Python for Finance, 3rd Edition

Chapter 9. Financial Time Series

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Financial time series are the backbone of almost everything in this book: prices, returns, volumes, FX rates, and index levels all arrive as timestamped data. This chapter shows how to load, inspect, and clean such series with pandas so that later chapters can focus on analytics rather than wrestling with CSV quirks.

The chapter shows how to load end-of-day (EOD) price data from CSV into well-structured pandas objects, configure a robust local/remote loading workflow, and handle common pitfalls around indexes, date parsing, and missing data. It explains how index_col and parse_dates change the resulting DataFrame, how to inspect structure with .head(), .info(), .dtypes, .shape, and .index, and how to validate imported time series before running analysis on top of them.

To follow along, you need a working python environment with pandas and numpy installed, local access to data/eod_data.csv or the remote fallback at hilpisch.com/eod_data.csv, and familiarity ...