November 2022
Intermediate to advanced
392 pages
10h 8m
Chinese
Content preview from Python算法交易
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144
｜
第
5
章
向下运动的分类问题。人们已经开发了不同的机器学习算法来解决这种分类问
题。本章介绍进行分类的典型基础算法——逻辑回归。
基于深度学习的策略
深度学习已被诸如
Facebook
之类的技术巨头所普及。与机器学习算法相似，基
于神经网络的深度学习算法让人们可以处理金融市场预测中面临的分类问题。
本章的组织如下。“使用线性回归进行市场波动预测”介绍了线性回归作为一种预
测指数水平和价格走势的技术。“使用机器学习进行市场波动预测”侧重于机器学习，
并在线性回归的基础上介绍了
scikit-learn
。它主要涵盖逻辑回归作为明确适用于
分类问题的替代线性模型。“使用深度学习进行市场运动预测”介绍了
Keras
来基
于神经网络算法预测股票市场运动的方向。
本章的主要目的是提供实用的方法，以根据过去的收益预测金融市场的未来价格走
势。基本的假设是有效的市场假设不能普遍适用，并且类似于股票价格图表的技术
分析背后的原因，历史可能会提供一些关于未来的见解，而这些见解可以通过统计
技术来挖掘。换句话说，假设金融市场中的某些模式会重复出现，从而可以利用过
去的观察来预测未来的价格走势。希尔皮斯（
Hilpisch
，
2020
）涵盖了更多细节。
5.1
使用线性回归进行市场走势预测
普通最小二乘法（
OLS
）和线性回归是数十年来的统计技术，已被证明在许多不同
的应用领域中都非常有用。本节使用线性回归进行价格预测。但是，它首先快速回
顾了基础知识并介绍了基本方法。
5.1.1
线性回归快速回顾
在应用线性回归之前，基于一些随机数据快速回顾该方法可能会有所帮助 ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.