mlsanityData to 7 Oct 2026

A Novel Deep Reinforcement Learning Based Automated Stock Trading System Using Cascaded LSTM Networks

arXiv 2212.02721 ↗First posted 6 Dec 2022q-fin.CP cs.AI q-fin.PMcited by 12

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Abstract (arXiv)

More and more stock trading strategies are constructed using deep reinforcement learning (DRL) algorithms, but DRL methods originally widely used in the gaming community are not directly adaptable to financial data with low signal-to-noise ratios and unevenness, and thus suffer from performance shortcomings. In this paper, to capture the hidden information, we propose a DRL based stock trading system using cascaded LSTM, which first uses LSTM to extract the time-series features from stock daily data, and then the features extracted are fed to the agent for training, while the strategy functions in reinforcement learning also use another LSTM for training. Experiments in DJI in the US market and SSE50 in the Chinese stock market show that our model outperforms previous baseline models in terms of cumulative returns and Sharp ratio, and this advantage is more significant in the Chinese stock market, a merging market. It indicates that our proposed method is a promising way to build a automated stock trading system.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 15 of its 61 references that are in the map. First posted on arXiv 6 Dec 2022.

Builds on

15 of 61 references.

References · 15

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3 more
  1. 2018Financial Trading as a Game: A Deep Reinforcement Learning Approachcited by 22
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  3. 2020A Novel Ensemble Deep Learning Model for Stock Prediction Based on Stock Prices and Newscited by 16

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Cited by year

Citing papers per year: 2024: 5; 2025: 7.

12 papers.

  1. 2024QADQN: Quantum Attention Deep Q-Network for Financial Market Predictioncited by 8
  2. 2024MacroHFT: Memory Augmented Context-aware Reinforcement Learning On High Frequency Tradingcited by 7
  3. 2025A novel multi-agent dynamic portfolio optimization learning system based on hierarchical deep reinforcement learningcited by 2
  4. 2024Combining supervised and unsupervised learning methods to predict financial market movementscited by 1
  5. 2025Finding Optimal Trading History in Reinforcement Learning for Stock Market Tradingcited by 1
  6. 2025A Deep Reinforcement Learning Approach to Automated Stock Trading, using xLSTM Networkscited by 1
  7. 2024A Hybrid Deep Learning Framework for Stock Price Prediction Considering the Investor Sentiment of Online Forum Enhanced by Popularitycited by 0
  8. 2024On-policy Actor-Critic Reinforcement Learning for Multi-UAV Explorationcited by 0
  9. 2025Directly Learning Stock Trading Strategies Through Profit Guided Loss Functionscited by 0
  10. 2025Quantum-Inspired DRL Approach with LSTM and OU Noise for Cut Order Planning Optimizationcited by 0
  11. 2025News-Aware Direct Reinforcement Trading for Financial Marketscited by 0
  12. 2025StarCraft+: Benchmarking Multi-agent Algorithms in Adversary Paradigmcited by 0