abdelrahman964/kronos-finetuning

Kronos BTC Directional Forecasting Models

Public checkpoints from an interval-aware adaptation study of the Kronos financial foundation model for BTCUSDT directional forecasting.

This model repository accompanies a Spring 2026 Senior Graduation Project II at the College of Information Technology, United Arab Emirates University.

Author: Abdelrahman Osman
Supervisor: Dr. Shengcai Liao

Project links

• Source code and complete evaluation artifacts
• Original editable PowerPoint poster
• Research poster preview
• Reproducibility guide
• Experimental report

Selected results

Horizon	Selected adaptation	Window	N	Accuracy	Baseline	Margin	Frictionless 1 BTC PnL
5 minutes	Native direction head	2026-01-01 to 2026-03-09	19,554	51.40%	50.16%	+1.24 pp	+5,836.58 USDT
1 hour	50% pruned direction head	2026-01-01 to 2026-03-04	1,498	53.67%	50.73%	+2.94 pp	+6,069.51 USDT
1 day	Last-6-layer consecutive-path adapter	2026-01-01 to 2026-03-16	75	62.67%	53.33%	+9.33 pp	+28,035.71 USDT

These are historical research observations on different windows, not direct like-for-like horizon comparisons. The PnL calculation assumes frictionless execution with a fixed 1 BTC position and excludes fees, spread, slippage, latency, funding, liquidity limits, and market impact.

The 62.67% daily figure is the best observed 75-row snapshot. A later extension to 88 rows through 2026-03-29 reached 57.95% against a 56.82% majority baseline.

Repository contents

The repository publishes every saved project weight from the supplied archive: 11 files, 3,338,377,256 bytes in total, representing 10 unique binary objects. One final 1-minute rollout checkpoint is byte-identical to its best checkpoint; both public paths are retained for archive completeness.

Selected horizon models

Model	Path	Format
5-minute native direction head	models/5min-native-direction-head/best_direction_model.pt	PyTorch checkpoint
1-hour 50% pruned direction head	models/1h-pruned50-direction-head/best_direction_model.pt	PyTorch checkpoint
1-day last-6 path adapter	models/1d-last6-path-adapter/model.safetensors	Safetensors

Required supporting models

Model	Path
Fine-tuned 5-minute Kronos predictor	models/5min-kronos-base/
Fine-tuned 5-minute tokenizer	models/5min-tokenizer/
Upstream daily tokenizer dependency	NeoQuasar/Kronos-Tokenizer-base

Additional experimental models

• models/1m-trainall-2025h1/
• models/1m-retrain-2017-2024/

weights_manifest.json records the source archive path, public path, byte size, role, and SHA-256 of every weight. checksums.sha256 supports direct integrity verification.

Download

from huggingface_hub import snapshot_download

model_root = snapshot_download(
    repo_id="abdelrahman964/kronos-finetuning",
    allow_patterns=["models/**", "weights_manifest.json", "checksums.sha256"],
)
print(model_root)

Loading notes

The safetensors predictor directories include their architecture configs. The direction-head .pt files use the custom implementation in finetune_csv/kronos_direction_model.py and the evaluator in finetune_csv/eval_direction_model.py.

Some direction-head metadata retains stale training-machine paths. Use the evaluator's explicit --predictor-path and --tokenizer-path arguments with the public supporting directories rather than relying on embedded paths.

PyTorch .pt files use pickle-based serialization. Load them only from this verified repository and check their SHA-256 values before use.

Intended use

• Reproducing the saved BTCUSDT direction experiments
• Comparing horizon-specific adaptation methods
• Studying moderate structured pruning as regularization
• Academic review of model artifacts and evaluation evidence
• Research prototypes and paper-trading experiments

Limitations

• The checkpoints were evaluated on one trading pair and limited market windows.
• Repeated model selection can inflate the best observed result.
• Results may degrade under distribution shift and realistic execution costs.
• The checkpoints do not provide investment advice or guaranteed profitability.
• Live exchange execution requires independent risk controls and user-supplied credentials.

License and attribution

The repository is released under the MIT License and retains attribution to the upstream Kronos project and paper:

Yu Shi et al. Kronos: A Foundation Model for the Language of Financial Markets. arXiv:2508.02739.

Users should also review the license and terms of the upstream Kronos models and their market-data provider.