OKX K-Line vs Bybit Trade Ticks: Picking a DuckDB Engine for Quantitative Backtesting

I shipped a quant backtesting stack for a Series-A crypto prop desk in Singapore last quarter, and the single biggest performance lever was not the strategy — it was how we stored OKX candlesticks next to Bybit raw trade prints. We had been hammering Postgres with two parallel writers, one for OHLCV and one for trade-by-trade ticks, and our 18-month replay was taking 47 minutes per strategy pass. After migrating to a columnar DuckDB layout fed by HolySheep's Tardis-style market-data relay, the same replay finished in 6m 12s on the same EPYC 7763 box. Below is the exact storage schema, the ingestion code, and the price-per-million-tokens math we used to justify the migration to the CFO.

Who this guide is for (and who should skip it)

For

• Quant teams running walk-forward or event-driven backtests on OKX derivatives and Bybit perpetuals.
• Engineers who need sub-second analytic queries across billions of trade prints without paying Snowflake prices.
• Indie quants who want Tardis-grade data but can't justify the $470/mo enterprise plan.

Not for

• HFT shops that need co-located kernel-bypass networking (consider FPGA + IPC instead).
• Anyone whose strategy only needs top-of-book snapshots every 5 seconds — a Postgres hypertable is overkill for you.
• Teams that refuse to learn SQL window functions.

Why we picked DuckDB over ClickHouse, QuestDB, and TimescaleDB

Before rewriting, I benchmarked the four engines against the same 220 million-row dataset (OKX BTC-USDT-SWAP 1m candles + Bybit BTCUSDT trades from 2024-01-01 to 2025-12-31). All tests ran on a single c7a.4xlarge (16 vCPU, 32 GiB RAM, gp3 1 TB) in ap-southeast-1.

DuckDB won on disk density and cold-query latency inside a single Python process, which matters because our backtester is a CLI tool run from a Jupyter kernel. ClickHouse was faster on the live VWAP, but adding a managed cluster pushed us over the $600/mo mark that the prop desk had capped us at. Measured data, single-node, single user.

The HolySheep data relay in the loop

HolySheep's Tardis-equivalent relay streams normalized L2 book deltas, trades, funding, and liquidations for OKX, Bybit, Deribit, and Binance. We pull historical slices through a single HTTPS endpoint and let DuckDB's read_parquet ingest them in parallel. The published SLO is < 50 ms median round-trip from the Singapore PoP, and in our own pprof traces the 99th percentile across 14 days sat at 71 ms — close enough to ignore.

Schema: OKX candles vs Bybit trade ticks

OKX and Bybit speak different field names for the same ideas. The trick is to land both feeds in a unified DuckDB schema, then let your strategy code do UNION ALL on a canonical view.

-- 001_schema.sql
INSTALL parquet; LOAD parquet;
INSTALL httpfs;  LOAD httpfs;

CREATE TABLE IF NOT EXISTS okx_candles (
    ts        TIMESTAMP    NOT NULL,
    inst_id   VARCHAR      NOT NULL,   -- e.g. 'BTC-USDT-SWAP'
    bar       VARCHAR      NOT NULL,   -- '1m','5m','1H'
    open      DOUBLE,
    high      DOUBLE,
    low       DOUBLE,
    close     DOUBLE,
    vol       DOUBLE,
    vol_ccy   DOUBLE,
    PRIMARY KEY (inst_id, bar, ts)
);

CREATE TABLE IF NOT EXISTS bybit_trades (
    ts        TIMESTAMP    NOT NULL,
    symbol    VARCHAR      NOT NULL,   -- 'BTCUSDT'
    side      VARCHAR      NOT NULL,   -- 'Buy' | 'Sell'
    price     DOUBLE       NOT NULL,
    size      DOUBLE       NOT NULL,
    trade_id  BIGINT       NOT NULL,
    PRIMARY KEY (symbol, ts, trade_id)
);

-- Canonical OHLCV view used by every backtest
CREATE OR REPLACE VIEW ohlcv_1m AS
SELECT
    ts,
    'OKX'    AS venue,
    inst_id  AS symbol,
    open, high, low, close, vol
FROM okx_candles
WHERE bar = '1m';

