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README.md

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+# 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.