mlsanityData to 14 Oct 2026

Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy

arXiv 2511.12120 ↗First posted 15 Nov 2025q-fin.TR q-fin.CP q-fin.PMcited by 15

On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance

Abstract (arXiv)

Stock trading strategies play a critical role in investment. However, it is challenging to design a profitable strategy in a complex and dynamic stock market. In this paper, we propose an ensemble strategy that employs deep reinforcement schemes to learn a stock trading strategy by maximizing investment return. We train a deep reinforcement learning agent and obtain an ensemble trading strategy using three actor-critic based algorithms: Proximal Policy Optimization (PPO), Advantage Actor Critic (A2C), and Deep Deterministic Policy Gradient (DDPG). The ensemble strategy inherits and integrates the best features of the three algorithms, thereby robustly adjusting to different market situations. In order to avoid the large memory consumption in training networks with continuous action space, we employ a load-on-demand technique for processing very large data. We test our algorithms on the 30 Dow Jones stocks that have adequate liquidity. The performance of the trading agent with different reinforcement learning algorithms is evaluated and compared with both the Dow Jones Industrial Average index and the traditional min-variance portfolio allocation strategy. The proposed deep ensemble strategy is shown to outperform the three individual algorithms and two baselines in terms of the risk-adjusted return measured by the Sharpe ratio. This work is fully open-sourced at hrefhttps://github.com/AI4Finance-Foundation/Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020GitHub.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods. Builds on 13 of its 47 references that are in the map. First posted on arXiv 15 Nov 2025.

Builds on

13 of 47 references.

References · 13

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015Continuous control with deep reinforcement learningcited by 3,407
  3. 2015Trust Region Policy Optimizationcited by 2,580
  4. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  5. 2016OpenAI Gymcited by 1,736
  6. 2018Large-Scale Study of Curiosity-Driven Learningcited by 280
  7. 2018Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendationcited by 101
  8. 2020An empirical investigation of the challenges of real-world reinforcement learningcited by 77
  9. 2019Deep Reinforcement Learning for Tradingcited by 73
  10. 2018Practical Deep Reinforcement Learning Approach for Stock Tradingcited by 49
  11. 2016Cryptocurrency Portfolio Management with Deep Reinforcement Learningcited by 32
  12. 2019Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysiscited by 15
1 more
  1. 2019DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial Newscited by 11

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 15.

15 papers.

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  1. 2026Agentic Trading: When LLM Agents Meet Financial Marketscited by 7
  2. 2026Deep Reinforcement Learning for Optimal Portfolio Allocation: A Comparative Study with Mean-Variance Optimizationcited by 2
  3. 2026Learning Markov Decision Processes under Fully Bandit Feedbackcited by 1
  4. 2026FinRL-X: An AI-Native Modular Infrastructure for Quantitative Tradingcited by 0
  5. 2026Decomposable Reward Modeling and Realistic Environment Design for Reinforcement Learning-Based Forex Tradingcited by 0
  6. 2026When Missing Becomes Structure: Intent-Preserving Policy Completion from Financial KOL Discoursecited by 0
  7. 2026TT-DAC-PS: Twin-Target Deterministic Actor-Critic with Policy Smoothing for Optimal Trade Executioncited by 0
  8. 2026A Unified Multi-Modal Framework for Intelligent Financial Systems: Integrating Reinforcement Learning, High-Frequency Trading, and Game-Theoretic Approaches with Cross-Modal Sentiment Analysiscited by 0
  9. 2026CLaC@FinMMEval 2026 Task 3: Sentiment-Augmented Deep Reinforcement Learning for Active Trading -- An Alpha-Reward Approachcited by 0
  10. 2026Learning Stock Trading Policies via Barycenter-Based Adversarial Inverse Reinforcement Learningcited by 0
  11. 2026Self-Supervised Auxiliary Task Discovery for Stable Reinforcement Learning in Stock Tradingcited by 0
  12. 2026Titans-QFWP: A Regime-Aware Hybrid Quantum Fast Weight Programmer for Portfolio Optimizationcited by 0
3 more
  1. 2026ViperQ: Order Flow Pattern Recognition via Auction Market Theory for Reinforcement Learning Tradingcited by 0
  2. 2026Deep Reinforcement Learning for Equity Trading: Benchmarking Actor-Critic Methods with Forward Retrainingcited by 0
  3. 2026PPO-HRAP: Proximal Policy Optimization with a Hybrid Regime-Aware Policy for Risk-Controlled Tradingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Deep Reinforcement Learning Framework for Diversified Portfolio Management Across Global Equity Markets
  2. 2024Deep Reinforcement Learning for Online Optimal Execution Strategies
  3. 2025Revisiting Ensemble Methods for Stock Trading and Crypto Trading Tasks at ACM ICAIF FinRL Contest 2023-2024
  4. 2025Can Artificial Intelligence Trade the Stock Market?
  5. 2024Deep Reinforcement Learning Strategies in Finance: Insights into Asset Holding, Trading Behavior, and Purchase Diversity
  6. 2024Learning the Market: Sentiment-Based Ensemble Trading Agents
  7. 2024A Deep Reinforcement Learning Framework For Financial Portfolio Management
  8. 2025A Deep Reinforcement Learning Approach to Automated Stock Trading, using xLSTM Networks