backtrader 回测基础：在加入 AI 之前先验证

python -m venv vbt-env
source vbt-env/bin/activate  # Windows 用：vbt-env\Scripts\activate
pip install --upgrade pip

pip install backtrader yfinance pandas matplotlib

import backtrader as bt
print(bt.__version__)

import backtrader as bt
import yfinance as yf
 
 
class SmaCross(bt.Strategy):
    params = (
        ('fast', 20),
        ('slow', 50),
    )
 
    def __init__(self):
        # 保存收盘价引用，用于日志输出
        self.dataclose = self.datas[0].close
 
        # 创建均线和交叉指标
        self.fast_sma = bt.indicators.SMA(self.data.close, period=self.p.fast)
        self.slow_sma = bt.indicators.SMA(self.data.close, period=self.p.slow)
        self.crossover = bt.indicators.CrossOver(self.fast_sma, self.slow_sma)
 
        # 跟踪未成交订单，防止同一根 K 线发出多个订单
        self.order = None
 
    def log(self, txt, dt=None):
        dt = dt or self.datas[0].datetime.date(0)
        print(f'{dt.isoformat()} {txt}')
 
    def notify_order(self, order):
        if order.status in [order.Submitted, order.Accepted]:
            return
 
        if order.status in [order.Completed]:
            if order.isbuy():
                self.log(f'买入成交, {order.executed.size} 股 @ {order.executed.price:.2f}')
            elif order.issell():
                self.log(f'卖出成交, {order.executed.size} 股 @ {order.executed.price:.2f}')
 
        elif order.status in [order.Canceled, order.Margin, order.Rejected]:
            self.log('订单取消/保证金不足/被拒绝')
 
        # 重置未成交订单跟踪器
        self.order = None
 
    def next(self):
        # 等待未成交订单完成后再发出新订单
        if self.order:
            return
 
        # 没有仓位时检查买入信号
        if not self.position:
            if self.crossover > 0:
                self.log(f'买入信号, {self.dataclose[0]:.2f}')
                self.order = self.buy()
        # 持有仓位时检查卖出信号
        else:
            if self.crossover < 0:
                self.log(f'卖出信号, {self.dataclose[0]:.2f}')
                self.order = self.sell()
 
 
if __name__ == '__main__':
    cerebro = bt.Cerebro()
 
    # 从 Yahoo Finance 下载 SPY 数据
    df = yf.download('SPY', start='2018-01-01', end='2023-12-31', auto_adjust=True)
    data = bt.feeds.PandasData(dataname=df)
    cerebro.adddata(data)
 
    # 添加策略
    cerebro.addstrategy(SmaCross)
 
    # 设置起始资金和手续费
    cerebro.broker.setcash(10000.0)
    cerebro.broker.setcommission(commission=0.001)  # 每笔交易 0.1%
 
    # 每次买入信号投入 95% 可用现金
    cerebro.addsizer(bt.sizers.PercentSizer, percents=95)
 
    # 附加分析器
    cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name='sharpe', riskfreerate=0.0)
    cerebro.addanalyzer(bt.analyzers.DrawDown, _name='drawdown')
    cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name='trades')
 
    print('初始账户价值: %.2f' % cerebro.broker.getvalue())
    results = cerebro.run()
    print('最终账户价值: %.2f' % cerebro.broker.getvalue())
 
    # 输出分析器结果
    strat = results[0]
    print(f"夏普比率: {strat.analyzers.sharpe.get_analysis()['sharperatio']:.3f}")
    print(f"最大回撤: {strat.analyzers.drawdown.get_analysis()['max']['drawdown']:.2f}%")
 
    cerebro.plot()

import matplotlib.pyplot as plt
 
figs = cerebro.plot(style='candlestick')
plt.savefig('sma_cross_equity.png')

from itertools import product
 
windows = [
    ('2018-01-01', '2020-12-31', '2021-01-01', '2021-06-30'),
    ('2018-07-01', '2021-06-30', '2021-07-01', '2021-12-31'),
    ('2019-01-01', '2021-12-31', '2022-01-01', '2022-06-30'),
]
 
best_out_of_sample_results = []
 
for train_start, train_end, test_start, test_end in windows:
    best_sharpe = -1e9
    best_params = None
 
    # 样本内优化
    for fast, slow in product(range(10, 31, 5), range(40, 91, 10)):
        if fast >= slow:
            continue
        df_train = yf.download('SPY', start=train_start, end=train_end, auto_adjust=True)
        # 用 (fast, slow) 在 df_train 上运行 cerebro 并记录夏普
        # ...
        if sharpe > best_sharpe:
            best_sharpe = sharpe
            best_params = (fast, slow)
 
    # 用最优参数做样本外测试
    df_test = yf.download('SPY', start=test_start, end=test_end, auto_adjust=True)
    # 在 df_test 上用 best_params 运行 cerebro
    # 把结果追加到 best_out_of_sample_results