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)

Encoding categorical variables

In the previous recipes, we have seen that some features are categorical variables (originally represented as either object or category data types). However, most machine learning algorithms work exclusively with numeric data. That is why we need to encode categorical features into a representation compatible with the models.

In this recipe, we cover some popular encoding approaches:

  • Label encoding
  • One-hot encoding

In label encoding, we replace the categorical value with a numeric value between 0 and # of classes - 1—for example, with three distinct classes, we use {0, 1, 2}.

This is already very similar to the outcome of converting to the category class in pandas . We can access the codes of the categories by running Additionally, we can recover the mapping by running dict(zip(, df_cat...