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

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Title: Algorithmic Trading Methods : Applications Using Advanced Statistics, Optimization, and Machine Learning Techniques
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
Authors: Robert Kissell
Resource Type: eBook.
Subjects: Algorithms, Investments, Stocks--Mathematical models, Portfolio management--Mathematical models, Program trading (Securities)
Categories: BUSINESS & ECONOMICS / Banks & Banking, BUSINESS & ECONOMICS / Finance / General
Database: eBook Collection (EBSCOhost)
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PubType: eBook
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  Data: 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
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      – Code: 332.60285
        Scheme: ddc
        Type: prePub
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Investments
        Type: general
      – SubjectFull: Stocks--Mathematical models
        Type: general
      – SubjectFull: Portfolio management--Mathematical models
        Type: general
      – SubjectFull: Program trading (Securities)
        Type: general
    Titles:
      – TitleFull: Algorithmic Trading Methods : Applications Using Advanced Statistics, Optimization, and Machine Learning Techniques
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            NameFull: Robert Kissell
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          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2021
            – D: 02
              M: 11
              Type: profile
              Y: 2020
          Identifiers:
            – Type: isbn-print
              Value: 9780128156308
            – Type: isbn-electronic
              Value: 9780128156315
          Titles:
            – TitleFull: Algorithmic Trading Methods : Applications Using Advanced Statistics, Optimization, and Machine Learning Techniques
              Type: main
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