May 2024
Intermediate to advanced
306 pages
5h 5m
Chinese
Content preview from Python算法交易实战
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第3章 通过基础机器学习预测市场
在第2章中，我们学习了如何设计交易策略、创建交易信号以及实施高级概念，例如交易工具的季节性。更仔细地理解这些概念涉及广阔的领域，包括随机过程、随机漫步、鞅和时间序列分析，你可以按照自己的节奏去探索。
那么接下来讲什么呢？让我们了解一种更先进的预测和预报方法：统计推理和预测。这就是所谓的机器学习，其基本原理是在19世纪和20世纪初发展起来的，并且一直在发展。近来，由于可以获得极具成本效益的处理能力和易于获得的大型数据集，人们对机器学习算法及其应用的兴趣再度上升。机器学习是一个庞大的领域，处于线性代数、多元微积分、概率论、频率论和贝叶斯统计学的交叉点，而深入分析机器学习也超出了本书的范围。然而，机器学习方法在Python中非常容易理解并且相当直观，所以我们将解释这些方法背后的直觉，并看看它们如何在算法交易中应用。首先，让我们介绍一些基本概念和符号。
本章将介绍以下主题。
● 了解术语和符号。
● 使用线性回归方法创建预测模型。
● 使用线性分类方法创建预测模型。
3.1 了解术语和符号
为了快速拓宽思路并建立关于供求关系的“直觉”，我们有一个简单且完全假设的数据集，其中包括从调查中获得的几个随机样本的身高（Height）、体重（Weight）和种族（Race）。让我们来看看这个数据集，如表3-1所示（1inch=25.4mm，1lb≈0.454kg）。
表3-1
|
Height /inch |
Weight /lb |
Race |
|---|---|---|
|
72 |
180 |
Asian |
|
66 |
150 |
Asian |
|
70 |
190 |
African |
|
75 |
210 |
Caucasian |
|
64 |
150 |
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