正在加载项目…
TradingAgents
智能体 / 应用TradingAgents: Multi-Agents LLM Financial Trading Framework
TauricResearchGitHub 公开资源索引
108,739 GitHub Stars0 项组成内容Stars 同步于 2026年9月27日
正在读取官方项目说明…
正在加载项目…
TradingAgents: Multi-Agents LLM Financial Trading Framework
正在读取官方项目说明…
内容来自 GitHub 原始文件，由原作者维护。在 GitHub 查看
Full release notes are in CHANGELOG.md.
TRADINGAGENTS_* env-var configurability with API-key auto-detection, remote Ollama support, non-US alpha benchmarks, and ticker path-traversal hardening.🚀 TradingAgents | ⚡ Installation & CLI | 🎬 Demo | 📦 Package Usage | 🤝 Contributing | 📄 Citation
🎉 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.
Clone TradingAgents:
git clone https://github.com/TauricResearch/TradingAgents.git
cd TradingAgents
Create a virtual environment in any of your favorite environment managers:
conda create -n tradingagents python=3.12
conda activate tradingagents
Or with uv:
uv venv --python 3.12
source .venv/bin/activate
Install the package and its dependencies (uv pip install . with uv):
pip install .
Alternatively, run with Docker:
cp .env.example .env # add your API keys
docker compose run --rm tradingagents
After updating the repository, rebuild the image with docker compose build.
For local models with Ollama:
docker compose --profile ollama run --rm tradingagents-ollama
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 DEEPSEEK_API_KEY=... # DeepSeek
export DASHSCOPE_API_KEY=... # Qwen — International (dashscope-intl.aliyuncs.com)
export DASHSCOPE_CN_API_KEY=... # Qwen — China (dashscope.aliyuncs.com)
export ZHIPU_API_KEY=... # GLM via Z.AI (international)
export ZHIPU_CN_API_KEY=... # GLM via BigModel (China, open.bigmodel.cn)
export MINIMAX_API_KEY=... # MiniMax — Global (api.minimax.io)
export MINIMAX_CN_API_KEY=... # MiniMax — China (api.minimaxi.com)
export OPENROUTER_API_KEY=... # OpenRouter
export MISTRAL_API_KEY=... # Mistral
export MOONSHOT_API_KEY=... # Kimi (Moonshot)
export GROQ_API_KEY=... # Groq
export NVIDIA_API_KEY=... # NVIDIA NIM
export FRED_API_KEY=... # FRED macro data (free, optional)
export ALPHA_VANTAGE_API_KEY=... # Alpha Vantage
export TYPESAFE_API_KEY=... # Jev social-post screening (optional)
For Azure OpenAI, copy .env.enterprise.example to .env.enterprise and fill in your credentials.
For AWS Bedrock, install the extra with pip install ".[bedrock]", set llm_provider: "bedrock", configure AWS credentials (environment variables, ~/.aws/credentials, or an IAM role) and AWS_DEFAULT_REGION, and use a Bedrock model ID, e.g. us.anthropic.claude-opus-4-8-v1:0.
For local models, configure Ollama with llm_provider: "ollama". The default endpoint is http://localhost:11434/v1; set OLLAMA_BASE_URL to point at a remote ollama-serve. Pull models with ollama pull <name>, and pick "Custom model ID" in the CLI for any model not listed by default.
For any other OpenAI-compatible server (vLLM, LM Studio, llama.cpp, or a custom relay), use llm_provider: "openai_compatible" and set the endpoint via backend_url (or TRADINGAGENTS_LLM_BACKEND_URL), e.g. http://localhost:8000/v1 for vLLM or http://localhost:1234/v1 for LM Studio. The model is whatever your server serves. No key is needed for local servers; set OPENAI_COMPATIBLE_API_KEY when the endpoint requires one.
