Datasets:

deusmos
/

cbb26-day-corpus-v1

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
schema_version: string
trade_date: timestamp[s]
session_timezone: string
session_start_utc_iso: timestamp[s]
bucket_count: int64
input_seconds: int64
training_anchor_seconds_iso: list<item: timestamp[s]>
  child 0, item: timestamp[s]
validation_anchor_seconds_iso: list<item: null>
  child 0, item: null
bundle_npz_relative: string
products: list<item: struct<product_id: string, tensor_key: string, shape_time: int64, shape_buckets: int64, s (... 536 chars omitted)
  child 0, item: struct<product_id: string, tensor_key: string, shape_time: int64, shape_buckets: int64, shape_channe (... 524 chars omitted)
      child 0, product_id: string
      child 1, tensor_key: string
      child 2, shape_time: int64
      child 3, shape_buckets: int64
      child 4, shape_channels: int64
      child 5, tensor_dtype: string
      child 6, lowest: string
      child 7, highest_exclusive: string
      child 8, price_bucket_width: string
      child 9, price_bucket_count: int64
      child 10, normalization_policy_id: string
      child 11, raw_max_abs_by_channel: list<item: double>
          child 0, item: double
      child 12, effective_divisors: list<item: double>
          child 0, item: double
      child 13, computed_from_start_iso: timestamp[s]
      child 14, computed_from_end_iso: timestamp[s]
      child 15, channel_names: list<item: string>
          child 0, item: string
      child 16, feature_set_version: string
      child 17, tensor_npz_relative: string
      child 18, artifact_normalization: string
      child 19, bucket_width_reference_mid: string
      child 20, bucket_width_midpoint_fraction: string
training_anchor_rows_npz_key: string
training_anchor_midpoints_npz_key: string
label_horizon_seconds: int64
training_anchor_labels_npz_key: string
loader_normalization: string
block_seconds: int64
product_order: list<item: string>
  child 0, item: string
array_shapes: list<item: list<item: int64>>
  child 0, item: list<item: int64>
      child 0, item: int64
padded_bucket_counts: list<item: int64>
  child 0, item: int64
created_unix_seconds: double
bytes: int64
source_tensors: list<item: struct<mtime_ns: int64, product_id: string, relative_path: string, size: int64, tensor_ke (... 11 chars omitted)
  child 0, item: struct<mtime_ns: int64, product_id: string, relative_path: string, size: int64, tensor_key: string>
      child 0, mtime_ns: int64
      child 1, product_id: string
      child 2, relative_path: string
      child 3, size: int64
      child 4, tensor_key: string
decimated_slabs_are_raw: bool
trailing_bucket_pad: int64
manifest_hash: string
dtype: string
to
{'array_shapes': List(List(Value('int64'))), 'block_seconds': Value('int64'), 'bytes': Value('int64'), 'created_unix_seconds': Value('float64'), 'decimated_slabs_are_raw': Value('bool'), 'dtype': Value('string'), 'loader_normalization': Value('string'), 'manifest_hash': Value('string'), 'padded_bucket_counts': List(Value('int64')), 'product_order': List(Value('string')), 'schema_version': Value('int64'), 'source_tensors': List({'mtime_ns': Value('int64'), 'product_id': Value('string'), 'relative_path': Value('string'), 'size': Value('int64'), 'tensor_key': Value('string')}), 'trade_date': Value('timestamp[s]'), 'trailing_bucket_pad': Value('int64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 299, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              schema_version: string
              trade_date: timestamp[s]
              session_timezone: string
              session_start_utc_iso: timestamp[s]
              bucket_count: int64
              input_seconds: int64
              training_anchor_seconds_iso: list<item: timestamp[s]>
                child 0, item: timestamp[s]
              validation_anchor_seconds_iso: list<item: null>
                child 0, item: null
              bundle_npz_relative: string
              products: list<item: struct<product_id: string, tensor_key: string, shape_time: int64, shape_buckets: int64, s (... 536 chars omitted)
                child 0, item: struct<product_id: string, tensor_key: string, shape_time: int64, shape_buckets: int64, shape_channe (... 524 chars omitted)
                    child 0, product_id: string
                    child 1, tensor_key: string
                    child 2, shape_time: int64
                    child 3, shape_buckets: int64
                    child 4, shape_channels: int64
                    child 5, tensor_dtype: string
                    child 6, lowest: string
                    child 7, highest_exclusive: string
                    child 8, price_bucket_width: string
                    child 9, price_bucket_count: int64
                    child 10, normalization_policy_id: string
                    child 11, raw_max_abs_by_channel: list<item: double>
                        child 0, item: double
                    child 12, effective_divisors: list<item: double>
                        child 0, item: double
                    child 13, computed_from_start_iso: timestamp[s]
                    child 14, computed_from_end_iso: timestamp[s]
                    child 15, channel_names: list<item: string>
                        child 0, item: string
                    child 16, feature_set_version: string
                    child 17, tensor_npz_relative: string
                    child 18, artifact_normalization: string
                    child 19, bucket_width_reference_mid: string
                    child 20, bucket_width_midpoint_fraction: string
              training_anchor_rows_npz_key: string
              training_anchor_midpoints_npz_key: string
              label_horizon_seconds: int64
              training_anchor_labels_npz_key: string
              loader_normalization: string
              block_seconds: int64
              product_order: list<item: string>
                child 0, item: string
              array_shapes: list<item: list<item: int64>>
                child 0, item: list<item: int64>
                    child 0, item: int64
              padded_bucket_counts: list<item: int64>
                child 0, item: int64
              created_unix_seconds: double
              bytes: int64
              source_tensors: list<item: struct<mtime_ns: int64, product_id: string, relative_path: string, size: int64, tensor_ke (... 11 chars omitted)
                child 0, item: struct<mtime_ns: int64, product_id: string, relative_path: string, size: int64, tensor_key: string>
                    child 0, mtime_ns: int64
                    child 1, product_id: string
                    child 2, relative_path: string
                    child 3, size: int64
                    child 4, tensor_key: string
              decimated_slabs_are_raw: bool
              trailing_bucket_pad: int64
              manifest_hash: string
              dtype: string
              to
              {'array_shapes': List(List(Value('int64'))), 'block_seconds': Value('int64'), 'bytes': Value('int64'), 'created_unix_seconds': Value('float64'), 'decimated_slabs_are_raw': Value('bool'), 'dtype': Value('string'), 'loader_normalization': Value('string'), 'manifest_hash': Value('string'), 'padded_bucket_counts': List(Value('int64')), 'product_order': List(Value('string')), 'schema_version': Value('int64'), 'source_tensors': List({'mtime_ns': Value('int64'), 'product_id': Value('string'), 'relative_path': Value('string'), 'size': Value('int64'), 'tensor_key': Value('string')}), 'trade_date': Value('timestamp[s]'), 'trailing_bucket_pad': Value('int64')}
              because column names don't match

