Python 投资组合分析入门
Charlotte Werger
Data Scientist
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# 定义无风险利率与目标回报为 0 rfr = 0 target_return = 0
# 由价格计算日收益率 apple_returns=pd.DataFrame(apple_price.pct_change())
# 仅选择负收益 negative_returns = apple_returns.loc[apple_returns['AAPL'] < target]
# 计算期望收益与下行收益的标准差 expected_return = apple_returns['AAPL'].mean() down_stdev = negative_returns.std()
# 计算索提诺比率 sortino_ratio = (expected_return - rfr)/down_stdev print(sortino_ratio)
0.07887683763760528
# 使用 rolling().max() 计算回报的最大值 roll_max = apple_price.rolling(min_periods=1,window=250).max() # 从滚动最大值计算日回撤 daily_drawdown = apple_price/roll_max - 1.0 # 计算最大日回撤 max_daily_drawdown = daily_drawdown.rolling(min_periods=1,window=250).min() # 绘制结果 daily_drawdown.plot() max_daily_drawdown.plot() plt.show()
# 使用 rolling().max() 计算回报的最大值 roll_max = apple_price.rolling(min_periods=1,window=250).max()
# 从滚动最大值计算日回撤 daily_drawdown = apple_price/roll_max - 1.0
# 计算最大日回撤 max_daily_drawdown = daily_drawdown.rolling(min_periods=1,window=250).min()
# 绘制结果 daily_drawdown.plot() max_daily_drawdown.plot() plt.show()
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