# crypto_volatility_forecaster_lstm

## Overview
This model is an advanced Long Short-Term Memory (LSTM) network designed to predict the 24-hour price volatility of major crypto assets (BTC, ETH, SOL). It processes multidimensional time-series data including price action, technical indicators, and on-chain metrics.

## Model Architecture
The architecture is optimized for non-linear temporal dependencies:
- **Input Layer**: Accepts a $60 \times 12$ tensor (60 time steps, 12 features).
- **Hidden Layers**: 3 stacked LSTM layers with 256 units each.
- **Regularization**: Dropout layers ($p=0.2$) between LSTM cells to prevent overfitting.
- **Output Layer**: Fully connected linear layer providing a single scalar representing the predicted volatility index.

## Intended Use
- Risk management for liquidity providers.
- Automated stop-loss adjustment strategies.
- Market health assessment for algorithmic trading.

## Limitations
- **Black Swan Events**: Cannot predict extreme outliers caused by exchange collapses or regulatory sudden shifts.
- **Lagging Indicators**: LSTM models are inherently reactive to historical patterns.
- **Feature Sensitivity**: Accuracy is highly dependent on the quality and latency of the input data provider.