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Datasets:
GregM
/
hyperliquid-perp-open-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
repo_id: string
generated_at: string
public_source_kinds: list<item: string>
  child 0, item: string
files: list<item: null>
  child 0, item: null
status: string
row_counts: struct<>
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
              repo_id: string
              generated_at: string
              public_source_kinds: list<item: string>
                child 0, item: string
              files: list<item: null>
                child 0, item: null
              status: string
              row_counts: struct<>
              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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Hyperliquid Perp Open Data

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

This repository is initialized for chain-derived Hyperliquid data. 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.

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/hyperliquid-perp-open-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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