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目录
一、Python 介绍 ....................................................................................................................................................... 4
1.1 Anaconda(Python IDE)的安装 ................................................................................................................ 4
1.2 Python 基础知识简介 ............................................................................................................................... 5
二、Python 的科学计算库 ....................................................................................................................................... 7
2.1 Numpy 库 ..................................................................................................................................................... 7
2.2 Pandas 库 ................................................................................................................................................... 7
2.3 Scipy 库 ..................................................................................................................................................... 9
2.4 Matplotlib 库 ......................................................................................................................................... 10
三、Python 的爬虫相关库 ......................................................................................................................................11
3.1 Scrapy 库 ................................................................................................................................................. 12
3.2 BeautifulSoup 库 ................................................................................................................................... 13
3.3 Pyquery 库 ............................................................................................................................................... 14
四、Python 的数据交互 ......................................................................................................................................... 14
4.1 Python 与数据库的交互 ......................................................................................................................... 14
4.2 Python 与 csv、excel 和 txt 文件的交互 ............................................................................................ 15
4.3 Python 与 Wind 客户端的交互 .............................................................................................................. 17
五、Python 自然语言的处理 ................................................................................................................................. 17
5.1 jieba 库 ................................................................................................................................................... 17
六、Python 与机器学习 ......................................................................................................................................... 19
6.1 Scikit-learn 库 ..................................................................................................................................... 19
6.2 分类算法，以朴素 Bayes 为例 .............................................................................................................. 20
6.3 回归算法：以 Logistic 为例 ............................................................................................................... 21
6.4 聚类算法：以 k-means 为例 ................................................................................................................. 22
七、Python 与深度学习 ......................................................................................................................................... 23
7.1 Keras 框架 ............................................................................................................................................... 23
7.2 长短期记忆网络 ...................................................................................................................................... 24
7.3 卷积神经网络 .......................................................................................................................................... 26
八、人工智能因子打分策略 ................................................................................................................................. 27
8.1 策略代码实战 ......................................................................................................................................... 27
8.2 策略结果(以 Logistic 为例) ............................................................................................................... 31
九、Python 股票策略打包成 Exe 文件 ................................................................................................................. 32
9.1 运用 pyinstaller 打包成 exe .............................................................................................................. 32
十、模型代码编写建议 ......................................................................................................................................... 32
