Upbit Candle Collector for Python
Upbit Candle Collector is a Python script that collects historical candle data from the Upbit API and saves it to a SQLite3 DB or CSV file. It allows you to specify the market, time interval, and date range for the data collection.
$ pip install pyubcc
$ ubcc BTC --timeframe day --days 30
Starting data collection for BTC...
Period: 30 days, Timeframe: day
KRW-BTC: 30 candles [00:00, 116.88 candles/s]
No missing candles found.
=== BTC Data Collection Results ===
Collection Period: 2025-01-18 09:00 ~ 2025-02-17 00:00
Timeframe: day (1440 minutes)
Collected Candles: 30
Data Gaps: 0
Timestamp Order Mismatches: 0
$ sqlite3 db/KRW-BTC_day.db "SELECT COUNT(*) FROM ohlcv;"
30
usage: ubcc [-h]
[--timeframe {minute1,minute3,minute5,minute10,minute15,minute30,minute60,minute240,day,week,month}]
[--days DAYS] [--db-path DB_PATH] [--export-csv] [--verbose]
coin
Upbit Candle Collector
positional arguments:
coin Coin symbol (e.g., BTC, ETH, DOGE) or full ticker (e.g., KRW-
BTC, USDT-BTC)
options:
-h, --help show this help message and exit
--timeframe {minute1,minute3,minute5,minute10,minute15,minute30,minute60,minute240,day,week,month}
Time interval (default: day)
--days DAYS Collection period in days (default: 30)
--db-path DB_PATH DB file path (default: db/{coin}_{timeframe}.db)
--export-csv Export data to CSV file
--verbose Enable detailed logging
from pyubcc import UpbitCandleCollector
# Initialize collector for BTC/KRW daily candles
collector = UpbitCandleCollector(
coin='BTC', # Coin symbol (e.g., BTC, ETH, DOGE)
timeframe='day', # Time interval (minute1 to month)
fiat='KRW', # Base currency (default: KRW)
verbose=True # Enable detailed logging
)
# Check database status
collector.check_db_status()
# Collect last 30 days of data
from datetime import datetime, timedelta
end_date = datetime.now()
start_date = end_date - timedelta(days=30)
results = collector.collect(start_date=start_date, end_date=end_date)
# Export collected data to CSV
collector.export_to_csv()
# Get data as pandas DataFrame
df = collector.get_ohlcv_data(start_date=start_date, end_date=end_date, filter_gaps=True)
print(df.head())The collect() method returns a tuple containing:
total_count: Number of collected candlesexpected_candles: Expected number of candles for the periodtimestamp_order_mismatches: Number of timestamp order mismatchesgaps: List of gaps in the data
The collected data includes:
timestamp: Candle timestampopen: Opening pricehigh: Highest pricelow: Lowest priceclose: Closing pricevolume: Trading volume
UpbitCandleCollector(coin, timeframe, fiat="KRW", db_path=None, verbose=False, show_progress=False)coin(str): Coin symbol (e.g., 'BTC', 'ETH', 'DOGE')timeframe(str): Time interval (minute1, minute3, minute5, minute10, minute15, minute30, minute60, minute240, day, week, month)fiat(str): Base currency (KRW, BTC, USDT, default: KRW)db_path(str, optional): DB file path (default: db/{coin}{timeframe}{fiat}.db)verbose(bool): Enable detailed loggingshow_progress(bool): Show progress bar (default: False)
Checks the database status and returns information about the first and last timestamps.
- Returns: bool - True if database contains data, False if empty
Collects historical data for the specified period.
- Parameters:
start_date(datetime, optional): Start date for data collectionend_date(datetime, optional): End date for data collection (default: current time)
- Returns: tuple (total_count, expected_candles, timestamp_order_mismatches, gaps)
Retrieves stored OHLCV data as a pandas DataFrame.
- Parameters:
start_date(datetime, optional): Start date for data retrievalend_date(datetime, optional): End date for data retrievalfilter_gaps(bool): Whether to filter data gaps (default: True)
- Returns: pandas.DataFrame with OHLCV data
Exports OHLCV data to a CSV file.
- Parameters:
start_date(datetime, optional): Start date for data exportend_date(datetime, optional): End date for data export
- Returns: str - Path to the exported CSV file, or None if no data to export
Analyzes gaps in candle data from database.
- Parameters:
start_date(datetime, optional): Start date for gap analysisend_date(datetime, optional): End date for gap analysis
- Returns: list of dictionaries containing gap information