AI Models
Train, validate and inspect the forecasting models. Every metric is an estimate on historical data — a model that beats its baseline once may still be wrong tomorrow.
LSTM
Recurrent net with long short-term memory over feature sequences.
GRU
Gated recurrent units — a lighter recurrent alternative to LSTM.
CNN
1-D convolutions over the time axis to detect local price patterns.
MLP
Feed-forward net over the latest feature snapshot and lagged returns.
Ensemble
Validation-accuracy-weighted blend of the four model forecasts.
Train models
Trains on historical candles with chronological splits — no lookahead. Runs execute in the background.
Active runs
No active training runs. Completed and failed runs appear in the training history.
A trained model is a probabilistic forecast, not a promise. Only models that beat the baseline with statistical significance are promoted to champion.
Latest ensemble forecast
BTC-USD · 1h — probabilistic estimate, not a guarantee
Feature importance
Not implemented yet — and we will not fake it.
Per-feature attribution (gradient saliency for the CNN/MLP heads, permutation importance for all models) is a planned next improvement. Until it is computed from real trained weights, this card intentionally shows no importance scores — invented numbers would be worse than none.
Feature families the models currently see
Which of these actually drive a given forecast is an open research question here — treat all model output as a statistical estimate.
Training history
Recent training runs with walk-forward validation metrics. Accuracy is measured on held-out data — treat every number as an estimate.