# 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.
---
# Agent Instructions: Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter:
```
GET https://docs.aarna.ai/aarna-agentic-engine/alpha-30-7.md?ask=
```
The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.