This model does not predict Indian equity returns. It is published as a documented negative result together with a real, measured calibration improvement. Do not trade it.

Headline

	pretrained Kronos-small	this fine-tune
Rank-IC (1d, 100 sessions)	−0.0116	−0.0114
IC t-statistic	−0.54	−0.50
Directional hit rate	50.25%	51.57%
Forecast dispersion ÷ realised	3.61×	1.85×
Forecasts implying >±10% in a day	11.82%	2.93%
Long/short decile spread	0.046%	0.139%

Fine-tuning roughly halved forecast dispersion and cut impossible one-day forecasts by 75%. It did not create ranking skill: the information coefficient is statistically indistinguishable from zero, and marginally negative.

For scale, the decile spread of 0.139% sits an order of magnitude below the ~10 bps round-trip cost of trading NSE equities, so it is not tradeable even if it were real.

The baseline beat it

A 12-1 momentum signal, measured on the identical 100 sessions at the identical horizon:

signal	rank-IC	t	permutation control
12-1 momentum	+0.0376	+3.51	+0.0003
this model	−0.0114	−0.50	—

A decades-old, six-line ranking rule outperforms the fine-tuned foundation model by a wide and statistically significant margin. That comparison is the most useful thing in this card.

(Momentum's own skill decays: +0.0596 over 2015-2019 versus +0.0290 over 2023-2026. And in a cost-aware backtest over 2023-2026 a concentrated top-10 momentum book returned 7.78% CAGR net against 20.46% for simply holding the universe equal-weighted. A positive IC is necessary for a tradeable strategy, not sufficient.)

Evaluation

Held-out window: 2026-01-01 → 2026-07-30, 100 sessions, never seen in training.
Universe: top 80 by 60-day median turnover, re-formed each session.
Observations: 7,465 forecasts per variant.
Metric: cross-sectional Spearman rank-IC of predicted vs realised next-session close-to-close return, aggregated as a t-statistic over sessions.
Sampling: 1-day horizon on daily dates, so windows do not overlap and the t-statistic is not inflated.

Four variants were evaluated. None showed skill:

variant	rank-IC	t	dispersion	impossible
pretrained Kronos-small	−0.0116	−0.54	3.61×	11.82%
fine-tuned, 4y raw (this model)	−0.0114	−0.50	1.85×	2.93%
fine-tuned, 15y corporate-action-adjusted	+0.0021	+0.09	10.19×	56.61%
fine-tuned, 15y, frozen tokenizer	−0.0315	−2.05	4.85×	17.86%

The 15-year variant, despite four times the data and corporate-action adjustment, regressed calibration badly. Freezing the tokenizer recovered about half of that, implicating tokenizer/predictor mismatch as a partial cause — the tokenizer received far more adaptation than the predictor.

The frozen-tokenizer arm's t of −2.05 is the only |t| above 2 in the study. It should not be believed: it is the fourth of four variants tested, where a |t| > 2 arises by chance roughly 18% of the time, and no prior hypothesis predicted negative skill.

Training

Base: NeoQuasar/Kronos-small (predictor), NeoQuasar/Kronos-Tokenizer-base
Data: NSE daily bars, ~300 symbols by liquidity, 2022-09 → 2025-06 targets
Windows: 174,645 (90-bar lookback, 10-bar horizon)
Schedule: tokenizer 3 epochs, then predictor against it; AdamW, lr 2e-4 / 4e-5, batch 16, grad-clip 3.0, MPS
Checkpoint selection: best validation loss. The predictor's validation loss turned after epoch 1 and epochs 2-3 were discarded — on a corpus five times larger the same thing happened, so this is not a data-volume limitation.
Normalisation: statistics from the lookback segment only, never the predicted segment.

Usage

python

from huggingface_hub import snapshot_download
from model import Kronos, KronosTokenizer, KronosPredictor  # upstream Kronos repo

path = snapshot_download("Sahilsingh0808/kronos-nse-1d")
tokenizer = KronosTokenizer.from_pretrained(f"{path}/tokenizer").eval()
predictor = Kronos.from_pretrained(f"{path}/predictor").eval()
kp = KronosPredictor(predictor, tokenizer, max_context=512)

.eval() matters: from_pretrained leaves these modules in train mode, where a non-zero attention dropout reaches scaled_dot_product_attention — an outright error on MPS, and silent noise in every forecast elsewhere.

Sample at temperature 0.6, not 1.0. At 1.0 the pretrained model's forecast spread measured 3.95× realised volatility with 12.6% impossible forecasts.

Intended use

Research into financial time-series foundation models; a calibration reference; a reproducible negative result for anyone evaluating whether to fine-tune Kronos on a new equity market.

Out of scope

Trading, investment decisions, or any use where the output is treated as a return forecast. The model has no measured predictive skill. It is not investment advice.

Limitations

Evaluated on one market (NSE), one horizon (1 day), one window (100 sessions of 2026). 100 sessions is enough to reject a large effect, not to rule out a small one — detecting an IC of 0.02 at |t| = 2 would need roughly 400 sessions.
The training corpus was not corporate-action adjusted, so split and bonus gaps appear as price crashes. The adjusted 15-year variant fixed that and performed worse, so this is not the binding constraint, but it is a real defect in this checkpoint.
Training universe is liquidity-ranked, biased toward large caps.
One market regime: 2022-2026 Indian equities were broadly rising.

Citation

bibtex

@misc{kronos_nse_1d_2026,
  title  = {Kronos-NSE-1d: fine-tuning a financial foundation model on Indian
            equities improves calibration but not predictive skill},
  author = {Sahil Singh},
  year   = {2026},
  note   = {Negative result. Rank-IC -0.0114 (t -0.50) over 100 held-out
            sessions; a 12-1 momentum baseline scored +0.0376 (t +3.51) on
            identical terms.},
  url    = {https://huggingface.co/Sahilsingh0808/kronos-nse-1d}
}

sahil007123/kronos-nse-1d

Kronos-NSE-1d — a fine-tune that improved calibration and produced no alpha