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)

Loading data and managing data types

In this recipe, we show how to load a dataset into Python. In order to show the entire pipeline—including working with messy data—we apply some transformation to the original dataset. For more information on applied changes, please refer to the accompanying GitHub repository.

How to do it...

Execute the following steps to load a dataset into Python.

  1. Import the libraries:
import pandas as pd
  1. Preview a CSV file:
!head -n 5 credit_card_default.csv

The output looks like this:

  1. Load the data from the CSV file:
df = pd.read_csv('credit_card_default.csv', index_col=0, 

The DataFrame has 30,000 rows and 24 columns.

  1. Separate...