Reinforcement Learning for Algorithmic Trading in Financial Markets
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| Publicado en: | ProQuest Dissertations and Theses (2025) |
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| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 66569 |2 nlm | ||
| 100 | 1 | |a Gityforoze, Soheil | |
| 245 | 1 | |a Reinforcement Learning for Algorithmic Trading in Financial Markets | |
| 260 | |b ProQuest Dissertations & Theses |c 2025 | ||
| 513 | |a Dissertation/Thesis | ||
| 520 | 3 | |a In multi-period algorithmic trading, determining algorithms that are ideal for riskaverse strategies is a challenging task. This study explored the application of model-free reinforcement learning (RL) in algorithmic trading and analyzed the relationship between risk-averse strategies and implementation of RL algorithms including Q-learning, Greedy-GQ, and SARSA. The data for this quantitative research included one year of the E-mini-NASDAQ-100 futures (2023-2024). Over 7,500 simulation results substantiated a proof of concept that Q-learning can successfully generate risk-adjusted trading signals in the highly liquid technology-focused futures market. With an optimized configuration of hyperparameters including look-back period, basis and reward functions, Q-learning delivered nearly twice the returns of the competing RL algorithms. Beyond absolute returns, Q-learning exhibited lower volatility across key risk metrics and outperformed the NASDAQ-100 benchmark by approximately 75 percentage points. These findings suggest reinforcement learning as a promising artificial intelligence and machine learning framework for alpha generating strategies in systematic trading. | |
| 653 | |a Artificial intelligence | ||
| 653 | |a Computer science | ||
| 653 | |a Finance | ||
| 773 | 0 | |t ProQuest Dissertations and Theses |g (2025) | |
| 786 | 0 | |d ProQuest |t ProQuest Dissertations & Theses Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3233860065/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3233860065/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |