b4653ca37b
- Created tests/fixtures/ with FixtureLoader class (14 loader methods) - Added stock_data fixtures: US, CN (with Chinese columns), standardized OHLCV - Added metadata fixtures: 5 analysis examples with datetime parsing - Added report_sections fixtures: 7 complete analyst report sections - Added api_responses fixtures: OpenAI embeddings and error responses - Added configurations fixtures: vendor and LLM provider configs - Created comprehensive README.md (595 lines) documenting fixture usage - Updated docs/testing/writing-tests.md with fixture examples 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> |
||
|---|---|---|
| .. | ||
| api | ||
| architecture | ||
| development | ||
| guides | ||
| sessions | ||
| testing | ||
| QUICKSTART.md | ||
| README.md | ||
README.md
TradingAgents Documentation
Welcome to the TradingAgents documentation. This guide will help you understand, use, and extend the TradingAgents multi-agent financial trading framework.
Overview
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 traders and risk management teams - the platform collaboratively evaluates market conditions and informs trading decisions.
Documentation Structure
Getting Started
- Quick Start Guide - Get up and running quickly with TradingAgents
- Development Setup - Set up your development environment
- Configuration Guide - Configure LLM providers, data vendors, and system settings
Architecture
Understand the system design and how components interact:
- Multi-Agent System - Agent roles, responsibilities, and collaboration patterns
- Data Flow - How data moves through the system
- LLM Integration - Provider abstraction and model selection
API Reference
Detailed API documentation for developers:
- TradingGraph API - Core orchestration API
- Agents API - Agent interfaces and implementations
- Data Flows API - Data vendor integrations
Guides
Step-by-step tutorials for common tasks:
- Adding a New Analyst - Extend the framework with custom analyst agents
- Adding an LLM Provider - Integrate new language model providers
- Configuration Options - Comprehensive configuration reference
Testing
Learn about the testing infrastructure:
- Testing Overview - Testing philosophy and structure
- Running Tests - How to run the test suite
- Writing Tests - Guidelines for writing new tests
Development
Contributing and development guidelines:
- Development Setup - Set up your development environment
- Contributing Guide - How to contribute to TradingAgents
Key Concepts
Multi-Agent Architecture
TradingAgents decomposes complex trading tasks into specialized roles:
- Analyst Team: Fundamentals, Sentiment, News, and Technical analysts
- Researcher Team: Bull and Bear researchers who debate insights
- Trader Agent: Makes trading decisions based on comprehensive analysis
- Risk Management: Evaluates portfolio risk and validates strategies
- Portfolio Manager: Final approval and execution oversight
LangGraph Framework
Built on LangGraph for:
- State management across agent workflows
- Tool orchestration for data access
- Conditional routing based on agent outputs
- Memory persistence for context retention
Data Vendor Abstraction
Flexible data sourcing through configurable vendors:
- yfinance: Stock prices and technical indicators
- Alpha Vantage: Fundamental data and news
- Google News: Alternative news sources
- Local: Offline backtesting data
Quick Links
- Installation Instructions
- API Keys Setup
- First Analysis
- Configuration Options
- GitHub Repository
- Research Paper
Support
- Discord: Join our community
- GitHub Issues: Report bugs or request features
- Twitter: @TauricResearch
License
TradingAgents is released under the MIT License. See the LICENSE file for details.
Disclaimer
TradingAgents is designed for research and educational purposes. It is not intended as financial, investment, or trading advice. Trading performance may vary based on many factors including model selection, data quality, and market conditions. See Tauric AI Disclaimer for full details.