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)

Investigating different approaches to handling imbalanced data

A very common issue when working with classification tasks is that of class imbalance: when one class is highly outnumbered in comparison to the second one (this can also be extended to multi-class). In general, we are talking about imbalance when the ratio of the two classes is not 1:1. In some cases, a delicate imbalance is not that big of a problem, but there are industries/problems in which we can encounter ratios of 100:1, 1000:1, or even worse.

In this recipe, we show an example of a credit card fraud problem, where the fraudulent class is only 0.17% of the entire sample. In such cases, gathering more data (especially of the fraudulent class) might simply not be feasible, and we need to resort to some techniques that can help us in understanding and avoiding the accuracy paradox.

Accuracy paradox refers to a...