A Market-Aware Dynamic Trading Strategy Based on Unsupervised Clustering and Reinforcement Learning

Yuan Zhuang

Abstract

The complexity and dynamics of the stock market make traditional absolute price prediction models based on supervised learning face great risks and limitations in practical business applications. This report proposes an innovative Hybrid Deep Reinforcement Learning trading framework to shift the forecast target from stock prices to optimal trading decisions in specific market conditions. The dataset for this experiment comes from a Malaysian listed company, GAMUDA BHD, from 2000 to 2024 on the Kaggle platform, and builds a decision-making structure with three stages. Use the Gaussian Mixture Model, the long-term and short-term memory network, and finally the Deep Q-Network to train the intelligent body to learn the optimal discrete trading strategy (buy, sell, hold) in a continuous complex environment. The research results show that, compared with the traditional buy-and-hold strategy and the single intensive learning model, the hybrid architecture that integrates market state perception shows significant commercial application value in terms of maximum pullback control and Sharp rate improvement. During a systemic market downturn, the GMM clusterer detected a surge in volatility, identifying the crisis. Based on this, DQN forced a "sell" order, successfully limiting the maximum drawdown. This strongly demonstrates the significance of hybrid models in mitigating black swan events in the market.

Source: semanticscholar · PDF

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