| # 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. |