TradingAgents v0.2.4: A Multi-Agent LLM Framework That Simulates an Entire Trading Firm

try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3798483) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } 정상록 Posted on Apr 28 TradingAgents v0.2.4: A Multi-Agent LLM Framework That Simulates an Entire Trading Firm #llm #ai #agents #machinelearning TL;DR UCLA Tauric Research released TradingAgents v0.2.4 (2026-04-25) — a LangGraph-based multi-agent LLM framework that mimics a real trading firm with 5 layers and ~12 agents. The new release adds Pydantic-typed structured outputs, LangGraph checkpoint resumption, a persistent decision-memory file, 5-tier rating, and 10 LLM provider integrations. Backtest on AAPL/GOOGL/AMZN shows 23-27% cumulative return. ⚠️ Disclaimer: backtest only. Not financial advice. Paper trade before any real capital. Why this is interesting beyond "another trading bot" Most LLM trading bots are a single model with a giant prompt. They suffer hard from confirmation bias — once they form an initial thesis, they cherry-pick evidence to support it. TradingAgents counters this structurally with 5 layers of explicit role-based agents that argue with each other: [Analyst Team x4] -> [Bull vs Bear debate] | v [Trader (3-tier)] | v [Risk Mgmt: Aggressive vs Conservative vs Neutral] | v [Portfolio Manager (5-tier)] -> Buy / Overweight / Hold / Underweight / Sell Enter fullscreen mode Exit fullscreen mode The whole pipeline runs on LangGraph state graph with explicit handoffs. You can replace any node, log any state, and resume from any checkpoint. v0.2.4 highlights for developers 1. Structured output decision agents The Research Manager, Trader, and Portfolio Manager now use llm.with_structured_output(Schema) with Pydantic schemas. This works across: OpenAI: json_schema Google Gemini: response_schema Anthropic Claude: tool-use Other models: function-calling fallback No more brittle text parsing for decision values. 2. LangGraph checkpoint resumption Pass --checkpoint to enable per-node state persistence: ~/.tradingagents/cache/checkpoints/<TICKER>.db Enter fullscreen mode Exit fullscreen mode If your run crashes after the Bull/Bear debate but before Risk Management, you resume from there instead of paying for the full pipeline again. Big API cost savings. 3. Persistent decision memory ~/.tradingagents/memory/trading_memory.md Enter fullscreen mode Exit fullscreen mode Every run appends a decision record. On the next run for the same ticker, the framework auto-injects: Previous decision Actual realized return (raw + alpha vs SPY) A one-paragraph retrospective into the Portfolio Manager prompt. This is automated trading-journal-as-context — the framework literally learns from its own past mistakes. 4. 5-tier rating system The Portfolio Manager now outputs: Rating Meaning Buy strong buy Overweight increase position Hold maintain Underweight reduce position Sell exit The Trader still uses 3-tier (Buy/Hold/Sell). Only the final Portfolio Manager gets the finer granularity. 5. 10 LLM providers config["llm_provider"] = "anthropic" # or: # openai, google, anthropic, xai, deepseek, # qwen, glm, openrouter, ollama, azure Enter fullscreen mode Exit fullscreen mode Local Ollama is the cost-killer if you don't need top-tier reasoning. Quick start from tradingagents.graph.trading_graph import TradingAgentsGraph from…

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