November 2022
Intermediate to advanced
392 pages
10h 8m
Chinese
Content preview from Python算法交易
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掌握向量化回测
｜
115
In [65]: temp = drawdown[drawdown == 0]
In [66]: periods = (temp.index[1:].to_pydatetime() -
temp.index[:-1].to_pydatetime())
In [67]: periods[12:15]
Out[67]: array([datetime.timedelta(days=1), datetime.timedelta(days=1),
datetime.timedelta(days=10)], dtype=object)
In [68]: periods.max()
Out[68]: datetime.timedelta(days=596)
过滤差等于零的行。
计算所有索引值之间的
timedelta
值。
选择最大
timedelta
值。
由于
pandas
包和主要的
DataFrame
类具有的功能，使用
pandas
进行向量化回测通
常是一项相当有效的工作。但是，当人们希望实施更大的回测程序（例如，优化
SMA
策略的参数）时，到目前为止演示的交互式方法不能很好地工作。为此，建议
使用更通用的方法。
事实证明，
pandas
是交易策略向量化分析的强大工具。通常，可以用一
行或几行代码来计算许多感兴趣的统计数据，例如对数收益，累计收益，
年化收益和波动率，最大跌幅和最大跌幅时间。能够通过简单的方法调
用来可视化结果是一个额外的好处。 ...
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