Python for Finance Cookbook - Second Edition

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Acquiring Financial Data

The first chapter of this book is dedicated to a very important (some may say the most important) part of any data science/quantitative finance project—gathering data. In line with the famous adage “garbage in, garbage out,” we should strive to obtain data of the highest possible quality and then correctly preprocess it for later use with statistical and machine learning algorithms. The reason for this is simple—the results of our analyses are highly dependent on the input data and no sophisticated model will be able to compensate for that. That is also why in our analyses, we should be able to use our (or someone else’s) understanding of the economic/financial domain to motivate certain data for, for example, ...