Algorithmic Trading Methods (eBook)

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.

Robert Kissell, Ph.D., is President of Kissell Research Group, a global financial and economic consulting firm specializing in quantitative modeling, statistical analysis, and algorithmic trading. He is also a professor at Molloy College in the School of Business and an adjunct professor at the Gabelli School of Business at Fordham University. He has held several senior leadership positions with prominent bulge bracket investment banks including UBS Securities where he was Executive Director of Execution Strategies and Portfolio Analysis, and at JP Morgan where he was Executive Director and Head of Quantitative Trading Strategies. He was previously at Citigroup/Smith Barney where he was Vice President of Quantitative Research, and at Instinet where he was Director of Trading Research. He began his career as an Economic Consultant at R.J. Rudden Associates specializing in energy, pricing, risk, and optimization. Dr. Kissell has written several books and published dozens of journal articles on Algorithmic Trading, Risk, and Finance. He is a coauthor of the CFA Level III reading titled 'Trade Strategy and Execution,” CFA Institute 2019.”

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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