Algorithmic trading methods : applications using advanced statistics, optimization, and machine learning techniques / created by Robert Kissell.
Material type:
TextPublisher: San Diego : Elsevier Inc, 2020Copyright date: ©2020Edition: Second EditionDescription: 588 pages 22 cm Illustrations (some color)Content type: - text
- unmediated
- volume
- 9780128156308
- HG4521 KIS
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Includes bibliographic references and index
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
"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"-- Provided by publisher.
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