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TradingAgents/docs/guides/configuration.md

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Configuration Guide

Complete reference for configuring TradingAgents.

Configuration File

Location: tradingagents/default_config.py

Default Configuration

DEFAULT_CONFIG = {
    # Directories
    "project_dir": "<auto-detected>",
    "results_dir": "./results",
    "data_cache_dir": "./dataflows/data_cache",

    # LLM settings
    "llm_provider": "openai",
    "deep_think_llm": "o4-mini",
    "quick_think_llm": "gpt-4o-mini",
    "backend_url": "https://api.openai.com/v1",

    # Workflow settings
    "max_debate_rounds": 1,
    "max_risk_discuss_rounds": 1,
    "max_recur_limit": 100,

    # Data vendors
    "data_vendors": {
        "core_stock_apis": "yfinance",
        "technical_indicators": "yfinance",
        "fundamental_data": "alpha_vantage",
        "news_data": "alpha_vantage"
    },

    # Tool-level overrides (optional)
    "tool_vendors": {}
}

Configuration Options

Directory Settings

project_dir

  • Type: str
  • Default: Auto-detected from package location
  • Description: Root directory of TradingAgents package

results_dir

  • Type: str
  • Default: "./results"
  • Environment Variable: TRADINGAGENTS_RESULTS_DIR
  • Description: Directory for storing analysis results
  • Example: 
    config["results_dir"] = "/path/to/results"
# Or set environment variable
export TRADINGAGENTS_RESULTS_DIR=/path/to/results

data_cache_dir

  • Type: str
  • Default: "./dataflows/data_cache"
  • Description: Directory for caching data vendor responses

LLM Settings

llm_provider

  • Type: str
  • Options: "openai", "anthropic", "google", "openrouter", "ollama"
  • Default: "openai"
  • Description: LLM provider selection

Examples:

# OpenAI (default)
config["llm_provider"] = "openai"

# Anthropic
config["llm_provider"] = "anthropic"

# OpenRouter (unified access)
config["llm_provider"] = "openrouter"

# Google Generative AI
config["llm_provider"] = "google"

# Ollama (local)
config["llm_provider"] = "ollama"

deep_think_llm

  • Type: str
  • Default: "o4-mini"
  • Description: Model for complex reasoning tasks
  • Use Cases: Research debates, trading decisions, risk assessment

Recommended Models by Provider:

# OpenAI
config["deep_think_llm"] = "o4-mini"  # Fast, affordable
config["deep_think_llm"] = "o1-preview"  # Best reasoning

# Anthropic
config["deep_think_llm"] = "claude-sonnet-4-20250514"

# OpenRouter
config["deep_think_llm"] = "anthropic/claude-sonnet-4.5"

# Google
config["deep_think_llm"] = "gemini-2.0-flash"

# Ollama
config["deep_think_llm"] = "mistral"

quick_think_llm

  • Type: str
  • Default: "gpt-4o-mini"
  • Description: Model for fast, routine tasks
  • Use Cases: Analyst reports, data summarization, tool calling

Recommended Models:

# OpenAI
config["quick_think_llm"] = "gpt-4o-mini"  # Most cost-effective

# Anthropic
config["quick_think_llm"] = "claude-sonnet-4-20250514"

# OpenRouter
config["quick_think_llm"] = "openai/gpt-4o-mini"

backend_url

  • Type: str
  • Default: "https://api.openai.com/v1"
  • Description: API endpoint for LLM provider

Examples:

# OpenAI
config["backend_url"] = "https://api.openai.com/v1"

# Anthropic
config["backend_url"] = "https://api.anthropic.com"

# OpenRouter
config["backend_url"] = "https://openrouter.ai/api/v1"

# Ollama (local)
config["backend_url"] = "http://localhost:11434/v1"

Workflow Settings

max_debate_rounds

  • Type: int
  • Default: 1
  • Range: 1-5
  • Description: Number of bull/bear debate rounds
  • Impact:
    • More rounds = deeper analysis
    • More rounds = higher cost and latency
    • Diminishing returns after 2-3 rounds

Examples:

# Fast, cost-effective
config["max_debate_rounds"] = 1

# Balanced
config["max_debate_rounds"] = 2

# Deep analysis
config["max_debate_rounds"] = 3

max_risk_discuss_rounds

  • Type: int
  • Default: 1
  • Range: 1-3
  • Description: Number of risk management discussion rounds

max_recur_limit

  • Type: int
  • Default: 100
  • Description: Maximum recursion limit for graph execution

Data Vendor Settings

data_vendors

  • Type: Dict[str, str]
  • Description: Category-level data vendor configuration

Available Categories:

core_stock_apis
  • Options: "yfinance", "alpha_vantage", "local"
  • Default: "yfinance"
  • Purpose: Stock prices and quotes
technical_indicators
  • Options: "yfinance", "alpha_vantage", "local"
  • Default: "yfinance"
  • Purpose: Technical indicators (MACD, RSI, etc.)
fundamental_data
  • Options: "openai", "alpha_vantage", "local"
  • Default: "alpha_vantage"
  • Purpose: Company financials and ratios
news_data
  • Options: "openai", "alpha_vantage", "google", "local"
  • Default: "alpha_vantage"
  • Purpose: News articles and events

