# ⏱️ CHRONOS-X **UQO Working Paper No. 41** — *Universal Intraday Representation Learning for Probabilistic Financial Forecasting* [](paper/main.pdf) [](pyproject.toml) [](docs/DATA_CARD.md) [](docs/REPRODUCIBILITY.md) [](https://uqo.ca) [](mailto:simon-pierre.boucher@uqo.ca) > **TL;DR** — Can a compact multi-resolution patch Transformer (MRPT) that reads the last 390 one-minute bars plus > five days of 5-minute and twenty days of 30-minute past-only aggregates produce better-calibrated distributions > of the next 5/15/30/60-minute returns than equal-budget one-minute-only models and well-specified volatility > baselines? Tested on 300 U.S. stocks and ETFs (2018–2025, 234 M one-minute bars from FinData OS), with > matched information sets, proper scoring rules, day-cluster inference, 60 held-out instruments and an untouched > 2025 test. **See `docs/PHASE_5..7_REPORT.md` and the paper for the measured answer; no claim is made beyond them.** --- ## 📌 Status | Item | Value | |---|---| | Version | 1.0 (November 2026) — built autonomously on 2026-11-02 under the owner's brief (`CLAUDE.md`) | | SSRN | not yet submitted | | Journal | — (research software + working paper) | | Code & data | reproducible from `scripts/` against FinData OS (private FirstRate Data license); synthetic example runs without a license | ## 🎯 What this paper does Five pre-registered, falsifiable hypotheses (`paper/sections/hypotheses.tex`): H1 multi-resolution context vs one-minute-only at equal parameters; H2 transfer to unseen instruments; H3 history-only regime conditioning and calibration under stress; H4 coherent joint paths (low-rank Gaussian copula over the 60 future minutes) vs independent marginals; H5 (exploratory) economic content after assumed costs. Targets are return distributions (per-minute Student-t marginals, cumulative returns, realised variance, tail events), never price levels. ## 🏆 Headline results (scaled CRPS of the cumulative return; 2024 development test, 986 468 origins; 2025 untouched, 1 008 425) | Object | 2024 (H=5 / H=60) | 2025 (H=5 / H=60) | |---|---|---| | Random walk (trailing scale) | 0.4699 / 0.4347 | 0.4759 / 0.4409 | | HAR-RV (minute scale, Student-t) | 0.4615 / **0.4166** | 0.4674 / **0.4232** | | MRPT-S multi-resolution (3.5 M params) | **0.4531** / 0.4187 | **0.4594** / 0.4256 | | MRPT-S one-minute only | 0.4533 / 0.4192 | 0.4594 / 0.4257 | | MRPT-B (20.7 M params) | 0.4532 / 0.4189 | 0.4595 / 0.4257 | | H1 multi-res vs 1m-only | −0.04 % / −0.10 % (t −5) | +0.01 % / +0.01 % (Holm p ≥ 0.36) → **rejected in substance** | | H2 zero-shot on 60 unseen instruments vs random walk | −4.1 % / −4.3 % (pooled −4.05 % / −4.27 %) → **no transfer penalty** | same in 2025 | | H3 regime conditioning | +0.02 … +0.03 % CRPS, identical stress-day coverage → **rejected** | | | H4 copula paths vs independent | energy −0.003 %, variogram −0.002 % (CIs ∋ 0) → **rejected** | | | H5 economic content | corr(median, realised) ≤ 0.025; net 0.0 bp [−1.7, +1.8] at 1 bp cost → **rejected** | | | Time-only inputs (no returns/volumes) | +0.2 … +0.3 % vs full model: ≈ 90 % of the skill is trailing volatility + clock | | ## 🗂 Repository layout ``` wp41_uqo/ ├── CLAUDE.md · README.md · pyproject.toml · Makefile ├── configs/ pilot · feasibility · main · ablations · baselines · final · synthetic_smoke (.yaml) ├── src/chronos_x/ │ ├── data/ client · exports · universe · calendar · quality · adjustments · features · windows · synthetic │ ├── models/ patches · encoder · fusion · regime · student_t · joint_path · model │ ├── train/ trainer · losses · sampling · checkpoint · tracking │ ├── eval/ scores · calibration · statistics · subgroups · economic · figures │ ├── baselines/ naive · har · garch · trees · neural · features │ ├── inference/ predict · serialization · cli │ └── pipeline.py · experiment.py · config.py ├── scripts/ audit_api · build_universe · audit_coverage · download_pilot · run_experiment · train_pilot · train_main │ · benchmark · evaluate · make_figures · build_references · make_synthetic · node_run.sh ├── tests/ unit · integration (live API, synthetic pipeline) · leakage · synthetic · numerical ├── data/manifests universe_main.json (hash inside) · universe_main_v1_precoverage.json · candidates · universe_synthetic.json ├── results/ metrics// (summaries, leaderboards, comparisons, train logs) · logs/ (provenance JSONL) ├── paper/ main.tex · sections/ · tables/ (generated) · figures/ (generated) · references.bib (Crossref-verified) · main.pdf └── docs/ DATA_CARD · MODEL_CARD · REPRODUCIBILITY · LICENSE · OPERATIONS · PHASE_0..9_REPORT ``` ## 🚀 Quick start ```bash make install && make test # venv + 26 offline tests .venv/bin/python scripts/make_synthetic.py # license-free fixtures in the FinData schema .venv/bin/python scripts/run_experiment.py --config configs/synthetic_smoke.yaml # bars -> grids -> baselines -> MRPT -> scores (~3 min) .venv/bin/python scripts/evaluate.py --exps synthetic_smoke && .venv/bin/python scripts/make_figures.py --exps synthetic_smoke cd paper && make # latexmk -> main.pdf ``` Full study: `docs/REPRODUCIBILITY.md`. Compute node and operations: `docs/OPERATIONS.md`. ## 🗃 Data FinData OS (`https://findata.spboucher.ai/api/v1`), FirstRate Data one-minute bars, private license (no raw data in this repository). Universe: 300 instruments selected point-in-time at 2017-12-29 (ADV ≥ 5 M USD, ≥ 80 % regular-hours minute coverage, stratified by asset class × dollar volume × volatility, SIC / ETF category balanced), 60 held out. Sample 2018-01-02 → 2025-12-31: 233 958 776 rows, 220 142 835 in regular hours. Details and conventions (bar-open stamps, 391/211-slot sessions, masks, split policy): `docs/DATA_CARD.md`, `docs/PHASE_0_REPORT.md`, `docs/PHASE_1_REPORT.md`. ## 🔬 Methodology 1. Audit the live API and conventions on real rows (`scripts/audit_api.py`, live tests). 2. Build the PIT universe, audit coverage, freeze the manifest (`build_universe.py`, `audit_coverage.py`). 3. Export bars per instrument, build dense grids and past-only aggregates (`download_pilot.py`, `data/features.py`). 4. Index origins and build causal windows and masked targets (`data/windows.py`; leakage tests). 5. Fit baselines on the same origins (`baselines/`, `experiment.fit_and_predict_baselines`). 6. Train MRPT variants with the Student-t NLL, early stopping and checkpoints (`train/trainer.py`). 7. Predict, sample, score, bootstrap and test (`inference/predict.py`, `eval/`, `pipeline.py`). 8. Ablations, zero-shot instruments, untouched 2025 (`configs/ablations.yaml`, `--heldout`, `configs/final.yaml`). 9. Tables, macros and figures from saved results only (`evaluate.py`, `make_figures.py`); LaTeX build. ## 🖼 Figures & tables inventory Generated into `paper/figures/` and `paper/tables/` by the scripts above (see `docs/PHASE_8_REPORT.md`). ## ⚠️ Limitations Survivor-heavy liquid universe (coverage gate; delisted archives partial), bars without quotes, regular hours only, single-node compute without mixed precision, foundation-model baselines not run. Full list: `paper/sections/limitations.tex`. ## 📝 Citation ```bibtex @techreport{boucher2026chronosx, author = {Boucher, Simon-Pierre}, title = {CHRONOS-X: Universal Intraday Representation Learning for Probabilistic Financial Forecasting}, institution = {Universit\'e du Qu\'ebec en Outaouais, D\'epartement des sciences administratives}, type = {Working Paper}, number = {41}, year = {2026}, month = {November} } ``` ## 👤 Author & contact **Simon-Pierre Boucher** — Département des sciences administratives, Université du Québec en Outaouais (UQO), Gatineau, QC. 📧 [simon-pierre.boucher@uqo.ca](mailto:simon-pierre.boucher@uqo.ca) ## 🗒 Notes - Directory named `wp41_uqo` as requested by the brief (the standard would be a descriptive slug). - Intraday data: FirstRate Data (private license), accessed through FinData OS; attribution required, redistribution prohibited.