Название: Quantitative Trading Strategies Using Python: Technical Analysis, Statistical Testing, and Machine LearningАвтор: Peng Liu
Издательство: Apress
Год: 2023
Страниц: 341
Язык: английский
Формат: pdf (true), epub
Размер: 16.3 MB
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.
Machine Learning can be used in pairs trading in several ways to improve the effectiveness of trading strategies. Examples include pair selection, feature engineering, spread prediction, etc. In this final chapter, we are going to focus on spread prediction using different Machine Learning algorithms in order to generate trading signals. Pairs trading is a type of quantitative trading strategy that involves transacting two highly correlated/cointegrated assets at the same time and in the opposite direction. The financial instruments could be two stocks or two indices, based on which the relative price difference is used to derive the spread series and generate trading signals. The primary assumption behind pairs trading is that the price spread between two highly correlated or cointegrated assets should exhibit a mean reversion behavior over time. Machine Learning models are predictive functions that generate predictions given a specific set of inputs. In this case, we intend to use a machine learning model in pairs trading to predict the spread between the two assets, which will then be used to identify profitable trading signals. Since the spread is a continuous quantity, we will explore regression models in this chapter, including support vector machine (SVM), random forest (RF), and neural network models.
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.
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