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Identifying Oversold Levels and Developing Low-Frequency Trading Algorithms for the S&P 500: An Analysis Using Stochastic Oscillator, Williams %R, and Trading Volume
Version 1
: Received: 4 September 2024 / Approved: 4 September 2024 / Online: 4 September 2024 (16:27:56 CEST)
How to cite:
Paik, C. K.; Choi, J.; Vaquero, I. U. Identifying Oversold Levels and Developing Low-Frequency Trading Algorithms for the S&P 500: An Analysis Using Stochastic Oscillator, Williams %R, and Trading Volume. Preprints2024, 2024090383. https://doi.org/10.20944/preprints202409.0383.v1
Paik, C. K.; Choi, J.; Vaquero, I. U. Identifying Oversold Levels and Developing Low-Frequency Trading Algorithms for the S&P 500: An Analysis Using Stochastic Oscillator, Williams %R, and Trading Volume. Preprints 2024, 2024090383. https://doi.org/10.20944/preprints202409.0383.v1
Paik, C. K.; Choi, J.; Vaquero, I. U. Identifying Oversold Levels and Developing Low-Frequency Trading Algorithms for the S&P 500: An Analysis Using Stochastic Oscillator, Williams %R, and Trading Volume. Preprints2024, 2024090383. https://doi.org/10.20944/preprints202409.0383.v1
APA Style
Paik, C. K., Choi, J., & Vaquero, I. U. (2024). Identifying Oversold Levels and Developing Low-Frequency Trading Algorithms for the S&P 500: An Analysis Using Stochastic Oscillator, Williams %R, and Trading Volume. Preprints. https://doi.org/10.20944/preprints202409.0383.v1
Chicago/Turabian Style
Paik, C. K., Jinhee Choi and Ivan Ureta Vaquero. 2024 "Identifying Oversold Levels and Developing Low-Frequency Trading Algorithms for the S&P 500: An Analysis Using Stochastic Oscillator, Williams %R, and Trading Volume" Preprints. https://doi.org/10.20944/preprints202409.0383.v1
Abstract
Recent algorithmic-based research in the stock market has predominantly focused on ultra-high-frequency trading and index estimation. We developed an improved algorithm to address the need for a low-frequency, real-world trading model to build on the existing high hit-ratio and low maximum drawdown. We utilized established price indicators, Stochastic and Williams %R, and introduced a volume factor to increase the model's safety to enhance the model's performance. This improved algorithm demonstrated superior returns while maintaining high hit-ratio and low maximum drawdown. Specifically, we leveraged the existing algorithm to trade using 2X and 3X signals, incorporating volume data, the 52-week average, standard deviation, and other factors. The data set comprised SPY ETF price and volume from 2010 to 2023, covering over 13 years. Our enhanced algorithmic trading model outperformed both the benchmark and previous results, achieving a hit rate of over 90%, a maximum drawdown of less than 1%, an average of 1.5 trades per year, a total return of +519.3%, and an annualized return (AnnR) of +15.1%. The analysis demonstrates that the model's simplicity, ease of application, and comprehensibility can assist investors in making informed investment decisions albeit the past performance does not guarantee future returns.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
