Quantitative Trading Strategies Using Python Technical Analysis Statistical Testing And Machine Learning Pengliu by Peng liu 9781484296752, 1484296753 instant download after payment.

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

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