metadata
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
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}
}