Book Image

Python for Finance Cookbook

By : Eryk Lewinson
Book Image

Python for Finance Cookbook

By: Eryk Lewinson

Overview of this book

Python is one of the most popular programming languages used in the financial industry, with a huge set of accompanying libraries. In this book, you'll cover different ways of downloading financial data and preparing it for modeling. You'll calculate popular indicators used in technical analysis, such as Bollinger Bands, MACD, RSI, and backtest automatic trading strategies. Next, you'll cover time series analysis and models, such as exponential smoothing, ARIMA, and GARCH (including multivariate specifications), before exploring the popular CAPM and the Fama-French three-factor model. You'll then discover how to optimize asset allocation and use Monte Carlo simulations for tasks such as calculating the price of American options and estimating the Value at Risk (VaR). In later chapters, you'll work through an entire data science project in the financial domain. You'll also learn how to solve the credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models. You'll then be able to tune the hyperparameters of the models and handle class imbalance. Finally, you'll focus on learning how to use deep learning (PyTorch) for approaching financial tasks. By the end of this book, you’ll have learned how to effectively analyze financial data using a recipe-based approach.
Table of Contents (12 chapters)

Getting data from Yahoo Finance

One of the most popular sources of free financial data is Yahoo Finance. It contains not only historical and current stock prices in different frequencies (daily, weekly, monthly), but also calculated metrics, such as the beta (a measure of the volatility of an individual asset in comparison to the volatility of the entire market) and many more. In this recipe, we focus on retrieving historical stock prices.

For a long period of time, the go-to tool for downloading data from Yahoo Finance was the pandas-datareader library. The goal of the library was to extract data from a variety of sources and store it in the form of a pandas DataFrame. However, after some changes to the Yahoo Finance API, this functionality was deprecated. It is still good to be familiar with this library, as it facilitates downloading data from sources such as FRED (Federal...