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Detecting Regime Change in Computational Finance: Data Science, Machine Learning and Algorithmic Trading [Minkštas viršelis]

3.71/5 (7 ratings by Goodreads)
(University of Essex, United Kingdom),
  • Formatas: Paperback / softback, 138 pages, aukštis x plotis: 234x156 mm, weight: 300 g, 16 Illustrations, color; 22 Illustrations, black and white
  • Išleidimo metai: 30-May-2022
  • Leidėjas: Chapman & Hall/CRC
  • ISBN-10: 0367540959
  • ISBN-13: 9780367540951
Kitos knygos pagal šią temą:
Detecting Regime Change in Computational Finance: Data Science, Machine Learning and Algorithmic TradingDidesnis paveikslėlis
  • Formatas: Paperback / softback, 138 pages, aukštis x plotis: 234x156 mm, weight: 300 g, 16 Illustrations, color; 22 Illustrations, black and white
  • Išleidimo metai: 30-May-2022
  • Leidėjas: Chapman & Hall/CRC
  • ISBN-10: 0367540959
  • ISBN-13: 9780367540951
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9780367540951.html
Raktažodžiai:
Based on interdisciplinary research into "Directional Change", a new data-driven approach to financial data analysis, Detecting Regime Change in Computational Finance: Data Science, Machine Learning and Algorithmic Trading applies machine learning to financial market monitoring and algorithmic trading. Directional Change is a new way of summarising price changes in the market. Instead of sampling prices at fixed intervals (such as daily closing in time series), it samples prices when the market changes direction ("zigzags"). By sampling data in a different way, this book lays out concepts which enable the extraction of information that other market participants may not be able to see. The book includes a Foreword by Richard Olsen and explores the following topics:











Data science: as an alternative to time series, price movements in a market can be summarised as directional changes





Machine learning for regime change detection: historical regime changes in a market can be discovered by a Hidden Markov Model





Regime characterisation: normal and abnormal regimes in historical data can be characterised using indicators defined under Directional Change





Market Monitoring: by using historical characteristics of normal and abnormal regimes, one can monitor the market to detect whether the market regime has changed





Algorithmic trading: regime tracking information can help us to design trading algorithms

It will be of great interest to researchers in computational finance, machine learning and data science.

About the Authors

Jun Chen received his PhD in computational finance from the Centre for Computational Finance and Economic Agents, University of Essex in 2019.

Edward P K Tsang is an Emeritus Professor at the University of Essex, where he co-founded the Centre for Computational Finance and Economic Agents in 2002.

Recenzijos

"This is the first book of its kind to build on the framework of Directional Change. The concept of Directional Change opens a whole new area of research."

-- From the Foreword by Richard Olsen, Founder and CEO of Lykke, co-founder of OANDA and pioneer in high frequency finance and fintech.

"Financial markets technology and the practice of trading are in a state of constant change. A book that details a completely new concept in trading, however, is very rare. Detecting Regime Change in Computational finance is one such book and Professor Tsang and Dr Chen should be applauded for producing this exciting new work. The concept and framework of directional change in prices is an area of research with much promise!"

-- Dr David Norman, Founder of TTC Institute and author of Professional Electronic Trading (Wileys 2001)

A creative start at a novel and difficult problem for investors large and small.

-- Professor M. A. H. Dempster, University of Cambridge & Cambridge Systems Associates Limited

"This book shows how AI could be a game-changer in finance"

-- Dr Amadeo Alentorn, Head of Research/Fund Manager at Merian Global Investors "This is the first book of its kind to build on the framework of Directional Change. The concept of Directional Change opens a whole new area of research."

-- From the Foreword by Richard Olsen, Founder and CEO of Lykke, co-founder of OANDA and pioneer in high frequency finance and fintech.

"Financial markets technology and the practice of trading are in a state of constant change. A book that details a completely new concept in trading, however, is very rare. Detecting Regime Change in Computational finance is one such book and Professor Tsang and Dr Chen should be applauded for producing this exciting new work. The concept and framework of directional change in prices is an area of research with much promise!"

-- Dr David Norman, Founder of TTC Institute and author of Professional Electronic Trading (Wileys 2001)

A creative start at a novel and difficult problem for investors large and small.

-- Professor M. A. H. Dempster, University of Cambridge & Cambridge Systems Associates Limited

"This book shows how AI could be a game-changer in finance"

