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TradingAgents Quick Start Guide

🚀 Overview

This guide will help you get started with TradingAgents quickly, including the new Chinese market features, database integration, and multi-LLM support.

⚡ Quick Setup (5 Minutes)

1. Prerequisites

# Python 3.8+ required
python --version

# Clone the repository
git clone https://github.com/your-repo/TradingAgents.git
cd TradingAgents

# Install dependencies
pip install -r requirements.txt
pip install pytdx beautifulsoup4  # For Chinese market support

2. Environment Configuration

# Copy environment template
cp .env.example .env

# Edit .env file with your API keys
nano .env  # or use your preferred editor

Minimum Required Configuration:

For US Stock Analysis Only:

# OpenAI or Google AI (Choose one)
OPENAI_API_KEY=your_openai_api_key_here
# OR
GOOGLE_API_KEY=your_google_api_key_here

# FinnHub (Required for financial data)
FINNHUB_API_KEY=your_finnhub_api_key_here

For China A-Share Analysis OR DashScope LLM:

# DashScope (Required for Chinese stocks or Qwen models)
DASHSCOPE_API_KEY=your_dashscope_api_key_here

# FinnHub (Required for financial data)
FINNHUB_API_KEY=your_finnhub_api_key_here

Note:

  • DashScope API key is only required when:
    • Analyzing Chinese A-share stocks (uses TongDaXin data + DashScope embeddings)
    • Choosing DashScope as your LLM provider (Qwen models)
  • For US stocks with OpenAI/Google models: DashScope is not needed

3. First Run

# Start the application
python -m cli.main

# Follow the interactive prompts:
# 1. Select Market: US Stock or China A-Share
# 2. Enter ticker symbol (e.g., AAPL or 000001)
# 3. Choose analysis date
# 4. Select analysts team
# 5. Choose LLM provider (DashScope recommended)
# 6. Run analysis

🌟 Feature Overview

🇺🇸 US Stock Analysis

  • Supported Symbols: AAPL, SPY, TSLA, NVDA, MSFT, etc.
  • Data Source: Yahoo Finance
  • Format: 1-5 letter symbols
  • Example: AAPL (Apple Inc.)

🇨🇳 China A-Share Analysis

  • Supported Exchanges:
    • Shanghai (60xxxx): 600036 (China Merchants Bank)
    • Shenzhen (00xxxx): 000001 (Ping An Bank)
    • ChiNext (30xxxx): 300001 (Technology stocks)
    • STAR Market (68xxxx): 688001 (Innovation companies)
  • Data Source: TongDaXin API
  • Format: 6-digit numeric codes

🤖 Multi-LLM Support

  • DashScope (Alibaba Cloud): Qwen models, Chinese-optimized
  • OpenAI: GPT-4o, GPT-4o-mini, o1, o3 series
  • Google AI: Gemini 2.0/2.5 Flash series
  • Anthropic: Claude 3.5/4 series

📋 Step-by-Step Walkthrough

Step 1: Market Selection

? Select Stock Market:
  US Stock - Examples: SPY, AAPL, TSLA
❯ China A-Share - Examples: 000001, 600036, 000858

Step 2: Ticker Input

Format requirement: 6-digit code (e.g., 600036, 000001)
Examples: 000001, 600036, 300001, 688001
? Enter China A-Share ticker symbol: 000001
✅ Valid A-share code: 000001 (will use TongDaXin data source)

Step 3: Analysis Configuration

? Select your research depth:
❯ Light (1 round) - Quick analysis
  Medium (2 rounds) - Balanced analysis  
  Deep (3 rounds) - Comprehensive analysis

? Select your LLM Provider:
❯ DashScope (Alibaba Cloud)
  OpenAI
  Google AI
  Anthropic

Step 4: Model Selection

? Select Your [Quick-Thinking LLM Engine]:
❯ Qwen-Turbo - Fast response, suitable for quick tasks
  Qwen-Plus - Balanced performance and cost
  Qwen-Max - Best performance for complex analysis

? Select Your [Deep-Thinking LLM Engine]:
❯ Qwen-Plus - Balanced performance and cost (Recommended)
  Qwen-Max - Best performance for complex analysis
  Qwen-Max-LongContext - Ultra-long context support

🗄️ Database Setup (Optional)

Enable High-Performance Caching

1. Start Database Services:

# MongoDB for persistent storage
docker run -d -p 27017:27017 --name mongodb mongo

# Redis for high-performance caching
docker run -d -p 6379:6379 --name redis redis

2. Enable in .env:

# Enable database caching
MONGODB_ENABLED=true
REDIS_ENABLED=true

# MongoDB configuration
MONGODB_HOST=localhost
MONGODB_PORT=27017
MONGODB_DATABASE=tradingagents

# Redis configuration  
REDIS_HOST=localhost
REDIS_PORT=6379
REDIS_DB=0

3. Restart Application:

python -m cli.main
# System will now use database caching for improved performance

