CoolFace
Modelpublic

Shadowell/Kairos-base-crypto

sourceHugging Facemitupdated 5mo agoView on Hugging Face
1likes1.9kdownloads
Model Card

Shadowell/Kairos-base-crypto

Fine-tuned Kronos-base on BTC/USDT + ETH/USDT 1-min K-lines (2024-01 ~ 2026-04) using [Kairos](https://github.com/Shadowell/Kairos). Architecture = Kronos + exogenous bypass channel (32-d) + quantile return head.

This run keeps the original tokenizer `NeoQuasar/Kronos-Tokenizer-base`, matching the original Kairos-small-crypto training flow. Training data comes from the public Binance Vision spot mirror, so the 5 crypto-native exogenous features (funding_rate / funding_rate_z / oi_change / basis / btc_dominance) remain padded to zero; the other 27 dimensions are real.

Results on test set (2026-01-01 04:16:00 ~ 2026-04-16 23:30:00, 304,710 1-min bars)

horizonmodelhit_raterank_icICIR
h1baseline50.78%+0.025+0.630
h1finetuned50.37%+0.011+0.051
h5baseline51.61%+0.029+0.385
h5finetuned50.95%+0.029+0.351
h30baseline52.49%+0.055+0.325
h30finetuned52.92%+0.076+0.484

Baseline = original Kronos-base weights + randomly initialised exog / return head. Tokenizer stays on the official NeoQuasar/Kronos-Tokenizer-base, matching the original crypto predictor flow. Training stopped at epoch 4; best val_ce = 2.4842.

Usage

python
from kairos import KronosTokenizer, KronosWithExogenous
tok = KronosTokenizer.from_pretrained("NeoQuasar/Kronos-Tokenizer-base")
model = KronosWithExogenous.from_pretrained("Shadowell/Kairos-base-crypto")

Training config (preset crypto-1min)

  • —lookback 256 min, predict 30 min
  • —batch 24, OneCycleLR, early-stop patience 3
  • —progressive unfreeze: only last transformer block + exog bypass + return head
  • —tokenizer source = NeoQuasar/Kronos-Tokenizer-base
  • —32-d EXOG = 24 common + 8 crypto-market features

Training recipe

Full command log, backtest commands, pitfalls and the reproduction checklist are in `docs/CRYPTO_BTC_ETH_RUN.md`.