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

• URL: http://arxiv.org/abs/2212.02721v2
• Date: Wed, 26 Jul 2023 09:47:55 GMT
• Title: A Novel Deep Reinforcement Learning Based Automated Stock Trading System Using Cascaded LSTM Networks
• Authors: Jie Zou, Jiashu Lou, Baohua Wang, Sixue Liu
• Abstract summary: 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. 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.
• Score: 3.593955557310285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs [74.35290684163718]
  A primary challenge in large language model (LLM) development is their onerous pre-training cost. This paper explores a promising paradigm to improve LLM pre-training efficiency and quality by leveraging a small language model (SLM)
  arXiv  Detail & Related papers  (2024-10-24T14:31:52Z)
• Social Debiasing for Fair Multi-modal LLMs [55.8071045346024]
  Multi-modal Large Language Models (MLLMs) have advanced significantly, offering powerful vision-language understanding capabilities. However, these models often inherit severe social biases from their training datasets, leading to unfair predictions based on attributes like race and gender. This paper addresses the issue of social biases in MLLMs by i) Introducing a comprehensive Counterfactual dataset with Multiple Social Concepts (CMSC) and ii) Proposing an Anti-Stereotype Debiasing strategy (ASD)
  arXiv  Detail & Related papers  (2024-08-13T02:08:32Z)
• Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM Alignment [65.15914284008973]
  We propose to leverage an Inverse Reinforcement Learning (IRL) technique to simultaneously build an reward model and a policy model. We show that the proposed algorithms converge to the stationary solutions of the IRL problem. Our results indicate that it is beneficial to leverage reward learning throughout the entire alignment process.
  arXiv  Detail & Related papers  (2024-05-28T07:11:05Z)
• Commodities Trading through Deep Policy Gradient Methods [0.0]
  It formulates the commodities trading problem as a continuous, discrete-time dynamical system. Two policy algorithms, namely actor-based and actor-critic-based approaches, are introduced. Backtesting on front-month natural gas futures demonstrates that DRL models increase the Sharpe ratio by $83%$ compared to the buy-and-hold baseline.
  arXiv  Detail & Related papers  (2023-08-10T17:21:12Z)
• Data Cross-Segmentation for Improved Generalization in Reinforcement Learning Based Algorithmic Trading [5.75899596101548]
  We propose a Reinforcement Learning (RL) algorithm that trades based on signals from a learned predictive model. We test our algorithm on 20+ years of equity data from Bursa Malaysia.
  arXiv  Detail & Related papers  (2023-07-18T16:00:02Z)
• HireVAE: An Online and Adaptive Factor Model Based on Hierarchical and Regime-Switch VAE [113.47287249524008]
  It is still an open question to build a factor model that can conduct stock prediction in an online and adaptive setting. We propose the first deep learning based online and adaptive factor model, HireVAE, at the core of which is a hierarchical latent space that embeds the relationship between the market situation and stock-wise latent factors. Across four commonly used real stock market benchmarks, the proposed HireVAE demonstrate superior performance in terms of active returns over previous methods.
  arXiv  Detail & Related papers  (2023-06-05T12:58:13Z)
• Asynchronous Deep Double Duelling Q-Learning for Trading-Signal Execution in Limit Order Book Markets [5.202524136984542]
  We employ deep reinforcement learning to train an agent to translate a high-frequency trading signal into a trading strategy that places individual limit orders. Based on the ABIDES limit order book simulator, we build a reinforcement learning OpenAI gym environment. We find that the RL agent learns an effective trading strategy for inventory management and order placing that outperforms a benchmark trading strategy having access to the same signal.
  arXiv  Detail & Related papers  (2023-01-20T17:19:18Z)
• Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification [83.23129279407271]
  We propose a method to efficiently retain the knowledge available in a neural network pre-trained on a set of securities. In our method, the prior knowledge encoded in a pre-trained neural network is maintained by keeping existing connections fixed. This knowledge is adjusted for the new securities by a set of augmented connections, which are optimized using the new data.
  arXiv  Detail & Related papers  (2022-07-23T18:54:10Z)
• Long Short-Term Memory Neural Network for Financial Time Series [0.0]
  We present an ensemble of independent and parallel long short-term memory neural networks for the prediction of stock price movement. With a straightforward trading strategy, comparisons with a randomly chosen portfolio and a portfolio containing all the stocks in the index show that the portfolio resulting from the LSTM ensemble provides better average daily returns and higher cumulative returns over time.
  arXiv  Detail & Related papers  (2022-01-20T15:17:26Z)
• Deep Stock Predictions [58.720142291102135]
  We consider the design of a trading strategy that performs portfolio optimization using Long Short Term Memory (LSTM) neural networks. We then customize the loss function used to train the LSTM to increase the profit earned. We find the LSTM model with the customized loss function to have an improved performance in the training bot over a regressive baseline such as ARIMA.
  arXiv  Detail & Related papers  (2020-06-08T23:37:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.