MPC Binance Backtest

Privacy-preserving cryptocurrency trading strategy backtester using Multi-Party Computation (MPC).

Quick Start

git clone https://github.com/f0x-sketch/mpc-binance-backtest.git
cd mpc-binance-backtest
pip install -r requirements.txt

Setup

1. Create config.json:

{
    "datadir": "user_data/data",
    "exchange": {
        "name": "binance",
        "key": "YOUR_API_KEY",
        "secret": "YOUR_SECRET"
    },
    "pairs": ["BTC/USDT", "ETH/USDT"],
    "timeframe": "5m",
    "strategy_name": "MyStrategy"
}

2. Download data:

freqtrade download-data --exchange binance --pairs BTC/USDT ETH/USDT --timeframe 5m

Create Strategy

Create strategies/my_strategy.py:

from mpc_backtest.base_strategy import MPCBaseStrategy

class MyStrategy(MPCBaseStrategy):
    def __init__(self):
        self.sma_short = 20
        self.sma_long = 50
        self.rsi_period = 14

    async def secure_populate_indicators(self, secure_close, secure_volume, mpc):
        # Calculate indicators
        sma_short = await self.calculate_sma(secure_close, self.sma_short, mpc)
        sma_long = await self.calculate_sma(secure_close, self.sma_long, mpc)
        rsi = await self.calculate_rsi(secure_close, self.rsi_period, mpc)
        
        entry_signals = []
        exit_signals = []
        
        for i in range(len(secure_close)):
            entry = (sma_short[i] > sma_long[i]) & (rsi[i] < 30)
            exit = (sma_short[i] < sma_long[i]) | (rsi[i] > 70)
            
            entry_signals.append(entry)
            exit_signals.append(exit)
        
        return {'entry': entry_signals, 'exit': exit_signals}

Run Backtest

Create run_backtest.py:

import asyncio
import json
from mpc_backtest import MPCBacktester
from strategies.my_strategy import MyStrategy

async def main():
    with open('config.json') as f:
        config = json.load(f)
    
    strategy = MyStrategy()
    backtester = MPCBacktester(config, strategy)
    
    results = await backtester.run_backtest(
        pair='BTC/USDT',
        timeframe='5m',
        timerange='20240101-'
    )
    
    print(json.dumps(results, indent=2))

if __name__ == "__main__":
    asyncio.run(main())

Run:

python run_backtest.py

Advanced Strategy Examples

See examples/ directory for:

• Multiple timeframe strategy
• Machine learning integration
• Custom indicator implementation
• Risk management examples

Documentation

• Strategy Development
• Indicator Reference
• Configuration Guide
• API Reference

License

MIT