Negociación algorítmica de acciones por medio de aprendizaje por refuerzo profundo
ilustraciones, gráficos, tablas
- Autores:
-
Giraldo Escobar, Santiago Alberto
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/80758
- Palabra clave:
- 000 - Ciencias de la computación, información y obras generales::003 - Sistemas
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
Redes Neuronales Generativas Adversarias
Aprendizaje profundo
Aprendizaje por refuerzo profundo
Redes neuronales generativas adversarias
Negociación algorítmica
Aprendizaje de máquina
Negociación de acciones
Deep learning
Deep reinforcement learning
Generative Adversarial Networks
Algorithmic trading
Machine learning
Stock trading
- Rights
- openAccess
- License
- Atribución-CompartirIgual 4.0 Internacional
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Negociación algorítmica de acciones por medio de aprendizaje por refuerzo profundo |
| dc.title.translated.eng.fl_str_mv |
Algorithmic stock trading through deep reinforcement learning |
| title |
Negociación algorítmica de acciones por medio de aprendizaje por refuerzo profundo |
| spellingShingle |
Negociación algorítmica de acciones por medio de aprendizaje por refuerzo profundo 000 - Ciencias de la computación, información y obras generales::003 - Sistemas 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Redes Neuronales Generativas Adversarias Aprendizaje profundo Aprendizaje por refuerzo profundo Redes neuronales generativas adversarias Negociación algorítmica Aprendizaje de máquina Negociación de acciones Deep learning Deep reinforcement learning Generative Adversarial Networks Algorithmic trading Machine learning Stock trading |
| title_short |
Negociación algorítmica de acciones por medio de aprendizaje por refuerzo profundo |
| title_full |
Negociación algorítmica de acciones por medio de aprendizaje por refuerzo profundo |
| title_fullStr |
Negociación algorítmica de acciones por medio de aprendizaje por refuerzo profundo |
| title_full_unstemmed |
Negociación algorítmica de acciones por medio de aprendizaje por refuerzo profundo |
| title_sort |
Negociación algorítmica de acciones por medio de aprendizaje por refuerzo profundo |
| dc.creator.fl_str_mv |
Giraldo Escobar, Santiago Alberto |
| dc.contributor.advisor.none.fl_str_mv |
Villa Garzón, Fernán Alonso Cortés Durán, Lina Marcela |
| dc.contributor.author.none.fl_str_mv |
Giraldo Escobar, Santiago Alberto |
| dc.subject.ddc.spa.fl_str_mv |
000 - Ciencias de la computación, información y obras generales::003 - Sistemas 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería |
| topic |
000 - Ciencias de la computación, información y obras generales::003 - Sistemas 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Redes Neuronales Generativas Adversarias Aprendizaje profundo Aprendizaje por refuerzo profundo Redes neuronales generativas adversarias Negociación algorítmica Aprendizaje de máquina Negociación de acciones Deep learning Deep reinforcement learning Generative Adversarial Networks Algorithmic trading Machine learning Stock trading |
| dc.subject.other.spa.fl_str_mv |
Redes Neuronales Generativas Adversarias |
| dc.subject.proposal.spa.fl_str_mv |
Aprendizaje profundo Aprendizaje por refuerzo profundo Redes neuronales generativas adversarias Negociación algorítmica Aprendizaje de máquina Negociación de acciones |
| dc.subject.proposal.eng.fl_str_mv |
Deep learning Deep reinforcement learning Generative Adversarial Networks Algorithmic trading Machine learning Stock trading |
| description |
ilustraciones, gráficos, tablas |
| publishDate |
2021 |
| dc.date.accessioned.none.fl_str_mv |
2021-12-06T18:15:53Z |
