crypto_volatility_forecaster

Overview

This model utilizes a Time-Series Transformer architecture to predict the volatility of major cryptocurrencies (e.g., BTC, ETH). By processing historical price action and volume data, it forecasts a probabilistic distribution of future price movements over a 24-hour window based on a 7-day look-back period.

Model Architecture

The model implements a specialized Encoder-Decoder Transformer designed for sequential numerical data.

• Temporal Embedding: Captures hourly and daily seasonalities.
• Distribution Head: Instead of point forecasts, it outputs parameters for a Student's t-distribution, which is better suited for the "fat tails" observed in financial market data.
• Context Window ($L$): 168 hours.
• Prediction Horizon ($H$): 24 hours.

The loss function used is the Negative Log-Likelihood ($NLL$): $$NLL=-\sum _{t=1}^H\log P(x_t\mathrm{\mid }{\theta }_t)NLL\; =\; -\backslash sum\_\{t=1\}^\{H\}\; \backslash log\; P(x\_t\; |\; \backslash theta\_t)$$

Intended Use

• Risk Management: Estimating potential Value at Risk (VaR) for digital asset portfolios.
• Algorithmic Trading: Providing volatility signals as features for automated execution strategies.
• Financial Research: Studying market regime shifts and anomaly detection.

Limitations

• Black Swan Events: Cannot predict volatility spikes caused by external shocks (e.g., regulatory changes, exchange failures) not present in historical price data.
• Stationarity: Financial markets are non-stationary; the model requires frequent retraining to adapt to new market conditions.
• Not Financial Advice: This model is for research purposes and should not be used as the sole basis for investment decisions.