# Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy

arXiv: https://arxiv.org/abs/2511.12120 · HTML: https://mlsanity.com/paper/2511.12120/

## 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.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

## Builds on (most cited first, 13 of 13)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Large-Scale Study of Curiosity-Driven Learning](https://mlsanity.com/paper/1808.04355/) (2018)
- [Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation](https://mlsanity.com/paper/1807.01473/) (2018)
- [An empirical investigation of the challenges of real-world reinforcement learning](https://mlsanity.com/paper/2003.11881/) (2020)
- [Deep Reinforcement Learning for Trading](https://mlsanity.com/paper/1911.10107/) (2019)
- [Practical Deep Reinforcement Learning Approach for Stock Trading](https://mlsanity.com/paper/1811.07522/) (2018)
- [Cryptocurrency Portfolio Management with Deep Reinforcement Learning](https://mlsanity.com/paper/1612.01277/) (2016)
- [Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis](https://mlsanity.com/paper/1906.11046/) (2019)
- [DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News](https://mlsanity.com/paper/1912.10806/) (2019)

## Built on by (most cited first, 15 of 15)

- [Agentic Trading: When LLM Agents Meet Financial Markets](https://mlsanity.com/paper/2605.19337/) (2026)
- [Deep Reinforcement Learning for Optimal Portfolio Allocation: A Comparative Study with Mean-Variance Optimization](https://mlsanity.com/paper/2602.17098/) (2026)
- [Learning Markov Decision Processes under Fully Bandit Feedback](https://mlsanity.com/paper/2602.02260/) (2026)
- [FinRL-X: An AI-Native Modular Infrastructure for Quantitative Trading](https://mlsanity.com/paper/2603.21330/) (2026)
- [Decomposable Reward Modeling and Realistic Environment Design for Reinforcement Learning-Based Forex Trading](https://mlsanity.com/paper/2604.00031/) (2026)
- [When Missing Becomes Structure: Intent-Preserving Policy Completion from Financial KOL Discourse](https://mlsanity.com/paper/2604.14333/) (2026)
- [TT-DAC-PS: Twin-Target Deterministic Actor-Critic with Policy Smoothing for Optimal Trade Execution](https://mlsanity.com/paper/2606.08379/) (2026)
- [A Unified Multi-Modal Framework for Intelligent Financial Systems: Integrating Reinforcement Learning, High-Frequency Trading, and Game-Theoretic Approaches with Cross-Modal Sentiment Analysis](https://mlsanity.com/paper/2606.10412/) (2026)
- [CLaC@FinMMEval 2026 Task 3: Sentiment-Augmented Deep Reinforcement Learning for Active Trading -- An Alpha-Reward Approach](https://mlsanity.com/paper/2607.16028/) (2026)
- [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)
- [Titans-QFWP: A Regime-Aware Hybrid Quantum Fast Weight Programmer for Portfolio Optimization](https://mlsanity.com/paper/2608.29093/) (2026)
- [ViperQ: Order Flow Pattern Recognition via Auction Market Theory for Reinforcement Learning Trading](https://mlsanity.com/paper/2609.13825/) (2026)
- [Deep Reinforcement Learning for Equity Trading: Benchmarking Actor-Critic Methods with Forward Retraining](https://mlsanity.com/paper/2609.31870/) (2026)
- [PPO-HRAP: Proximal Policy Optimization with a Hybrid Regime-Aware Policy for Risk-Controlled Trading](https://mlsanity.com/paper/2610.01325/) (2026)

## 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.