Chainticks/perp-data

Chainticks Perp Data

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

import pandas as pd

DATE = "YYYY-MM-DD"
URL = "https://huggingface.co/datasets/Chainticks/perp-data/resolve/main/hyperliquid_chain/trades/date={DATE}/part-0000.parquet"

trades = pd.read_parquet(URL.format(DATE=DATE))  # first shard; see _manifest.json for all part files
print(trades.head())

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 publishes Hyperliquid funding, trades, markets, open interest, and liquidations 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
  trades/date=YYYY-MM-DD/part-0001.parquet
  markets/date=YYYY-MM-DD/part-0000.parquet
  open_interest/date=YYYY-MM-DD/part-0000.parquet
  liquidations/date=YYYY-MM-DD/part-0000.parquet
_schema.json
_manifest.json
LATEST_DATE.txt

Quickstart

import pandas as pd
from huggingface_hub import HfApi

repo = "Chainticks/perp-data"
date = "YYYY-MM-DD"
api = HfApi()
files = [
    path for path in api.list_repo_files(repo, repo_type="dataset")
    if path.startswith(f"hyperliquid_chain/trades/date={date}/") and path.endswith(".parquet")
]
urls = [f"https://huggingface.co/datasets/{repo}/resolve/main/{path}" for path in files]
trades = pd.concat([pd.read_parquet(url) for url in urls], ignore_index=True)
print(trades.head(), len(trades))

import duckdb

date = "YYYY-MM-DD"
url = f"https://huggingface.co/datasets/Chainticks/perp-data/resolve/main/hyperliquid_chain/liquidations/date={date}/part-0000.parquet"
rows = duckdb.sql("select symbol, count(*) as n from read_parquet(?) group by 1 order by 2 desc", [url]).df()
print(rows)

import polars as pl

date = "YYYY-MM-DD"
url = f"https://huggingface.co/datasets/Chainticks/perp-data/resolve/main/hyperliquid_chain/open_interest/date={date}/part-0000.parquet"
oi = pl.read_parquet(url)
print(oi.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.

Agent Prompt Snippet

You can query Chainticks Perp Data directly from Hugging Face as partitioned Parquet. Use URLs shaped like:
https://huggingface.co/datasets/Chainticks/perp-data/resolve/main/<provider>/<dataset>/date=YYYY-MM-DD/part-0000.parquet

Valid provider for v1: hyperliquid_chain.
Valid datasets: funding, trades, markets, open_interest, liquidations.
Large dates may have multiple part-*.parquet files. Read _schema.json before generating queries. Read _manifest.json for available files, row counts, and UTC time ranges.
Read LATEST_DATE.txt for the newest published UTC partition.
Only treat rows as public-source eligible when source_kind is one of: on_chain_event, chain_rpc, hypercore_s3.

Machine Metadata

• Schema sidecar: _schema.json
• Manifest sidecar: _manifest.json
• Latest partition pointer: LATEST_DATE.txt
• Croissant metadata: https://huggingface.co/api/datasets/Chainticks/perp-data/croissant

Chainticks is independent and is not affiliated with, endorsed by, or sponsored by Hyperliquid Labs or any protocol whose data appears here. Protocol names are used descriptively.