Datasets:

deusmos
/

cbb26-timeseries-db

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
schema: string
trade_date: timestamp[s]
product_id: string
day_start_utc: timestamp[s]
day_end_utc: timestamp[s]
row_counts: struct<orderbook_replay_anchors: int64, orderbook_second_deltas: int64, orderbook_checkpoints: int64 (... 35 chars omitted)
  child 0, orderbook_replay_anchors: int64
  child 1, orderbook_second_deltas: int64
  child 2, orderbook_checkpoints: int64
  child 3, orderbook_replay_metadata: int64
dump_relpath: string
dump_size_bytes: int64
export_schema: string
format: string
database_bytes: int64
shards: list<item: null>
  child 0, item: null
table_bytes: struct<orderbook_second_deltas: int64, orderbook_checkpoints: int64, orderbook_replay_anchors: int64 (... 89 chars omitted)
  child 0, orderbook_second_deltas: int64
  child 1, orderbook_checkpoints: int64
  child 2, orderbook_replay_anchors: int64
  child 3, orderbook_replay_metadata: int64
  child 4, bucket_definitions: int64
  child 5, artifact_manifests: int64
migrations_included: list<item: string>
  child 0, item: string
shard_count: int64
scope_tables: list<item: string>
  child 0, item: string
hf_repo: string
time_ranges: struct<orderbook_second_deltas: struct<min: timestamp[s], max: timestamp[s]>, orderbook_replay_ancho (... 118 chars omitted)
  child 0, orderbook_second_deltas: struct<min: timestamp[s], max: timestamp[s]>
      child 0, min: timestamp[s]
      child 1, max: timestamp[s]
  child 1, orderbook_replay_anchors: struct<min: timestamp[s], max: timestamp[s]>
      child 0, min: timestamp[s]
      child 1, max: timestamp[s]
  child 2, orderbook_checkpoints: struct<min: timestamp[s], max: timestamp[s]>
      child 0, min: timestamp[s]
      child 1, max: timestamp[s]
dump_layout: string
filters: struct<start_date: timestamp[s], end_date: timestamp[s], products: null>
  child 0, start_date: timestamp[s]
  child 1, end_date: timestamp[s]
  child 2, products: null
dump_basename: string
database_target: struct<host: string, port: int64, dbname: string>
  child 0, host: string
  child 1, port: int64
  child 2, dbname: string
export_mode: string
created_at: timestamp[s]
sync_mode: bool
archive_basename: string
pg_dump_docker_image: string
restore_notes: list<item: string>
  child 0, item: string
scope: string
hypertable_bytes: int64
to
{'schema': Value('string'), 'created_at': Value('timestamp[s]'), 'export_mode': Value('string'), 'scope': Value('string'), 'scope_tables': List(Value('string')), 'format': Value('string'), 'dump_layout': Value('string'), 'export_schema': Value('string'), 'dump_basename': Value('string'), 'archive_basename': Value('string'), 'database_target': {'host': Value('string'), 'port': Value('int64'), 'dbname': Value('string')}, 'database_bytes': Value('int64'), 'hypertable_bytes': Value('int64'), 'table_bytes': {'orderbook_second_deltas': Value('int64'), 'orderbook_checkpoints': Value('int64'), 'orderbook_replay_anchors': Value('int64'), 'orderbook_replay_metadata': Value('int64'), 'bucket_definitions': Value('int64'), 'artifact_manifests': Value('int64')}, 'row_counts': {'orderbook_checkpoints': Value('int64'), 'orderbook_replay_anchors': Value('int64'), 'orderbook_replay_metadata': Value('int64'), 'orderbook_second_deltas': Value('int64')}, 'time_ranges': {'orderbook_second_deltas': {'min': Value('timestamp[s]'), 'max': Value('timestamp[s]')}, 'orderbook_replay_anchors': {'min': Value('timestamp[s]'), 'max': Value('timestamp[s]')}, 'orderbook_checkpoints': {'min': Value('timestamp[s]'), 'max': Value('timestamp[s]')}}, 'pg_dump_docker_image': Value('string'), 'restore_notes': List(Value('string')), 'migrations_included': List(Value('string')), 'shard_count': Value('int64'), 'shards': List(Value('null')), 'sync_mode': Value('bool'), 'hf_repo': Value('string'), 'filters': {'start_date': Value('timestamp[s]'), 'end_date': Value('timestamp[s]'), 'products': Value('null')}}
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 2815, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2352, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2377, 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 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 419, 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 310, 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 130, 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 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              schema: string
              trade_date: timestamp[s]
              product_id: string
              day_start_utc: timestamp[s]
              day_end_utc: timestamp[s]
              row_counts: struct<orderbook_replay_anchors: int64, orderbook_second_deltas: int64, orderbook_checkpoints: int64 (... 35 chars omitted)
