Explainability-driven decision gating for risk-controlled algorithmic trading

This paper proposes a decision framework based on deep learning techniques that is applicable to algorithmic trading. A deep neural network is used to predict the direction of the market based on financial time-series data and technical indicators.The novelty of this research is that the decision gate, which is based on the use of explainability, is integrated into the decision-making process. In this case, the use of explainability is not limited to the interpretability of the decision but is used to validate the decision before the trade is executed. Therefore, the decision is executed only if the local feature attribution supports the decision.This decision framework is tested based on walk-forward validation under realistic constraints. The performance of the decision framework is tested, and the results showed that the decision gate reduces the unnecessary trades while improving the performance which demonstrates control over the black box behaviour of Deep Neural Network using Explainable Artificial Intelligence (XAI).This research showed that explainability is used not only to interpret decisions but also to control risk.The results demonstrate that the proposed approach reduces trading activity by approximately 60% while maintaining or improving overall portfolio performance, leading to lower transaction costs and more stable equity behaviour.