mirror of
https://github.com/wassname/libcryptomarket.git
synced 2026-09-09 11:25:52 +08:00
[#20] Support extracting candles to csv script
This commit is contained in:
Gavin.Chan
@@ -65,3 +65,4 @@ target/
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.editorconfig
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.github/
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.idea/
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.ipynb_checkpoints
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@@ -48,6 +48,7 @@ clean-test: ## remove test and coverage artifacts
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rm -fr htmlcov/
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autopep8: ## autopep8 to clean
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autopep8 --aggressive --in-place --recursive libcryptomarket/*/*/*.py
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autopep8 --aggressive --in-place --recursive libcryptomarket/*/*.py
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autopep8 --aggressive --in-place --recursive libcryptomarket/*.py
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autopep8 --aggressive --in-place --recursive tests/*.py
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@@ -0,0 +1,75 @@
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import argparse
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import logging
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import pandas as pd
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from libcryptomarket.core import candles, FREQUENCY_TO_SEC_DICT
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LOG_FORMAT = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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def get_args():
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"""Get input arguments.
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"""
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parser = argparse.ArgumentParser(description=(
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'Query historical candles to files.'))
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parser.add_argument('--exchange', action='store', dest='exchange',
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help='Exchange name.', required=True)
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parser.add_argument('--symbols', action='store', dest='symbols',
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help='List of symbols',
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type=str, nargs='+', required=True)
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parser.add_argument('--frequency', action='store', dest='frequency',
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help='Frequency.',
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choices=list(FREQUENCY_TO_SEC_DICT.keys()),
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required=True)
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parser.add_argument('--start-time', action='store', dest='start_time',
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help='Start time in format of \'YYYY-MM-DD\'',
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required=True)
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parser.add_argument('--end-time', action='store', dest='end_time',
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help='End time in format of \'YYYY-MM-DD\'',
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required=True)
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parser.add_argument('--output', action='store', dest='output',
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help='Output filename', required=True)
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return parser.parse_args()
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def main():
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"""Main.
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"""
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args = get_args()
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logging.basicConfig(format=LOG_FORMAT, level=logging.INFO)
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start_time = pd.Timestamp(args.start_time)
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logging.info('Start time: %s', start_time)
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end_time = pd.Timestamp(args.end_time)
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logging.info('End time: %s', end_time)
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logging.info('Starting querying to exchange %s with frequency %s...',
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args.exchange, args.frequency)
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all_data = {}
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for symbol in args.symbols:
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logging.info('Querying symbol %s...', symbol)
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data = candles(source=args.exchange,
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symbol=symbol,
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start_time=start_time,
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end_time=end_time,
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frequency=args.frequency)
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all_data[symbol] = data.set_index(['start_time'], ['end_time'])
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logging.info('Cleaning the data...')
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all_data = pd.concat(all_data, axis=1, names=['symbol', 'value'])
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all_data = all_data.stack(level=0)
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logging.info('Exporting to path (%s)...', args.output)
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all_data.to_csv(args.output)
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logging.info('Exported all the historical prices')
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if __name__ == '__main__':
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main()
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@@ -1,6 +1,6 @@
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# pylint: disable-msg=W0401
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# flake8: noqa
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from libcryptomarket.core.historical import historical_ticker
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from libcryptomarket.core.instrument import instruments
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from libcryptomarket.core.order_book import order_book
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from libcryptomarket.core.candle import candles, latest_candles
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from libcryptomarket.core.candle import (
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candles, latest_candles, FREQUENCY_TO_SEC_DICT)
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@@ -1,8 +1,29 @@
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from datetime import datetime, timedelta
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from time import sleep
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import pandas as pd
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import ccxt
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from .exchanges import * # noqa
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FREQUENCY_TO_SEC_DICT = {
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'1m': 60,
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'5m': 300,
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'15m': 900,
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'30m': 1800,
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'1h': 3600,
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'3h': 10800,
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'6h': 21600,
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'12h': 43200,
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'1d': 86400,
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'1w': 86400 * 7,
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'2w': 86400 * 7 * 2,
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'1M': 86400 * 30,
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}
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FREQUENCY_TO_SEC_DICT.update(dict(
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[(value, value) for value in FREQUENCY_TO_SEC_DICT.values()]))
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def candles(source, symbol, start_time, end_time, frequency):
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"""Return candles of a given period and frequency.
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@@ -14,32 +35,49 @@ def candles(source, symbol, start_time, end_time, frequency):
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:param frequency: `int` frequency in seconds.
