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)

Splitting data into training and test sets

Having completed the EDA, the next step is to split the dataset into training and test sets. The idea is to have two separate datasets:

  • Training set—On this part of the data, we train a machine learning model
  • Test set—This part of the data was not seen by the model during training, and is used to evaluate the performance

What we want to achieve by splitting the data is preventing overfitting. Overfitting is a phenomenon whereby a model finds too many patterns in data used for training and performs well only on that particular data. In other words, it fails to generalize to unseen data.

This is a very important step in the analysis, as doing it incorrectly can introduce bias, for example, in the form of data leakage. Data leakage can occur when, during the training phase, a model observes information to which it should...