dc.contributor.advisor | Teitelman, Lawrence | |
dc.contributor.author | Leitner, Talia | |
dc.date.accessioned | 2022-06-09T19:49:10Z | |
dc.date.available | 2022-06-09T19:49:10Z | |
dc.date.issued | 2022-05-19 | |
dc.identifier.citation | Leitner, T. (2022, May 19). Algorithmic Trading. Undergraduate honors thesis, Yeshiva University. | en_US |
dc.identifier.uri | https://hdl.handle.net/20.500.12202/8267 | |
dc.description | Undergraduate honors thesis / Open Access | en_US |
dc.description.abstract | This 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.sponsorship | The S. Daniel Abraham Honors Program | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | Yeshiva University | en_US |
dc.relation.ispartofseries | S. Daniel Abraham Honors Student Theses;May 19, 2022 | |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.subject | algorithmic trading | en_US |
dc.subject | machine learning | en_US |
dc.title | Algorithmic Trading | en_US |
dc.type | Thesis | en_US |