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CBB26 Day Corpus v1 (`cbb26-day-corpus-v1`)

Materialized day_corpus_bundle_v1 training corpora derived from cbb26 Timescale replay — published for cloud training and research without re-running materialization locally.

Canonical Hub repo: deusmos/cbb26-day-corpus-v1

Source-of-truth for this card (edit here, then publish): docs/datasets/cbb26-day-corpus-v1/README.md in the cbb26 git repo.

Standards index: docs/standards/README.md

Start here

I want…	Use this
ML tensors / training corpus	This repo — Path A Quickstart (~10–15 min)
Raw L2 replay / own materialize	`cbb26-timeseries-db`
Run a live collector	cbb26 repo + local Docker stack

Not a row-oriented HF dataset. Files are NPZ/JSON day bundles. Do not expect load_dataset() to return training rows. Use hf download + verify scripts below.

NDDS = Normalized Decimated Day Slabs — 10-second decimated, loader-normalized tensor slabs cached under _ndds_cache/ (~300 MiB/day on Hub). Implementation: libs/day_raster_corpus/ndds_cache.py.

What this dataset is

This is not raw Timescale replay. It is the materialized corpus layer above replay — one fixed materialization profile (10-second decimation, loader normalization, 128-row windows per day_corpus_bundle_v1). That profile is lossy by design: it encodes cbb26's training geometry, not every choice you might want for your own experiments.

