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)

Time Series Modeling

In this chapter, we will introduce the basics of time series modeling. We start by explaining the building blocks of time series and how to separate them using decomposition methods. Later, we will introduce the concept of stationarity—why it is important, how to test for it, and ultimately how to achieve it in case the original series is not stationary.

We will also look into two of the most widely used approaches to time series modeling—the exponential smoothing methods and ARIMA class models. In both cases, we will show you how to fit the models, evaluate the goodness of fit, and forecast future values of the time series. Additionally, we will present a novel approach to modeling a time series using the additive model from Facebook's Prophet library.

We cover the following recipes in this chapter:

  • Decomposing time series
  • Decomposing...