tradingagents

TradingAgents is a multi-agent trading framework built with LangGraph that simulates the dynamics of real-world trading firms. It deploys specialized LLM-powered agents across four teams: **Analyst Team:** Fundamentals Analyst evaluates company financials; Sentiment Analyst aggregates news, StockTwits, and Reddit into sentiment signals; News Analyst monitors macroeconomic indicators; Technical Analyst uses indicators like MACD and RSI to detect patterns. **Researcher Team:** Bullish and bearish researchers critically assess analyst insights through structured debates, balancing potential gains against risks. **Trader Agent:** Composes reports from analysts and researchers to determine trade timing and magnitude. **Risk Management & Portfolio Manager:** Continuously evaluates portfolio risk, adjusts strategies, and the Portfolio Manager approves or rejects transaction proposals before execution against a simulated exchange. The framework supports a wide range of LLM providers including OpenAI (GPT-5.x), Google (Gemini 3.x), Anthropic (Claude 4.x), xAI (Grok 4.x), DeepSeek, Qwen, GLM, MiniMax, OpenRouter, Ollama for local models, Azure OpenAI, AWS Bedrock, and any OpenAI-compatible endpoint. Markets are sourced from Yahoo Finance, covering US stocks, Hong Kong, Tokyo, London, India, Canada, Australia, China A-shares, and crypto. Usage is available via interactive CLI (`tradingagents`) or Python API (`TradingAgentsGraph.propagate()`). Key features include persistent decision logging with reflection, LangGraph checkpoint resume for crash recovery, configurable debate rounds and model selection, and temperature-controlled sampling for improved reproducibility. The framework is explicitly designed for research purposes and is not intended as financial advice. Recent releases (v0.4.0) include look-ahead/point-in-time fixes, GPT-5.6 and GLM-5.3 model support, working CLI checkpoint resume, and hardening against ticker path traversal.