Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.12202/8267
Title: | Algorithmic Trading |
Authors: | Teitelman, Lawrence Leitner, Talia |
Keywords: | algorithmic trading machine learning |
Issue Date: | 19-May-2022 |
Publisher: | Yeshiva University |
Citation: | Leitner, T. (2022, May 19). Algorithmic Trading. Undergraduate honors thesis, Yeshiva University. |
Series/Report no.: | S. Daniel Abraham Honors Student Theses;May 19, 2022 |
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. |
Description: | Undergraduate honors thesis / Open Access |
URI: | https://hdl.handle.net/20.500.12202/8267 |
Appears in Collections: | S. Daniel Abraham Honors Student Theses |
Files in This Item:
File | Description | Size | Format | |
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Talia Leitner HonorsThesis OA May19, 2022.pdf | 175.08 kB | Adobe PDF | View/Open |
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