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)

Decomposing time series using Facebook's Prophet

An alternative approach to time series decomposition is to use an additive model, in which a time series is represented as a combination of patterns on different time scales (daily, weekly, monthly, yearly, and so on) together with the overall trend. Facebook's Prophet does exactly that, along with more advanced functionalities such as accounting for changepoints (rapid changes in behavior), holidays, and much more. A practical benefit of using this library is that we are able to forecast future values of the time series, along with a confidence interval indicating the level of uncertainty.

In this recipe, we will try fitting Prophet's additive model to daily gold prices from 2000-2004 and predicting the prices over 2005.

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