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Basic Information

TradingAgents is an open-source multi-agent framework for research into LLM-driven financial trading that models the structure and workflow of real trading firms. It composes specialized agents including fundamentals, sentiment, news, technical analysts, bullish and bearish researchers, a trader agent, and a risk management and portfolio manager to collaboratively analyze markets and propose trades. The project provides both a Python package interface and a command-line interface for selecting tickers, dates, models, and research depth. It is implemented with LangGraph for modularity and requires FinnHub for market data and OpenAI for LLMs. The framework supports configurable debate rounds, online tools or cached data from a curated Tauric TradingDB, simulated exchange execution, and explicit warnings that the code is for research and not financial advice.

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App Details

Features
Modular multi-agent architecture with role specialization for fundamentals, sentiment, news, technical analysis, researchers, trader, and risk management. Debate and research workflows allow hostile and supportive researchers to critique analyst outputs across configurable rounds. LangGraph integration provides flexible, composable pipelines and easy configuration through default_config.py. A Python API exposes a TradingAgentsGraph class with a propagate function to request decisions programmatically. A CLI demonstrates interactive selection of tickers, LLMs, dates, and progress visualization. Supports simulated order execution with a portfolio manager approval step and optional online tools versus cached Tauric TradingDB data. Explicit support notes for model choices and cost-saving recommendations due to high API call volume.
Use Cases
The repository helps researchers and developers prototype, experiment with, and study multi-agent LLM approaches to market analysis and automated decision-making. It provides an end-to-end environment to compare different LLM backbones, tune debate and analysis depth, and observe how specialized agents interact to form trading proposals. The simulated exchange and portfolio manager let users test execution and risk controls without live trading. LangGraph modularity and configurable defaults make it straightforward to swap components, run backtests on cached data, or enable online data tools for live experiments. The README emphasizes research use, reproducibility aids such as default configuration and examples, and cautions about variability in real-world trading outcomes.

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