Eryk Lewinson - Python for Finance Cookbook: Over 50 recipes for applying modern Python libraries to financial data analysis

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Eryk Lewinson received his master's degree in quantitative finance from Erasmus University Rotterdam (EUR). In his professional career, he gained experience in the practical application of data science methods while working for two "Big 4" companies and a Dutch FinTech scale-up. In his work, he focuses on using machine learning to provide business value to his company. In his spare time, he enjoys writing about topics related to data science, playing video games, and traveling with his girlfriend.

Ratanlal Mahanta is currently working as a Managing Partner a t bittQsrv, a global quantitative research company offering quant models for its investors. He has several years' experience in the modeling and simulation of quantitative trading. Ratanlal holds a master's degree in science in computational finance, and his research areas include quant trading, optimal execution, and high-frequency trading. He has over 9 years' work experience in the finance industry, and is gifted at solving difficult problems that lie at the intersection of the market, technology, research, and design.

Jiri Pik is an artificial intelligence architect and strategist who works with major investment banks, hedge funds, and other players. He has architected and delivered breakthrough trading, portfolio, and risk management systems, as well as decision support systems, across numerous industries.

Jiri's consulting firm, Jiri Pik RocketEdge, provides its clients with certified expertise, judgment, and execution at the drop of a hat.

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This book begins by exploring various ways of downloading financial data and preparing it for modeling. We check the basic statistical properties of asset prices and returns, and investigate the existence of so-called stylized facts. We then calculate popular indicators used in technical analysis (such as Bollinger Bands, Moving Average Convergence Divergence (MACD), and Relative Strength Index (RSI )) and backtest automatic trading strategies built on their basis.

The next section introduces time series analysis and explores popular models such as exponential smoothing, AutoRegressive Integrated Moving Average (ARIMA), and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) (including multivariate specifications). We also introduce you to factor models, including the famous Capital Asset Pricing Model (CAPM) and the Fama-French three-factor model. We end this section by demonstrating different ways to optimize asset allocation, and we use Monte Carlo simulations for tasks such as calculating the price of American options or estimating the