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Datasets:
GregM
/
perp-data

Tasks:
Tabular Regression
Tags:
finance
crypto
defi
trading
parquet
time-series
License:
Dataset card Files
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
allowed_public_source_kinds: list<item: string>
  child 0, item: string
datasets: struct<funding: list<item: string>, open_interest: list<item: string>, trades: list<item: string>>
  child 0, funding: list<item: string>
      child 0, item: string
  child 1, open_interest: list<item: string>
      child 0, item: string
  child 2, trades: list<item: string>
      child 0, item: string
public_dataset_rule: string
schema_version: int64
files: list<item: null>
  child 0, item: null
row_counts: struct<>
status: string
generated_at: string
repo_id: string
public_source_kinds: list<item: string>
  child 0, item: string
to
{'files': List(Value('null')), 'generated_at': Value('string'), 'public_source_kinds': List(Value('string')), 'repo_id': Value('string'), 'row_counts': {}, 'status': Value('string')}
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
              allowed_public_source_kinds: list<item: string>
                child 0, item: string
              datasets: struct<funding: list<item: string>, open_interest: list<item: string>, trades: list<item: string>>
                child 0, funding: list<item: string>
                    child 0, item: string
                child 1, open_interest: list<item: string>
                    child 0, item: string
                child 2, trades: list<item: string>
                    child 0, item: string
              public_dataset_rule: string
              schema_version: int64
              files: list<item: null>
                child 0, item: null
              row_counts: struct<>
              status: string
              generated_at: string
              repo_id: string
              public_source_kinds: list<item: string>
                child 0, item: string
              to
              {'files': List(Value('null')), 'generated_at': Value('string'), 'public_source_kinds': List(Value('string')), 'repo_id': Value('string'), 'row_counts': {}, 'status': Value('string')}
              because column names don't match

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Perp Data

Free, daily-updated perpetuals market data intended for quant research, backtesting, and market microstructure analysis.

This repository is initialized for chain-derived perp DEX data, starting with Hyperliquid. The public dataset must only contain records whose provenance is public chain/archive state, not venue REST API resale. The first production feed will publish Hyperliquid funding, trades, and open interest as partitioned Parquet, under an explicit hyperliquid_chain/ provider partition.

Status

Initialized. Data publication starts after the Hetzner chain-derived hyperliquid_chain sink is live.

Planned Layout 

hyperliquid_chain/
  funding/date=YYYY-MM-DD/part-0000.parquet
  trades/date=YYYY-MM-DD/part-0000.parquet
  open_interest/date=YYYY-MM-DD/part-0000.parquet
_schema.json
_manifest.json

Quickstart 

import pandas as pd

repo = "GregM/perp-data"
url = f"https://huggingface.co/datasets/{repo}/resolve/main/hyperliquid_chain/funding/date=YYYY-MM-DD/part-0000.parquet"
funding = pd.read_parquet(url)
print(funding.head())

Provenance

Eligible public rows use one of these source_kind values:

  • on_chain_event
  • chain_rpc
  • hypercore_s3

API-sourced internal research rows are intentionally excluded from this public dataset.
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