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dc.contributor.advisorTeitelman, Lawrence
dc.contributor.authorLeitner, Talia
dc.identifier.citationLeitner, T. (2022, May 19). Algorithmic Trading. Undergraduate honors thesis, Yeshiva University.en_US
dc.descriptionUndergraduate honors thesis / Open Accessen_US
dc.description.abstractThis paper gives an overview of algorithmic trading. It is intended to inform interested traders of the value of incorporating algorithms into their trading strategies. They must also be warned of the dangers of implementing faulty algorithms. This paper highlights the usefulness of categorizing algorithms. An overview of the most popular algorithms is also given. Methods for comparing the performance of different algorithms in trading specific stocks is discussed. The metrics used to evaluate their performance is also mentioned. Challenges within the field of algorithmic trading are important for traders to consider when choosing which algorithms to implement in specific situations. Finally, the integration of machine learning into the field of algorithmic trading is discussed. This revelation has significantly broadened the scope of algorithmic trading. Machine learning now serves as an essential tool for traders to compete in the fast-paced markets of modern day.en_US
dc.description.sponsorshipThe S. Daniel Abraham Honors Programen_US
dc.publisherYeshiva Universityen_US
dc.relation.ispartofseriesS. Daniel Abraham Honors Student Theses;May 19, 2022
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.subjectalgorithmic tradingen_US
dc.subjectmachine learningen_US
dc.titleAlgorithmic Tradingen_US

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Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States