Adaptive Deep Reinforcement Learning for Dynamic Stock Price Prediction and Trading Strategy Optimization

Abstract

Financial markets' nonlinear and time-varying characteristics pose ongoing challenges to predicting stock prices and optimizing trading strategies. Conventional statistics and supervised learning models tend to fail to adapt to abrupt market changes and to convert the prediction into an executable strategy. In this research, the objective is to create an integrated architecture for decision-making to fill this gap through adaptive deep reinforcement learning. The proposed model combines learning through Long Short-Term Memory (LSTM) networks and policy learning through Deep Q-Network (DQN) and Proximal Policy Optimization (PPO). On the basis of the Nifty-50 dataset representing over two decades, the model adaptively learns trade strategies through interaction in a synthetic trading environment. Experimental analysis shows an advantage of this new proposal, realizing a return of 160.77, a Sharpe ratio of 6.99, and a maximum drawdown of 173.16, outperforming considerably established benchmarks in prior research. The end trading signal realized was BUY, indicating strong confident direction forecasting. The results highlight the importance of unifying sequential learning and decision-making in one adaptive framework, finding it to be worthwhile for institutions that seek to bolster trading under volatile situations.