Example:

config["data_vendors"] = {
    "core_stock_apis": "yfinance",
    "technical_indicators": "yfinance",
    "fundamental_data": "alpha_vantage",
    "news_data": "google"  # Use Google for news
}

tool_vendors

  • Type: Dict[str, str]
  • Description: Tool-level vendor overrides (takes precedence over categories)

Example:

config["tool_vendors"] = {
    "get_stock_data": "alpha_vantage",  # Override category default
    "get_news": "google"  # Override category default
}

Environment Variables

LLM Provider API Keys

# OpenAI (required for OpenAI provider or embeddings)
export OPENAI_API_KEY=sk-your_key_here

# Anthropic (required for Anthropic provider)
export ANTHROPIC_API_KEY=sk-ant-your_key_here

# OpenRouter (required for OpenRouter provider)
export OPENROUTER_API_KEY=sk-or-v1-your_key_here

# Google (required for Google provider)
export GOOGLE_API_KEY=your_key_here

Data Vendor API Keys

# Alpha Vantage (required for fundamental and news data)
export ALPHA_VANTAGE_API_KEY=your_key_here

Application Settings

# Results directory
export TRADINGAGENTS_RESULTS_DIR=./results

Configuration Examples

Production Configuration

from tradingagents.default_config import DEFAULT_CONFIG

config = DEFAULT_CONFIG.copy()

# Use production-grade models
config["llm_provider"] = "openai"
config["deep_think_llm"] = "o1-preview"  # Best reasoning
config["quick_think_llm"] = "gpt-4o"  # High quality

# Deep analysis
config["max_debate_rounds"] = 2
config["max_risk_discuss_rounds"] = 2

# Reliable data sources
config["data_vendors"] = {
    "core_stock_apis": "alpha_vantage",
    "technical_indicators": "alpha_vantage",
    "fundamental_data": "alpha_vantage",
    "news_data": "alpha_vantage"
}

Development/Testing Configuration

config = DEFAULT_CONFIG.copy()

# Use cost-effective models
config["llm_provider"] = "openai"
config["deep_think_llm"] = "o4-mini"
config["quick_think_llm"] = "gpt-4o-mini"

# Fast analysis
config["max_debate_rounds"] = 1
config["max_risk_discuss_rounds"] = 1

# Free data sources
config["data_vendors"] = {
    "core_stock_apis": "yfinance",
    "technical_indicators": "yfinance",
    "fundamental_data": "alpha_vantage",  # Free tier
    "news_data": "google"
}

Cost-Optimized Configuration

config = DEFAULT_CONFIG.copy()

# Use OpenRouter for competitive pricing
config["llm_provider"] = "openrouter"
config["deep_think_llm"] = "anthropic/claude-sonnet-4.5"
config["quick_think_llm"] = "openai/gpt-4o-mini"
config["backend_url"] = "https://openrouter.ai/api/v1"

# Minimal debate rounds
config["max_debate_rounds"] = 1
config["max_risk_discuss_rounds"] = 1

Offline/Local Configuration

config = DEFAULT_CONFIG.copy()

# Use local Ollama models
config["llm_provider"] = "ollama"
config["deep_think_llm"] = "mistral"
config["quick_think_llm"] = "mistral"
config["backend_url"] = "http://localhost:11434/v1"

# Use local data cache
config["data_vendors"] = {
    "core_stock_apis": "local",
    "technical_indicators": "local",
    "fundamental_data": "local",
    "news_data": "local"
}

Using Configuration

In Code

from tradingagents.graph.trading_graph import TradingAgentsGraph
from tradingagents.default_config import DEFAULT_CONFIG

# Create custom config
config = DEFAULT_CONFIG.copy()
config["llm_provider"] = "anthropic"
config["max_debate_rounds"] = 2

# Initialize with config
ta = TradingAgentsGraph(config=config)

# Run analysis
state, decision = ta.propagate("NVDA", "2024-05-10")

In CLI

The CLI reads configuration from default_config.py and allows runtime overrides through the interactive menu.

With .env File

Create .env file:

# LLM Provider
OPENAI_API_KEY=sk-your_key_here
ANTHROPIC_API_KEY=sk-ant-your_key_here

# Data Vendor
ALPHA_VANTAGE_API_KEY=your_key_here

# Application
TRADINGAGENTS_RESULTS_DIR=./results

Load in Python:

from dotenv import load_dotenv
load_dotenv()

# Now environment variables are available

Best Practices

  1. Never Hardcode Keys: Use environment variables
  2. Copy Default Config: Always config = DEFAULT_CONFIG.copy()
  3. Start Minimal: Use 1 debate round initially
  4. Test Locally: Use Ollama for development
  5. Monitor Costs: Track LLM API usage
  6. Cache Aggressively: Use local data vendor when possible
  7. Validate Configuration: Check keys before running

Troubleshooting

Missing API Keys

Error: ValueError: OPENAI_API_KEY environment variable is required

Solution:

export OPENAI_API_KEY=your_key_here

Invalid Model Names

Error: Invalid model name: 'gpt-5'

Solution: Check provider documentation for valid model names

Data Vendor Errors

Error: VendorError: Alpha Vantage API key invalid

Solution: Verify API key is correct and has remaining quota

See Also