Deep Reinforcement Learning Approach for Trading Automation in the Stock Market
Open accessAbstract
Deep Reinforcement Learning (DRL) algorithms can scale to previously intractable problems. The automation of profit generation in the stock market is possible using DRL, by combining the financial assets price "prediction" step and the "allocation" step of the portfolio in one unified process to produce fully autonomous systems capable of interacting with their environment to make optimal decisions through trial and error. This work represents a DRL model to generate profitable trades in the stock market, effectively overcoming the limitations of supervised learning approaches. We formulate the trading problem as a Partially Observed Markov Decision Process (POMDP) model, considering the constraints imposed by the stock market, such as liquidity and transaction costs. We then solve the formulated POMDP problem using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm reporting a 2.68 Sharpe Ratio on unseen data set (test data). From the point of view of stock market forecasting and the intelligent decision-making mechanism, this paper demonstrates the superiority of DRL in financial markets over other types of machine learning and proves its credibility and advantages in strategic decision-making.
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Researchers have focused on developing automated trading systems that can adapt to market changes and make predictions from a more generalized perspective.
“To address this, researchers have focused on developing automated trading systems that can adapt to market changes and make predictions from a more generalized perspective [1].”
From A Deep Reinforcement Learning Approach to Automated Stock Trading, using xLSTM Networks (2025) · cites this paper as [1]
Cites
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“Most works that studied the RL’s applications in financial markets and particularly in trading stocks considered discrete action spaces [9], [10], [11], [12], i.”
BackgroundMarkov decision processes: a tool for sequential decision making under uncertainty
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“5) Moving Average Convergence/Divergence (MACD) ∈ RN : Is one of the most used momentum indicators to identify the relationship between two moving averages of the stock price and it helps the agent to understand whether the bullish or bearish movement in the price is strengthening or weakening [29].”
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A Deep Reinforcement Learning Approach to Automated Stock Trading, using xLSTM Networks
Sarlakifar, F., Mohammadzadeh Asl, M., Rezvani Khaledi, S., & Salimi-Badr, A. (2025) · RePEc: Research Papers in Economics · doi:10.48308/jicse.2025.239844.1077 · Cited by 1 · see citing works
“To address this, researchers have focused on developing automated trading systems that can adapt to market changes and make predictions from a more generalized perspective [1].”
A Deep Reinforcement Learning Approach to Automated Stock Trading, using xLSTM Networks
Sarlakifar, F., Asl, M. M., Khaledi, S. R., & Salimi-Badr, A. (2025) · arXiv · doi:10.48550/arxiv.2503.09655
“To address this, researchers have focused on developing automated trading systems that can adapt to market changes and make predictions from a more generalized perspective [1].”
- Authors
- Kabbani, T., & Duman, E.
- Affiliations
- Department of Industrial Engineering, Özyeðin University, Istanbul, Turkey
- Venue
- IEEE Access
- Published
- 2022
- Volume
- 10
- Pages
- 93564-93574
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4294691400
- Cited by
- 88
- References
- 8
- Citations by year
- 2026: 18 · 2025: 31 · 2024: 25 · 2023: 12 · 2022: 2
- FWCI
- 10.72
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- IEEE Access (publishedVersion, cc-by-nc-nd)
- SSRN Electronic Journal (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
- Social Sciences › Business, Management and Accounting › Accounting › Financial Analysis and Corporate Governance
- Keywords
- deep reinforcement learning, algorithmic trading, stock market, asset allocation, stock price prediction, partially observable Markov decision processes, TD3, Sharpe ratio, transaction costs, liquidity constraints, financial decision-making
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