DDPG based on multi-scale strokes for financial time series trading strategy

@article{Chen2022DDPGBO,
  title={DDPG based on multi-scale strokes for financial time series trading strategy},
  author={Jun Chen and Cong Chen and L J Duan and Zhiqiang Cai},
  journal={Proceedings of the 2022 8th International Conference on Computer Technology Applications},
  year={2022}
}
  • Jun ChenCong Chen Zhiqiang Cai
  • Published 12 May 2022
  • Computer Science
  • Proceedings of the 2022 8th International Conference on Computer Technology Applications
With the development of artificial intelligence, more and more financial practitioners apply deep reinforcement learning to financial trading strategies. However, it is difficult to extract accurate features due to the characteristics of considerable noise, highly non-stationary, and non-linearity of single-scale time series, which makes it hard to obtain high returns. In this paper, we extract a multi-scale feature matrix on multiple time scales of financial time series, according to the… 

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References

SHOWING 1-10 OF 24 REFERENCES

Using Data Augmentation Based Reinforcement Learning for Daily Stock Trading

A framework named data augmentation based reinforcement learning (DARL) which uses minute-candle data (open, high, low, close) to train the agent and is used to guide daily stock trading, which finds proximal policy optimization (PPO) is the most stable algorithm to achieve high risk-adjusted returns.

Deep Direct Reinforcement Learning for Financial Signal Representation and Trading

This work introduces a recurrent deep neural network for real-time financial signal representation and trading and proposes a task-aware backpropagation through time method to cope with the gradient vanishing issue in deep training.

Application of deep reinforcement learning in stock trading strategies and stock forecasting

This paper proves the feasibility of deep reinforcement learning in financial markets and the credibility and advantages of strategic decision-making and compares the model with the traditional model to prove its advantages.

Practical Deep Reinforcement Learning Approach for Stock Trading

The proposed deep reinforcement learning approach is shown to outperform the two baselines in terms of both the Sharpe ratio and cumulative returns.

An intelligent financial portfolio trading strategy using deep Q-learning

Learning to trade via direct reinforcement

It is demonstrated how direct reinforcement can be used to optimize risk-adjusted investment returns (including the differential Sharpe ratio), while accounting for the effects of transaction costs.

Stock Prediction Based on Technical Indicators Using Deep Learning Model

Results indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms, and aids investors in making profitable investment decisions as it presents trend-based forecasting.

Stock Selection via Spatiotemporal Hypergraph Attention Network: A Learning to Rank Approach

STHAN-SR, a neural hypergraph architecture for stock selection that combines a hypergraph and a temporal Hawkes attention mechanism to tailor a new spatiotemporal attention hypergraph network architecture to rank stocks based on profit is proposed.