TradingAgents/docs/en-US/quick_reference.md

TradingAgents Quick Reference Card

🚀 Quick Start

1. Change LLM Provider

# Edit main.py
config["llm_provider"] = "google"  # or "openai", "anthropic"
config["backend_url"] = "https://generativelanguage.googleapis.com/v1"
config["deep_think_llm"] = "gemini-2.0-flash"
config["quick_think_llm"] = "gemini-2.0-flash"

2. Modify Debate Rounds

# Edit main.py or default_config.py
config["max_debate_rounds"] = 3  # Increase to 3 rounds
config["max_risk_discuss_rounds"] = 2  # Risk discussion 2 rounds

3. Enable/Disable Online Tools

config["online_tools"] = True   # Enable online APIs
config["online_tools"] = False  # Use local data

📁 Key File Locations

Content to Modify	File Path	Specific Location
System Config	tradingagents/default_config.py	Entire file
Runtime Config	main.py	Lines 15-22
Market Analyst Prompts	tradingagents/agents/analysts/market_analyst.py	Lines 24-50
Fundamentals Analyst Prompts	tradingagents/agents/analysts/fundamentals_analyst.py	Lines 23-26
News Analyst Prompts	tradingagents/agents/analysts/news_analyst.py	Lines 20-23
Social Media Analyst Prompts	tradingagents/agents/analysts/social_media_analyst.py	Lines 19-22
Bull Researcher Prompts	tradingagents/agents/researchers/bull_researcher.py	Lines 25-43
Bear Researcher Prompts	tradingagents/agents/researchers/bear_researcher.py	Lines 25-43
Trader Prompts	tradingagents/agents/trader/trader.py	Lines 30-36
Reflection System Prompts	tradingagents/graph/reflection.py	Lines 15-47
Cache Config	tradingagents/dataflows/cache_manager.py	Lines 20-35

🎯 Common Modification Templates

1. Professional Prompt Template

system_message = f"""
You are a professional {role_name} with the following characteristics:

Expertise Areas:
- {domain_1}
- {domain_2}
- {domain_3}

Analysis Requirements:
1. Provide detailed analysis reasoning
2. Include risk warnings
3. Summarize key indicators in table format

Output Format:
{output_format}

Important Notes:
- Avoid simply saying "trends are mixed"
- Provide specific data support
- Consider market-specific factors
"""

2. Risk Control Template

system_message = f"""
You are a risk-conscious {role_name}.

Risk Control Principles:
1. Risk first, returns second
2. Strict stop-loss, protect capital
3. Diversified investment, reduce risk
4. Data-driven, rational decisions

Must Include:
- Risk assessment level (Low/Medium/High)
- Recommended stop-loss points
- Maximum position suggestion
- Risk warning description

Decision Format:
Final Recommendation: **BUY/HOLD/SELL**
Risk Level: **Low/Medium/High**
Stop-Loss Point: **Specific price**
Suggested Position: **Percentage**
"""

3. Technical Analysis Template

system_message = f"""
You are a professional technical analyst focusing on the following indicators:

Core Indicators:
- Moving Averages: SMA, EMA
- Momentum Indicators: RSI, MACD
- Volatility Indicators: Bollinger Bands, ATR
- Volume Indicators: VWMA

Analysis Framework:
1. Trend identification (Up/Down/Sideways)
2. Support and resistance levels
3. Buy/sell signal identification
4. Risk-reward ratio calculation

Output Requirements:
- Clear trend judgment
- Specific entry/exit points
- Technical indicator divergence analysis
- Volume-price relationship analysis
"""

⚙️ Configuration Parameters Quick Reference

LLM Configuration

"llm_provider": "openai" | "google" | "anthropic"
"deep_think_llm": "model_name"  # Deep thinking model
"quick_think_llm": "model_name"  # Quick thinking model
"backend_url": "API_address"

Google Models Quick Reference

# Fast Models: gemini-2.0-flash-lite, gemini-2.0-flash ⭐, gemini-2.5-flash-preview-05-20
# Deep Models: gemini-2.0-flash ⭐, gemini-2.5-flash-preview-05-20, gemini-2.5-pro-preview-06-05

# Google API Setup
export GOOGLE_API_KEY="your_key_here"

Debate Configuration

"max_debate_rounds": 1-5        # Debate rounds
"max_risk_discuss_rounds": 1-3  # Risk discussion rounds
"max_recur_limit": 100          # Recursion limit

Tool Configuration

"online_tools": True | False    # Whether to use online tools
"data_cache_dir": "cache_directory_path"
"results_dir": "results_output_directory"

Cache Configuration

# In cache_manager.py
'us_stock_data': {'ttl_hours': 2}     # US stock cache 2 hours
'china_stock_data': {'ttl_hours': 1}  # A-share cache 1 hour

🔧 Common Commands

Test Configuration

# Run basic tests
cd tests && python test_cache_manager.py

# Run integration tests
cd tests && python test_integration.py

# Run performance tests
cd tests && python test_performance.py

Backup and Restore

# Backup configuration files
cp tradingagents/default_config.py tradingagents/default_config.py.backup

# Backup prompt files
cp tradingagents/agents/trader/trader.py tradingagents/agents/trader/trader.py.backup

# Restore files
cp tradingagents/default_config.py.backup tradingagents/default_config.py

Git Management

# Check modification status
git status

# Commit configuration changes
git add tradingagents/default_config.py
git commit -m "feat: Update LLM configuration to Google Gemini"

# Commit prompt changes
git add tradingagents/agents/trader/trader.py
git commit -m "feat: Optimize trader prompts, add risk control"

🚨 Important Notes

⚠️ Must Do Before Modification

1. Backup Files: Always backup original files before modification
2. Test Environment: Validate modifications in test environment
3. Version Control: Use Git to track all changes

⚠️ Common Errors

1. Forgot to Restart: Need to restart application after config changes
2. Path Errors: Ensure file paths are correct
3. Syntax Errors: Python syntax must be correct
4. Encoding Issues: Use UTF-8 encoding for content

⚠️ Performance Considerations

1. Prompt Length: Avoid overly long prompts (recommend <4000 tokens)
2. API Call Frequency: Be aware of API call limits
3. Cache Settings: Set reasonable cache TTL times

🆘 Troubleshooting

Issue: Configuration not taking effect

# Solution: Force reload configuration
from tradingagents.dataflows.config import reload_config
reload_config()

Issue: API call failures

# Solution: Check API keys and network connection
import os
print("OpenAI API Key:", os.getenv("OPENAI_API_KEY", "Not set"))
print("Google API Key:", os.getenv("GOOGLE_API_KEY", "Not set"))

Issue: High memory usage

# Solution: Enable cache cleanup
config["cache_settings"]["cache_size_limit_mb"] = 500  # Limit cache size
config["cache_settings"]["cache_cleanup_interval"] = 1800  # Clean every 30 minutes

📞 Getting Help

1. View Detailed Documentation: docs/en-US/configuration_guide.md
2. Run Tests: Test files in tests/ directory
3. View Examples: examples/ directory (if available)
4. GitHub Issues: Submit issues in project repository

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💡 Tip: Recommend bookmarking this document for easy reference!