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)

Implementing scikit-learn's pipelines

In the previous recipes, we showed all the steps required to build a machine learning model —starting with loading data, splitting it into a training and a test set, imputing missing values, encoding categorical features, and—lastly—fitting a decision tree classifier.

The process requires multiple steps to be executed in a certain order, which can sometimes be tricky with a lot of modifications to the pipeline mid-work. That is why scikit-learn introduced Pipelines. By using Pipelines, we can sequentially apply a list of transformations to the data, and then train a given estimator (model).

One important point to be aware of is that the intermediate steps of the Pipeline must have the fit and transform methods (the final estimator only needs the fit method, though). Using Pipelines has several benefits:

  • The flow...