🔧 Configuration Examples

Example 1: US Stock Analysis with OpenAI

# Only need OpenAI and FinnHub for US stocks
OPENAI_API_KEY=your_openai_key
FINNHUB_API_KEY=your_finnhub_key

CLI Selections:

  • Market: US Stock
  • Ticker: AAPL
  • LLM Provider: OpenAI
  • Models: GPT-4o-mini (quick), o1 (deep)

Note: DashScope not required for US stock analysis with OpenAI

Example 2: US Stock Analysis with Google AI

# Only need Google AI and FinnHub for US stocks
GOOGLE_API_KEY=your_google_key
FINNHUB_API_KEY=your_finnhub_key

CLI Selections:

  • Market: US Stock
  • Ticker: TSLA
  • LLM Provider: Google AI
  • Models: Gemini 2.0 Flash (quick), Gemini 2.5 Flash (deep)

Note: DashScope not required for US stock analysis with Google AI

Example 3: China A-Share Analysis (DashScope Required)

# DashScope required for Chinese stock analysis
DASHSCOPE_API_KEY=your_dashscope_key
FINNHUB_API_KEY=your_finnhub_key

CLI Selections:

  • Market: China A-Share
  • Ticker: 000001
  • LLM Provider: DashScope
  • Models: qwen-turbo (quick), qwen-plus (deep)

Note: DashScope API key is required for Chinese stock analysis (TongDaXin data + embeddings)

Example 4: US Stocks with DashScope LLM (DashScope Required)

# DashScope required when using Qwen models
DASHSCOPE_API_KEY=your_dashscope_key
FINNHUB_API_KEY=your_finnhub_key

CLI Selections:

  • Market: US Stock
  • Ticker: SPY
  • LLM Provider: DashScope (Alibaba Cloud)
  • Models: qwen-turbo (quick), qwen-plus (deep)

Note: DashScope API key is required when choosing DashScope as LLM provider

Example 5: Full Features with Database

# Choose based on your use case
OPENAI_API_KEY=your_openai_key          # For US stocks with OpenAI
# OR
DASHSCOPE_API_KEY=your_dashscope_key    # For Chinese stocks or DashScope LLM

FINNHUB_API_KEY=your_finnhub_key
MONGODB_ENABLED=true
REDIS_ENABLED=true

Benefits:

  • Faster data retrieval
  • Persistent analysis history
  • Advanced caching strategies
  • Usage analytics

🛠️ Troubleshooting

Common Issues

1. API Key Errors:

Error: Invalid API key
Solution: Check .env file and ensure correct API key format

2. TongDaXin Connection Issues:

Error: TongDaXin API unavailable
Solution: System automatically falls back to cached data

3. Database Connection Issues:

Error: MongoDB/Redis connection failed
Solution: System falls back to file cache automatically

4. Invalid Ticker Format:

Error: Invalid ticker format
Solution: 
- US stocks: Use 1-5 letter symbols (AAPL)
- A-shares: Use 6-digit codes (000001)

Debug Mode

# Enable debug logging
export TRADINGAGENTS_LOG_LEVEL=DEBUG
python -m cli.main

📊 Sample Analysis Output

US Stock Analysis (AAPL)

📈 Analysis Results for AAPL (Apple Inc.)
Market: US Stock Exchange
Data Source: Yahoo Finance

🔍 Technical Analysis:
- Current Price: $150.25 (+2.3%)
- RSI: 65.2 (Neutral to Bullish)
- Moving Averages: Above 20-day and 50-day MA

💰 Fundamental Analysis:
- P/E Ratio: 28.5
- Revenue Growth: 8.2% YoY
- Market Cap: $2.4T

📰 News Sentiment: Positive (0.72/1.0)
🎯 Recommendation: BUY with target $165

China A-Share Analysis (000001)

📈 Analysis Results for 000001 (平安银行)
Market: Shenzhen Stock Exchange
Data Source: TongDaXin API

🔍 Technical Analysis:
- Current Price: ¥12.85 (+1.8%)
- RSI: 58.3 (Neutral)
- Volume: Above average

💰 Fundamental Analysis:
- P/E Ratio: 5.2
- ROE: 12.8%
- Book Value: ¥15.20

📰 News Sentiment: Neutral (0.55/1.0)
🎯 Recommendation: HOLD with target ¥14.50

🎯 Next Steps

Explore Advanced Features

  1. Custom Prompts: Modify agent prompts for specific strategies
  2. Database Analytics: Analyze historical performance
  3. Multi-Market Comparison: Compare US and Chinese stocks
  4. Risk Management: Configure risk parameters

Learn More

Get Support

  • GitHub Issues: Report bugs and feature requests
  • Documentation: Comprehensive guides and examples
  • Community: Join discussions and share strategies

🎉 Congratulations! You're now ready to analyze both US and Chinese markets with TradingAgents. The system provides intelligent fallbacks, multi-LLM support, and enterprise-grade caching for optimal performance.