trading-agents-llm

# trading-agents-llm USE FOR: - "build multi-agent trading system" - "LLM-powered stock analysis pipeline" - "analyst + researcher + trader + risk manager agent workflow" - "AI agent debate for trading decisions" - "integrate Claude / GPT / Gemini into trading research" - "A-share / HK / US equity LLM analysis" - "automate fundamental + sentiment + news + technical analysis" tags: [multi-agent, LLM, trading, AI, equities, fundamentals, sentiment, technical, risk, LangGraph, Claude, GPT, research] kind: framework category: quant-ml-trading --- ## What Is TradingAgents? Open-source multi-agent LLM framework that simulates a trading firm: - Specialized agents collaborate across the full research → decision pipeline - Uses **LangGraph** for agent orchestration - Supports 6+ LLM providers including Anthropic Claude - **Research only** — not financial advice Repos: - Original: https://github.com/TauricResearch/TradingAgents - CN Enhanced Fork: https://github.com/hsliuping/TradingAgents-CN --- ## Agent Architecture ``` ┌─────────────────── ANALYST TEAM ───────────────────┐ │ Fundamentals Analyst → Financial metrics & value │ │ Sentiment Analyst → Social media & mood │ │ News Analyst → Macro news & events │ │ Technical Analyst → MACD, RSI, patterns │ └─────────────────────────────────────────────────────┘ ↓ Reports fed into ↓ ┌─────────────── RESEARCHER TEAM ────────────────────┐ │ Bullish Researcher ↔ Bearish Researcher (debate) │ │ Critical assessment of analyst findings │ └─────────────────────────────────────────────────────┘ ↓ Debate synthesis ↓ ┌─────────────── TRADER AGENT ───────────────────────┐ │ Synthesizes all reports → trading decision │ │ Determines timing and position magnitude │ └─────────────────────────────────────────────────────┘ ↓ Proposal submitted ↓ ┌─────────── RISK MANAGEMENT TEAM ───────────────────┐ │ Portfolio Manager → approves / rejects trades │ │ Risk evaluator → volatility + liquidity check │ └─────────────────────────────────────────────────────┘ ``` --- ## Installation (Original) ```bash git clone https://github.com/TauricResearch/TradingAgents.git cd TradingAgents conda create -n tradingagents python=3.13 conda activate tradingagents pip install -r requirements.txt ``` **Required API Keys:** ```bash export OPENAI_API_KEY="sk-..." # or any supported provider export ANTHROPIC_API_KEY="sk-ant-..." # for Claude export ALPHA_VANTAGE_API_KEY="..." # market data ``` --- ## Usage ### CLI (Interactive) ```bash python -m cli.main # Select: ticker, date, LLM provider, research depth ``` ### Python API ```python from tradingagents.graph.trading_graph import TradingAgentsGraph from tradingagents.default_config import DEFAULT_CONFIG config = DEFAULT_CONFIG.copy() config["llm_provider"] = "anthropic" # Use Claude config["deep_think_llm"] = "claude-opus-4-6" # Complex reasoning config["quick_think_llm"] = "claude-haiku-4-5-20251001" # Fast tasks config["max_debate_rounds"] = 3 # Researcher debate depth config["online_tools"] = True # Live market data ta = TradingAgentsGraph(debug=True, config=config) state, decision = ta.propagate("NVDA", "2026-01-15") print(decision) # BUY / SELL / HOLD + rationale ``` --- ## LLM Provider Configuration | Provider | `llm_provider` | Models | |----------|---------------|--------| | Anthropic | `"anthropic"` | claude-opus-4-6, claude-sonnet-4-6, claude-haiku-4-5 | | OpenAI | `"openai"` | gpt-4o, gpt-4o-mini, o1 | | Google | `"google"` | gemini-2.0-flash, gemini-1.5-pro | | xAI | `"xai"` | grok-2 | | OpenRouter | `"openrouter"` | Any model via router | | Ollama | `"ollama"` | Local models (llama3, mistral, etc.) | | DeepSeek | `"deepseek"` | deepseek-chat (CN fork) | | Alibaba | `"alibaba"` | qwen models (CN fork) | --- ## CN Fork (TradingAgents-CN) — Key Enhancements ### Architecture Upgrade - **Original**: Streamlit UI - **CN Fork**: FastAPI + Vue 3 (enterprise-grade) ### Regional Market Support | Market | Data Source | |--------|-------------| | A-shares (China) | Tushare, AkShare, BaoStock | | HK Stocks | AkShare | | US Equities | Alpha Vantage | ### Additional Features - MongoDB + Redis dual database (persistent sessions, caching) - Docker support (amd64 + ARM64) - Report export: Markdown, Word, PDF - Batch portfolio analysis - SSE + WebSocket real-time progress - News quality filtering + multi-layer assessment - User auth + operation logging ### CN Fork Installation ```bash git clone https://github.com/hsliuping/TradingAgents-CN.git cd TradingAgents-CN docker-compose up -d # Easiest path (MongoDB + Redis included) # or pip install -r requirements.txt ``` --- ## Trading Workflow (Step-by-Step) ``` 1. Input: ticker + date 2. Analysts run in parallel → 4 reports 3. Researcher debate (N rounds) → bull/bear synthesis 4. Trader synthesizes → trade proposal (BUY/SELL/HOLD + size) 5. Risk manager evaluates volatility + liquidity 6. Portfolio manager: APPROVE or REJECT 7. Output: final decision + reasoning chain ``` --- ## Integration With Claude Use Claude as the reasoning backbone: ```python config = { "llm_provider": "anthropic", "deep_think_llm": "claude-opus-4-6", # Analyst/Researcher deep work "quick_think_llm": "claude-sonnet-4-6", # Fast classification tasks "max_debate_rounds": 2, "online_tools": True, } ``` Claude's strength in structured reasoning makes it ideal for: - Fundamental analysis reports (long-form reasoning) - Researcher debate synthesis - Risk rationale explanation --- ## Key Design Patterns (for building similar agents) ```python # Pattern: Analyst role definition analyst_prompt = """ You are a Fundamental Analyst. Evaluate the company's: - Revenue growth, margins, P/E, debt ratios - Competitive moat and sector dynamics Return: structured report with BUY/NEUTRAL/SELL signal + confidence """ # Pattern: Debate orchestration (LangGraph) from langgraph.graph import StateGraph graph = StateGraph(TradingState) graph.add_node("fundamentals_analyst", run_fundamentals) graph.add_node("sentiment_analyst", run_sentiment) graph.add_node("researcher_debate", run_debate) graph.add_node("trader_decision", run_trader) graph.add_node("risk_check", run_risk_manager) graph.add_edge("fundamentals_analyst", "researcher_debate") # ... ``` ---