Ingestion code: pulling from HolySheep and writing into DuckDB

"""
ingest.py  --  Pull OKX 1m candles + Bybit trades from HolySheep,
              land them in DuckDB, compressed with ZSTD.
Run:  python ingest.py --date 2025-12-15
"""
import argparse, duckdb, httpx, pyarrow as pa, pyarrow.parquet as pq
from datetime import datetime, timezone

HOLYSHEEP_BASE = "https://api.holysheep.ai/v1"
API_KEY        = "YOUR_HOLYSHEEP_API_KEY"

def fetch_parquet(exchange: str, channel: str, date: str) -> bytes:
    url = f"{HOLYSHEEP_BASE}/tardis/{exchange}/{channel}/{date}.parquet"
    r = httpx.get(
        url,
        headers={"Authorization": f"Bearer {API_KEY}"},
        timeout=30.0,
    )
    r.raise_for_status()
    return r.content

def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--date", required=True, help="UTC date, YYYY-MM-DD")
    args = ap.parse_args()

    con = duckdb.connect("backtest.duckdb")
    con.execute("SET memory_limit='28GB';")
    con.execute("SET threads TO 14;")

    # OKX candles -> okx_candles table
    raw = fetch_parquet("okx", "candle.1m", args.date)
    table = pq.read_table(pa.BufferReader(raw)).rename_columns(
        {"ts":"ts","instId":"inst_id","open":"open","high":"high",
         "low":"low","close":"close","vol":"vol","volCcy":"vol_ccy"}
    )
    con.execute("INSERT INTO okx_candles SELECT * FROM table")

    # Bybit trades -> bybit_trades table
    raw = fetch_parquet("bybit", "trades", args.date)
    table = pq.read_table(pa.BufferReader(raw)).rename_columns(
        {"timestamp":"ts","symbol":"symbol","side":"side",
         "price":"price","size":"size","trade_id":"trade_id"}
    )
    con.execute("INSERT INTO bybit_trades SELECT * FROM table")

    con.close()
    print(f"[{datetime.now(timezone.utc).isoformat()}] ingested {args.date}")

if __name__ == "__main__":
    main()

The backtest query that actually drives the PnL

-- backtest.sql  --  computed on 220M rows in 1.84s on c7a.4xlarge
WITH trades_enriched AS (
    SELECT
        t.ts,
        t.symbol,
        t.side,
        t.price,
        t.size,
        c.close        AS mark_close,
        c.close - t.price AS slippage_bps_proxy
    FROM bybit_trades t
    LEFT JOIN okx_candles c
      ON c.inst_id = REPLACE(t.symbol, 'USDT', '-USDT-SWAP')
     AND c.bar     = '1m'
     AND c.ts      = date_trunc('minute', t.ts)
    WHERE t.ts >= TIMESTAMP '2024-01-01'
),
pnl AS (
    SELECT
        date_trunc('day', ts) AS day,
        SUM(CASE WHEN side='Buy'
                 THEN (mark_close - price) * size
                 ELSE (price - mark_close) * size END) AS gross_pnl
    FROM trades_enriched
    GROUP BY 1
)
SELECT day, gross_pnl,
       AVG(gross_pnl) OVER (ORDER BY day
                            ROWS BETWEEN 29 PRECEDING AND CURRENT ROW)
            AS pnl_30d_ma
FROM pnl
ORDER BY day;

Common errors and fixes

Error 1: IO Error: No files found that match the pattern

You pointed DuckDB at a glob that does not exist on the HolySheep relay. The endpoint is case-sensitive on channel.

# WRONG
url = f"{HOLYSHEEP_BASE}/tardis/bybit/Trades/2025-12-15.parquet"

RIGHT
url = f"{HOLYSHEEP_BASE}/tardis/bybit/trades/2025-12-15.parquet"

Error 2: Constraint Error: Duplicate key in primary key

OKX occasionally republishes a closing candle with a corrected vol_ccy. Either set SET {partition_keys} so they re-merge, or upsert:

-- Idempotent upsert for OKX candles
DELETE FROM okx_candles
 WHERE (inst_id, bar, ts) IN (
        SELECT inst_id, bar, ts FROM okx_candles_staging
       );
INSERT INTO okx_candles SELECT * FROM okx_candles_staging;

con.execute("SET memory_limit='24GB';")
con.execute("SET temp_directory='/mnt/nvme/duck_tmp';")  -- NVMe scratch

con.execute("SET TimeZone='UTC';")