Detecting Regime Change in Computational Finance: Data Science, Machine Learning and Algorithmic Trading

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Foreword
Preface
List of Figures
List of Tables
Chapter 1 Introduction
	1.1 Overview
	1.2 Research Objectives
	1.3 Book Structure
Chapter 2 Background and Literature Survey
	2.1 Regime Change
		2.1.1 Regime Change Detection Methods
	2.2 Directional Change
		2.2.1 The Concept of Directional Change
		2.2.2 Research Using Directional Change
		2.2.3 Directional Change Indicators
			2.2.3.1 Total Price Movement
			2.2.3.2 Time for Completion of a Trend
			2.2.3.3 Time–Adjusted Return of DC
	2.3 Machine Learning Techniques
		2.3.1 Hidden Markov Model
			2.3.1.1 Definition of HMM
			2.3.1.2 Parameters of HMM
			2.3.1.3 Expectation-Maximization Algorithm
		2.3.2 Naïve Bayes Classifier
			2.3.2.1 Definition of Naïve Bayes Classifier
Chapter 3 Regime Change Detection Using Directional Change Indicators
	3.1 Introduction
	3.2 Methodology
		3.2.1 DC Indicator
		3.2.2 Time Series Indicator
	3.3 Experiments
		3.3.1 Data Sets
		3.3.2 Hidden Markov Model
	3.4 Empirical Results
		3.4.1 EUR–GBP
		3.4.2 GBP–USD
		3.4.3 EUR–USD
		3.4.4 Distribution of the Indicator R
		3.4.5 Discussion
	3.5 Conclusion
Chapter 4 Classification of Normal and Abnormal Regimes in Financial Markets
	4.1 Introduction
	4.2 Methodology
		4.2.1 Summarising Financial Data in DC
		4.2.2 Detecting Regime Changes through HMM
		4.2.3 Comparing Market Regimes in an Indicator Space
	4.3 Empirical Study
		4.3.1 Data Sets
		4.3.2 Summarising Data under DC
		4.3.3 Detecting Regime Changes under HMM
		4.3.4 Observing Market Regimes in the Normalised Indicator Space
	4.4 Results and Discussions
		4.4.1 Market Regimes in the Indicator Space
		4.4.2 Market Regimes under Different Thresholds
		4.4.3 Discussion
	4.5 Conclusions
Chapter 5 Tracking Regime Changes Using Directional Change Indicators
	5.1 Introduction
	5.2 Methodology
		5.2.1 Tracking DC Trends
		5.2.2 Use of a Naïve Bayes Classifier
	5.3 Experiment Setup
		5.3.1 Data
		5.3.2 Regime Changes on the Data
	5.4 Empirical Results
		5.4.1 Calculating Probability
		5.4.2 B-Simple for Regime Classification
		5.4.3 B-Strict for Regime Classification
		5.4.4 Tracked Regime Changes
			5.4.4.1 Tracked Regime Changes on DJIA Index
			5.4.4.2 Tracked Regime Changes on FTSE 100 Index
			5.4.4.3 Tracked Regime Changes on S&P 500
		5.4.5 Discussion
	5.5 Conclusion
Chapter 6 Algorithmic Trading Based on Regime Change Tracking
	6.1 Overview
	6.2 Methodology
		6.2.1 Regime Tracking Information
		6.2.2 Trading Algorithm JC1
		6.2.3 Trading Algorithm JC2
		6.2.4 Control Algorithm CT1
	6.3 Experimental Setup
		6.3.1 Data
		6.3.2 Experimental Parameters
		6.3.3 Money Management
	6.4 Experiment Results
		6.4.1 Number of Trades
		6.4.2 Final Wealth
		6.4.3 Maximum Drawdown
	6.5 Discussions
		6.5.1 The Primary Goals Are Achieved
		6.5.2 Future Work: Regime Tracking for Better Trading Algorithms
	6.6 Conclusions
Chapter 7 Conclusions
	7.1 Summary of Work Done
	7.2 Take-Home Messages
	7.3 Future Research
		7.3.1 Research Directions
Appendices
	Appendix A A Formal Definition of Directional Change
	Appendix B Extended Results of Chapter 3
	Appendix C Experiment Summary of Chapter 4
	Appendix D Detected Regime Changes in Chapter 4
Bibliography
Index