                child 0, orderbook_replay_anchors: int64
                child 1, orderbook_second_deltas: int64
                child 2, orderbook_checkpoints: int64
                child 3, orderbook_replay_metadata: int64
              dump_relpath: string
              dump_size_bytes: int64
              export_schema: string
              format: string
              database_bytes: int64
              shards: list<item: null>
                child 0, item: null
              table_bytes: struct<orderbook_second_deltas: int64, orderbook_checkpoints: int64, orderbook_replay_anchors: int64 (... 89 chars omitted)
                child 0, orderbook_second_deltas: int64
                child 1, orderbook_checkpoints: int64
                child 2, orderbook_replay_anchors: int64
                child 3, orderbook_replay_metadata: int64
                child 4, bucket_definitions: int64
                child 5, artifact_manifests: int64
              migrations_included: list<item: string>
                child 0, item: string
              shard_count: int64
              scope_tables: list<item: string>
                child 0, item: string
              hf_repo: string
              time_ranges: struct<orderbook_second_deltas: struct<min: timestamp[s], max: timestamp[s]>, orderbook_replay_ancho (... 118 chars omitted)
                child 0, orderbook_second_deltas: struct<min: timestamp[s], max: timestamp[s]>
                    child 0, min: timestamp[s]
                    child 1, max: timestamp[s]
                child 1, orderbook_replay_anchors: struct<min: timestamp[s], max: timestamp[s]>
                    child 0, min: timestamp[s]
                    child 1, max: timestamp[s]
                child 2, orderbook_checkpoints: struct<min: timestamp[s], max: timestamp[s]>
                    child 0, min: timestamp[s]
                    child 1, max: timestamp[s]
              dump_layout: string
              filters: struct<start_date: timestamp[s], end_date: timestamp[s], products: null>
                child 0, start_date: timestamp[s]
                child 1, end_date: timestamp[s]
                child 2, products: null
              dump_basename: string
              database_target: struct<host: string, port: int64, dbname: string>
                child 0, host: string
                child 1, port: int64
                child 2, dbname: string
              export_mode: string
              created_at: timestamp[s]
              sync_mode: bool
              archive_basename: string
              pg_dump_docker_image: string
              restore_notes: list<item: string>
                child 0, item: string
              scope: string
              hypertable_bytes: int64
              to
              {'schema': Value('string'), 'created_at': Value('timestamp[s]'), 'export_mode': Value('string'), 'scope': Value('string'), 'scope_tables': List(Value('string')), 'format': Value('string'), 'dump_layout': Value('string'), 'export_schema': Value('string'), 'dump_basename': Value('string'), 'archive_basename': Value('string'), 'database_target': {'host': Value('string'), 'port': Value('int64'), 'dbname': Value('string')}, 'database_bytes': Value('int64'), 'hypertable_bytes': Value('int64'), 'table_bytes': {'orderbook_second_deltas': Value('int64'), 'orderbook_checkpoints': Value('int64'), 'orderbook_replay_anchors': Value('int64'), 'orderbook_replay_metadata': Value('int64'), 'bucket_definitions': Value('int64'), 'artifact_manifests': Value('int64')}, 'row_counts': {'orderbook_checkpoints': Value('int64'), 'orderbook_replay_anchors': Value('int64'), 'orderbook_replay_metadata': Value('int64'), 'orderbook_second_deltas': Value('int64')}, 'time_ranges': {'orderbook_second_deltas': {'min': Value('timestamp[s]'), 'max': Value('timestamp[s]')}, 'orderbook_replay_anchors': {'min': Value('timestamp[s]'), 'max': Value('timestamp[s]')}, 'orderbook_checkpoints': {'min': Value('timestamp[s]'), 'max': Value('timestamp[s]')}}, 'pg_dump_docker_image': Value('string'), 'restore_notes': List(Value('string')), 'migrations_included': List(Value('string')), 'shard_count': Value('int64'), 'shards': List(Value('null')), 'sync_mode': Value('bool'), 'hf_repo': Value('string'), 'filters': {'start_date': Value('timestamp[s]'), 'end_date': Value('timestamp[s]'), 'products': Value('null')}}
              because column names don't match

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CBB26 Timescale market data (`cbb26-timeseries-db`)

Public Timescale/Postgres replay shards for the cbb26 monorepo: canonical Coinbase Advanced Trade level-2 order book history stored in the market_data schema, packaged as restorable pg_dump files for research, corpus materialization, and reproducibility.

