alpha 30/7

In aarna’s alpha 30/7 neural network architecture, the initial processing begins with a Variational Autoencoder (VAE), which transforms the input dataset of 93 features into 32 latent spaces. These latent spaces are then passed into LSTM layers that capture and analyze temporal dependencies within the sequence. Enhancing this analysis, an attention mechanism focuses selectively on the most pertinent aspects of the LSTM outputs, ensuring that critical information is emphasized for subsequent layers. The processed data is then integrated and further interpreted in the Dense layers (ANN), which apply non-linear transformations to consolidate the insights derived from earlier stages. The architecture is fortified with a risk management framework, incorporating a probability filter designed to avoid predictions when the model lacks confidence and a dynamic stop-loss mechanism adjusted based on market moments to reduce downside risks. This flow ensures that the network not only predicts effectively but also guards against potential financial uncertainties.