Table of Contents:

1 Market Data
2 Forecasting
3 Forecast Combining
4 Risk
5 Trading Costs
6 Portfolio Construction
7 Simulation

This book provides a comprehensive overview of quantitative trading of shares, also known as statistical arbitrage. This book teaches you how to collect financial data, discover asset return patterns from historical data, produce and integrate various predictions, manage risk, construct a stock portfolio that is optimal for risk and trading expenses, and execute trades. In this book, readers learn: methods for estimating stock returns using machine learning in efficient financial markets. How to merge several forecasts into one utilising secondary machine learning, dimensionality reduction, and other techniques. Methods for avoiding overfitting and the curse of dimensionality, including active research areas such as "benign overfitting" in machine learning. This book is ideal for financial professionals, such as quantitative traders and portfolio managers, and will also find a place in the libraries of data scientists and students in a range of statistical and quantitative subjects. This book is a must-read for anyone seeking a deeper grasp of how to use data science, machine learning, and optimization to the stock market.