Algorithmic Trading Methods: Applications Using Advanced Statistics, Optimization, and Machine Learning Techniques

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.

1. New Financial Markets 2. Algorithmic Trading 3. Market Microstructure 4. Transaction Cost Analysis 5. Market Impact Models 6. Estimating I-Star Model Parameters 7. Volatility and Risk Models 8. Advanced Forecasting Techniques – "Volume Forecasting Models" 9. Algorithmic Decision-Making Framework 10. Portfolio Algorithms & Trade Schedule Optimization 11. Pre-Trade and Post-Trade Models 12. Liquidation Cost Analysis 13. Compliance and Regulatory Reporting 14. Portfolio Construction 15. Quantitative Portfolio Management Techniques 16. Multi-Asset Trading Costs, ETFs, Fixed Income, etc. 17. High Frequency Trading and Black Box Models 18. Cost Index – Historical TCA Patterns, Costs by Market Cap, and Investment Style 19. TCA with Excel, MATLAB, & Python 20. Advanced Topics – TCA ETFs, Stat Arb, Liquidity Trading 21. Best Execution Process – Model Validation, and Best Execution Process for Brokers and for Investors