August 2022
Intermediate to advanced
394 pages
12h 32m
Chinese
Content preview from 金融人工智能：用Python实现AI量化交易
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向量化回测
｜
237
In [29]: data[['r', 's', 's_']].std()
➊
Out[29]: r 0.0054
s 0.0054
s_ 0.0054
dtype: float64
In [30]: data[['r', 's', 's_']].std() * math.sqrt(252)
➋
Out[30]: r 0.0853
s 0.0853
s_ 0.0855
dtype: float64
➊
日波动率。
➋
年化波动率。
向量化回测
向量化回测是一种强大而有效的方法，可以回测基于预测的交易策略的“纯”
收益。它还可以考虑成比例交易成本的影响。然而，它并不太适合分析典型
的风险管理措施，比如（跟踪）止损单或获利单。第
11
章会对此进行论述。
10.2
基于
DNN
的每日策略的回测
上一节在一个简单且易于可视化的交易策略的基础上列出了向量化回测方案。同样的方案
也可以应用于基于
DNN
的交易策略
，只需进行最小的技术调整。下面训练一个
Keras
的
DNN
模型
，其所使用的数据与上一个例子中的数据相同。但是，如第
7
章所述，需要向
DataFrame
对象添加不同的特征和滞后项。
In [31]: data = pd.DataFrame(pd.read_csv(url, index_col=0,
parse_dates=True).dropna()[symbol])
In [32]: data.info()
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: ...
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