Straight Arrow Strategy Implementation

A clean, focused AI-powered investment analysis system using the proven Straight Arrow strategy (60% VTI, 30% BNDX, 10% GSG).

• Straight Arrow Strategy: Fixed allocation (60% VTI, 30% BNDX, 10% GSG)
• Portfolio Analysis: Drift analysis and rebalancing recommendations
• Real-time Market Data: Alpha Vantage integration with fallback to mock data
• AI Analysis: Educational investment guidance with OpenAI integration
• Database Storage: Portfolio history tracking with Supabase
• Simple API: Clean, focused endpoints

FinAgent Simplified
├── Straight Arrow Strategy (Fixed Allocation)
├── Market Data Service (Alpha Vantage + Mock)
├── AI Service (OpenAI + Mock)
├── Database (Supabase PostgreSQL)
└── FastAPI REST API

Python 3.8+
Supabase account (optional)
API Keys: OpenAI, Alpha Vantage (optional)

pip install -r requirements_simple.txt

export SUPABASE_URL="your-supabase-url"
export SUPABASE_ANON_KEY="your-supabase-key"
export OPENAI_API_KEY="your-openai-key"
export ALPHA_VANTAGE_API_KEY="your-alpha-vantage-key"

# Run in your Supabase SQL editor
cat database_simple.sql

python main_simplified.py
# Server available at http://localhost:8000
# API docs at http://localhost:8000/docs

python test_simple.py

GET /health

POST /api/analyze-portfolio
{
  "portfolio": {
    "VTI": 25000,
    "BNDX": 20000,
    "GSG": 5000
  },
  "total_value": 50000
}

POST /api/market-data
{
  "symbols": ["VTI", "BNDX", "GSG"]
}

POST /api/agents/data-analyst
{
  "query": "What is the Straight Arrow strategy?",
  "symbols": ["VTI", "BNDX", "GSG"]
}

GET /api/portfolio-history

Target Allocation:

• 60% VTI - Vanguard Total Stock Market ETF
• 30% BNDX - Vanguard Total International Bond ETF
• 10% GSG - iShares GSCI Commodity-Indexed Trust

Benefits:

• Simple 3-fund portfolio
• Broad diversification
• Low costs
• Easy to rebalance
• Suitable for beginners

# Start server
python main_simplified.py

# Run tests
python test_simple.py

# Check health
curl http://localhost:8000/health

# Deploy to Render/Heroku
# Set environment variables
# Update database connection

import requests

response = requests.post("http://localhost:8000/api/analyze-portfolio", json={
    "portfolio": {"VTI": 30000, "BNDX": 15000, "GSG": 5000},
    "total_value": 50000
})

analysis = response.json()["analysis"]
print(f"Strategy: {analysis['strategy']}")
print(f"Needs rebalancing: {analysis['risk_assessment']['needs_rebalancing']}")

response = requests.post("http://localhost:8000/api/agents/data-analyst", json={
    "query": "Should I rebalance my portfolio?",
    "symbols": ["VTI", "BNDX", "GSG"]
})

ai_response = response.json()["analysis"]
print(ai_response["analysis"])

• Set ALPHA_VANTAGE_API_KEY for real market data
• Set OPENAI_API_KEY for AI analysis
• Set SUPABASE_URL and SUPABASE_ANON_KEY for database

• System works with mock data
• No external dependencies
• Perfect for testing and development

• main_simplified.py - Main application
• requirements_simple.txt - Dependencies
• database_simple.sql - Database schema
• test_simple.py - Test suite
• README.md - This file

1. Connect GitHub repository
2. Set environment variables
3. Deploy from main_simplified.py
4. Run database schema in Supabase

SUPABASE_URL=your-supabase-url
SUPABASE_ANON_KEY=your-supabase-key
OPENAI_API_KEY=your-openai-key
ALPHA_VANTAGE_API_KEY=your-alpha-vantage-key
PORT=8000

Try the live API:

• Health: GET https://your-app.onrender.com/health
• Docs: https://your-app.onrender.com/docs

1. Fork repository
2. Create feature branch
3. Make changes
4. Test thoroughly
5. Submit pull request

MIT License - see LICENSE file

FinAgent Simplified - Clean, focused investment analysis 🎯