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)

Modeling time series with exponential smoothing methods

Exponential smoothing methods are suitable for non-stationary data (that is, data with a trend and/or seasonality) and work similarly to exponential moving averages. The forecasts are weighted averages of past observations. These models put more emphasis on recent observations as the weights become exponentially smaller with time. Smoothing methods are popular because they are fast (not a lot of computations are required) and relatively reliable when it comes to forecasts:

Simple exponential smoothing: The most basic model is called Simple Exponential Smoothing (SES). This class of models is most apt for cases when the considered time series does not exhibit any trend or seasonality. They also work well with series with only a few data points.

The model is parameterized by a smoothing parameter α with values between...