commanderzee/1s-crypto-data
1-Second Crypto OHLCV Data (Binance) Historical 1-second kline (OHLCV) data for 6 major cryptocurrencies downloaded from Binance Vision. Updated daily — never more than 24h behind. Assets Symbol Start Updated BTCUSDT 2019-01 daily ETHUSDT 2019-01 daily BNBUSDT 2019-01 daily XRPUSDT 2019-01 daily DOGEUSDT 2019-07 daily SOLUSDT 2020-10 daily File Structure data/{SYMBOL}_1s.parquet ← full history, one file per asset… See the full description on the dataset page: https://huggingface.co/datasets/commanderzee/1s-crypto-data.
1-Second Crypto OHLCV Data (Binance)
Historical 1-second kline (OHLCV) data for 6 major cryptocurrencies downloaded from Binance Vision. Updated daily — never more than 24h behind.
Assets
File Structure
data/{SYMBOL}_1s.parquet ← full history, one file per asset
scripts/daily_update.py ← run to append latest daily dataSchema
Usage
import pandas as pd
# Load one asset (full history, ~200M rows for BTC)
df = pd.read_parquet(
"hf://datasets/commanderzee/1s-crypto-data/data/BTCUSDT_1s.parquet"
)
# Load all assets
SYMBOLS = ["BTCUSDT","ETHUSDT","BNBUSDT","XRPUSDT","DOGEUSDT","SOLUSDT"]
frames = {
sym: pd.read_parquet(
f"hf://datasets/commanderzee/1s-crypto-data/data/{sym}_1s.parquet"
)
for sym in SYMBOLS
}Daily Auto-Update
The scripts/daily_update.py checks the latest timestamp in each asset's Parquet, downloads any missing days from Binance Vision's daily 1s endpoint, appends new rows, and re-uploads.
# Run manually (requires HF_TOKEN with write access):
HF_TOKEN=<your_token> python scripts/daily_update.py
# Dry-run (shows what would be fetched, no upload):
HF_TOKEN=<your_token> python scripts/daily_update.py --dry-run
# Update specific symbols only:
HF_TOKEN=<your_token> python scripts/daily_update.py --symbols BTCUSDT ETHUSDTBinance publishes daily files roughly 2–4h after UTC midnight.
Source
All data from Binance Vision public archives. Timestamps normalised to Unix seconds (integer division from raw ms/μs timestamps). Duplicate bars removed; rows sorted ascending by open_time_s.