Canonical Hub repo: deusmos/cbb26-timeseries-db

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

Start here

I want…	Use this
ML tensors / training corpus	`cbb26-day-corpus-v1` — Path A Quickstart
Raw L2 replay / own materialize	This repo — restore + materialize (Path B, hours)
Run a live collector	cbb26 repo + local Docker stack

Not a row-oriented HF dataset. Files are pg_dump shards and JSON sidecars. Do not expect load_dataset() to return tabular rows. Use hf download + the restore workflow below.

NDDS = Normalized Decimated Day Slabs — 10-second decimated, loader-normalized tensor slabs cached under _ndds_cache/ (~300 MiB/day on Hub). For pre-built NDDS bundles use cbb26-day-corpus-v1. Implementation: libs/day_raster_corpus/ndds_cache.py.

Why replay shards?

Timescale replay is base truth for what happened on the Coinbase L2 order book: checkpoints, anchors, and per-second deltas with carry-forward semantics. Materializing to tensors is lossy — bucket sizes, normalization, and decimation are design choices, not facts stored in the exchange feed.

Publishing replay as restorable pg_dump shards lets you pick your own materialization profile instead of being locked into the maintainer's. cbb26-day-corpus-v1 is the fast path when you want our standard geometry; this repo is for when you need fidelity and flexibility. Restore + materialize takes longer — that is an intentional tradeoff.

Layer	Fidelity	User flexibility
This dataset (replay)	Lossless L2 replay truth	Choose resolution, buckets, normalization
Day corpus / NDDS on Hub	Lossy, fixed profile	Fast path for one training geometry

What this dataset is

This is not a training corpus of pre-built tensors. It is the raw replay layer that cbb26 services write and read before materialization:

Layer	What it is	Where it lives
This dataset	Timescale `market_data` replay tables, one shard per UTC day × product	HF `data/{YYYY-MM-DD}/{PRODUCT}.dump`
Materialized corpus	Day-bundle rasters derived from replay	Local disk or `deusmos/cbb26-day-corpus-v1`
Training run	Checkpoints, TensorBoard, eval metrics	Separate HF model repos / local `tmp/`

Schema (`market_data`)

Each shard exports the four core replay tables required for deterministic order-book replay in cbb26:

Table	Role
`orderbook_second_deltas`	One row per `(product_id, UTC second)` where the end-of-second book changed vs. the last persisted second
`orderbook_replay_anchors`	Full retained L2 book at anchor seconds (includes baseline anchor at or before day start)
`orderbook_checkpoints`	Full retained L2 book once per product per UTC hour
`orderbook_replay_metadata`	Operational replay continuity / gap metadata (not source of truth for book state)

DDL and migrations ship in-repo on the dataset:

migrations/0001_init.sql … 0004_replay_metadata_window_end_index.sql
schema/market_data_schema.dump — schema-only pg_dump -Fc for empty restores

Hypertables use TimescaleDB 2.x on Postgres 16. See services/timeseries-db/migrations/ in cbb26 for the live definition.

Products (20)

Shards use Coinbase product ids (BASE-QUOTE):

AAVE-USD, ADA-USD, APT-USD, ATOM-USD, AVAX-USD, BCH-USD, BTC-USD, DOGE-USD, DOT-USD, ETH-USD, HBAR-USD, LINK-USD, LTC-USD, NEAR-USD, PEPE-USD, SHIB-USD, SOL-USD, UNI-USD, XLM-USD, XRP-USD

This matches the default MARKET_COLLECTOR_PRODUCTS basket in deploy/compose/slice-a.yml.