If you need a different bucket size, normalization policy, or decimation rate, start from cbb26-timeseries-db replay shards and materialize locally. This dataset is the convenience layer; replay preserves your options.

Layer	Dataset	Fidelity	User flexibility
Raw replay	`deusmos/cbb26-timeseries-db`	Lossless L2 replay truth	Choose your own materialization
This dataset	`cbb26-day-corpus-v1`	Lossy, fixed profile	Fast path for standard training geometry
Training runs	separate model repos	—	checkpoints (v2 standard)

Hub layout (NDDS-only default)

train/{YYYY-MM-DD}/
  bundle_manifest.json
  anchors.npz
  _ndds_cache/metadata.json
  _ndds_cache/decimated_slabs.npz
eval/{YYYY-MM-DD}/…

~315 MiB/day average (NDDS-only). Full tensor_*.npz files are not on Hub by default — materialize locally from replay if you need raw slabs.

Schema

Manifest: schema_version: day_corpus_bundle_v1
Spec: docs/standards/specs/DAY_CORPUS_BUNDLE_v1.md

Coverage snapshot (live)

Metric	Value
Last verified	2026-05-25 22:41 UTC
Hub revision	`a82f1331f4c7f512ee6a126e410c17dd9c32da5f`
Train days	57
Eval days	2
Total bundle days	59
Train span	`2025-01-01` → `2026-05-23`
Eval span	`2024-12-01` → `2026-05-06`

Refresh: uv run python scripts/generate_hf_dataset_coverage.py --update-readme

Download

Quick verify path (~10–15 min):

git clone https://github.com/deusmos/cbb26.git && cd cbb26
uv sync
./examples/download_and_verify_corpus_day.sh

Manual download:

export HF_TOKEN=...   # optional for public repo
export HF_DATASET_REVISION=<pin-sha-after-upload>

uv run hf download deusmos/cbb26-day-corpus-v1 \
  --repo-type dataset \
  --revision "$HF_DATASET_REVISION" \
  --local-dir ./corpus-hf

Single day:

uv run hf download deusmos/cbb26-day-corpus-v1 \
  --repo-type dataset \
  --include "train/2024-12-01/*" \
  --local-dir ./corpus-hf

Verify locally

uv run python scripts/verify_day_raster_bundle_dir.py \
  ./corpus-hf/train/2024-12-01 --require-ndds

Root verify (profile days):

uv run python scripts/verify_free_17d_day_raster_output_root.py ./corpus-hf/train

Tools for researchers

Tool	Command / link
Download + verify	`examples/download_and_verify_corpus_day.sh`
Preview PNG + HTML gallery	`examples/preview_corpus_day.sh`
PyTorch DataLoader	`libs/day_raster_corpus/pytorch_loader.py`, `examples/pytorch_day_corpus_dataloader.py`
Interactive flashcard (labels required)	`scripts/run_flashcard_drill.sh -- --bundle-day-dir ...`
Hub thumbnails	browse `previews/` on this repo
Live peek	HF Space `deusmos/cbb26-corpus-peek`

Visual preview (local)

One command after clone — download one train day, render window PNGs, open HTML gallery:

./examples/preview_corpus_day.sh
# opens file://.../tmp/examples_corpus_previews/index.html

Underlying render script: scripts/render_day_bundle_window_samples.py (writes gallery-compatible *_summary.json sidecars).

Visual previews (Hub)

Static montage PNGs are published under previews/{split}/{YYYY-MM-DD}/ with an index at previews/manifest.json.

Colormap: signed quantity / depth / delta channels mapped to RGB via libs/tensor_materializer/visualization.py (tensor_to_rgb_image_array).

