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Algorithmic Trading Methods

Applications Using Advanced Statistics, Optimization, and Machine Learning Techniques
 
Edition number: 2
Publisher: Academic Press
Date of Publication:
 
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Long 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.


  • 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:
1. New Financial Markets2. Algorithmic Trading3. Market Microstructure4. Transaction Cost Analysis5. Market Impact Models6. Estimating I-Star Model Parameters7. Volatility and Risk Models8. Advanced Forecasting Techniques - "Volume Forecasting Models"9. Algorithmic Decision-Making Framework10. Portfolio Algorithms & Trade Schedule Optimization11. Pre-Trade and Post-Trade Models12. Liquidation Cost Analysis13. Compliance and Regulatory Reporting14. Portfolio Construction15. Quantitative Portfolio Management Techniques16. Multi-Asset Trading Costs, ETFs, Fixed Income, etc.17. High Frequency Trading and Black Box Models18. Cost Index - Historical TCA Patterns, Costs by Market Cap, and Investment Style19. TCA with Excel, MATLAB, & Python20. Advanced Topics - TCA ETFs, Stat Arb, Liquidity Trading21. Best Execution Process - Model Validation, and Best Execution Process for Brokers and for Investors