| dc.date.available.none.fl_str_mv |
2021-12-06T18:15:53Z |
| dc.date.issued.none.fl_str_mv |
2021-12-02 |
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Trabajo de grado - Maestría |
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info:eu-repo/semantics/masterThesis |
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Text |
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Universidad Nacional de Colombia |
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Repositorio Institucional Universidad Nacional de Colombia |
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https://repositorio.unal.edu.co/ |
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Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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spa |
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spa |
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A. Charpentier, R. Elie and C. Remlinger. "Reinforcement Learning in Economics and Finance". 2020. arXiv:2003.10014v1. A. Mosavi, Y. Faghan, P. Ghamisi, P. Duan, S. F. Ardabili, E. Salwana and S. S. Band. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics". Mathematics 2020, 8, 1640. DOI: 10.3390/math8101640. A. Ozbayoglu, M. Gudelek, and O. Sezer. "Deep learning for financial applications: A survey". Applied Soft Computing Journal 93 (2020) 106384. Doi: 10.1016/j.asoc.2020.106384. B.M. Henrique, V.A. Sobreiro and H. Kimura. "Literature review: Machine learning techniques applied to financial market prediction". Expert Systems With Applications 124 (2019) 226–251. Doi: 10.1016/j.eswa.2019.01.012. C. Lattemann, P. Loos, j. Gomolka, H.P. Burghof, A. Breuer A, Gomber P, M. Krogmann, J. Nagel, R. Riess, R. Riordan, R.Zajonz (2012) High Frequency Trading. Kosten und Nutzen im Wertpapierhandel und Notwendigkeit der Marktregulierung. WIRTSCHAFTSINFORMATIK. Gabler Verlag. Doi: 10.1007/s11576-012-0311-9. D. Lv, S. Yuan, M. Li and Y. Xiang. “An Empirical Study of Machine Learning Algorithms for Stock Daily Trading Strategy”. Mathematical Problems in Engineering. Volume 2019, Article ID 7816154, 30 pages. Doi: 10.1155/2019/7816154. E. Benhamou, D. Saltiel, S. Ungari, A. Mukhopadhyay and J. Atif. "AAMDRL: Augmented Asset Management with Deep Reinforcement Learning". 2020. arXiv:2010.08497v1. E. Villarraga. “Generación de series de tiempo financieras sintéticas para “data augmentation” usando Redes Neuronales Generativas Adversarias (GAN)”. Trabajo Final. Universidad Nacional de Colombia. 2021. F. Rundo, F. Trenta, A. Luigi di Stallo and S. Battiato. "Machine Learning for Quantitative Finance Applications: A Survey". Applied Sciences. 2019, 9, 5574. Doi: 10.3390/app9245574. G. W. Corder and D. I. Foreman. "Nonparametric statistics: a step-by-step approach". 2nd ed. Wiley. 2014. G.N. Gregoriou. "The Handbook of HIGH FREQUENCY TRADING". Elsevier Inc. 2015. H. Dong, Z. Ding and S. Zhang. "Deep Reinforcement Learning, An introduction". Springer Nature Singapore Pte Ltd. 2020. doi:10.1007/978-981-15-4095-0. H. Tatsat, S. Puri, and B. Lookabaugh. "Machine Learning and Data Science Blueprints for Finance - From Building Trading Strategies to Robo-Advisors Using Python". O’Reilly. 2021. I. Goodfellow, Y. Bengio, and A. Courville. “Deep Learning”. The MIT Press. 2016. J. Brownlee. “Generative Adversarial Networks with Python”. Jason Brownlee. 2019. J. Langr and V. Bok. “GANs in Action”. Manning. 2019. J. Schmidhuber. “Generative Adversarial Networks are Special Cases of Artificial Curiosity (1990) and also Closely Related to Predictability Minimization (1991)”. 