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

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

Autor Robert Kissell
en Limba Engleză Paperback – 13 sep 2020
The Science of Algorithmic Trading and Portfolio Management, Second Edition,focuses on trading strategies and methods, including new insights on the evolution of financial markets, pre-trade models and post-trade analysis, liquidation cost and risk analysis required for regulatory reporting, and compliance and regulatory reporting requirements. Highlighting new investment styles, it adds new material on best execution processes for investors and brokers, including model validation, quality and assurance, limit order model testing, and smart order model testing. Using basic programming tools, such as Excel, MATLAB, and Python, this book provides a process to create TCA low cost exchange traded funds.


  • Provides insights into all necessary components of algorithmic trading, including transaction costs analysis, market impact, risk and optimization, and a thorough and detailed discussion of trading algorithms
  • Includes increased coverage of mathematics, statistics and machine learning
  • Presents broad coverage of Alpha Model construction
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Specificații

ISBN-13: 9780128156308
ISBN-10: 0128156309
Pagini: 612
Dimensiuni: 191 x 235 x 34 mm
Greutate: 1.04 kg
Ediția:2
Editura: ELSEVIER SCIENCE

Public țintă

Upper-division undergraduates, graduate students, researchers, and professionals working in financial economics, especially trading.

Cuprins

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

Descriere

The Science of Algorithmic Trading and Portfolio Management, Second Edition, focuses on trading strategies and methods, including new insights on the evolution of financial markets, pre-trade models and post-trade analysis, liquidation cost and risk analysis required for regulatory reporting, and compliance and regulatory reporting requirements. Highlighting new investment styles, it adds new material on best execution processes for investors and brokers, including model validation, quality and assurance, limit order model testing, and smart order model testing. Using basic programming tools, such as Excel, MATLAB, and Python, this book provides a process to create TCA low cost exchange traded funds.

 

 

 

  • Provides insights into all necessary components of algorithmic trading, including transaction costs analysis, market impact, risk and optimization, and a thorough and detailed discussion of trading algorithms
  • Includes increased coverage of mathematics, statistics and machine learning
  • Presents broad coverage of Alpha Model construction