November 2022
Intermediate to advanced
392 pages
10h 8m
Chinese
Content preview from Python算法交易
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128
｜
第
4
章
用先前的值向前填充所有
NaN
位置；将所有剩余的
NaN
值替换为
0
。
用索引头寸列
SMA
开始绘制市场头寸结果。
图 4-15：基于均值回归策略为 GDX 生成的市场头寸
最后一步是得出如图
4-16
所示的策略收益。尽管特殊参数导致长期处于中立地位（非
多头也非空头），但该策略的表现要大幅领先于
GDX
ETF
。这些中立地位反映在
图
4-16
中策略曲线的平坦部分中：
In [110]: data['strategy'] = data['position'].shift(1) * data['returns']
In [111]: data[['returns', 'strategy']].dropna().cumsum(
).apply(np.exp).plot(figsize=(10, 6));
4.4.2
方法通用化
和以前一样，基于一个
Python
类实现向量化回测会更有效。“均值反向测试类”中
介绍的
MRVectorBacktester
类继承自
MomVectorBacktester
类，并且仅替换
run_
strategy()
方法就可适应均值回归策略的细节。
掌握向量化回测
｜
129
图 4-16： GDX ETF 的总体表现和均值回归策略（SMA=25，阈值
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