Deep reinforcement learning approach for reliable stock price prediction using financial time series data

Stock price forecasting is a difficult task in volatile and nonlinear financial markets for traditional statistical and deep learning models because of the complex temporal dependencies and rapidly fluctuating market conditions. In this study, we present a novel Deep Reinforcement Learning (DRL) model for credible stock price forecasting with financial time series data, which is implemented on the Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) architectures. Data preprocessing, feature engineering, and reward-guided training are also included in the framework, pushing towards the accuracy of prediction and trading performance. Results on historical datasets demonstrate that the DRL approach can reduce RMSE, MAE and MAPE, and increase the directional accuracy when compared with some current state-of-the-art techniques, such as ARIMA, LSTM, and GRU. Backtesting indicates better cumulative returns, stronger Sharpe ratios, and reduced maximum drawdowns, indicating higher profits and less risk. Experimental results based on comparisons with the state-of-the-art show the robustness and generalization of the proposed approach, indicating that it is promising for real-world automatic trading and financial decision-making in dynamic markets.