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

arXiv: https://arxiv.org/abs/2212.02721 · HTML: https://mlsanity.com/paper/2212.02721/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 61 references that are in the map.
- First posted on arXiv 6 Dec 2022.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO](https://mlsanity.com/paper/2005.12729/) (2020)
- [Playing FPS Games with Deep Reinforcement Learning](https://mlsanity.com/paper/1609.05521/) (2016)
- [Qlib: An AI-oriented Quantitative Investment Platform](https://mlsanity.com/paper/2009.11189/) (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)
- [HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information](https://mlsanity.com/paper/2110.13716/) (2021)
- [Enhancing Time Series Momentum Strategies Using Deep Neural Networks](https://mlsanity.com/paper/1904.04912/) (2019)
- [Financial Trading as a Game: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/1807.02787/) (2018)
- [Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Review](https://mlsanity.com/paper/2106.00123/) (2021)
- [A Novel Ensemble Deep Learning Model for Stock Prediction Based on Stock Prices and News](https://mlsanity.com/paper/2007.12620/) (2020)

## Built on by (most cited first, 12 of 12)

- [QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction](https://mlsanity.com/paper/2408.03088/) (2024)
- [MacroHFT: Memory Augmented Context-aware Reinforcement Learning On High Frequency Trading](https://mlsanity.com/paper/2406.14537/) (2024)
- [A novel multi-agent dynamic portfolio optimization learning system based on hierarchical deep reinforcement learning](https://mlsanity.com/paper/2501.06832/) (2025)
- [Combining supervised and unsupervised learning methods to predict financial market movements](https://mlsanity.com/paper/2409.03762/) (2024)
- [Finding Optimal Trading History in Reinforcement Learning for Stock Market Trading](https://mlsanity.com/paper/2502.12537/) (2025)
- [A Deep Reinforcement Learning Approach to Automated Stock Trading, using xLSTM Networks](https://mlsanity.com/paper/2503.09655/) (2025)
- [A Hybrid Deep Learning Framework for Stock Price Prediction Considering the Investor Sentiment of Online Forum Enhanced by Popularity](https://mlsanity.com/paper/2405.10584/) (2024)
- [On-policy Actor-Critic Reinforcement Learning for Multi-UAV Exploration](https://mlsanity.com/paper/2409.11058/) (2024)
- [Directly Learning Stock Trading Strategies Through Profit Guided Loss Functions](https://mlsanity.com/paper/2507.19639/) (2025)
- [Quantum-Inspired DRL Approach with LSTM and OU Noise for Cut Order Planning Optimization](https://mlsanity.com/paper/2508.16611/) (2025)
- [News-Aware Direct Reinforcement Trading for Financial Markets](https://mlsanity.com/paper/2510.19173/) (2025)
- [StarCraft+: Benchmarking Multi-agent Algorithms in Adversary Paradigm](https://mlsanity.com/paper/2512.16444/) (2025)

## 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.