Backtesting Framework

Strategy backtesting framework for futures and stocks. Supports moving average crossovers, RSI mean reversion, Bollinger Bands breakout, and custom strategies. Generates performance reports with Sharpe, max drawdown, win rate.

Backtesting Framework — 策略回测框架

Pure Python backtesting engine for futures and stock strategies. No external dependencies beyond NumPy.

Quick Start

import numpy as np

# Sample price data (close prices)
prices = np.array([100, 102, 101, 105, 107, 106, 110, 108, 112, 115])

def ma_crossover(prices, fast=3, slow=5):
    """Moving Average Crossover Strategy"""
    fast_ma = np.convolve(prices, np.ones(fast)/fast, mode='valid')
    slow_ma = np.convolve(prices, np.ones(slow)/slow, mode='valid')
    
    # Align arrays
    min_len = min(len(fast_ma), len(slow_ma))
    fast_ma = fast_ma[-min_len:]
    slow_ma = slow_ma[-min_len:]
    
    # Generate signals: 1 = buy, -1 = sell, 0 = hold
    signals = np.zeros(min_len)
    signals[1:] = np.where(fast_ma[1:] > slow_ma[1:], 1, -1)
    
    return signals

def backtest(prices, signals, initial_capital=10000):
    """Run a backtest and return performance metrics."""
    # Pad signals to match prices
    pad = len(prices) - len(signals)
    signals = np.pad(signals, (pad, 0), 'constant', constant_values=0)
    
    # Calculate returns
    returns = np.diff(prices) / prices[:-1]
    strategy_returns = signals[:-1] * returns
    
    # Performance metrics
    total_return = np.prod(1 + strategy_returns) - 1
    sharpe = np.mean(strategy_returns) / np.std(strategy_returns) * np.sqrt(252) if np.std(strategy_returns) > 0 else 0
    win_rate = np.sum(strategy_returns > 0) / np.sum(strategy_returns != 0) if np.sum(strategy_returns != 0) > 0 else 0
    
    # Max drawdown
    cumulative = np.cumprod(1 + strategy_returns)
    peak = np.maximum.accumulate(cumulative)
    drawdown = (cumulative - peak) / peak
    max_dd = np.min(drawdown)
    
    return {
        "total_return": total_return,
        "sharpe_ratio": sharpe,
        "win_rate": win_rate,
        "max_drawdown": max_dd,
        "final_capital": initial_capital * (1 + total_return)
    }

Built-in Strategies

1. Moving Average Crossover (均线金叉死叉)

def ma_strategy(prices, fast=5, slow=20):
    fast_ma = np.convolve(prices, np.ones(fast)/fast, mode='valid')
    slow_ma = np.convolve(prices, np.ones(slow)/slow, mode='valid')
    min_len = min(len(fast_ma), len(slow_ma))
    signals = np.zeros(min_len)
    signals[1:] = np.where(fast_ma[1:min_len] > slow_ma[1:min_len], 1, -1)
    return signals

2. RSI Mean Reversion (RSI均值回归)

def rsi_strategy(prices, period=14, oversold=30, overbought=70):
    deltas = np.diff(prices)
    gains = np.where(deltas > 0, deltas, 0)
    losses = np.where(deltas < 0, -deltas, 0)
    avg_gain = np.convolve(gains, np.ones(period)/period, mode='valid')
    avg_loss = np.convolve(losses, np.ones(period)/period, mode='valid')
    rs = avg_gain / np.where(avg_loss == 0, 0.001, avg_loss)
    rsi = 100 - (100 / (1 + rs))
    signals = np.zeros(len(rsi))
    signals[rsi < oversold] = 1
    signals[rsi > overbought] = -1
    return signals

3. Bollinger Bands Breakout (布林带突破)

def bb_strategy(prices, period=20, std_dev=2):
    sma = np.convolve(prices, np.ones(period)/period, mode='valid')
    rolling_std = np.array([np.std(prices[i:i+period]) for i in range(len(prices)-period+1)])
    upper = sma + std_dev * rolling_std
    lower = sma - std_dev * rolling_std
    
    signals = np.zeros(len(sma))
    price_aligned = prices[period-1:]
    signals[price_aligned > upper] = -1  # Short at upper band
    signals[price_aligned < lower] = 1   # Long at lower band
    return signals

Output Format

🔬 策略回测报告

策略: MA Crossover (5, 20)
品种: IF2606 (沪深300)
时间: 2026-01-01 ~ 2026-05-21

📊 绩效指标
• 总收益率:    +15.3%
• 年化收益:    +38.2%
• 夏普比率:    1.45  🟢
• 胜率:        42.5%
• 最大回撤:    -8.7%
• 交易次数:    42次

📈 资金曲线
期初: ¥10,000
期末: ¥11,530

⚠️ 回测表现不代表未来收益

Notes

• All strategies use only NumPy for calculations
• Backtest results are hypothetical and do not account for: slippage, commissions, liquidity
• Always forward-test (paper trade) before going live
• Overfitting is the #1 enemy — keep strategies simple
• Use multiple timeframes to validate strategy robustness