Whether it's fixing a bug, improving documentation, or suggesting a new feature, your input helps make this project better. If you are interested in this line of research, please consider joining our open-source financial AI research community [Tauric Research](https://tauric.ai/). ## Citation Please reference our work if you find *TradingAgents* provides you with some help :) ``` @misc{xiao2025tradingagentsmultiagentsllmfinancial, title={TradingAgents: Multi-Agents LLM Financial Trading Framework}, author={Yijia Xiao and Edward Sun and Di Luo and Wei Wang}, year={2025}, eprint={2412.20138}, archivePrefix={arXiv}, primaryClass={q-fin.TR}, url={https://arxiv.org/abs/2412.20138}, } ```