A reproducible approach to equity backtesting

dc.contributor.advisorGebbie, Timothy
dc.contributor.authorArbi, Riaz
dc.date.accessioned2020-02-18T10:44:14Z
dc.date.available2020-02-18T10:44:14Z
dc.date.issued2019
dc.date.updated2020-02-18T10:40:39Z
dc.description.abstractResearch findings relating to anomalous equity returns should ideally be repeatable by others. Usually, only a small subset of the decisions made in a particular backtest workflow are released, which limits reproducability. Data collection and cleaning, parameter setting, algorithm development and report generation are often done with manual point-and-click tools which do not log user actions. This problem is compounded by the fact that the trial-and-error approach of researchers increases the probability of backtest overfitting. Borrowing practices from the reproducible research community, we introduce a set of scripts that completely automate a portfolio-based, event-driven backtest. Based on free, open source tools, these scripts can completely capture the decisions made by a researcher, resulting in a distributable code package that allows easy reproduction of results.
dc.identifier.apacitationArbi, R. (2019). <i>A reproducible approach to equity backtesting</i>. (). ,Faculty of Science ,Department of Statistical Sciences. Retrieved from http://hdl.handle.net/11427/31158en_ZA
dc.identifier.chicagocitationArbi, Riaz. <i>"A reproducible approach to equity backtesting."</i> ., ,Faculty of Science ,Department of Statistical Sciences, 2019. http://hdl.handle.net/11427/31158en_ZA
dc.identifier.citationArbi, R. 2019. A reproducible approach to equity backtesting.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Arbi, Riaz AB - Research findings relating to anomalous equity returns should ideally be repeatable by others. Usually, only a small subset of the decisions made in a particular backtest workflow are released, which limits reproducability. Data collection and cleaning, parameter setting, algorithm development and report generation are often done with manual point-and-click tools which do not log user actions. This problem is compounded by the fact that the trial-and-error approach of researchers increases the probability of backtest overfitting. Borrowing practices from the reproducible research community, we introduce a set of scripts that completely automate a portfolio-based, event-driven backtest. Based on free, open source tools, these scripts can completely capture the decisions made by a researcher, resulting in a distributable code package that allows easy reproduction of results. DA - 2019 DB - OpenUCT DP - University of Cape Town KW - Equity Backtesting KW - Reproducible Research KW - Event-based Backtesting KW - R KW - RStudio LK - https://open.uct.ac.za PY - 2019 T1 - A reproducible approach to equity backtesting TI - A reproducible approach to equity backtesting UR - http://hdl.handle.net/11427/31158 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/31158
dc.identifier.vancouvercitationArbi R. A reproducible approach to equity backtesting. []. ,Faculty of Science ,Department of Statistical Sciences, 2019 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/31158en_ZA
dc.language.rfc3066eng
dc.publisher.departmentDepartment of Statistical Sciences
dc.publisher.facultyFaculty of Science
dc.subjectEquity Backtesting
dc.subjectReproducible Research
dc.subjectEvent-based Backtesting
dc.subjectR
dc.subjectRStudio
dc.titleA reproducible approach to equity backtesting
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMSc
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