November 2022
Intermediate to advanced
392 pages
10h 8m
Chinese
Content preview from Python算法交易
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99
第 4 章
掌握向量化回测
认为可以通过过去就能预测未来是可笑的。
注
1
——The Economist
在交易准备阶段，为算法交易程序发掘想法和假设通常更有创造力，甚至有时是很
有趣的部分。通常，对它们进行彻底的测试是更需要技术和耗时的部分。本章介绍
了不同算法交易策略的向量化回测。它涵盖以下类型的策略（另请参阅第
1
章的“交
易策略”）：
基于简单移动平均线（
SMA
）的策略
SMA
用于生成买卖信号的基本思想已经存在数十年了。
SMA
是所谓的股价技
术分析中的主要工具。例如，当在较短时间窗口（例如
42
天）上定义的
SMA
超过了在较长时间窗口（例如
252
天）上定义的
SMA
时，就得到一个信号。
动量策略
这类策略基于以下假设，即最近的表现将持续额外的时间。例如，一个下降趋
势的股票要被卖空的原因就是假设了这个趋势会持续更长的时间。
均值回归策略
均值回归策略背后的原因是，当股票价格或其他金融工具的价格偏离过多时，
它们往往会回归到某个均值水平或某个趋势水平。
注 1： 来源：“过往能预测未来吗？” The Economist, 2009 年 9 月 23 日。
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