November 2022
Intermediate to advanced
392 pages
10h 8m
Chinese
Content preview from Python算法交易
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61
第 3 章
处理金融数据
显然，数据胜过算法。没有全面的数据，你往往会得到不全面的预测。
——罗伯
·
托马斯（2016）
在算法交易中，通常必须处理四种类型的数据，如表
3-1
所示。尽管它已经简化了
金融数据世界，但在技术环境中，将历史数据与实时数据，结构化数据与非结构化
数据对进行区分通常是很有用的。
表 3-1：金融数据的类型（样例）
结构化
非结构化
历史数据
收盘价
财经新闻文章
实时数据
外汇买卖价
推特的消息
本书主要涉及历史和实时类型的结构化数据（数字，表格数据）。本章特别关注历
史结构数据，例如在法兰克福证券交易所交易的
SAP SE
股票的日终收盘价。但是，
盘中数据也是此类数据，例如在纳斯达克证券交易所交易的
Apple
，
Inc.
股票的
1
分钟柱线数据。第
7
章介绍了实时结构化数据的处理。
算法交易项目通常从需要基于历史金融数据进行（回测）的交易想法或假设开始。
这是本章的内容，其计划如下。“从不同来源读取金融数据”使用
pandas
从不同
的基于文件和基于
Web
的源读取数据。“使用开放数据源”介绍
Quandl
（
http://
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