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--- |
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license: mit |
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tags: |
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- time-series |
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- forecasting |
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- finance |
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- crypto |
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--- |
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# crypto_volatility_forecaster |
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## Overview |
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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. |
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## Model Architecture |
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The model implements a specialized **Encoder-Decoder Transformer** designed for sequential numerical data. |
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- **Temporal Embedding**: Captures hourly and daily seasonalities. |
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- **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. |
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- **Context Window ($L$):** 168 hours. |
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- **Prediction Horizon ($H$):** 24 hours. |
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The loss function used is the Negative Log-Likelihood ($NLL$): |
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$$NLL = -\sum_{t=1}^{H} \log P(x_t | \theta_t)$$ |
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## Intended Use |
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- **Risk Management**: Estimating potential Value at Risk (VaR) for digital asset portfolios. |
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- **Algorithmic Trading**: Providing volatility signals as features for automated execution strategies. |
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- **Financial Research**: Studying market regime shifts and anomaly detection. |
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## Limitations |
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- **Black Swan Events**: Cannot predict volatility spikes caused by external shocks (e.g., regulatory changes, exchange failures) not present in historical price data. |
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- **Stationarity**: Financial markets are non-stationary; the model requires frequent retraining to adapt to new market conditions. |
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- **Not Financial Advice**: This model is for research purposes and should not be used as the sole basis for investment decisions. |
