Quantitative Trading Strategies Using Python: Technical Analysis, Statistical Testing, and Machine Learning

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

• Master the fundamental concepts of quantitative trading
• Use Python and its popular libraries to build trading models and strategies from scratch
• Perform data analysis and visualization, algorithmic trading, backtesting, risk management, optimization, and machine learning for trading strategies using Python
• Utilize common trading strategies such as trend-following, momentum trading, and pairs trading
• Evaluate different quantitative trading strategies by applying the relevant performance measures and statistics in a scientific manner during backtesting

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.

使用Python建立和實施交易策略。本書將介紹量化交易的基本概念，並展示如何使用Python和流行的庫從頭開始建立交易模型和策略。它涵蓋了實用的交易策略，並提供了逐步實施的示例，涉及到各種主題，包括數據分析和可視化、算法交易、回測、風險管理、優化和機器學習，並配有Python的實際示例。

《Python量化交易策略》第一部分介紹了交易策略的基礎知識，包括量化交易的介紹、電子市場、風險和回報以及期貨合約。第二部分介紹了常見的交易策略，包括趨勢跟隨、動量交易和通過回測進行評估的過程。第三部分涵蓋了更高級的主題，包括使用假設檢驗進行統計套利、使用貝葉斯優化優化交易參數以及使用機器學習方法生成交易信號。

無論您是一位有經驗的交易員想要自動化您的交易策略，還是一位初學者有興趣學習量化交易，本書都將是一個寶貴的資源。本書以清晰簡潔的風格撰寫，使複雜的主題易於理解，並且充滿了示例和練習，以幫助鞏固關鍵概念，您將從中獲得對核心交易策略的牢固理解，以及如何使用Python來實施它們。

您將學到什麼：
- 掌握量化交易的基本概念
- 使用Python及其流行庫從頭開始建立交易模型和策略
- 使用Python進行數據分析和可視化、算法交易、回測、風險管理、優化和機器學習等交易策略
- 使用趨勢跟隨、動量交易和配對交易等常見交易策略
- 在回測期間以科學的方式應用相關績效指標和統計數據評估不同的量化交易策略

本書適合對量化交易有興趣的初學者和專業人士，對金融感興趣的數據科學家，以及研究量化金融、金融工程或相關領域的研究人員或學生。

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).

彭柳（Peng Liu）是新加坡管理大學的量化金融（實務）助理教授，也是新加坡國立大學的兼職研究員。他在新加坡國立大學獲得統計學博士學位，並在銀行、科技和酒店業等行業擁有十年的數據科學家工作經驗。彭柳是《Bayesian Optimization》（Apress，2023）的作者。