# Deep Reinforcement Learning Approach for Trading Automation in The Stock Market

arXiv: https://arxiv.org/abs/2208.07165 · HTML: https://mlsanity.com/paper/2208.07165/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 43 references that are in the map.
- First posted on arXiv 5 Jul 2022.

## Builds on (most cited first, 10 of 10)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Deep Reinforcement Learning that Matters](https://mlsanity.com/paper/1709.06560/) (2017)
- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem](https://mlsanity.com/paper/1706.10059/) (2017)
- [Practical Deep Reinforcement Learning Approach for Stock Trading](https://mlsanity.com/paper/1811.07522/) (2018)
- [Mid-price Prediction Based on Machine Learning Methods with Technical and Quantitative Indicators](https://mlsanity.com/paper/1907.09452/) (2019)
- [Capturing Financial markets to apply Deep Reinforcement Learning](https://mlsanity.com/paper/1907.04373/) (2019)

## Built on by (most cited first, 9 of 9)

- [FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making](https://mlsanity.com/paper/2407.06567/) (2024)
- [INVESTORBENCH: A Benchmark for Financial Decision-Making Tasks with LLM-based Agent](https://mlsanity.com/paper/2412.18174/) (2024)
- [SleeperNets: Universal Backdoor Poisoning Attacks Against Reinforcement Learning Agents](https://mlsanity.com/paper/2405.20539/) (2024)
- [Hierarchical Reinforced Trader (HRT): A Bi-Level Approach for Optimizing Stock Selection and Execution](https://mlsanity.com/paper/2410.14927/) (2024)
- [A Deep Reinforcement Learning Approach to Automated Stock Trading, using xLSTM Networks](https://mlsanity.com/paper/2503.09655/) (2025)
- [An Ensemble Method of Deep Reinforcement Learning for Automated Cryptocurrency Trading](https://mlsanity.com/paper/2309.00626/) (2023)
- [Provably Efficient Partially Observable Risk-Sensitive Reinforcement Learning with Hindsight Observation](https://mlsanity.com/paper/2402.18149/) (2024)
- [Learning Stock Trading Policies via Barycenter-Based Adversarial Inverse Reinforcement Learning](https://mlsanity.com/paper/2608.15770/) (2026)
- [Self-Supervised Auxiliary Task Discovery for Stable Reinforcement Learning in Stock Trading](https://mlsanity.com/paper/2608.15841/) (2026)

## Abstract (arXiv)

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 of strategic decision-making.