-- Dr Amadeo Alentorn, Head of Research/Fund Manager at Merian Global Investors

Foreword xi
Preface xix
List of Figures
xxi
List of Tables
xxv
Chapter 1 Introduction
1(4)
1.1 Overview
1(1)
1.2 Research Objectives
2(2)
1.3 Book Structure
4(1)
Chapter 2 Background and Literature Survey
5(16)
2.1 Regime Change
5(2)
2.1.1 Regime Change Detection Methods
6(1)
2.2 Directional Change
7(6)
2.2.1 The Concept of Directional Change
9(2)
2.2.2 Research Using Directional Change
11(1)
2.2.3 Directional Change Indicators
12(1)
2.2.3.1 Total Price Movement
12(1)
2.2.3.2 Time for Completion of a Trend
12(1)
2.2.3.3 Time---Adjusted Return of DC
13(1)
2.3 Machine Learning Techniques
13(8)
2.3.1 Hidden Markov Model
13(2)
2.3.1.1 Definition of HMM
15(1)
2.3.1.2 Parameters of HMM
15(1)
2.3.1.3 Expectation-Maximization Algorithm
16(1)
2.3.2 Naive Bayes Classifier
17(1)
2.3.2.1 Definition of Naive Bayes Classifier
18(3)
Chapter 3 Regime Change Detection Using Directional Change Indicators
21(18)
3.1 Introduction
22(3)
3.2 Methodology
25(3)
3.2.1 DC Indicator
26(1)
3.2.2 Time Series Indicator
27(1)
3.3 Experiments
28(1)
3.3.1 Data Sets
28(1)
3.3.2 Hidden Markov Model
28(1)
3.4 Empirical Results
28(9)
3.4.1 EUR GBP
29(2)
3.4.2 GBP USD
31(2)
3.4.3 EUR-USD
33(2)
3.4.4 Distribution of the Indicator R
35(1)
3.4.5 Discussion
35(2)
3.5 CONCLUSION
37(2)
Chapter 4 Classification of Normal and Abnormal Regimes in Financial Markets
39(20)
4.1 Introduction
40(1)
4.2 Methodology
41(4)
4.2.1 Summarising Financial Data in DC
41(2)
4.2.2 Detecting Regime Changes through HMM
43(1)
4.2.3 Comparing Market Regimes in an Indicator Space
44(1)
4.3 Empirical Study
45(5)
4.3.1 Data Sets
46(1)
4.3.2 Summarising Data under DC
47(1)
4.3.3 Detecting Regime Changes under HMM
47(1)
4.3.4 Observing Market Regimes in the Normalised Indicator Space
48(2)
4.4 Results and Discussions
50(6)
4.4.1 Market Regimes in the Indicator Space
51(1)
4.4.2 Market Regimes under Different Thresholds
52(2)
4.4.3 Discussion
54(2)
4.5 Conclusions
56(3)
Chapter 5 Tracking Regime Changes Using Directional Change Indicators
59(20)
5.1 Introduction
60(1)
5.2 Methodology
61(4)
5.2.1 Tracking DC Trends
61(1)
5.2.2 Use of a Naive Bayes Classifier
62(3)
5.3 Experiment Setup
65(1)
5.3.1 Data
65(1)
5.3.2 Regime Changes on the Data
66(1)
5.4 Empirical Results
66(10)
5.4.1 Calculating Probability
68(1)
5.4.2 B-Simple for Regime Classification
69(1)
5.4.3 B-Strict for Regime Classification
70(1)
5.4.4 Tracked Regime Changes
71(1)
5.4.4.1 Tracked Regime Changes on DJIA Index
71(2)
5.4.4.2 Tracked Regime Changes on FTSE 100 Index
73(1)
5.4.4.3 Tracked Regime Changes on S&P 500
74(1)
5.4.5 Discussion
74(2)
5.5 Conclusion
76(3)
Chapter 6 Algorithmic Trading Based on Regime Change Tracking
79(14)
6.1 Overview
79(1)
6.2 Methodology
80(4)
6.2.1 Regime Tracking Information
80(1)
6.2.2 Trading Algorithm JC1
81(2)
6.2.3 Trading Algorithm JC2
83(1)
6.2.4 Control Algorithm CT1
83(1)
6.3 Experimental Setup
84(2)
6.3.1 Data
84(1)
6.3.2 Experimental Parameters
84(1)
6.3.3 Money Management
85(1)
6.4 Experiment Results
86(3)
6.4.1 Number of Trades
86(1)
6.4.2 Final Wealth
87(1)
6.4.3 Maximum Drawdown
88(1)
6.5 Discussions
89(1)
6.5.1 The Primary Goals Are Achieved
89(1)
6.5.2 Future Work: Regime Tracking for Better Trading Algorithms
90(1)
6.6 Conclusions
90(3)
Chapter 7 Conclusions
93(8)
7.1 Summary of Work Done
93(4)
7.2 Take-Home Messages
97(1)
7.3 Future Research
98(3)
7.3.1 Research Directions
99(2)
Appendices
101(22)
Appendix A A Formal Definition of Directional Change
101(6)
Appendix B Extended Results of
Chapter 3
107(4)
Appendix C Experiment Summary of
Chapter 4
111(8)
Appendix D Detected Regime Changes in
Chapter 4
119(4)
Bibliography 123(6)
Index 129
Jun Chen received his PhD in computational finance from the Centre for Computational Finance and Economic Agents, University of Essex in 2019.

Edward P K Tsang is an Emeritus Professor at the University of Essex, where he co-founded the Centre for Computational Finance and Economic Agents in 2002. He is a Visiting Professor at University of Hong Kong.