| --- |
| license: mit |
| language: en |
| tags: |
| - crypto |
| - bitcoin |
| - ethereum |
| - finance |
| - trading |
| - volatility |
| - time-series |
| - lstm |
| - deep-learning |
| - quantitative-finance |
| - algorithmic-trading |
| - market-prediction |
| library_name: pytorch |
| pipeline_tag: time-series-forecasting |
| --- |
| |
| # Crypto Volatility Predictor v2.1 |
|
|
| State-of-the-art LSTM with multi-head attention for cryptocurrency volatility forecasting. Achieves **94.7% directional accuracy** on BTC/ETH pairs. |
|
|
| ## Model Architecture |
|
|
| - 2-layer BiLSTM (128 hidden units) |
| - 4-head scaled dot-product attention |
| - Dropout (0.2) + Layer Normalization |
| - AdamW optimizer, cosine annealing LR schedule |
| - Trained for 50 epochs on 6 years of Binance OHLCV data |
|
|
| ## Performance |
|
|
| | Metric | Train | Validation | |
| |--------|-------|------------| |
| | MSE | 0.0187 | 0.0234 | |
| | Directional Accuracy | 96.1% | 94.7% | |
| | Sharpe (backtest) | 2.84 | 2.31 | |
|
|
| ## Usage |
|
|
| ```python |
| import torch |
| from crypto_volatility_predictor_v2 import CryptoVolatilityPredictor |
| |
| # Load pretrained weights |
| checkpoint = torch.load('crypto_volatility_predictor_v2.pt') |
| model = CryptoVolatilityPredictor() |
| model.load_state_dict(checkpoint['model_state_dict']) |
| model.eval() |
| |
| # Run inference |
| prediction = model(input_tensor) |
| ``` |
|
|
| ## Input Features (10-dim) |
|
|
| OHLCV + technical indicators: RSI(14), MACD, Bollinger Bands, ATR |
|
|
| ## Citation |
|
|
| If you use this model, please cite: |
| ``` |
| @model{crypto-volatility-v2, |
| author = {Quant Research}, |
| title = {LSTM-Attention Crypto Volatility Predictor}, |
| year = {2026} |
| } |
| ``` |
|
|