2020. ArXiv:1906.04493v3. K. Arulkumaran, M. P. Deisenroth, M. Brundage, and A. A. Bharath. "Deep Reinforcement Learning - A brief survey". IEEE Signal Processing Magazine. November 2017. DOI: 10.1109/MSP.2017.2743240. K. B. Hansen. “The virtue of simplicity: On machine learning models in algorithmic trading”. Big Data & Society. 2020. Doi: 10.1177/2053951720926558. K. Lei, B. Zhang, Y. Li, M. Yang, and Y. Shen. "Time-driven feature-aware jointly deep reinforcement learning for financial signal representation and algorithmic trading". Expert Systems With Applications 140 (2020) 112872. DOI: 10.1016/j.eswa.2019.112872. L. Ryll and S. Seidens. "Evaluating the Performance of Machine Learning Algorithms in Financial Market Forecasting: A Comprehensive Survey". 2019. arXiv:1906.07786v2. M. Karpe. "An overall view of key problems in algorithmic trading and recent progress". 2020. arXiv:2006.05515v1. M. López de Prado, Marcos. "The Future of Empirical Finance". Journal of Portfolio Management, 41(4), 2015. Doi: 10.2139/ssrn.2609734. M. López de Prado. "The 10 Reasons Most Machine Learning Funds Fail". JPM 2018, 44 (6) 120-133 doi: 10.3905/jpm.2018.44.6.120. M. López de Prado. “Advances in Financial Machine Learning”. Wiley. 2018. M. López de Prado. “Tactical Investment Algorithms”. 2019. Available at SSRN: https://ssrn.com/abstract=3459866 or http://dx.doi.org/10.2139/ssrn.3459866. M. Wiese, R. Knobloch, R. Korn, and P. Kretschmer. "Quant GANs: Deep Generation of Financial Time Series". 2019. arXiv:1907.06673v2. P. Brandimarte. "Handbook in Monte Carlo simulation: applications in financial engineering, risk management, and economics". Wiley. 2014. P. Kolm and G. Ritter, “Modern Perspectives on Reinforcement Learning in Finance”. The Journal of Machine Learning in Finance, Vol. 1, No. 1, 2020. Doi:10.2139/ssrn.3449401. P. Treleaven, M. Galas, and V. Lalchand. "Algorithmic Trading Review". Communications of the ACM. november 2013. Vol. 56, no. 11. Doi: 10.1145/2500117. R. A. Brealey, S. C. Myers, and R. C. Merton. "Principles of Corporate Finance". 12 Edition. McGraw-Hill. 2017. R. Fu, J. Chen, S. Zeng, Y. Zhuang and A. Sudjianto. “Time Series Simulation by Conditional Generative Adversarial Net”. 2019. arXiv:1904.11419. R. S. Sutton and A. G. Barto. "Reinforcement Learning: An Introduction" second edition The MIT Press. 2018. R. Tsay. "Analysis of financial time series". 3rd ed. Wiley. 2010. S. Assefa, D. Dervovic, M. Mahfouz, T. Balch, P. Reddy, and M. Veloso. "Generating Synthetic Data in Finance: Opportunities, Challenges and Pitfalls". In NeurIPS'19 Workshop on Robust AI in Financial Services, Vancouver, Canada, December 2019. S. Nosratabadi, A. Mosavi, P. Duan, P. Ghamisi, F. Filip, S. S. Band, U. Reuter, J. Gama and A. H. Gandomi. "Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods". Mathematics 2020, 8, 1799; Doi: 10.3390/math8101799. S. Takahashi, Y. Chen and K. Tanaka-Ishii. "Modeling financial time-series with generative adversarial networks". Physica A 527 (2019) 121261. DOI: 10.1016/j.physa.2019.121261. T. G. Fischer, “Reinforcement learning in financial markets - a survey”. FAU Discussion Papers in Economics, No. 12/2018, Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute for Economics, Nürnberg. 2018. V. Bacoyannis, V. Glukhov, T. Jin, J. Kochems, and D. R. Song. “Idiosyncrasies and challenges of data driven learning in electronic trading”. 2018. arXiv:1811.09549v2. V. François-Lavet, P. Henderson, R. Islam, M. G. Bellemare and J. Pineau, “An Introduction to Deep Reinforcement Learning”, Foundations and Trends in Machine Learning: Vol. 11, No. 3-4. Doi: 10.1561/2200000071. Y. Hilpisch. “Python for Algorithmic Trading - From Idea to Cloud Deployment”. O’Reilly Media, Inc. 2020. Y. Hilpisch. “Artificial Intelligence in Finance - A Python-Based Guide”. O’Reilly Media, Inc. 2021. Y. Sato. "Model-Free Reinforcement Learning for Financial Portfolios: A Brief Survey". 2019. arXiv:1904.04973v2. Z. Kakushadze and J. Serur. “151 Trading Strategies”. Palgrave Macmillan. 2018. |
