November 2022
Intermediate to advanced
392 pages
10h 8m
Chinese
Content preview from Python算法交易
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166
｜
第
5
章
scikit-learn
在以统一的方式提供对各种机器学习模型的访问方面做得
很好。这些示例表明，用于逻辑回归的
API
与用于线性回归的
API
没有
区别。因此，
scikit-learn
非常适合在特定应用程序场景中测试许多适
当的机器学习模型，而无需太多更改
Python
代码。
配备了基础知识之后，下一步就是将逻辑回归应用于预测市场方向的问题。
5.2.3
使用逻辑回归预测市场方向
在机器学习中，人们通常说特征，而不是像回归语境中的自变量或解释变量。简单
分类示例仅具有一个特征：学习的小时数。在实践中，一个对象常常具有不止一种
可用于分类的特征。在本章介绍的预测方法下，可以通过滞后识别特征。因此，使
用时间序列数据中的三个滞后意味着存在三个特征。作为可能的结果或类别，向上
和向下移动分别只有
+1
和
-1
。
尽管措辞有所变化，但形式上保持不变，尤其是在
推导矩阵（现在称为特征矩阵）方面。
以下代码为创建基于
pandas
的
DataFrame
的“特征矩阵”提供了一种替代方法，如
果不采用
Python
方式，则三步过程同样适用。现在，特征矩阵是原始数据集中各列
的子集：
In [89]: symbol = 'GLD'
In [90]: data = pd.DataFrame(raw[symbol])
In [91]: data.rename(columns={symbol: 'price'}, inplace=True)
In [92]: data['return'] = np.log(data['price'] ...
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