What this book covers

Chapter 1, Acquiring Financial Data, covers a few of the most popular sources of high-quality financial data, including Yahoo Finance, Nasdaq Data Link, Intrinio, and Alpha Vantage. It focuses on leveraging dedicated Python libraries and processing data for further analysis.

Chapter 2, Data Preprocessing, describes various techniques used to preprocess data. It describes the crucial steps between obtaining the data and using it for building machine learning models or investigating trading strategies. As such, it covers topics such as converting prices to returns, adjusting them for inflation, imputing missing values, or aggregating trade data into various kinds of bars.

Chapter 3, Visualizing Financial Time Series, focuses on visualizing financial (and not only) time series data. By plotting the data, we can visually identify some patterns, such as trends, seasonality, and changepoints, which we can further confirm using statistical tests. The insights gathered at this point can lead to making better decisions while choosing the modeling approach.

Chapter 4, Exploring Financial Time Series Data, shows how to use various algorithms and statistical tests to automatically identify potential issues with time series data, such as the existence of outliers. Additionally, it covers analyzing data for the existence of trends or other patterns such as mean-reversion. Lastly, it explores the stylized facts of asset returns. Together, those concepts are crucial while working with financial data, as we want to make sure that the models/strategies we are building can accurately capture the dynamics of asset returns.

Chapter 5, Technical Analysis and Building Interactive Dashboards, explains the basics of technical analysis in Python by showing how to calculate some of the most popular indicators and automatically recognize patterns in candlestick data. It also demonstrates how to create a Streamlit-based web app, which enables us to visualize and inspect the predefined TA indicators in an interactive fashion.

Chapter 6, Time Series Analysis and Forecasting, introduces the basics of time series modeling. It starts by looking into the building blocks of time series and how to separate them using various decomposition methods. Then, it covers the concept of stationarity, how to test for it, and how to achieve it in case the original series is not stationary. Lastly, it shows how to use two of the most widely used statistical approaches to time series modeling—the exponential smoothing methods and ARIMA class models.

Chapter 7, Machine Learning-Based Approaches to Time Series Forecasting, starts by explaining different ways of validating time series models. Then, it provides an overview of feature engineering approaches. It also introduces a tool for automatic feature extraction which generates hundreds or thousands of features with a few lines of code. Furthermore, it explains the concept of reduced regression and how to use Meta’s popular Prophet algorithm. The chapter concludes with an introduction to one of the popular AutoML frameworks for time series forecasting.

Chapter 8, Multi-Factor Models, covers estimating various factor models, starting with the simplest one-factor model (CAPM) and then extending it to the more advanced three-, four-, and five-factor models.

Chapter 9, Modeling Volatility with GARCH Class Models, focuses on volatility and the concept of conditional heteroskedasticity. It shows how to use univariate and multivariate GARCH models, which are one of the most popular ways of modeling and forecasting volatility.

Chapter 10, Monte Carlo Simulations in Finance, explains how to use Monte Carlo methods for various tasks, such as simulating stock prices, pricing derivatives with no closed-form solution (American/Exotic options), or estimating the uncertainty of a portfolio (for example, by calculating Value-at-Risk and Expected Shortfall).

Chapter 11, Asset Allocation, starts by explaining the most basic asset allocation strategy, and on its basis, showing how to evaluate the performance of portfolios. Then it shows three different approaches to obtaining the efficient frontier. Lastly, it explores Hierarchical Risk Parity, which is a novel approach to asset allocation based on the combination of graph theory and machine learning.

Chapter 12, Backtesting Trading Strategies, presents how to run backtests of various trading strategies using two approaches (vectorized and event-driven) with the help of popular Python libraries. To do so, it uses a few examples of strategies built on the basis of popular technical indicators or mean-variance portfolio optimization.

Chapter 13, Applied Machine Learning: Identifying Credit Default, shows how to approach a real-life machine learning task of predicting loan defaults. It covers the entire scope of a machine learning project, from gathering and cleaning data to building and tuning a classifier. An important takeaway from this chapter is understanding the general approach to machine learning projects, which can then be applied to many different tasks, be it churn prediction or estimating the price of new real estate in a neighborhood.

Chapter 14, Advanced Concepts for Machine Learning Projects, continues from the workflow introduced in the preceding chapter and demonstrates possible extensions to the MVP stage of ML projects. It starts with presenting more advanced classifiers. Then, it covers alternative approaches to encoding categorical features and describes a few methods of dealing with imbalanced data.

Furthermore, it shows how to create stacked ensembles of ML models and leverage Bayesian hyperparameter tuning to improve upon exhaustive grid search. It also explores various approaches to calculating feature importance and using it to select the most informative predictors. Lastly, it touches upon the rapidly developing field of explainable AI.

Chapter 15, Deep Learning in Finance, describes how to apply some of the recent neural network architectures to two possible use cases in the financial domain—predicting credit card default (a classification task) and forecasting time series.