Coverage snapshot (live)

Metric	Value
Last verified	2026-05-25 22:41 UTC
Hub revision	`b6c9773744ca346dba5c37c05939349f1ae3225e`
Shard files (`.dump`)	1220
UTC days with ≥1 shard	61
Days with 20/20 products	61
Distinct products	20
Date span	`2024-12-01` → `2026-05-25`
Manifest DB bytes (operator snapshot)	111622336995 (~104.0 GiB)

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

Each shard sidecar (data/.../{product}.json) records row_counts, dump_size_bytes, and UTC day bounds (cbb26_timeseries_shard_manifest_v1).

Data dictionary (replay rows)

Full contract: docs/standards/specs/STORAGE_REPLAY.md · JSON source: libs/contracts/storage_and_snapshot_spec.json

Column	Type	Meaning
`product_id`	TEXT	Coinbase product, e.g. `BTC-USD`
`changed_second`	TIMESTAMPTZ	UTC second truncated; primary key with `product_id`
`source_sequence_num_start` / `_end`	BIGINT	L2 sequence range incorporated in this second
`best_bid` / `best_ask`	NUMERIC	End-of-second BBO after applying `changes`
`changes`	JSONB	Array of `[side, price, new_quantity]` — `bid` or `offer`; quantity `0` removes level
`change_count`	INTEGER	Length of `changes` array

Example orderbook_second_deltas row (sanitized static illustration):

{
  "product_id": "BTC-USD",
  "changed_second": "2024-12-01T12:34:56+00:00",
  "source_sequence_num_start": 9876543210,
  "source_sequence_num_end": 9876543299,
  "best_bid": "96543.21000000",
  "best_ask": "96543.22000000",
  "changes": [
    ["bid", "96540.00", "1.25000000"],
    ["offer", "96543.22", "0.50000000"],
    ["offer", "96544.00", "0"]
  ],
  "change_count": 3
}

Repository layout

deusmos/cbb26-timeseries-db/
├── README.md                          ← dataset card (this file on Hub)
├── backup_manifest.json               ← export inventory + restore notes (updated at bootstrap)
├── schema/
│   └── market_data_schema.dump        ← schema-only pg_dump (market_data)
├── migrations/
│   ├── 0001_init.sql
│   ├── 0002_checkpoint_delta_upgrade.sql
│   ├── 0003_replay_anchor_support.sql
│   └── 0004_replay_metadata_window_end_index.sql
└── data/
    └── {YYYY-MM-DD}/
        ├── {PRODUCT}.dump             ← pg_dump -Fc (core-replay tables for that day+product)
        └── {PRODUCT}.json             ← shard sidecar (row counts, sizes)

Naming conventions

{YYYY-MM-DD} — UTC calendar trade date (inclusive day window 00:00:00 … 23:59:59 UTC).
{PRODUCT} — uppercase Coinbase id, e.g. BTC-USD (regex ^[A-Z0-9]+-[A-Z0-9]+$).
One Hub commit per shard during sync upload (rate-limit friendly; ~30 s spacing).

Manifests and revision pinning

File	Purpose
`backup_manifest.json`	Dataset-level manifest (`cbb26_timeseries_db_backup_manifest_v2`): scope, table list, row counts, time ranges, restore notes, migration list
`data/.../*.json`	Per-shard sidecar with `row_counts` and `dump_size_bytes`
Git revision SHA	Pin downloads with `HF_TIMESERIES_DATASET_REVISION=<sha>` or `hf download … --revision <sha>`

There are no separate checksum files; verify restores with sidecar row_counts, pg_restore exit code, and scripts/smoke_restore_hf_shard.sh.

Download and restore

Quick inspect (no Postgres)

git clone https://github.com/deusmos/cbb26.git && cd cbb26
./examples/download_one_timeseries_shard.sh

Prerequisites

Docker (for pg_dump/pg_restore client Postgres 16 — use image pgvector/pgvector:pg16 or timescale/timescaledb:2.14.2-pg16)
A running Postgres 16 + TimescaleDB target (see Run your own collector below)
Hugging Face CLI: uv run hf auth login or HF_TOKEN in environment

Download

Full dataset (large):

export HF_TOKEN=...   # optional for public repo; required for uploads
uv run hf download deusmos/cbb26-timeseries-db --repo-type dataset --local-dir ./cbb26-timeseries-db