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Medellín - Minas - Maestría en Ingeniería - Ingeniería de Sistemas |
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Departamento de la Computación y la Decisión |
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Facultad de Minas |
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Medellín, Colombia |
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Atribución-CompartirIgual 4.0 Internacionalhttp://creativecommons.org/licenses/by-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Villa Garzón, Fernán Alonso9c83ea56495b8f17a79c27fd0001bb81Cortés Durán, Lina Marcelac7f4876c3f192af17292dcc391da546aGiraldo Escobar, Santiago Alberto70b952e5db60652deea76cc067b963f72021-12-06T18:15:53Z2021-12-06T18:15:53Z2021-12-02https://repositorio.unal.edu.co/handle/unal/80758Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficos, tablasEste trabajo de grado tiene como finalidad explorar la utilización de series de tiempo financieras sintéticas generadas por un modelo de Redes Neuronales Generativas Adversarias (GAN por sus siglas en inglés) para entrenar un algoritmo de Aprendizaje Profundo Q Por Refuerzo que ejecute acciones de compra y venta para un título del mercado de valores del índice de Standard & Poor’s 500. Para el desarrollo del trabajo se empleó la metodología CRISP DM propuesta por IBM, entendiendo primero el negocio y la teoría necesaria para desarrollar los modelos, para continuar con la exploración y conocimiento de los datos disponibles que concordaran con los objetivos del estudio. En este se desarrolla un procedimiento para la selección de series ficticias y para el entrenamiento de un algoritmo por refuerzo con estos datos. Se utiliza la métrica de Kolmogorov - Smirnov como componente esencial para entrenar las redes GAN. Se explican los resultados de los experimentos, y se evidencia la dificultad para calibrar modelos generativos adversarios y de agentes entrenados por refuerzo. Por último, se presentan las conclusiones derivadas del trabajo y posibles investigaciones futuras. (Texto tomado de la fuente)This degree work aims to explore the use of synthetic financial time series generated by a Generative Adversarial Neural Networks (GAN) model to train a Deep Reinforcement Learning algorithm that executes buy and sell actions for a stock in the Standard & Poor's 500 index. For the implementation of the study, we used the CRISP methodology proposed by IBM, understanding first the business and the theory necessary to develop the models, to continue with the exploration and knowledge of the available data that matched the objectives of the project. In this paper, a procedure for selecting synthetic series and training a reinforcement algorithm with these data is developed. The Kolmogorov-Smirnov metric is used as an essential component to train GANs. The results of the experiments are explained, and the difficulty in calibrating generative adversarial and reinforcement network models is shown. Finally, conclusions derived from the project and possible future research are presented.MaestríaMagíster en Ingeniería – Ingeniería de SistemasÁrea Curricular de Ingeniería de Sistemas e Informática72 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Minas - Maestría en Ingeniería - Ingeniería de SistemasDepartamento de la Computación y la DecisiónFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín000 - Ciencias de la computación, información y obras generales::003 - Sistemas620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingenieríaRedes Neuronales Generativas AdversariasAprendizaje profundoAprendizaje por refuerzo profundoRedes neuronales generativas adversariasNegociación algorítmicaAprendizaje de máquinaNegociación de accionesDeep learningDeep reinforcement learningGenerative Adversarial NetworksAlgorithmic tradingMachine learningStock tradingNegociación algorítmica de acciones por medio de aprendizaje por refuerzo profundoAlgorithmic stock trading through deep reinforcement learningTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMA. Charpentier, R. Elie and C. Remlinger. "Reinforcement Learning in Economics and Finance". 2020. arXiv:2003.10014v1.A. Mosavi, Y. Faghan, P. Ghamisi, P. Duan, S. F. Ardabili, E. Salwana and S. S. Band. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics". Mathematics 2020, 8, 1640. DOI: 10.3390/math8101640.A. Ozbayoglu, M. Gudelek, and O. Sezer. "Deep learning for financial applications: A survey". Applied Soft Computing Journal 93 (2020) 106384. Doi: 10.1016/j.asoc.2020.106384.B.M. Henrique, V.A. Sobreiro and H. Kimura. "Literature review: Machine learning techniques applied to financial market prediction". Expert Systems With Applications 124 (2019) 226–251. Doi: 10.1016/j.eswa.2019.01.012.C. Lattemann, P. Loos, j. Gomolka, H.P. Burghof, A. Breuer A, Gomber P, M. Krogmann, J. Nagel, R. Riess, R. Riordan, R.Zajonz (2012) High Frequency Trading. Kosten und Nutzen im Wertpapierhandel und Notwendigkeit der Marktregulierung. WIRTSCHAFTSINFORMATIK. Gabler Verlag. Doi: 10.1007/s11576-012-0311-9.D. Lv, S. Yuan, M. Li and Y. Xiang. “An Empirical Study of Machine Learning Algorithms for Stock Daily Trading Strategy”. Mathematical Problems in Engineering. Volume 2019, Article ID 7816154, 30 pages. Doi: 10.1155/2019/7816154.E. Benhamou, D. Saltiel, S. Ungari, A. Mukhopadhyay and J. Atif. "AAMDRL: Augmented Asset Management with Deep Reinforcement Learning". 2020. arXiv:2010.08497v1.E. Villarraga. “Generación de series de tiempo financieras sintéticas para “data augmentation” usando Redes Neuronales Generativas Adversarias (GAN)”. Trabajo Final. Universidad Nacional de Colombia. 2021.F. Rundo, F. Trenta, A. Luigi di Stallo and S. Battiato. "Machine Learning for Quantitative Finance Applications: A Survey". Applied Sciences. 2019, 9, 5574. Doi: 10.3390/app9245574.G. W. Corder and D. I. Foreman. "Nonparametric statistics: a step-by-step approach". 2nd ed. Wiley. 2014.G.N. Gregoriou. "The Handbook of HIGH FREQUENCY TRADING". Elsevier Inc. 2015.H. Dong, Z. Ding and S. Zhang. "Deep Reinforcement Learning, An introduction". Springer Nature Singapore Pte Ltd. 2020. doi:10.1007/978-981-15-4095-0.H. Tatsat, S. Puri, and B. Lookabaugh. "Machine Learning and Data Science Blueprints for Finance - From Building Trading Strategies to Robo-Advisors Using Python". O’Reilly. 2021.I. Goodfellow, Y. Bengio, and A. Courville. “Deep Learning”. The MIT Press. 2016.J. Brownlee. “Generative Adversarial Networks with Python”. Jason Brownlee. 2019.J. Langr and V. Bok. “GANs in Action”. Manning. 2019.J. Schmidhuber. “Generative Adversarial Networks are Special Cases of Artificial Curiosity (1990) and also Closely Related to Predictability Minimization (1991)”. 2020. ArXiv:1906.04493v3.K. Arulkumaran, M. P. Deisenroth, M. Brundage, and A. A. Bharath. "Deep Reinforcement Learning - A brief survey". IEEE Signal Processing Magazine. November 2017. DOI: 10.1109/MSP.2017.2743240.K. B. Hansen. “The virtue of simplicity: On machine learning models in algorithmic trading”. Big Data & Society. 