Build and implement trading strategies using Python. This book will introduce you to the fundamental concepts of quantitative trading and shows how to use Python and popular libraries to build trading models and strategies from scratch. It covers practical trading strategies coupled with step-by-step implementations that touch upon a wide range of topics, including data analysis and visualization, algorithmic trading, backtesting, risk management, optimization, and machine learning, all coupled with practical examples in Python.
Part one of Quantitative Trading Strategies with Python covers the fundamentals of trading strategies, including an introduction to quantitative trading, the electronic market, risk and return, and forward and futures contracts. Part two introduces common trading strategies, including trend-following, momentum trading, and evaluation process via backtesting. Part three covers more advanced topics, including statistical arbitrage using hypothesis testing, optimizing trading parameters using Bayesian optimization, and generating trading signals using a machine learning approach.
Whether you're an experienced trader looking to automate your trading strategies or a beginner interested in learning quantitative trading, this book will be a valuable resource. Written in a clear and concise style that makes complex topics easy to understand, and chock full of examples and exercises to help reinforce the key concepts, you'll come away from it with a firm understanding of core trading strategies and how to use Python to implement them.
What You Will Learn
Who This Book Is For
Aspiring quantitative traders and analysts, data scientists interested in finance, and researchers or students studying quantitative finance, financial engineering, or related fields.
Show moreBuild and implement trading strategies using Python. This book will introduce you to the fundamental concepts of quantitative trading and shows how to use Python and popular libraries to build trading models and strategies from scratch. It covers practical trading strategies coupled with step-by-step implementations that touch upon a wide range of topics, including data analysis and visualization, algorithmic trading, backtesting, risk management, optimization, and machine learning, all coupled with practical examples in Python.
Part one of Quantitative Trading Strategies with Python covers the fundamentals of trading strategies, including an introduction to quantitative trading, the electronic market, risk and return, and forward and futures contracts. Part two introduces common trading strategies, including trend-following, momentum trading, and evaluation process via backtesting. Part three covers more advanced topics, including statistical arbitrage using hypothesis testing, optimizing trading parameters using Bayesian optimization, and generating trading signals using a machine learning approach.
Whether you're an experienced trader looking to automate your trading strategies or a beginner interested in learning quantitative trading, this book will be a valuable resource. Written in a clear and concise style that makes complex topics easy to understand, and chock full of examples and exercises to help reinforce the key concepts, you'll come away from it with a firm understanding of core trading strategies and how to use Python to implement them.
What You Will Learn
Who This Book Is For
Aspiring quantitative traders and analysts, data scientists interested in finance, and researchers or students studying quantitative finance, financial engineering, or related fields.
Show moreChapter 1: Introduction to Quantitative Trading.- Chapter 2: Understanding the Electronic Market.- Chapter 3: Understanding Risk and Return.- Chapter 4: Forward and Futures Contracts.- Chapter 5: Trend Following Strategy.- Chapter 6: Momentum Trading Strategy.- Chapter 7: Backtesting A Trading Strategy.- Chapter 8: Statistical Arbitrage with Hypothesis Testing.- Chapter 9: Optimizing Trading Strategies with Bayesian Optimization.- Chapter 10: Optimizing Trading Strategies with Machine Learning.
Peng Liu is an assistant professor of quantitative
finance (practice) at Singapore Management University and an
adjunct researcher at the National University of Singapore. He
holds a Ph.D. in statistics from the National University of
Singapore and has ten years of working experience as a data
scientist across the banking, technology, and hospitality
industries. Peng is the author of Bayesian Optimization (Apress,
2023).
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