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 advanced classifiers

In Chapter 8, Identifying Credit Default with Machine Learning, we learned how to build an entire pipeline, with the goal of predicting customer default, that is, their inability to repay their debts. For the machine learning part, we used a decision tree classifier, which is one of the basic algorithms.

There are a few ways to possibly improve the performance of the model, some of them include:

  • Gathering more observations
  • Adding extra features—either by gathering additional data or through feature engineering
  • Using more advanced models
  • Tuning the hyperparameters

There is a common rule that data scientists spend 80% of their time on a project gathering and cleaning data while spending only 20% on the actual modeling. In line with this, adding more data might greatly improve a model's performance, especially when dealing with imbalanced...