🚀 LocoHFT

High-frequency trading engine powered by Rust + Python AI

LocoHFT is a hybrid high-frequency trading system combining Rust's blazing-fast execution speed with Python's AI/ML capabilities. Features an interactive terminal UI, risk management, and both practice and live trading modes.

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✨ Features

Feature	Description
⚡ Ultra-Low Latency	Sub-microsecond order execution via Rust engine
🤖 AI Trading Strategies	Python-embedded ML algorithms via PyO3
📊 Smart Analysis	Bollinger Bands mean reversion, momentum detection
🛡️ Risk Management	Position limits, VaR calculations, automatic stop-loss
🎮 Practice Mode	Paper trading with $100K virtual balance
💡 AI Recommendations	Personalized portfolio allocation based on goals
📚 Learning Center	Built-in educational content for beginners
💾 SQLite Persistence	Portfolio and trade history storage

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🏗️ Architecture

┌─────────────────────────────────────────────────────────────┐
│                        Terminal UI                           │
│                  (dialoguer + indicatif)                     │
└──────────────────────┬──────────────────────────────────────┘
                       │
┌──────────────────────▼──────────────────────────────────────┐
│                      Rust Core                               │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐       │
│  │   Engine     │  │  Risk Mgmt   │  │   Portfolio  │       │
│  │  Execution   │  │     VaR      │  │   Manager    │       │
│  └──────┬───────┘  └──────────────┘  └──────────────┘       │
└─────────┼────────────────────────────────────────────────────┘
          │ PyO3 Bridge
┌─────────▼────────────────────────────────────────────────────┐
│                    Python AI Layer                           │
│  ┌──────────────────┐      ┌──────────────────────────┐     │
│  │ LocoHFTStrategy  │      │ Recommendation Engine    │     │
│  │ (Bollinger Bands)│      │ (Portfolio Optimizer)    │     │
│  └──────────────────┘      └──────────────────────────┘     │
└──────────────────────────────────────────────────────────────┘

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🚀 Quick Start

Prerequisites

• Rust 1.70+
• Python 3.8+

Build & Run

# Clone the repository
git clone https://github.com/yourusername/LocoHFT.git
cd LocoHFT

# Build release binary
cargo build --release

# Run the application
./target/release/LocoHFT.exe

Windows Standalone Build

# Run the standalone build script (downloads required tools automatically)
build-standalone.bat

# Output will be in dist/LocoHFT.exe

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📖 Usage Guide

Main Menu

Option	Description
📈 Stock Analysis	Analyze individual stocks, view market overview
💰 My Portfolio	View holdings, trade history, buy/sell stocks
🤖 AI Trading	Enable automated AI trading with risk controls
📚 Learning Center	Educational content for beginners
⚙️ Settings	Toggle practice/live mode, configure API keys

AI Trading Strategy

The built-in LocoHFTStrategy uses Bollinger Bands Mean Reversion:

# Buys when price < lower band (oversold)
# Sells when price > upper band (overbought)
upper = SMA + 2σ
lower = SMA - 2σ

Practice Mode vs Live Trading

Mode	Risk	Balance	Purpose
🎮 Practice	None	$100,000 virtual	Learn & test strategies
🔴 Live	Real	Your actual funds	Real trading

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⚙️ Configuration

API Keys (Optional)

For real market data, get a free API key from Alpha Vantage:

Settings → API Keys → Enter your key

Without API keys, the app uses demo data for learning.

Risk Settings

Edit python/strategy.py to customize:

self.lookback = 20          # Bollinger Bands period
self.position_limit = 1000  # Max shares per symbol
self.var_limit = 0.02       # 2% Value-at-Risk limit

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🛠️ Development

Project Structure

LocoHFT/
├── src/
│   ├── main.rs           # TUI application entry
│   ├── engine.rs         # Execution & risk engine
│   └── python_bridge.rs  # PyO3 Python interop
├── python/
│   ├── strategy.py       # AI trading strategies
│   ├── recommendations.py # Portfolio optimizer
│   └── analytics.py      # Data analysis tools
├── Cargo.toml
└── README.md

Adding Custom Strategies

1. Edit python/strategy.py
2. Implement your logic in on_market_data()
3. Return a Signal dict with action, size, price, confidence

def on_market_data(self, symbol, price, volume, timestamp):
    # Your custom logic here
    if should_buy:
        return Signal("BUY", 100, price, 0.85, {}).dict()
    return None

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🧪 Testing

# Run unit tests
cargo test

# Run with logging
RUST_LOG=debug cargo run

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📦 Dependencies

Rust Crates

• tokio - Async runtime
• pyo3 - Python interoperability
• dialoguer / indicatif - Terminal UI
• rusqlite - Embedded database
• crossbeam - High-performance channels
• reqwest - HTTP client for APIs

Python Libraries

• Standard library only (embedded)

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⚠️ Risk Disclaimer

Trading involves significant risk of loss.

• This software is for educational purposes
• Past performance does not guarantee future results
• Only trade with money you can afford to lose
• The AI is a tool, not financial advice
• Always do your own research

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📄 License

MIT License - See LICENSE for details.

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🙏 Acknowledgments

• Built with ❤️ in Rust
• Python AI integration via PyO3
• Terminal UI powered by console-rs

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Made for traders who love speed and intelligence
🦀 ⚡ 🐍