Last quarter I was helping a three-person quant shop in Singapore stand up a new market-making backtest for OKX perpetual swaps. The bottleneck was not strategy logic — it was data. Every backtest iteration spent 14 minutes just to load six months of trades from gzipped CSVs. We rebuilt the pipeline around Parquet + DuckDB, dropped iteration time to under a minute, and shipped the model two sprints early. This is the exact playbook, including the AI-assisted regime-labeling step that runs on the HolySheep LLM endpoint.
Why Parquet for crypto trade data
OKX historical trade dumps are tall and narrow — millions of rows per day, only six or seven columns. Columnar formats are a perfect fit because most analytical queries touch only a subset of columns (price, side, amount) over a date range. Parquet also gives you:
- Column pruning: reading only
priceandamountskips everything else. - Predicate pushdown: row-group statistics let DuckDB skip whole chunks.
- Built-in compression: Zstd shrinks ~6 GB of raw trades to roughly 700 MB.
- Schema: types are stored once and respected across readers.
For the OKX BTC-USDT-SWAP feed I worked with, DuckDB on a partitioned Parquet dataset was 47× faster than the equivalent pandas read_csv pipeline — measured locally on an M2 Pro, 32 GB RAM. That figure is consistent with published benchmarks from the DuckDB team showing 30–50× speedups on columnar-friendly workloads.
Pipeline at a glance
- Stream raw OKX trades via HolySheep's Tardis-compatible market-data relay.
- Normalize timestamps to UTC nanoseconds, fix side encoding, drop duplicates.
- Partition by trade date and write Parquet with PyArrow.
- Use the HolySheep AI API to auto-label each day with a regime tag.
- Validate with DuckDB sanity queries.
Step 1: Stream raw OKX trades
HolySheep operates a Tardis.dev-style relay at https://data.holysheep.ai/v1/okx/trades. Each request returns a streaming gzip response with the canonical Tardis schema (timestamp, local_timestamp, id, side, price, amount). Because the relay is replay-stable, you can resume an interrupted download by tracking the last seen id.
import os, gzip, requests
from datetime import datetime, timedelta, timezone
API_KEY = os.environ["HOLYSHEEP_DATA_KEY"]
BASE = "https://data.holysheep.ai/v1/okx/trades"
def fetch_day(symbol: str, day: str, out_path: str) -> None:
"""symbol e.g. BTC-USDT-SWAP, day YYYY-MM-DD."""
url = f"{BASE}/{symbol}/{day}"
headers = {"Authorization": f"Bearer {API_KEY}"}
with requests.get(url, headers=headers, stream=True, timeout=60) as r:
r.raise_for_status()
with gzip.open(r.raw, "rb") as gz, open(out_path, "wb") as out:
for line in gz:
out.write(line)
print(f"wrote {out_path}")
if __name__ == "__main__":
start = datetime(2025, 1, 1, tzinfo=timezone.utc)
for i in range(7):