Actions for Performance of Semi-High Frequency Trading Algorithms in Python Based on Dark Pool Movements

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Performance of Semi-High Frequency Trading Algorithms in Python Based on Dark Pool Movements

Author
Fan, Mutian
Published
[University Park, Pennsylvania] : Pennsylvania State University, 2022.
Physical Description
1 electronic document
Additional Creators
Velikov, Mihail and Schreyer Honors College
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Open Access.
Summary
Dark pools are hidden stock markets, which do not show trades before they occur as opposed to a transparent market such as the New York Stock Exchange. Not much research has been done in algorithmic trading based on dark pools; thus, the purpose of this thesis is to see if dark pools are able to predict movements in the market and generate a positive return in the market. This will be done using algorithmic trading done in the Python programming language, through TD Ameritrade's trading platform which allows foreign programs to access market information. The trading program will be set up using a web scraper to gather live dark pool data, as there is a lack of historical information to back test an algorithm. Then, it will log this information to be analyzed later. An analysis through looking at the assets of the algorithm given a starting amount of $25,000 will be done and compared with the price movement on SPY during the period of data collection. Risk-based analysis will be done using a Sharpe ratio with the risk-free rate of the U.S. treasury yield. After the analysis, it was shown that the algorithm performed extremely well despite heavy limitations on how many shares it could buy at any given time. Although some assumptions were made for live market performance, it can be said that dark pools are a valid way to make a good semi-high frequency execution algorithm.
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Dissertation Note
B.S. Pennsylvania State University 2022.
Technical Details
The full text of the dissertation is available as an Adobe Acrobat .pdf file ; Adobe Acrobat Reader required to view the file.
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