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LSTM-DDPG for Trading with Variable Positions.

Zhichao Jia1, Qiang Gao1,2, Xiaohong Peng3

  • 1School of Electronics and Information Engineering, Beihang University, Beijing 100191, China.

Sensors (Basel, Switzerland)
|October 13, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces LSTM-DDPG, a novel deep reinforcement learning model for trading, enabling variable position sizing. The model demonstrates superior performance in return and risk management compared to fixed-size strategies.

Keywords:
deep reinforcement learningreward functiontrading strategyvariable positions

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Area of Science:

  • Quantitative Finance
  • Artificial Intelligence
  • Computational Economics

Background:

  • Machine learning for trading is extensively researched, focusing on determining trade direction and size.
  • Existing models often use fixed or limited position sizes, neglecting market condition variability.
  • No prior research has integrated variable position sizing into trading models.

Purpose of the Study:

  • To propose a novel deep reinforcement learning model, LSTM-DDPG, for making trading decisions with variable position sizes.
  • To address the limitation of fixed position sizing in current algorithmic trading strategies.
  • To enhance trading performance by adapting position size to dynamic market conditions.

Main Methods:

  • The trading process is modeled as a Partially Observable Markov Decision Process.
  • A Long Short-Term Memory (LSTM) network is employed for extracting market state features.
  • The Deep Deterministic Policy Gradient (DDPG) framework is utilized for decision-making on trade direction and variable position size.
  • The model was tested on China stock market index futures (IF300) data.

Main Results:

  • The LSTM-DDPG model with variable positions significantly outperformed models with fixed or few-level positions in terms of return and risk.
  • Testing on IF300 data confirmed the efficacy of variable position sizing in algorithmic trading.
  • The differential Sharpe ratio reward function proved more effective in maximizing investment potential than a simple profit reward function.

Conclusions:

  • The proposed LSTM-DDPG model offers a significant advancement in algorithmic trading by incorporating variable position sizing.
  • Variable position sizing, driven by market conditions, leads to improved trading performance and risk management.
  • The differential Sharpe ratio is a more suitable reward function for optimizing trading strategies in terms of investment potential.