---
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}
}
```