TradingAgents/docs/FASTAPI_SETUP.md

TradingAgents FastAPI Setup Guide

Overview

This guide explains how to run TradingAgents as a FastAPI server and integrate it with your Swift app.

Prerequisites

• Python 3.8+
• API keys for OpenAI (or other LLM providers)
• Swift/SwiftUI project with ReSwift

Python Server Setup

1. Install Dependencies

pip install -r requirements.txt

2. Set Environment Variables

Create a .env file in the project root:

# Required
OPENAI_API_KEY=your_openai_api_key_here

# Optional (defaults shown)
DEEP_THINK_MODEL=gpt-4o-mini
QUICK_THINK_MODEL=gpt-4o-mini
BACKEND_URL=https://api.openai.com/v1

3. Run the FastAPI Server

python run_api.py

The server will start at http://localhost:8000

4. Test the API

# Test health endpoint
curl http://localhost:8000/health

# Test analysis endpoint
curl -X POST http://localhost:8000/analyze \
  -H "Content-Type: application/json" \
  -d '{"ticker": "AAPL"}'

Swift Integration

1. Add ReSwift to Your Project

In Xcode, go to File → Add Package Dependencies and add:

https://github.com/ReSwift/ReSwift

2. Copy Swift Files

Copy these files to your Swift project:

• TradingAgents_Swift_Integration.swift - Redux architecture and networking
• TradingAnalysisView.swift - SwiftUI views

3. Update Your App

In your main app file, initialize the store and show the view:

import SwiftUI
import ReSwift

@main
struct YourApp: App {
    var body: some Scene {
        WindowGroup {
            TradingAnalysisView()
        }
    }
}

4. Configure API URL

If running on a real device or different network, update the base URL in TradingAgentsAPIService:

private let baseURL = "http://your-server-ip:8000"

API Endpoints

POST /analyze

Analyzes a stock ticker.

Request:

{
  "ticker": "AAPL"
}

Response:

{
  "ticker": "AAPL",
  "analysis_date": "2024-01-15",
  "market_report": "Technical analysis...",
  "sentiment_report": "Social sentiment analysis...",
  "news_report": "Recent news analysis...",
  "fundamentals_report": "Fundamental analysis...",
  "investment_plan": "Research team recommendation...",
  "trader_investment_plan": "Trading strategy...",
  "final_trade_decision": "Final decision...",
  "processed_signal": "BUY"
}

Production Deployment

Docker (Recommended)

Create a Dockerfile:

FROM python:3.9-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .

CMD ["uvicorn", "api:app", "--host", "0.0.0.0", "--port", "8000"]

Build and run:

docker build -t tradingagents-api .
docker run -p 8000:8000 --env-file .env tradingagents-api

Security Considerations

1. Use HTTPS in production
2. Add API authentication
3. Implement rate limiting
4. Validate and sanitize inputs
5. Use environment-specific configurations

Troubleshooting

Common Issues

1. Import errors: Ensure all dependencies are installed
2. API key errors: Check your .env file
3. Connection refused: Verify the server is running and accessible
4. CORS errors: Check CORS configuration matches your Swift app's needs

Performance Tips

• The analysis can take 30-60 seconds depending on the LLM models
• Consider implementing caching for repeated requests
• Use background processing for long-running analyses