README.md

metadata

title: AlgoQuant Backend API
emoji: 🚀
colorFrom: blue
colorTo: green
sdk: docker
pinned: false
license: mit

AlgoQuant Backend API 🚀

Production-grade FastAPI backend for algorithmic cryptocurrency trading with AI-powered strategies.

Features

• 🤖 HMM-SVR Walk-Forward Strategy - Zero lookahead bias backtesting
• 📊 Pairs Trading - Statistical arbitrage (ETH/BTC)
• 💼 Paper Trading - Simulated trading with $10,000 starting capital
• 🔒 Secure Auth - JWT authentication with bcrypt
• ⚡ Real-Time Data - Binance Testnet + Yahoo Finance

API Documentation

Once deployed, access the interactive API docs at:

• Swagger UI: https://your-space-name.hf.space/docs
• ReDoc: https://your-space-name.hf.space/redoc

Endpoints

Authentication

• POST /signup - Create new user account
• POST /login - Get JWT access token

Trading

• POST /backtest - Run strategy backtesting
• POST /start-live-trading - Start simulated trading session
• POST /stop-live-trading/{session_id} - Stop trading session
• GET /portfolio - Get user portfolio balance
• GET /trading-sessions - List all trading sessions
• GET /trades - Get trade history

Data

• GET /price/{ticker} - Get current price for ticker
• GET /dashboard - Get dashboard metrics

Environment Variables

Required for production:

DATABASE_URL=postgresql://user:password@host:5432/dbname
SECRET_KEY=your-secret-key-here

Tech Stack

• FastAPI - Modern async web framework
• PostgreSQL - Production database
• SQLModel - SQL ORM with type safety
• scikit-learn - Machine learning
• hmmlearn - Hidden Markov Models
• yfinance - Free market data

Local Development

# Install dependencies
pip install -r requirements.txt

# Train HMM model
python train_hmm_model.py

# Run server
uvicorn main:app --reload --port 8000

License

MIT License - See LICENSE for details

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Built with 🧠 for quantitative traders

Part of the AlgoQuant AI-Powered Trading Platform