Detecting regime change in computational finance: data science, machine learning and algorithmic trading
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| Main Author: | |
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
Milton
CRC Press LLC
2020
|
| Online Access: | DE-M347 DE-739 Volltext |
| Item Description: | Description based on publisher supplied metadata and other sources |
| Physical Description: | 1 Online-Ressource (164 Seiten) |
| ISBN: | 9781003087595 9781000220360 |
| DOI: | 10.1201/9781003087595 |
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| 245 | 1 | 0 | |a Detecting regime change in computational finance |b data science, machine learning and algorithmic trading |
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| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
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| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Chen, Jun |t Detecting Regime Change in Computational Finance |d Milton : CRC Press LLC,c2020 |z 9780367536282 |
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| contents | 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 |
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| doi_str_mv | 10.1201/9781003087595 |
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| spelling | Chen, Jun Verfasser aut Detecting regime change in computational finance data science, machine learning and algorithmic trading Milton CRC Press LLC 2020 ©2021 1 Online-Ressource (164 Seiten) txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources 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 Tsang, Edward P. K. Sonstige (DE-588)134150465 oth Erscheint auch als Druck-Ausgabe Chen, Jun Detecting Regime Change in Computational Finance Milton : CRC Press LLC,c2020 9780367536282 https://doi.org/10.1201/9781003087595 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Chen, Jun 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 |
| title | Detecting regime change in computational finance data science, machine learning and algorithmic trading |
| title_auth | Detecting regime change in computational finance data science, machine learning and algorithmic trading |
| title_exact_search | Detecting regime change in computational finance data science, machine learning and algorithmic trading |
| title_exact_search_txtP | Detecting regime change in computational finance data science, machine learning and algorithmic trading |
| title_full | Detecting regime change in computational finance data science, machine learning and algorithmic trading |
| title_fullStr | Detecting regime change in computational finance data science, machine learning and algorithmic trading |
| title_full_unstemmed | Detecting regime change in computational finance data science, machine learning and algorithmic trading |
| title_short | Detecting regime change in computational finance |
| title_sort | detecting regime change in computational finance data science machine learning and algorithmic trading |
| title_sub | data science, machine learning and algorithmic trading |
| url | https://doi.org/10.1201/9781003087595 |
| work_keys_str_mv | AT chenjun detectingregimechangeincomputationalfinancedatasciencemachinelearningandalgorithmictrading AT tsangedwardpk detectingregimechangeincomputationalfinancedatasciencemachinelearningandalgorithmictrading |