#### 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.
Preface
Financial Data and Preprocessing
Free Chapter
Technical Analysis in Python
Identifying Credit Default with Machine Learning
Advanced Machine Learning Models in Finance
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# Testing for stationarity in time series

A stationary time series is a series in which statistical properties such as mean, variance, and covariance are constant over time. Stationarity is a desired characteristic of time series as it makes modeling and extrapolating (forecasting) into the future more feasible. Some drawbacks of non-stationary data are:

• Variance can be misspecified by the model
• Worse model fit
• Cannot leverage valuable time-dependent patterns in the data

In this recipe, we show you how to test the time series for stationarity. To do so, we employ the following methods:

• The Augmented Dickey-Fuller (ADF) test
• The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test
• Plots of the (partial) autocorrelation function (PACF/ACF)

We investigate the stationarity of monthly gold prices from the years 2000-2011.

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