计算收益最大回撤的思想和代码

Posted 2020-09-13 寻自己

在财务风险指标中，最大回撤是一个很重要的指标。

自己在计算最大回撤时，最开始是网上想找一些成熟的解决方案。

但是最后发现，要不是代码不通用，要不就是性能不好，还有一些本来就是错误的。

所以最后只能理解最大回撤的算法思路，再写出代码，最后再优化性能了！

 

例子：

初始资金是1块钱的话，资金连续一周的变化如下：

周一：1.01

周二：1.02

周三：0.98

周四：1.0

周五：0.97

那么我们要计算的样本为：[1, 1.01, 1.02, 0.98, 1.0, 0.97]

如果计算截至到周二的最大回撤，那么我们要拿到周二的值：1.02，再拿周二之前最大的值，即，周一的1.01，再计算回撤：(1.02/1.01-1)*100=0.99%，这时间 0.99% >0 ，所以是不存在回撤的。

如果计算截至到周三的最大回撤，那么我们要拿到周三的值：0.98，再拿周三之前最大的值，即，周二的1.02，再计算回撤：(0.98/1.02-1)*100=-3.9215%，这时间 -3.9215% < 0 ，所以是存在回撤的。

如果计算截至到周五的最大回撤，那么我们要拿到周五的值：0.97，再拿周五之前最大的值，即，周二的1.02，再计算回撤：(0.97/1.02-1)*100=-4.9019%，这时间 -4.9019% < 0 ，所以是存在回撤的。

这时间再比较 Min(-4.9019%, -3.9215%)，得出最大回撤为 -4.9019%。

 

用Python 代码来实现：

returns = [1.0, 1.01, 1.05, 1.1, 1.11, 1.07, 1.03, 1.03, 1.01, 1.02, 1.04, 1.05, 1.07, 1.06, 1.05, 1.06, 1.07, 1.09, 1.12, 1.18, 1.15, 1.15, 1.18, 1.16, 1.19, 1.17, 1.17, 1.18, 1.19, 1.23, 1.24, 1.25, 1.24, 1.25, 1.24, 1.25, 1.24, 1.25, 1.24, 1.27, 1.23, 1.22, 1.18, 1.2, 1.22, 1.25, 1.25, 1.27, 1.26, 1.31, 1.32, 1.31, 1.33, 1.33, 1.36, 1.33, 1.35, 1.38, 1.4, 1.42, 1.45, 1.43, 1.46, 1.48, 1.52, 1.53, 1.52, 1.55, 1.54, 1.53, 1.55, 1.54, 1.52, 1.53, 1.53, 1.5, 1.45, 1.43, 1.42, 1.41, 1.43, 1.42, 1.45, 1.45, 1.49, 1.49, 1.51, 1.54, 1.53, 1.56, 1.52, 1.53, 1.58, 1.58, 1.58, 1.61, 1.63, 1.61, 1.59, 1.58, 1.6, 1.6, 1.61, 1.61, 1.62, 1.6, 1.61, 1.61, 1.62, 1.64, 1.64, 1.67, 1.66, 1.67, 1.66, 1.67, 1.68, 1.68, 1.7, 1.72, 1.73, 1.73, 1.74, 1.76, 1.75, 1.75, 1.75, 1.73, 1.73, 1.73, 1.73, 1.75, 1.74, 1.74, 1.74, 1.75, 1.75, 1.74, 1.74, 1.74, 1.76, 1.78, 1.81, 1.79, 1.81, 1.8, 1.79, 1.8, 1.8, 1.82, 1.8, 1.8, 1.81, 1.82, 1.82, 1.83, 1.81, 1.78, 1.76, 1.75, 1.72, 1.68, 1.69, 1.69, 1.7, 1.71, 1.71, 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# # Bak Performance 
# max_draw_down = 0
# for i in range(1, len(returns)):
#     for j in range(0, i):
#         temp_draw_down = returns[i]/returns[j] - 1
#         max_draw_down = min(max_draw_down, temp_draw_down)
# print(str(max_draw_down))

# Good Performance 
max_draw_down = 0
temp_max_value = 0
for i in range(1, len(returns)):
    temp_max_value = max(temp_max_value, returns[i-1])
    max_draw_down = min(max_draw_down, returns[i]/temp_max_value-1)
print(str(max_draw_down))

注视掉的代码是性能很差劲，请勿使用。

同时，如果你拿到到的是每日的涨跌幅，而不是资金总量变化，计算思想和上面类似，但是要考虑回撤的复利情况，不能直接套用, 但是思想相同！