Examining the Feasibility and Effectiveness of Utilizing Dark Pool Transactions in Retail Algorithmic Trading
Open Access
- Author:
- Qu, James
- Area of Honors:
- Finance
- Degree:
- Bachelor of Science
- Document Type:
- Thesis
- Thesis Supervisors:
- Mihail Velikov, Thesis Supervisor
Brian Spangler Davis, Thesis Honors Advisor - Keywords:
- Dark Pool
Finance
Algorithmic Trading
Trading
Performance
S&P 500
Black Box
Alpha
TD
TD Ameritrade
API - Abstract:
- This paper analyzes if retail investors who are individual non-professional investors buying and selling securities can utilize dark pool information to generate positive returns and outperform the overall market. Specifically, this paper utilizes a very specific personal algorithmic trading strategy that has been created which utilizes dark pool trading information to outperform the S&P 500. Dark Pools are private exchanges where institutional investors can make large trades to not affect prices on public exchanges, ensuring price security when filling a large block of securities being exchanged. The Literature Review section will go future in- depth on what these are and how they work. The results of this study were extremely promising. The algorithmic trading program created to test this thesis ran on a paper portfolio. The Results and Analysis chapter details the specifics of how the paper portfolio was set up. The portfolio generated ~1,163.84% in returns in 60 active days of trading over the course of 6 months. The algorithm bought the share count of each trade to roughly $8,000.00 in value rounded down to the nearest share, generating excess returns of $92,401.44. This paper is not trying to prove the effectiveness of any specific algorithm or algorithmic strategy to beat the market, but rather to prove that dark pool information can be effectively used by an algorithm to beat the market. Due to the nature of dark pools being secretive along with the use of an algorithm, we can only make informed hypotheses on the exact market functions behind the outperformance; however, with our high amount of data points and strong results, we are able to conclude that retail investors are able to leverage dark pool information and the trades of institutional investors in these private exchanges to outperform the market if the investor is able to effectively utilize the information. Details of the analysis are explored in Chapter 4 Results and Analysis section of this paper.