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, regre...
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| Format: | eBook |
| Language: | English |
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London, United Kingdom
Academic Press
2021
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| Edition: | Second edition |
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| Online Access: | |
| Collection: | O'Reilly - Collection details see MPG.ReNa |
Table of Contents:
- 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
- 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
- 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
- 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
- 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