Deep reinforcement learning with positional context for intraday trading

TL;DR

This paper introduces a deep reinforcement learning model for intraday trading that incorporates positional context features, leading to improved profitability and risk management across diverse assets over nearly a decade.

Contribution

It presents a novel DRL approach that integrates positional contextual features into the state space for intraday trading, enhancing decision-making performance.

Findings

Model achieves notable profitability and risk-adjusted metrics.

Positional features significantly contribute to model performance.

Patterns in trading activity support the model's effectiveness.

Abstract

Deep reinforcement learning (DRL) is a well-suited approach to financial decision-making, where an agent makes decisions based on its trading strategy developed from market observations. Existing DRL intraday trading strategies mainly use price-based features to construct the state space. They neglect the contextual information related to the position of the strategy, which is an important aspect given the sequential nature of intraday trading. In this study, we propose a novel DRL model for intraday trading that introduces positional features encapsulating the contextual information into its sparse state space. The model is evaluated over an extended period of almost a decade and across various assets including commodities and foreign exchange securities, taking transaction costs into account. The results show a notable performance in terms of profitability and risk-adjusted metrics. The feature importance results show that each feature incorporating contextual information contributes to the overall performance of the model. Additionally, through an exploration of the agent's intraday trading activity, we unveil patterns that substantiate the effectiveness of our proposed model.