#### 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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# Converting prices to returns

Asset prices are usually non-stationary, that is, their statistics, such as mean and variance (mathematical moments) change over time. This could also mean observing some trends or seasonality in the price series (see Chapter 3, Time Series Modeling). By transforming the prices into returns, we attempt to make the time series stationary, which is the desired property in statistical modeling.

There are two types of returns:

• Simple returns: They aggregate over assets; the simple return of a portfolio is the weighted sum of the returns of the individual assets in the portfolio. Simple returns are defined as:
• Log returns: They aggregate over time; it is easier to understand with the help of an examplethe log return for a given month is the sum of the log returns of the days within that month. Log returns are defined as:

Pt is the price of...