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Title: :  A Review of Deep Reinforcement Learning Techniques in Algorithmic and Quantitative Trading
PaperId: :  25394
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 10 Issue 6 2024
DUI:    16.0415/IJARIIE-25394
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Taaran JainPoornima Institute of Engineering and Technology
Vikas KumarPoornima Institute of Engineering and Technology

Abstract

Artificial Intelligence, Machine Learning, Algorithmic Trading and Quantitative Trading
Deep Reinforcement Learning, Algorithmic Trading, Quantitative Trading, Portfolio Optimization, Financial Markets, Machine Learning, Sim-to-Real Transfer, Market Volatility
Deep gaining knowledge of Deep Reinforcement Learning (DRL) has turned out to be an innovative generation within the algorithmic and quantitative trading industries with huge upgrades over conventional device learning. This review explores the latest traits inside the Deep Reinforcement Learning (DRL) framework and its packages in financial markets, focusing on portfolio optimization, throughput, and plenty of business ideas. By studying marketplace power techniques such as AlphaOptimizerNet, QTNet, and the open-source FinRL framework, we compare how DRL-primarily based systems solve key problems of market volatility trade, transaction fees, and the stability between exploration and exploitation. In addition, this paper discusses the combination of simulation-to-reality translation in robotics and mathematical physics, in addition to the usage of deep gaining knowledge of methods along with Double Deep Q-Networks (DDQN) and Reinforced Deep Markov Models (RDMM) to enhance decision making. While Deep Reinforcement Learning (DRL) has demonstrated advanced overall performance in actual-world markets and backtesting, this evaluation also highlights the need for additional use in enterprise environments to be considered robust and capable. Through this evaluation, we take advantage of the perception of the future capacity and limitations of DRL inside the automation industry and spotlight the want for new extensions and real-global testing.

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IJARIIE Taaran Jain, and Vikas Kumar. "A Review of Deep Reinforcement Learning Techniques in Algorithmic and Quantitative Trading" International Journal Of Advance Research And Innovative Ideas In Education Volume 10 Issue 6 2024 Page 1074-1087
MLA Taaran Jain, and Vikas Kumar. "A Review of Deep Reinforcement Learning Techniques in Algorithmic and Quantitative Trading." International Journal Of Advance Research And Innovative Ideas In Education 10.6(2024) : 1074-1087.
APA Taaran Jain, & Vikas Kumar. (2024). A Review of Deep Reinforcement Learning Techniques in Algorithmic and Quantitative Trading. International Journal Of Advance Research And Innovative Ideas In Education, 10(6), 1074-1087.
Chicago Taaran Jain, and Vikas Kumar. "A Review of Deep Reinforcement Learning Techniques in Algorithmic and Quantitative Trading." International Journal Of Advance Research And Innovative Ideas In Education 10, no. 6 (2024) : 1074-1087.
Oxford Taaran Jain, and Vikas Kumar. 'A Review of Deep Reinforcement Learning Techniques in Algorithmic and Quantitative Trading', International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 6, 2024, p. 1074-1087. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/A_Review_of_Deep_Reinforcement_Learning_Techniques_in_Algorithmic_and_Quantitative_Trading_ijariie25394.pdf (Accessed : 02 December 2024).
Harvard Taaran Jain, and Vikas Kumar. (2024) 'A Review of Deep Reinforcement Learning Techniques in Algorithmic and Quantitative Trading', International Journal Of Advance Research And Innovative Ideas In Education, 10(6), pp. 1074-1087IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/A_Review_of_Deep_Reinforcement_Learning_Techniques_in_Algorithmic_and_Quantitative_Trading_ijariie25394.pdf (Accessed : 02 December 2024)
IEEE Taaran Jain, and Vikas Kumar, "A Review of Deep Reinforcement Learning Techniques in Algorithmic and Quantitative Trading," International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 6, pp. 1074-1087, Nov-Dec 2024. [Online]. Available: https://ijariie.com/AdminUploadPdf/A_Review_of_Deep_Reinforcement_Learning_Techniques_in_Algorithmic_and_Quantitative_Trading_ijariie25394.pdf [Accessed : 02 December 2024].
Turabian Taaran Jain, and Vikas Kumar. "A Review of Deep Reinforcement Learning Techniques in Algorithmic and Quantitative Trading." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 10 number 6 (02 December 2024).
Vancouver Taaran Jain, and Vikas Kumar. A Review of Deep Reinforcement Learning Techniques in Algorithmic and Quantitative Trading. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2024 [Cited : 02 December 2024]; 10(6) : 1074-1087. Available from: https://ijariie.com/AdminUploadPdf/A_Review_of_Deep_Reinforcement_Learning_Techniques_in_Algorithmic_and_Quantitative_Trading_ijariie25394.pdf
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