pyubcc 0.1.1

Upbit Candle Collector

pyubcc

Upbit Candle Collector for Python

Description

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.

Quick Start

$ 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

CLI

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

Module

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())

Return Values

The collect() method returns a tuple containing:

• total_count: Number of collected candles
• expected_candles: Expected number of candles for the period
• timestamp_order_mismatches: Number of timestamp order mismatches
• gaps: List of gaps in the data

Data Structure

The collected data includes:

• timestamp: Candle timestamp
• open: Opening price
• high: Highest price
• low: Lowest price
• close: Closing price
• volume: Trading volume

Public API

Constructor

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 logging
• show_progress (bool): Show progress bar (default: False)

Methods

check_db_status()

Checks the database status and returns information about the first and last timestamps.

• Returns: bool - True if database contains data, False if empty

collect(start_date=None, end_date=None)

Collects historical data for the specified period.

• Parameters:
  • start_date (datetime, optional): Start date for data collection
  • end_date (datetime, optional): End date for data collection (default: current time)
• Returns: tuple (total_count, expected_candles, timestamp_order_mismatches, gaps)

get_ohlcv_data(start_date=None, end_date=None, filter_gaps=True)

Retrieves stored OHLCV data as a pandas DataFrame.

• Parameters:
  • start_date (datetime, optional): Start date for data retrieval
  • end_date (datetime, optional): End date for data retrieval
  • filter_gaps (bool): Whether to filter data gaps (default: True)
• Returns: pandas.DataFrame with OHLCV data

export_to_csv(start_date=None, end_date=None)

Exports OHLCV data to a CSV file.

• Parameters:
  • start_date (datetime, optional): Start date for data export
  • end_date (datetime, optional): End date for data export
• Returns: str - Path to the exported CSV file, or None if no data to export

analyze_gaps(start_date=None, end_date=None)

Analyzes gaps in candle data from database.

• Parameters:
  • start_date (datetime, optional): Start date for gap analysis
  • end_date (datetime, optional): End date for gap analysis
• Returns: list of dictionaries containing gap information