Fork maintained by Zac Bi

• [2026-02] TradingAgents v0.2.0 released with multi-provider LLM support (GPT-5.x, Gemini 3.x, Claude 4.x, Grok 4.x) and improved system architecture.
• [2026-01] Trading-R1 Technical Report released, with Terminal expected to land soon.

🎉 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 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 technical analysts, 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.

Our framework decomposes complex trading tasks into specialized roles. This ensures the system achieves a robust, scalable approach to market analysis and decision-making.

• 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.
• Technical Analyst: Utilizes technical indicators (like MACD and RSI) to detect trading patterns and forecast price movements.

• 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.

• 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.

• 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.

Clone TradingAgents:

git clone https://github.com/TauricResearch/TradingAgents.git
cd TradingAgents

Install with uv (recommended) or pip:

# Using uv (recommended)
uv sync

# Or with pip
pip install -e .

TradingAgents supports multiple LLM providers. Set the API key for your chosen provider:

export OPENAI_API_KEY=...          # OpenAI (GPT)
export GOOGLE_API_KEY=...          # Google (Gemini)
export ANTHROPIC_API_KEY=...       # Anthropic (Claude)
export XAI_API_KEY=...             # xAI (Grok)
export OPENROUTER_API_KEY=...      # OpenRouter
export ALPHA_VANTAGE_API_KEY=...   # Alpha Vantage

For local models, configure Ollama with llm_provider: "ollama" in your config.

Alternatively, copy .env.example to .env and fill in your keys:

cp .env.example .env

Run the CLI (interactive analysis):

uv run tradingagents analyze

Or after install: tradingagents analyze. 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.

We built TradingAgents with LangGraph to ensure flexibility and modularity. The framework supports multiple LLM providers: OpenAI, Google, Anthropic, xAI, OpenRouter, and Ollama.

To use TradingAgents inside your code, import the tradingagents module and initialize a TradingAgentsGraph(). The .propagate() method returns a decision. Example:

from tradingagents.config import DEFAULT_CONFIG
from tradingagents.graph.trading_graph import TradingAgentsGraph

ta = TradingAgentsGraph(debug=True, config=DEFAULT_CONFIG.copy())
_, decision = ta.propagate("NVDA", "2026-01-15")
print(decision)

To customize LLM provider, models, and debate rounds:

from tradingagents.config import DEFAULT_CONFIG
from tradingagents.graph.trading_graph import TradingAgentsGraph

config = DEFAULT_CONFIG.copy()
config["llm_provider"] = "openai"        # openai, google, anthropic, xai, openrouter, ollama
config["deep_think_llm"] = "gpt-5.2"     # Model for complex reasoning
config["quick_think_llm"] = "gpt-5-mini" # Model for quick tasks
config["max_debate_rounds"] = 2

ta = TradingAgentsGraph(debug=True, config=config)
_, decision = ta.propagate("NVDA", "2026-01-15")
print(decision)

See tradingagents.config (e.g. DEFAULT_CONFIG in config.defaults) for all configuration options. A minimal run script is in scripts/run_single_propagate.py.

• Backtest: Run a backtest from persisted decisions (SQLite or CSV):
  uv run tradingagents backtest --ticker AAPL --start 2024-01-01 --end 2024-12-31
  Use --db-path for the SQLite DB or --csv path/to/decisions.csv (columns: ticker, trade_date, final_decision).

• Dashboard: The dashboard is built with Streamlit (open source). Install optional dependencies with uv sync --extra dashboard (or pip install streamlit plotly altair), then run:

  uv run streamlit run dashboard/app.py

  You can view agent decisions (with ticker and date range filters, and decision distribution), daily NAV and cumulative return (with date range), and run statistics. Set TRADINGAGENTS_DB_PATH to point to your SQLite database if it is not the default tradingagents.db.

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.

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}, 
}