---
license: mit
tags: [time-series, finance, kronos, kairos, crypto]
library_name: pytorch
pipeline_tag: time-series-forecasting
---

# 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`](https://huggingface.co/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)

| horizon | model | hit_rate | rank_ic | ICIR |
|---|---|---:|---:|---:|
| h1 | baseline | 50.78% | +0.025 | +0.630 |
| h1 | finetuned | 50.37% | +0.011 | +0.051 |
| h5 | baseline | 51.61% | +0.029 | +0.385 |
| h5 | finetuned | 50.95% | +0.029 | +0.351 |
| h30 | baseline | 52.49% | +0.055 | +0.325 |
| h30 | finetuned | 52.92% | +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`](https://github.com/Shadowell/Kairos/blob/main/docs/CRYPTO_BTC_ETH_RUN.md).