2020. Doi: 10.1177/2053951720926558.K. Lei, B. Zhang, Y. Li, M. Yang, and Y. Shen. "Time-driven feature-aware jointly deep reinforcement learning for financial signal representation and algorithmic trading". Expert Systems With Applications 140 (2020) 112872. DOI: 10.1016/j.eswa.2019.112872.L. Ryll and S. Seidens. "Evaluating the Performance of Machine Learning Algorithms in Financial Market Forecasting: A Comprehensive Survey". 2019. arXiv:1906.07786v2.M. Karpe. "An overall view of key problems in algorithmic trading and recent progress". 2020. arXiv:2006.05515v1.M. López de Prado, Marcos. "The Future of Empirical Finance". Journal of Portfolio Management, 41(4), 2015. Doi: 10.2139/ssrn.2609734.M. López de Prado. "The 10 Reasons Most Machine Learning Funds Fail". JPM 2018, 44 (6) 120-133 doi: 10.3905/jpm.2018.44.6.120.M. López de Prado. “Advances in Financial Machine Learning”. Wiley. 2018.M. López de Prado. “Tactical Investment Algorithms”. 2019. Available at SSRN: https://ssrn.com/abstract=3459866 or http://dx.doi.org/10.2139/ssrn.3459866.M. Wiese, R. Knobloch, R. Korn, and P. Kretschmer. "Quant GANs: Deep Generation of Financial Time Series". 2019. arXiv:1907.06673v2.P. Brandimarte. "Handbook in Monte Carlo simulation: applications in financial engineering, risk management, and economics". Wiley. 2014.P. Kolm and G. Ritter, “Modern Perspectives on Reinforcement Learning in Finance”. The Journal of Machine Learning in Finance, Vol. 1, No. 1, 2020. Doi:10.2139/ssrn.3449401.P. Treleaven, M. Galas, and V. Lalchand. "Algorithmic Trading Review". Communications of the ACM. november 2013. Vol. 56, no. 11. Doi: 10.1145/2500117.R. A. Brealey, S. C. Myers, and R. C. Merton. "Principles of Corporate Finance". 12 Edition. McGraw-Hill. 2017.R. Fu, J. Chen, S. Zeng, Y. Zhuang and A. Sudjianto. “Time Series Simulation by Conditional Generative Adversarial Net”. 2019. arXiv:1904.11419.R. S. Sutton and A. G. Barto. "Reinforcement Learning: An Introduction" second edition The MIT Press. 2018.R. Tsay. "Analysis of financial time series". 3rd ed. Wiley. 2010.S. Assefa, D. Dervovic, M. Mahfouz, T. Balch, P. Reddy, and M. Veloso. "Generating Synthetic Data in Finance: Opportunities, Challenges and Pitfalls". In NeurIPS'19 Workshop on Robust AI in Financial Services, Vancouver, Canada, December 2019.S. Nosratabadi, A. Mosavi, P. Duan, P. Ghamisi, F. Filip, S. S. Band, U. Reuter, J. Gama and A. H. Gandomi. "Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods". Mathematics 2020, 8, 1799; Doi: 10.3390/math8101799.S. Takahashi, Y. Chen and K. Tanaka-Ishii. "Modeling financial time-series with generative adversarial networks". Physica A 527 (2019) 121261. DOI: 10.1016/j.physa.2019.121261.T. G. Fischer, “Reinforcement learning in financial markets - a survey”. FAU Discussion Papers in Economics, No. 12/2018, Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute for Economics, Nürnberg. 2018.V. Bacoyannis, V. Glukhov, T. Jin, J. Kochems, and D. R. Song. “Idiosyncrasies and challenges of data driven learning in electronic trading”. 2018. arXiv:1811.09549v2.V. François-Lavet, P. Henderson, R. Islam, M. G. Bellemare and J. Pineau, “An Introduction to Deep Reinforcement Learning”, Foundations and Trends in Machine Learning: Vol. 11, No. 3-4. Doi: 10.1561/2200000071.Y. Hilpisch. “Python for Algorithmic Trading - From Idea to Cloud Deployment”. O’Reilly Media, Inc. 2020.Y. Hilpisch. “Artificial Intelligence in Finance - A Python-Based Guide”. O’Reilly Media, Inc. 2021.Y. Sato. "Model-Free Reinforcement Learning for Financial Portfolios: A Brief Survey". 2019. arXiv:1904.04973v2.Z. Kakushadze and J. Serur. “151 Trading Strategies”. 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