# A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading Rules

arXiv: https://arxiv.org/abs/2101.03867 · HTML: https://mlsanity.com/paper/2101.03867/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 45 references that are in the map.
- First posted on arXiv 8 Jan 2021.

## Builds on (most cited first, 14 of 14)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Long-term Recurrent Convolutional Networks for Visual Recognition and Description](https://mlsanity.com/paper/1411.4389/) (2014)
- [Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning](https://mlsanity.com/paper/1612.01887/) (2016)
- [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)
- [AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks](https://mlsanity.com/paper/1908.02646/) (2019)
- [Cryptocurrency Portfolio Management with Deep Reinforcement Learning](https://mlsanity.com/paper/1612.01277/) (2016)
- [An Application of Deep Reinforcement Learning to Algorithmic Trading](https://mlsanity.com/paper/2004.06627/) (2020)
- [An intelligent financial portfolio trading strategy using deep Q-learning](https://mlsanity.com/paper/1907.03665/) (2019)
- [Deep Learning for Spatio-Temporal Modeling: Dynamic Traffic Flows and High Frequency Trading](https://mlsanity.com/paper/1705.09851/) (2017)
- [Learning Financial Asset-Specific Trading Rules via Deep Reinforcement Learning](https://mlsanity.com/paper/2010.14194/) (2020)
- [Dreaming machine learning: Lipschitz extensions for reinforcement learning on financial markets](https://mlsanity.com/paper/1907.05697/) (2019)
- [VLSTM: Very Long Short-Term Memory Networks for High-Frequency Trading](https://mlsanity.com/paper/1809.01506/) (2018)

## Built on by (most cited first, 2 of 2)

- [News-Aware Direct Reinforcement Trading for Financial Markets](https://mlsanity.com/paper/2510.19173/) (2025)
- [Regime-Aware Portfolio Management via Retrieval-Augmented LLM-Guided Expert Switching](https://mlsanity.com/paper/2608.28252/) (2026)

## Abstract (arXiv)

A wide variety of deep reinforcement learning (DRL) models have recently been proposed to learn profitable investment strategies. The rules learned by these models outperform the previous strategies specially in high frequency trading environments. However, it is shown that the quality of the extracted features from a long-term sequence of raw prices of the instruments greatly affects the performance of the trading rules learned by these models. Employing a neural encoder-decoder structure to extract informative features from complex input time-series has proved very effective in other popular tasks like neural machine translation and video captioning in which the models face a similar problem. The encoder-decoder framework extracts highly informative features from a long sequence of prices along with learning how to generate outputs based on the extracted features. In this paper, a novel end-to-end model based on the neural encoder-decoder framework combined with DRL is proposed to learn single instrument trading strategies from a long sequence of raw prices of the instrument. The proposed model consists of an encoder which is a neural structure responsible for learning informative features from the input sequence, and a decoder which is a DRL model responsible for learning profitable strategies based on the features extracted by the encoder. The parameters of the encoder and the decoder structures are learned jointly, which enables the encoder to extract features fitted to the task of the decoder DRL. In addition, the effects of different structures for the encoder and various forms of the input sequences on the performance of the learned strategies are investigated. Experimental results showed that the proposed model outperforms other state-of-the-art models in highly dynamic environments.