With TYPESAFE_API_KEY set, the Sentiment Analyst screens StockTwits and Reddit posts with TypeSafe's Jev before reading them. Posts that are not about the company are dropped, and each source opens with a count of the remaining posts by stance: bullish, bearish, neutral, or unclear. Without the key, posts pass through unscreened. jev-latest moves with new releases; set TYPESAFE_DEFAULT_MODEL to a versioned ID such as jev-1.13.0 to hold it fixed across runs.
Alternatively, copy .env.example to .env and fill in your keys:
cp .env.example .env
Launch the interactive CLI:
tradingagents # installed command
python -m cli.main # alternative: run directly from source
You will see a screen where you can select your desired tickers, analysis date, LLM provider, research depth, and more. Your previous run's answers come back as the defaults, so pressing Enter accepts them. The TRADINGAGENTS_* variables in .env still skip their step entirely.
TradingAgents works with any market Yahoo Finance covers, using the exchange-suffixed ticker. Company identity and the alpha benchmark resolve automatically per market.
AAPL, SPY0700.HK · Tokyo: 7203.T · London: AZN.LRELIANCE.NS, .BO · Canada: .TO · Australia: .AX.SS, Shenzhen .SZ (e.g. 600519.SS for Kweichow Moutai)BTC-USD, ETH-USD
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, DeepSeek, Qwen (Alibaba DashScope, international and China endpoints), GLM (Zhipu), MiniMax (global + China), OpenRouter, Ollama for local models, and Azure OpenAI for enterprise.
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:
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", "2026-09-01")
print(decision)
You can also adjust the default configuration to set your own choice of LLMs, debate rounds, etc.
from tradingagents.graph.trading_graph import TradingAgentsGraph
from tradingagents.default_config import DEFAULT_CONFIG
config = DEFAULT_CONFIG.copy()
config["llm_provider"] = "openai" # e.g. openai, google, anthropic, deepseek, groq, ollama; openai_compatible covers any OpenAI-compatible endpoint (vLLM, LM Studio, llama.cpp, ...)
config["deep_think_llm"] = "gpt-6-sol" # Model for complex reasoning
config["quick_think_llm"] = "gpt-6-luna" # Model for quick tasks
config["max_debate_rounds"] = 2
ta = TradingAgentsGraph(debug=True, config=config)
_, decision = ta.propagate("NVDA", "2026-09-01")
print(decision)
See tradingagents/default_config.py for all configuration options.
US company statements can come from SEC EDGAR, which records the date every figure was filed. A run dated in the past then reads the statements exactly as they stood that day: a fiscal year that has ended but has not been filed yet is not served, and a figure restated later still reads as first reported. Apple's 2008 total assets were filed as $39.6B and restated to $36.2B in 2010, so a run dated in between reads $39.6B.
EDGAR needs no account or API key. Add the vendor to the chain:
config["data_vendors"]["fundamental_data"] = "sec_edgar,yfinance"
SEC asks callers to identify themselves and refuses requests that carry no contact address, so a default one is sent. Set your own so SEC can reach you rather than the project:
SEC_EDGAR_USER_AGENT="Your Name your@email.com"
It covers companies that file with the SEC, including foreign companies listed in the US. Anything else, such as Hong Kong or A-share listings, falls through to the next vendor in the chain. EDGAR's machine-readable filings begin in 2009, and a fourth quarter is reported as unavailable rather than derived, because filers publish it only inside the annual figure.
By default the agents do not know what you hold, so their guidance is written for a reader who applies it to their own position. Pass a portfolio to have the trader, the risk analysts and the portfolio manager work against your actual book.
from tradingagents.portfolio import PortfolioContext
portfolio = PortfolioContext.model_validate({
"cash": 25000.0,
"currency": "USD",
"positions": [{"ticker": "NVDA", "quantity": 120, "average_price": 150.0}],
})
_, decision = ta.propagate("NVDA", "2026-09-01", portfolio=portfolio)
The CLI takes the same content as a JSON file: tradingagents --portfolio my_book.json.
An empty positions list means a flat book, which is different from passing nothing. A run without a portfolio is never treated as flat.
TradingAgents persists two kinds of state across runs.
