Algorithmic trading methods : applications using advanced statistics, optimization, and machine learning techniques /

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Kissell, Robert, 1967- (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London, United Kingdom : Academic Press, [2021]
Edición:Second edition.
Temas:
Investments.
Algorithms.
Stocks > Mathematical models.
Program trading (Securities)
Portfolio management > Mathematical models.
Investments
Algorithms
Investissements.
Algorithmes.
Actions (Titres de soci�et�e) > Mod�eles math�ematiques.
N�egociation assist�ee par ordinateur (Valeurs mobili�eres)
Gestion de portefeuille > Mod�eles math�ematiques.
algorithms.
Portfolio management > Mathematical models
Stocks > Mathematical models
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Intro
  • Title page
  • Table of Contents
  • Copyright
  • Preface
  • Acknowledgments
  • Chapter 1. Introduction
  • What is Electronic Trading?
  • What is Algorithmic Trading?
  • Trading Algorithm Classifications
  • Trading Algorithm Styles
  • Investment Cycle
  • Investment Objective
  • Information Content
  • Investment Styles
  • Investment Strategies
  • Research Data
  • Broker Trading Desks
  • Research Function
  • Sales Function
  • Implementation Types
  • Algorithmic Decision-Making Process
  • Chapter 2. Algorithmic Trading
  • Advantages
  • Disadvantages
  • Growth in Algorithmic Trading
  • Market Participants
  • Classifications of Algorithms
  • Types of Algorithms
  • Algorithmic Trading Trends
  • Day of Week Effect
  • Intraday Trading Profiles
  • Trading Venue Classification
  • Types of Orders
  • Revenue Pricing Models
  • Execution Options
  • Algorithmic Trading Decisions
  • Algorithmic Analysis Tools
  • High Frequency Trading
  • Direct Market Access
  • Chapter 3. Transaction Costs
  • What are transaction costs?
  • What is best execution?
  • What is the goal of implementation?
  • Unbundled Transaction Cost Components
  • Transaction Cost Classification
  • Transaction Cost Categorization
  • Transaction Cost Analysis
  • Implementation Shortfall
  • Implementation Shortfall Formulation
  • Evaluating Performance
  • Comparing Algorithms
  • Independent Samples
  • Median Test
  • Distribution Analysis
  • Chi-Square Goodness of Fit
  • Kolmogorov-Smirnov Goodness of Fit
  • Experimental Design
  • Final Note on Posttrade Analysis
  • Chapter 4. Market Impact Models
  • Introduction
  • Definition
  • Derivation of Models
  • I-Star Market Impact Model
  • Model Formulation
  • Chapter 5. Probability and Statistics
  • Introduction
  • Random Variables
  • Probability Distributions
  • Probability Distribution Functions
  • Continuous Distribution Functions
  • Discrete Distributions
  • Chapter 6. Linear Regression Models
  • Introduction
  • Linear Regression
  • Matrix Techniques
  • Log Regression Model
  • Polynomial Regression Model
  • Fractional Regression Model
  • Chapter 7. Probability Models
  • Introduction
  • Developing a Probability Model
  • Solving Probability Output Models
  • Examples
  • Comparison of Power Function to Logit Model
  • Conclusions
  • Chapter 8. Nonlinear Regression Models
  • Introduction
  • Regression Models
  • Nonlinear Formulation
  • Solving Nonlinear Regression Model
  • Estimating Parameters
  • Nonlinear least squares (Non-OLS)
  • Hypothesis Testing
  • Evaluate Model Performance
  • Sampling Techniques
  • Random Sampling
  • Sampling With Replacement
  • Sampling Without Replacement
  • Monte Carlo Simulation
  • Bootstrapping Techniques
  • Jackknife Sampling Techniques
  • Chapter 9. Machine Learning Techniques
  • Introduction
  • Types of Machine Learning
  • Examples
  • Classification
  • Regression
  • Neural Networks
  • Chapter 10. Estimating I-Star Market Impact Model Parameters
  • Introduction

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