Project · Machine Learning
Crypto Volume Forecasting
A supervised ML pipeline that forecasts 7-day-ahead trading volume for Bitcoin, Ethereum, and Litecoin. Models are trained offline on 730 days of daily OHLCV data from Yahoo Finance using a chronological 80/20 split with a 7-day horizon gap to prevent target leakage. Feature engineering produces ~45 candidate predictors: price and volume lags, rolling statistics, realised volatility, momentum, and calendar effects. A two-pass importance-based pruning step selects the top 20 per model.
Select an asset and model below. Inference loads the pre-trained artifact and runs a live prediction against the most recent market data in under a second.
Run a Forecast
Autoregressive Rolling Forecast
A separate T+1 model predicts next-day volume for the selected asset and model class. Each prediction is fed back as a lag feature to generate T+2, T+3, and so on. Price is held constant across the rollout; only volume updates autoregressively. Forecast error compounds with each step.
Pipeline Details
Data
730 days · BTC, ETH, LTC
Daily OHLCV via Yahoo Finance
Models
Ridge · XGBoost · LightGBM
Trained offline · inference on demand
Target
log-volume, 7 days ahead
Inverted to real scale at inference
Split
80% train · 7-day gap · 20% test
Gap prevents horizon leakage
Features
Top 20 of 45 engineered
Two-pass importance pruning per model
Evaluation
RMSE · MAE · MAPE · R² · Dir. Acc.
Held-out test set · volatility regime