策略案例

[https://bigquant.com/experimentshare/6774a21c87834dd48de568308a1fd3ee](https://bigquant.com/experimentshare/6774a21c87834dd48de568308a1fd3ee

策略案例

[https://bigquant.com/experimentshare/06c5d16cb44b46e7a8ffe45ad9a69a95](https://bigquant.com/experimentshare/06c5d16cb44b46e7a8ffe45ad9a69a95

策略案例

[https://bigquant.com/experimentshare/a8cfb3df111c43fc9bec8f70654ecd5e](https://bigquant.com/experimentshare/a8cfb3df111c43fc9bec8f70654ecd

策略案例

[https://bigquant.com/experimentshare/587dfa30731644aeac4499c052f9a686](https://bigquant.com/experimentshare/587dfa30731644aeac4499c052f9a6

策略案例

[https://bigquant.com/experimentshare/5c5e31cf67c94de099b00aeab9676e48](https://bigquant.com/experimentshare/5c5e31cf67c94de099b00aeab9676e

策略案例

[https://bigquant.com/experimentshare/21cf886fbd794a66be617bfd57a0cb88](https://bigquant.com/experimentshare/21cf886fbd794a66be617bfd57a0cb

反转因子计算方法

反转因子的W式切割经过长期反复的摸索，我们找到了一个反转因子的有效切割方案，简称W式切割。具体操作步骤如下：

1. 在每个月底，对于股票s，回溯其过去N个交易日的数据（为方便处理，N取偶数）；
2. 对于股票s，逐日计算平均单笔成交金额D（D=当日成交金额/当日成交笔数

导语

特征选择是除数据之外最关键的步骤。尽管这一步非常必要，但很多指导文章中却完全忽略这一过程。

本文将展示一些很棒的特征选择方法，帮助读者在机器学习中更加如鱼得水。

特征选择是什么？实际问题中，需要什么样的特征来帮助解决建模并不总是很清晰。在这个问题上，数据总是存在各种问题，比如数据过

策略案例

[https://bigquant.com/experimentshare/05251c753111424eaff32648838ac24f](https://bigquant.com/experimentshare/05251c753111424eaff32648838ac24f