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SMA.py
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SMA.py
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import yfinance as yf
import pandas as pd
import matplotlib.pyplot as plt
# 获取股票数据
symbol = "aapl"
start_date = "2021-01-01"
end_date = "2024-06-01"
data = yf.download(symbol, start=start_date, end=end_date)
# 计算移动平均
data['SMA_50'] = data['Close'].rolling(window=50).mean()
data['SMA_200'] = data['Close'].rolling(window=200).mean()
# 初始化交叉信号列
data['Signal'] = 0
# 计算交叉信号
data.loc[data['SMA_50'] > data['SMA_200'], 'Signal'] = 1
data.loc[data['SMA_50'] < data['SMA_200'], 'Signal'] = -1
# 计算每日收益率
data['Daily_Return'] = data['Close'].pct_change()
# 计算策略信号的收益率(shift(1) 是为了避免未来数据的偏差)
data['Strategy_Return'] = data['Signal'].shift(1) * data['Daily_Return']
# 计算累计收益
data['Cumulative_Return'] = (1 + data['Strategy_Return']).cumprod()
# 输出策略表现
strategy_performance = {
'Total Return': data['Cumulative_Return'].iloc[-1] - 1,
'Annualized Return': (data['Cumulative_Return'].iloc[-1] ** (252 / len(data))) - 1,
'Max Drawdown': (data['Cumulative_Return'] / data['Cumulative_Return'].cummax() - 1).min(),
}
print("策略表现:")
for key, value in strategy_performance.items():
print(f"{key}: {value:.4f}")
# 绘制累计收益曲线
plt.figure(figsize=(10, 6))
plt.plot(data['Cumulative_Return'], label='Strategy Cumulative Return', color='b')
plt.plot(data['Close'] / data['Close'].iloc[0], label='Stock Cumulative Return', color='g')
plt.title("Cumulative Return of Strategy vs. Stock")
plt.xlabel("Date")
plt.ylabel("Cumulative Return")
plt.legend()
plt.show()