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)

Tuning hyperparameters using grid searches and cross-validation

Cross-validation, together with grid search, is commonly used to tune the hyperparameters of the model in order to achieve better performance. Below, we outline the differences between hyperparameters and parameters.


  • External characteristic of the model
  • Not estimated based on data
  • Can be considered the model's settings
  • Set before the training phase
  • Tuning them can result in better performance


  • Internal characteristic of the model
  • Estimated based on data, for example, the coefficients of linear regression
  • Learned during the training phase

One of the challenges of machine learning is training models that are able to generalize well to unseen data (overfitting versus underfitting; a bias-variance trade-off). While tuning the model's hyperparameters, we would like to evaluate...