Pin a revision (recommended for reproducible materialization):

export HF_TIMESERIES_DATASET_REVISION=b6c9773744ca346dba5c37c05939349f1ae3225e  # example; get latest from Hub
uv run hf download deusmos/cbb26-timeseries-db \
  --repo-type dataset \
  --revision "$HF_TIMESERIES_DATASET_REVISION" \
  --local-dir ./cbb26-timeseries-db

Single day / product:

uv run hf download deusmos/cbb26-timeseries-db \
  --repo-type dataset \
  --include "data/2024-12-01/BTC-USD.*" \
  --local-dir ./cbb26-timeseries-db

Python (huggingface_hub):

import os

from huggingface_hub import snapshot_download

path = snapshot_download(
    repo_id="deusmos/cbb26-timeseries-db",
    repo_type="dataset",
    revision=os.environ["HF_TIMESERIES_DATASET_REVISION"],  # pin SHA
    allow_patterns=["data/2024-12-01/*", "migrations/*", "schema/*"],
)

Restore workflow

Start Timescale with an empty or compatible database (see collector section).

Apply migrations if the volume is fresh:

# migrations are in the downloaded tree under migrations/
psql -h 127.0.0.1 -U cbb26 -d cbb26 -f migrations/0001_init.sql
# … or rely on docker-entrypoint-initdb.d on first volume init

Restore schema (if needed):

docker run --rm --network host \
  -e PGPASSWORD="$POSTGRES_PASSWORD" \
  -v "$PWD/cbb26-timeseries-db:/in" \
  pgvector/pgvector:pg16 \
  pg_restore -h 127.0.0.1 -p 5432 -U cbb26 -d cbb26 --schema-only \
  /in/schema/market_data_schema.dump

Restore one shard (data-only into staging schema):
```
scripts/smoke_restore_hf_shard.sh \
  --shard-dir ./cbb26-timeseries-db \
  --date 2024-12-01 --product BTC-USD
```
Or manually with pg_restore + merge SQL (see script for merge statements).
Verify sidecar expectations — script fails closed on row-count mismatch vs sidecar.

Replay invariant: Each shard includes the baseline replay anchor at or before day_start_utc. Without it, same-day replay cannot be reconstructed.

Run your own collector (local Docker stack)

You can collect the same schema locally and later upload missing shards (see Contributing).

Prerequisites

Docker + Docker Compose
Linux or macOS host with ~50+ GiB free for sustained multi-product collection
Network access to Coinbase Advanced Trade websocket

1. Configure environment

From the cbb26 repo root:

cp .env.example .env
# Edit POSTGRES_PASSWORD, optional ports, and MARKET_COLLECTOR_PRODUCTS

Key variables (see .env.example and deploy/compose/slice-a.yml):

Variable	Default	Purpose
`POSTGRES_DB`	`cbb26`	Database name
`POSTGRES_USER`	`cbb26`	Database user
`POSTGRES_PASSWORD`	`change-me`	Change in production
`POSTGRES_PORT`	`5432`	Host-published Postgres port
`MARKET_COLLECTOR_PRODUCTS`	20-product CSV	Coinbase products to subscribe
`MARKET_COLLECTOR_PORT`	`8080`	Collector HTTP / metrics
`TENSOR_MATERIALIZER_PORT`	`8081`	Materializer API

Inside Compose containers, services use DB_HOST=timeseries-db (not localhost).

2. Start the stack

scripts/up_slice_a.sh
scripts/check_slice_a.sh

Services: timeseries-db, market-collector, tensor-materializer, prometheus, grafana.

3. Verify health

Expected endpoints:

Collector: http://localhost:8080/healthz, http://localhost:8080/readyz
Materializer: http://localhost:8081/healthz

After the collector runs, confirm replay rows exist:

DB_HOST=localhost DB_PORT=5432 uv run python scripts/_timeseries_db_hf_backup.py inventory --scope core-replay

Internal runbook: docs/runbooks/slice-a.md.

Contributing missing shards and days

See docs/standards/CONTRIBUTING_DATA.md (operator guide).

