Metadata-Version: 2.2
Name: pyubcc
Version: 0.1.1
Summary: Upbit Candle Collector
Author-email: Kyungwook Park <parksama@gmail.com>
License: MIT License
        
        Copyright (c) 2025 Kyungwook, Park
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
        
Project-URL: Homepage, https://github.com/kyungw00k/pyubcc
Keywords: upbit,업비트,cryptocurrency,candle,trading
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Office/Business :: Financial :: Investment
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: requests>=2.25.1

# 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
```python
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
```python
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

