November 2022
Intermediate to advanced
392 pages
10h 8m
Chinese
Content preview from Python算法交易
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154
｜
第
5
章
在示例情况下，预测是
1250
次正确和
1242
次错误，这意味着命中率约为
49.9%
或
几乎恰好是
50%
：
In [38]: hits = np.sign(data['return'] *
data['prediction']).value_counts()
In [39]: hits
Out[39]: 1.0 1250
-1.0 1242
0.0 13
dtype: int64
In [40]: hits.values[0] / sum(hits)
Out[40]: 0.499001996007984
计算市场产品和预期回报，对结果取符号并计算计数值。
打印出两种可能值的计数。
计算命中率，定义为基于所有预测后正确预测的数量。
5.1.5
预测未来市场方向
出现的问题是，通过直接基于用作因变量值的对数回报的符号实施线性回归，是否
可以提高命中率。至少从理论上讲，这简化了从预测绝对返回值到返回值的符号
的问题。
Python
代码中实现此推理的唯一更改是将符号值（在
Python
中为
1.0
或
-1.0
）用于回归步骤。确实，命中次数增加到
1301
，命中率增加到约
51.9%
，提高
了两个百分点：
In [41]: reg = np.linalg.lstsq(data[cols], np.sign(data['return']),
rcond=None)[0]
In ...
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