The dataset grows by incremental sync from contributors with a populated Timescale instance. Uploads are idempotent: shards already on Hub are skipped.

export HF_TIMESERIES_DATASET_REPO=deusmos/cbb26-timeseries-db
scripts/upload_timeseries_db_to_hf.sh --sync \
  --start-date 2026-05-22 --end-date 2026-05-31

Tools for researchers

This repo is replay truth, not pre-rendered tensors. To see what NDDS windows look like without restoring Postgres, use the day-corpus tooling:

Tool	Link
Preview PNG + gallery	`examples/preview_corpus_day.sh` on `cbb26-day-corpus-v1`
PyTorch DataLoader	`libs/day_raster_corpus/pytorch_loader.py`
Hub thumbnails	`previews/` on day-corpus
Live peek	HF Space `deusmos/cbb26-corpus-peek`

Raw shard preview without Postgres restore is not shipped in v1 — restore + materialize (Path B) or use day-corpus previews. See shared FAQ.

FAQ

Can I use this commercially?

Market data is governed by Coinbase market data terms. Software and schema in cbb26 are MIT licensed. Not legal advice.

Why Postgres dumps not Parquet?

Replay fidelity requires full L2 book state. See ADR-003.

Why two datasets?

Raw replay (this repo) vs materialized NDDS day bundles (cbb26-day-corpus-v1).

How complete is UTC day X?

20/20 products under data/{YYYY-MM-DD}/; sidecar orderbook_second_deltas ≈ 86400 for a full continuous day.

How do I cite this?

@dataset{cbb26_timeseries_db,
  title  = {CBB26 Coinbase L2 Order Book Replay Shards},
  author = {deusmos},
  year   = {2026},
  url    = {https://huggingface.co/datasets/deusmos/cbb26-timeseries-db}
}

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 / schema / scripts: MIT License (cbb26 repository).
Market data: sourced from Coinbase public market feeds. Respect Coinbase market data terms. Provided for research and reproducibility without warranty.

Policy: docs/standards/DATA_USE_POLICY.md.

Related (downstream)

Restoring HF shards into local Timescale lets you materialize new days/products without operator infrastructure. Pre-built NDDS day bundles for cloud training live in deusmos/cbb26-day-corpus-v1.

flowchart LR
  CB[Coinbase L2 websocket] --> MC[market-collector]
  MC --> TS[(timeseries-db / market_data)]
  TS --> TM[tensor-materializer / materialize scripts]
  TM --> CORP[Corpus artifacts]
  TS --> HF[(HF timeseries shards)]
  CORP --> HFC[(HF day-corpus-v1 NDDS)]
  CORP --> TR[train_latent_encoder.py]

Materialization entrypoints: scripts/materialize_valid_day_bundles.py, scripts/materialize_free_tardis_17d_day_bundles.py. Training: scripts/train_latent_encoder.py (v2 contract). Vast/JOJAT runbook: docs/runbooks/vast_jojat_training.md.

References

Resource	Location
Orderbook data standards (v1)	`docs/standards/README.md`
Examples	`examples/download_one_timeseries_shard.sh`
cbb26 repo	GitHub `deusmos/cbb26`
Upload entrypoint	`scripts/upload_timeseries_db_to_hf.sh`
Coverage refresh	`scripts/generate_hf_dataset_coverage.py`
Materialized training corpus (NDDS)	`deusmos/cbb26-day-corpus-v1`
Publish this README to Hub	`scripts/publish_timeseries_dataset_readme.sh`

Changelog

Date	Notes
2026-05-25	A+++ upgrade: decision tree, live coverage, restore smoke enforcement, FAQ, BibTeX
2026-05-25	Initial public dataset card (shards `2024-12-01`+, 20 products, core-replay export)

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Total file size:

30.1 GB

CBB26 Timescale market data (cbb26-timeseries-db)

Start here

Why replay shards?

What this dataset is

Schema (market_data)

Products (20)

Coverage snapshot (live)

Data dictionary (replay rows)

Repository layout

Naming conventions

Manifests and revision pinning

Download and restore

Quick inspect (no Postgres)

Prerequisites

Download

Restore workflow

Run your own collector (local Docker stack)

Prerequisites

1. Configure environment

2. Start the stack

3. Verify health

Contributing missing shards and days

Tools for researchers

FAQ

Can I use this commercially?

Why Postgres dumps not Parquet?

Why two datasets?

How complete is UTC day X?

How do I cite this?

Is this affiliated with Coinbase?

Licensing and data use

Related (downstream)

References

Changelog

CBB26 Timescale market data (`cbb26-timeseries-db`)

Schema (`market_data`)