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Chapter 1 - Introduction
Pages 1-21 - Book chapterNo access
Chapter 2 - Algorithmic Trading
Pages 23-56 - Book chapterNo access
Chapter 3 - Transaction Costs
Pages 57-97 - Book chapterNo access
Chapter 4 - Market Impact Models
Pages 99-128 - Book chapterNo access
Chapter 5 - Probability and Statistics
Pages 129-150 - Book chapterNo access
Chapter 6 - Linear Regression Models
Pages 151-173 - Book chapterNo access
Chapter 7 - Probability Models
Pages 175-195 - Book chapterNo access
Chapter 8 - Nonlinear Regression Models
Pages 197-219 - Book chapterNo access
Chapter 9 - Machine Learning Techniques
Pages 221-231 - Book chapterNo access
Chapter 10 - Estimating I-Star Market Impact Model Parameters
Pages 233-267 - Book chapterNo access
Chapter 11 - Risk, Volatility, and Factor Models
Pages 269-299 - Book chapterNo access
Chapter 12 - Volume Forecasting Techniques
Pages 301-322 - Book chapterNo access
Chapter 13 - Algorithmic Decision-Making Framework
Pages 323-348 - Book chapterNo access
Chapter 14 - Portfolio Algorithms and Trade Schedule Optimization
Pages 349-374 - Book chapterNo access
Chapter 15 - Advanced Algorithmic Modeling Techniques
Pages 375-403 - Book chapterNo access
Chapter 16 - Decoding and Reverse Engineering Broker Models with Machine Learning Techniques
Pages 405-428 - Book chapterNo access
Chapter 17 - Portfolio Construction with Transaction Cost Analysis
Pages 429-467 - Book chapterNo access
Chapter 18 - Quantitative Analysis with TCA
Pages 469-518 - Book chapterNo access
Chapter 19 - Machine Learning and Trade Schedule Optimization
Pages 519-542 - Book chapterNo access
Chapter 20 - TCA Analysis Using MATLAB, Excel, and Python
Pages 543-558 - Book chapterNo access
Chapter 21 - Transaction Cost Analysis (TCA) Library
Pages 559-567 - Book chapterNo access
References
Pages 569-576 - Book chapterNo access
Index
Pages 577-588
About the book
Description
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.
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.
Key Features
- 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
- 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
Details
ISBN
978-0-12-815630-8
Language
English
Published
2020
Copyright
Copyright © 2021 Elsevier Inc. All rights reserved.
Imprint
Academic Press