crypto_volatility_forecaster_lstm

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.