Maintainers regenerate thumbnails:

scripts/upload_corpus_previews_to_hf.sh --corpus-root ./corpus-hf

PyTorch DataLoader (research)

Use the shareable loader module (wraps verified NDDS window code; no latent-encoder labels):

from libs.day_raster_corpus.pytorch_loader import Cbb26DayCorpusDataset, load_day_corpus_dataloader

# After: ./examples/download_and_verify_corpus_day.sh
day_dir = "./tmp/examples_corpus_day/train/2024-12-01"

dataset = Cbb26DayCorpusDataset(day_dir, ndds_cache_mode="readonly")
sample = dataset[0]
# sample["tensor"]: (P, 128, bucket_count, 3) float16
# sample["anchor_row_decimated"], sample["anchor_second_index"], sample["trade_date"]

loader = load_day_corpus_dataloader(day_dir, batch_size=4, shuffle=True, ndds_cache_mode="readonly")
batch = next(iter(loader))
# batch["tensor"]: (B, P, 128, bucket_count, 3)

Multi-day local corpus:

loader = load_day_corpus_dataloader("./corpus-hf", split="train", batch_size=8)

Stream from Hub without a full local tree (requires HF_TOKEN if gated):

dataset = Cbb26DayCorpusDataset(
    "train/2024-12-01",
    corpus_source="hf",
    hf_dataset_repo="deusmos/cbb26-day-corpus-v1",
    ndds_cache_mode="readonly",
)

Notes:

Prefer num_workers=0 — full-day NDDS slabs are held in process memory.
Default normalization: sample_window_max_abs_per_channel (see libs/day_raster_corpus/loader_normalization.py).
Raw timeseries is not a PyTorch loader. Restore cbb26-timeseries-db to Postgres and materialize day bundles first.

Demo script: examples/pytorch_day_corpus_dataloader.py

Produce bundles from replay

Requires populated Timescale (collector, restore, or backfill):

DB_HOST=localhost DB_PORT=5432 \
uv run python scripts/materialize_valid_day_bundles.py \
  --output-root ./corpus \
  --dates 2024-12-01

See Quickstart Path B and timeseries dataset card.

Upload (maintainers)

export HF_DATASET_REPO=deusmos/cbb26-day-corpus-v1
scripts/upload_corpus_to_hf_sequential.sh \
  --corpus-root ./corpus \
  --batch-by day

Default: --corpus-artifacts ndds-only. Full tensors: --corpus-artifacts full.

Publish this README:

scripts/publish_day_corpus_dataset_readme.sh

Publish Hub preview thumbnails (maintainers):

scripts/upload_corpus_previews_to_hf.sh --corpus-root ./corpus-hf

Contributing missing days

Materialize and verify locally (verify_day_raster_bundle_dir.py)
Upload via sequential script or open Hub Discussion for promotion
See docs/standards/CONTRIBUTING_DATA.md

FAQ

Can I use this commercially?

Market data is governed by Coinbase market data terms. Software and manifests are MIT licensed. Not legal advice.

Why Postgres dumps not Parquet?

Raw replay uses pg_dump; this corpus layer uses NPZ/JSON bundles. See ADR-002 and ADR-003.

Why two datasets?

Raw replay (timeseries-db) vs materialized NDDS (this repo).

How complete is UTC day X?

Bundle manifest lists products; upstream replay day should have 20/20 shards before materialization.

How do I cite this?

@dataset{cbb26_day_corpus_v1,
  title  = {CBB26 Day Corpus v1 (NDDS Day Bundles)},
  author = {deusmos},
  year   = {2026},
  url    = {https://huggingface.co/datasets/deusmos/cbb26-day-corpus-v1}
}

Is this affiliated with Coinbase?

No. Independent research project using public market feeds.

Full FAQ source: docs/datasets/_shared/FAQ.md.

Licensing and data use

Software / manifests: MIT License
Market data: derived from Coinbase feeds — see Coinbase market data terms and DATA_USE_POLICY.md

References

Resource	Location
PRD-05	`docs/prd/PRD-05_day-corpus-public-standard.md`
Examples	`examples/download_and_verify_corpus_day.sh`, `examples/preview_corpus_day.sh`, `examples/pytorch_day_corpus_dataloader.py`
Corpus peek Space	`deusmos/cbb26-corpus-peek`
Vast/JOJAT runbook	`docs/runbooks/vast_jojat_training.md`
Raw replay dataset	`deusmos/cbb26-timeseries-db`
Coverage refresh	`scripts/generate_hf_dataset_coverage.py`
Terminology	`terminology.md`

Changelog

Date	Notes
2026-05-25	A+++ upgrade: decision tree, live coverage, FAQ, BibTeX, examples link

Downloads last month: 656

Total file size:

19.5 GB

CBB26 Day Corpus v1 (cbb26-day-corpus-v1)