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)

Forecasting using ARIMA class models

In this recipe, we focus on using the ARIMA class models to forecast future observations of a given time series.

We compare the forecasting performance of the models we built in the Modeling time series with ARIMA class models recipe, where we investigated Google's stock prices in 2015-2018. We manually selected an ARIMA(2,1,1) model, while auto_arima suggested ARIMA(3,1,2). In this recipe, we use both models as they were initially estimated using different libraries, offering slightly different possibilities in terms of forecasting.

We forecast Google's weekly stock prices over the first 3 months of 2019.

Getting ready

The ARIMA(2,1,1) model from the Modeling time series with...