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)

Decomposing time series

The goal of time series decomposition is to increase our understanding of the data by breaking down the series into multiple components. It provides insight in terms of modeling complexity and which approaches to follow in order to accurately capture each of the components.

These components can be divided into two types: systematic and non-systematic. The systematic ones are characterized by consistency and the fact that they can be described and modeled. By contrast, the non-systematic ones cannot be modeled directly.

The following are the systematic components:

  • level: The mean value in the series.
  • trend: An estimate of the trend, that is, the change in value between successive time points at any given moment. It can be associated with the slope (increasing/decreasing) of the series.
  • seasonality: Deviations from the mean caused by repeating short-term...