Algorithmic Trading Methods

Applications Using Advanced Statistics, Optimization, and Machine Learning Techniques

Robert Kissell

• Publisher: Academic Press
• Year: 2020
• eBook ISBN: 9780128156315
• Edition: 2
• Topic: Economics
• Subtopic: Finance
• Index: Economics

Algorithmic Trading Methods: Applications using Advanced Statistics, Optimization, and Machine Learning Techniques, Second Edition, is a sequel to The Science of Algorithmic Trading and Portfolio Management. This edition includes new chapters on algorithmic trading, advanced trading analytics, regression analysis, optimization, and advanced statistical methods. Increasing its focus on trading strategies and models, this edition includes new insights into the ever-changing financial environment, pre-trade and post-trade analysis, liquidation cost & risk analysis, and compliance and regulatory reporting requirements. Highlighting new investment techniques, this book includes material to assist in the best execution process, model validation, quality and assurance testing, limit order modeling, and smart order routing analysis. Includes advanced modeling techniques using machine learning, predictive analytics, and neural networks. The text provides readers with a suite of transaction cost analysis functions packaged as a TCA library. These programming tools are accessible via numerous software applications and programming languages. - Provides insight into all necessary components of algorithmic trading including: transaction cost analysis, market impact estimation, risk modeling and optimization, and advanced examination of trading algorithms and corresponding data requirements - Increased coverage of essential mathematics, probability and statistics, machine learning, predictive analytics, and neural networks, and applications to trading and finance - Advanced multiperiod trade schedule optimization and portfolio construction techniques - Techniques to decode broker-dealer and third-party vendor models - Methods to incorporate TCA into proprietary alpha models and portfolio optimizers - TCA library for numerous software applications and programming languages including: MATLAB, Excel Add-In, Python, Java, C/C++, .Net, Hadoop, and as standalone .EXE and .COM applications

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Preface
• Acknowledgments
• Chapter 1. Introduction
• Chapter 2. Algorithmic Trading
• Chapter 3. Transaction Costs
• Chapter 4. Market Impact Models
• Chapter 5. Probability and Statistics
• Chapter 6. Linear Regression Models
• Chapter 7. Probability Models
• Chapter 8. Nonlinear Regression Models
• Chapter 9. Machine Learning Techniques
• Chapter 10. Estimating I-Star Market Impact Model Parameters
• Chapter 11. Risk, Volatility, and Factor Models
• Chapter 12. Volume Forecasting Techniques
• Chapter 13. Algorithmic Decision-Making Framework
• Chapter 14. Portfolio Algorithms and Trade Schedule Optimization
• Chapter 15. Advanced Algorithmic Modeling Techniques
• Chapter 16. Decoding and Reverse Engineering Broker Models with Machine Learning Techniques
• Chapter 17. Portfolio Construction with Transaction Cost Analysis
• Chapter 18. Quantitative Analysis with TCA
• Chapter 19. Machine Learning and Trade Schedule Optimization
• Chapter 20. TCA Analysis Using MATLAB, Excel, and Python
• Chapter 21. Transaction Cost Analysis (TCA) Library
• References
• Index