November 2022
Intermediate to advanced
392 pages
10h 8m
Chinese
Content preview from Python算法交易
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291
第 10 章
自动化交易操作
人们担心计算机会变得太聪明而掌控整个世界，但真正的问题是它们太愚蠢
并且已经掌控了整个世界。
——佩德罗
·
多明戈斯（Pedro Domingos）
你可能会想，“接下来干什么呢？”。交易平台提供了获取历史数据和流数据的功
能。它还允许下达买卖订单并检查账户状态。本书引入了许多不同的方法来通过预
测市场价格走势推导算法交易策略。你可能会问：“那么，将这些全部放在一起就
可以以自动化的方式进行工作了吗？”这个问题不能以任何通用的方式回答。本章
讨论了这个场景下许多很重要的主题。本章假定将部署单个自动化的算法交易策略。
这样可以简化诸如资本和风险管理之类的问题。
本章涵盖以下主题。
10.1
节根据策略特征和可用的交易资金讨论了凯利准则，凯利
准则有助于确定交易规模。要获得对算法交易策略的信心，需要对该策略的性能和
风险特征进行彻底的回测。
10.2
节根据
1.5
节中介绍的基于机器学习（
ML
）的分类
算法对示例策略进行了回测。要将算法交易策略部署到自动交易中，需要将其转换
为可实时处理传入流数据的在线算法。
10.3
节介绍了将离线算法转换为在线算法的
过程。
10.4
节着手确保自动算法交易策略可以在云平台中稳健可靠地运行。我们不太可能
仔细地考虑到所有相关主题，但从可用性，性能和安全性的角度来看，云部署似乎
是唯一可行的选择。
10.5
节介绍了日志和监视。在部署自动交易策略期间，日志对
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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.