Datasets:
license: cc-by-4.0
pretty_name: Chainticks Perp Data
tags:
- finance
- crypto
- defi
- trading
- parquet
- time-series
- pandas
- duckdb
- polars
- mlcroissant
task_categories:
- tabular-regression
configs:
- config_name: funding
data_files:
- split: train
path: hyperliquid_chain/funding/date=*/part-*.parquet
- config_name: trades
data_files:
- split: train
path: hyperliquid_chain/trades/date=*/part-*.parquet
- config_name: markets
data_files:
- split: train
path: hyperliquid_chain/markets/date=*/part-*.parquet
- config_name: open_interest
data_files:
- split: train
path: hyperliquid_chain/open_interest/date=*/part-*.parquet
- config_name: liquidations
data_files:
- split: train
path: hyperliquid_chain/liquidations/date=*/part-*.parquet
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_eventchain_rpchypercore_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.