August 2022
Intermediate to advanced
394 pages
12h 32m
Chinese
Content preview from 金融人工智能：用Python实现AI量化交易
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82
｜
第
4
章
然而，就像
OLS
回归一般的应用一样简单和直接，该方法依赖于许多假设，其中大多数与
残差有关，而这些假设在实践中并不总是成立。
线性
对系数和残差来说，模型的参数是线性的。
独立性
自变量之间并不完全（高度）相关（没有
多重共线性
）。
零均值
残差的平均值是（或接近）零。
不相关
残差与自变量不（强）相关。
同方差性
残差的标准差（几乎）是恒定的。
无自相关
残差之间不存在（强）相关性。
在实践中，对于给定的数据集，检验这些假设的有效性通常相当简单。
4.3
数据可用性
金融计量经济学是由回归等统计方法和可用的金融数据驱动的。从
20
世纪
50
年代到
90
年代，甚至到
21
世纪初
，理论和实证的金融研究与今天的标准相比，主要是由相对较小
的数据集驱动的，并且由日终数据（
EOD data
）组成
。在过去
10
年左右，数据可用性发
生了巨大的变化，可用的金融数据类型和其他数据类型越来越多，其粒度越来越细，数量
越来越大，速度也越来越快。
4.3.1
可编程
API
就数据驱动的金融学而言，重要的不仅是可用的数据，还包括如何访问和处理这些数据。
在相当长的一段时间里，金融专业人士一直依赖于
Ref
initiv
或彭博等公司的数据终端，但
这只是其中两家领先的提供商。报纸、杂志、财经报道等媒介早已被这种终端所取代，成
为财经信息的主要来源。然而，这些终端所提供的海量和丰富的数据不能被单个用户甚至
大批的金融专业人士系统地使用。因此，数据驱动的金融学要取得重大突破，关键在于通
过应用程序接口（
API
）实现数据的
编程可用性
。
API
允许使用计算机代码段选择 ...
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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.
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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.