Stock market forecasting is not an easy task to undertake because of the volatility and the price movements which are non-stationary. In this work, the authors propose a hybrid deep learning architecture that combines the use of Discrete Wavelet Transform (DWT)-based feature extraction with multi-architecture aggregation with LSTM, GRU, RNN, and CNN models. The model was tested on six Indian stocks based on ten-years of daily historical returns under a rolling walk-forward validation procedure, with the model being trained on a five-year window and tested on out-of-sample periods. It has been shown experimentally that the hybrid aggregation method gives smaller prediction errors than individual architectures. Using the proposed model on the NIFTY 50 index, the RMSE was 0.2102, and the directional accuracy was 65.18%, indicating improved predictive stability compared to baseline models. An ablation analysis also supports the fact that wavelet-based pre-processing helps to reduce errors and increase consistency of trends. These results indicate that wavelet-based feature extraction with heterogeneous deep learning models can be more robust when used in daily stock forecasting. In future work, statistical significance tests and cost-based trading simulations may be incorporated to further assess practical relevance.