TradingAgents: 멀티 에이전트 LLM 금융 트레이딩 프레임워크

Deutsch | Español | français | 日本語 | 한국어 | Português | Русский | 中文 TradingAgents: Multi-Agents LLM Financial Trading Framework News [2026-08] TradingAgents v0.4.0 released with look-ahead / point-in-time fixes across FRED macro, social sentiment, and the decision-log memory; clearer decision signals; working CLI checkpoint resume; Trader price grounding; and the GPT-5.6 and GLM-5.3 models. See CHANGELOG.md for the full list. [2026-07] TradingAgents v0.3.1 released with correctness and stability fixes: Alpha Vantage look-ahead filtering, graph-router crash-safety, graph-shape-aware checkpoint resume, working crypto sentiment sources, a configurable LLM retry budget, Bedrock API-key auth, and Claude Sonnet 5 / Fable 5 support. [2026-06] TradingAgents v0.3.0 released with a verified data-access contract, an expanded provider registry (NVIDIA, Kimi, Groq, Mistral, Bedrock, and any OpenAI-compatible endpoint), FRED and Polymarket data vendors, a current-generation model catalog, and a CI gate. [2026-05] TradingAgents v0.2.5 released with the grounded Sentiment Analyst, GPT-5.5 etc. model coverage, Qwen/GLM/MiniMax dual-region support, TRADINGAGENTS_* env-var configurability with API-key auto-detection, remote Ollama support, non-US alpha benchmarks, and ticker path-traversal hardening. [2026-04] TradingAgents v0.2.4 released with structured-output agents (Research Manager, Trader, Portfolio Manager), LangGraph checkpoint resume, persistent decision log, DeepSeek/Qwen/GLM/Azure provider support, Docker, and a Windows UTF-8 encoding fix. [2026-03] TradingAgents v0.2.3 released with multi-language support, GPT-5.4 family models, unified model catalog, backtesting date fidelity, and proxy support. [2026-03] TradingAgents v0.2.2 released with GPT-5.4/Gemini 3.1/Claude 4.6 model coverage, five-tier rating scale, OpenAI Responses API, Anthropic effort control, and cross-platform stability. [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 | ⚡ 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 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 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. Analyst Team Fundamentals Analyst: Evaluates company financials and performance metrics, identifying intrinsic values and potential red flags. Sentiment Analyst: Aggregates news headlines, StockTwits, and Reddit chatter into a single sentiment read 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. 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, determining the timing and magnitude of trades. 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: 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 Install the package and its dependencies: pip install . Docker Alternatively, run with Docker: cp .env.example .env # add your API keys docker compose run --rm tradingagents For local models with Ollama: docker compose --profile ollama run --rm tradingagents-ollama Required APIs 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 ALPHA_VANTAGE_API_KEY=... # Alpha Vantage 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. Alternatively, copy .env.example to .env and fill in your keys: cp .env.example .env CLI Usage 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. Markets and tickers TradingAgents works with any market Yahoo Finance covers, using the exchange-suffixed ticker. Company identity and the alpha benchmark resolve automatically per market. US: AAPL , SPY Hong Kong: 0700.HK · Tokyo: 7203.T · London: AZN.L India: RELIANCE.NS , .BO · Canada: .TO · Australia: .AX China A-shares: Shanghai .SS , Shenzhen .SZ (e.g. 600519.SS for Kweichow Moutai) Crypto: BTC-USD , ETH-USD An interface will appear showing results as they load, letting you track the agent's progress as it runs. TradingAgents Package I