The decision log is always on. Each completed run appends its decision to ~/.tradingagents/memory/trading_memory.md. On the next run for the same ticker, TradingAgents fetches the realised return (raw, and alpha against the instrument's regional benchmark), generates a one-paragraph reflection, and injects the most recent same-ticker decisions plus recent cross-ticker lessons into the Portfolio Manager prompt, so each analysis carries forward what worked and what didn't.
Override the path with TRADINGAGENTS_MEMORY_LOG_PATH.
Checkpoint resume is opt-in via --checkpoint. When enabled, LangGraph saves state after each node so a crashed or interrupted run resumes from the last successful step instead of starting over. The run view says whether it resumed a saved run or started fresh. Checkpoints are cleared automatically on successful completion.
Per-ticker SQLite databases live at ~/.tradingagents/cache/checkpoints/<TICKER>.db (override the base with TRADINGAGENTS_CACHE_DIR). Use --clear-checkpoints to reset all of them before a run.
tradingagents --checkpoint # enable for this run
tradingagents --clear-checkpoints # reset before running
config = DEFAULT_CONFIG.copy()
config["checkpoint_enabled"] = True
ta = TradingAgentsGraph(config=config)
_, decision = ta.propagate("NVDA", "2026-09-01")
One run gives one decision, which cannot tell you whether the system decides well. run_backtest runs the same pipeline over a grid of tickers and dates, writes to a decision log of its own, and scores the decisions whose holding window has since traded.
from tradingagents.backtest import iter_grid, run_backtest, summarize
dates = iter_grid("2026-06-01", "2026-08-01", every_n_days=7)
result = run_backtest(["NVDA", "AAPL"], dates, config, selected_analysts=["market", "news"])
print(summarize(result).render())
From the CLI:
tradingagents backtest NVDA,AAPL --start 2026-06-01 --end 2026-08-01 --every 7
Each cell is scored on realized alpha against the instrument's regional benchmark, grouped by rating. Your own decision log is never written to, and re-running the same grid with run_id=result.run_id skips the cells that already ran, so an interrupted sweep continues where it stopped.
TradingAgents is LLM-driven, so two runs of the same ticker and date can differ. This is expected for a research tool built on language models, not a defect. The variation comes from a few distinct sources, and it helps to separate them.
Language model sampling is non-deterministic. Even at a fixed temperature, providers do not guarantee byte-identical output across calls, and reasoning models (the default GPT-6 family, and any thinking-mode model) vary the most because their internal reasoning is itself sampled.
Live data moves. News, StockTwits, and Reddit return different content as time passes, so a run today sees different inputs than a run last week even for the same historical trade date. Pin the analysis date to hold the price and indicator window fixed, but the social and news sources still reflect "now".
To reduce variation you can lower the sampling temperature. Set temperature in your config (or TRADINGAGENTS_TEMPERATURE in .env); lower values make models that honor it more repeatable. The current curated models are reasoning-first and largely ignore temperature, so for tighter reproducibility name a non-reasoning model in your config, or in TRADINGAGENTS_DEEP_THINK_LLM and TRADINGAGENTS_QUICK_THINK_LLM. Any model ID your provider serves is accepted, whether or not the picker lists it.
config = DEFAULT_CONFIG.copy()
config["llm_provider"] = "openai"
config["temperature"] = 0.0
# Reasoning models ignore temperature. For tighter reproducibility, name a
# non-reasoning model in deep_think_llm / quick_think_llm.
What does not vary anymore: the analyzed company identity is resolved deterministically from the ticker before any agent runs, and the market analyst grounds exact price and indicator claims in a verified data snapshot. Earlier reports of "different companies" or fabricated price levels across runs are addressed by these two mechanisms.
Backtest results are not guaranteed to match any published figure. Returns depend on the model, the temperature, the date range, data quality, and the sampling above. Treat the framework as a research scaffold for studying multi-agent analysis, not as a strategy with a fixed, replicable return.
Contributions are welcome: bug fixes, documentation, and feature ideas; past contributions are credited per release in CHANGELOG.md.
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},
}