我们在创建策略的过程中, 需要通过不同的评估指标来验证策略的有效性. 这些指标可以帮助我们调整策略, 得到更好的结果.

常见的策略评估指标有:

• 回测收益率
• 回测年化收益率
• Alpha, Beta

获取取票数据

• 以伊利股份和贵州茅台为例
• 分析区间 2019-01-01 到 2021-01-01

注: 贵州茅台兑牛奶, 那叫一个爽.

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
plt.style.use("fivethirtyeight")

# 定义获取数据时间段
start_date = "2019-01-01"  # 开始日期
end_date = "2021-01-01"  # 结束日期

# 获取洋河股份数据
data = get_price(["600887.XSHG", "600519.XSHG"], start_date=start_date, end_date=end_date, fields="close")
data = pd.DataFrame(data, index=data.index)

# 调试输出
print(data.head())

# 绘制走势图
data.plot(figsize=(16, 10), subplots=True)

输出结果:

            600887.XSHG  600519.XSHG
date                                
2019-01-02      21.3696     583.4249
2019-01-03      21.1985     574.6781
2019-01-04      21.4076     586.3665
2019-01-07      21.6453     589.7659
2019-01-08      21.4837     589.0840

回测收益率

# 计算每天的累计收益
data_new = data / data.iloc[0]
print(data_new.head())

# 画图
data_new.plot(figsize=(16, 10))

# 区间累计收益率
total_return = data_new.iloc[-1] - 1  # 用最后一个值减去1
total_return = pd.DataFrame(total_return.values, columns=["累计收益"], index=total_return.index)
print(total_return)

输出结果:

            600887.XSHG  600519.XSHG
date                                
2019-01-02     1.000000     1.000000
2019-01-03     0.991993     0.985008
2019-01-04     1.001778     1.005042
2019-01-07     1.012902     1.010869
2019-01-08     1.005339     1.009700
                 累计收益
600887.XSHG  1.076314
600519.XSHG  2.424605

回测年华收益率

代码:

# 查看数据长度
data_length = len(data_new)
print("数据长度:", data_length)

# 计算年化收益率
annual_return = pow(data_new.iloc[-1], 250 / data_length) - 1

# 调试输出
print("年化收益:")
print(annual_return)

调试输出:

数据长度: 487
年化收益:
600887.XSHG    0.455062
600519.XSHG    0.881225
Name: 2020-12-31 00:00:00, dtype: float64

# 计算累计最大值
max = data.cummax()

# 调试输出
print(data.head())
print(max.head())

# 计算每天回撤
drawdown_daily = (data.cummax() - data) / data.cummax()

# 调试输出
print(drawdown_daily.head())

# 计算最大回测
total_drawdown = drawdown_daily.max()

# 改成df
total_drawdown = pd.DataFrame([str(i * 100) + "%" for i in total_drawdown], columns=["最大回撤"], index= total_drawdown.index)

# 调试输出
print(total_drawdown.head())

输出结果:

# 计算累计最大值
max = data.cummax()

# 调试输出
print(data.head())
print(max.head())

# 计算每天回撤
drawdown_daily = (data.cummax() - data) / data.cummax()

# 调试输出
print(drawdown_daily.head())

# 计算最大回测
total_drawdown = drawdown_daily.max()

# 改成df
total_drawdown = pd.DataFrame([str(i * 100) + "%" for i in total_drawdown], columns=["最大回撤"], index= total_drawdown.index)

# 调试输出
print(total_drawdown.head())

输出结果:

            600887.XSHG  600519.XSHG
date                                
2019-01-02      21.3696     583.4249
2019-01-03      21.1985     574.6781
2019-01-04      21.4076     586.3665
2019-01-07      21.6453     589.7659
2019-01-08      21.4837     589.0840
            600887.XSHG  600519.XSHG
date                                
2019-01-02      21.3696     583.4249
2019-01-03      21.3696     583.4249
2019-01-04      21.4076     586.3665
2019-01-07      21.6453     589.7659
2019-01-08      21.6453     589.7659
            600887.XSHG  600519.XSHG
date                                
2019-01-02     0.000000     0.000000
2019-01-03     0.008007     0.014992
2019-01-04     0.000000     0.000000
2019-01-07     0.000000     0.000000
2019-01-08     0.007466     0.001156
                            最大回撤
600887.XSHG  21.322799729408366%
600519.XSHG  19.270518955514174%

夏普比率指的是策略承受一单位总风险, 会产生多少的超额报酬.

• 假设无风险收益率为年化 3%
• 超额收益率以无风险收益率为基准

代码:

# 向后填补缺失值
data_fill = data.fillna(method='pad')

# 计算每日收益率
return_rate = data_fill.apply(lambda x: x / x.shift(1) - 1)[1:]  # 去除首个NaN

# 调试输出
print(return_rate.head())

# 计算超额回报率
exReturn = return_rate - 0.03 / 250

#计算夏普比率
sharpe_rate = np.sqrt(len(exReturn)) * exReturn.mean() / exReturn.std()

#夏普比率的输出结果
SHR = pd.DataFrame(sharpe_rate,columns=['夏普比率'])

# 输出伊利和茅台的夏普比率
print(SHR)

输出结果:

|  |  |
|--|--|
|  |  |

            600519.XSHG  600887.XSHG
date                                
2019-01-03    -0.014992    -0.008007
2019-01-04     0.020339     0.009864
2019-01-07     0.005797     0.011104
2019-01-08    -0.001156    -0.007466
2019-01-09     0.018734     0.008849
                 夏普比率
600519.XSHG  3.048383
600887.XSHG  1.715563