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"""
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if source.lower() == 'poloniex':
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source = getattr(ccxt, source.lower())()
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source = source.lower()
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func_name = "%s_candles" % source
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func = globals().get(func_name)
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data = source.public_get_returnchartdata(params={
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"currencyPair": symbol,
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"start": round(start_time.timestamp()),
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"end": round(end_time.timestamp()),
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"period": frequency
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})
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if source == "bitfinex":
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# Always use version 2 for bitfinex
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source += "2"
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data = pd.DataFrame(data).rename(columns={
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'date': 'start_time',
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'quoteVolume': 'quote_volume',
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'weightedAverage': 'weighted_average'
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})
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exchange = getattr(ccxt, source.lower())()
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describe = exchange.describe()
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data.loc[:, 'start_time'] = data['start_time'].apply(
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lambda x : pd.Timestamp.fromtimestamp(x).tz_localize('UTC'))
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data['end_time'] = data['start_time'] + pd.DateOffset(
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seconds=frequency)
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return data
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else:
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if func is None:
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raise ValueError("Source {} is not implemented".format(
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source.__class__.__name__))
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# Initialization
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frequency = describe['timeframes'][frequency]
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all_data = []
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last_start_time = None
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while start_time < end_time:
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sleep(describe['rateLimit'] / 1000)
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data = func(source=exchange, symbol=symbol, start_time=start_time,
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end_time=end_time, frequency=frequency)
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if len(data) == 0:
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break
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if (last_start_time is not None and
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data["start_time"].iloc[0] >= last_start_time):
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break
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all_data.append(data)
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start_time = data["end_time"].iloc[-1]
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if len(all_data) == 0:
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raise ValueError("Start time cannot be after end time.")
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elif len(all_data) == 1:
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return all_data[0]
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else:
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return pd.concat(all_data)
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def latest_candles(source, symbols, frequency, frequency_count, end_time=None):
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"""Return the latest candles based on the frequency and its count.
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@@ -58,9 +96,9 @@ def latest_candles(source, symbols, frequency, frequency_count, end_time=None):
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end_time = datetime.utcnow()
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closest_end_time = pd.Timestamp(end_time).floor(
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timedelta(seconds=frequency))
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timedelta(seconds=FREQUENCY_TO_SEC_DICT[frequency]))
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start_time = closest_end_time - timedelta(
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seconds=frequency * frequency_count)
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seconds=FREQUENCY_TO_SEC_DICT[frequency] * frequency_count)
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all_data = []
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for symbol in symbols:
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@@ -0,0 +1,72 @@
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import pandas as pd
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def poloniex_candles(source, symbol, start_time, end_time, frequency):
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"""Poloniex candles.
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"""
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data = source.public_get_returnchartdata(params={
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"currencyPair": symbol,
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"start": round(start_time.timestamp()),
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"end": round(end_time.timestamp()),
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"period": frequency
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})
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data = pd.DataFrame(data).rename(columns={
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'date': 'start_time',
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'quoteVolume': 'quote_volume',
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'weightedAverage': 'weighted_average'
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})
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data.loc[:, 'start_time'] = data['start_time'].apply(
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lambda x: pd.Timestamp.utcfromtimestamp(x))
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data['end_time'] = data['start_time'].shift(-1)
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return data.iloc[:-1, :]
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def bitfinex_candles(source, symbol, start_time, end_time, frequency):
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"""Bitfinex candles.
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"""
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data = source.request(
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path='candles/trade:{}:{}/hist'.format(frequency, symbol),
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params={
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"start": round(start_time.timestamp() * 1000),
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"end": round(end_time.timestamp() * 1000),
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"sort": 1
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})
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data = pd.DataFrame(data, columns=["start_time", "open", "close", "high",
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"low", "volume"])
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data.loc[:, 'start_time'] = data['start_time'].apply(
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lambda x: pd.Timestamp.utcfromtimestamp(x / 1000))
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data['end_time'] = data['start_time'].shift(-1)
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return data.iloc[:-1, :]
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def gdax_candles(source, symbol, start_time, end_time, frequency):
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"""GDAX candles.
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"""
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end_time += pd.DateOffset(seconds=1)
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data = source.request(
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path='products/{}/candles'.format(symbol),
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params={
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"granularity": frequency,
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"start": start_time.isoformat(),
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"end": end_time.isoformat(),
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})
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if len(data) == 0:
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return data
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data = pd.DataFrame(data, columns=["start_time", "low", "high", "open",
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"close", "volume"])
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data.loc[:, 'start_time'] = data['start_time'].apply(
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lambda x: pd.Timestamp.utcfromtimestamp(x))
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data = data.sort_values(['start_time'])
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data['end_time'] = data['start_time'].shift(-1)
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return data.iloc[:-1, :]
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@@ -1,212 +0,0 @@
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from functools import partial
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from datetime import datetime, timedelta
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from time import sleep
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import pandas as pd
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def historical_ticker(source, symbol, period, start_time=None, end_time=None,
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**kwargs):
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"""Return historical ticker.
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:param source: Source, an Exchange API object.
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:param symbol: Symbol, string object.
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:param period: Period or frequency, followed with exchange protocol, string
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object.
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:param start_time: Start time, datetime object.
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:param end_time: Start time, datetime object.
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:param wait_sec: Seconds to wait between queries, int. Optional.
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"""
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# Validation
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if start_time is not None and not isinstance(start_time, datetime):
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raise ValueError("Start time is not a datetime object.")
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if end_time is not None and not isinstance(end_time, datetime):
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raise ValueError("End time is not a datetime object.")
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# Source object name
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source_name = source.__class__.__name__.lower().replace("api", "")
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if source_name == "poloniex":
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return _historical_ticker_poloniex(
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source=source, symbol=symbol, period=period,
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start_time=start_time, end_time=end_time,
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**kwargs)
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elif source_name == "gdax":
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return _historical_ticker_gdax(
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source=source, symbol=symbol, period=period,
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start_time=start_time, end_time=end_time,
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**kwargs)
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elif source_name == "bitfinex":
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return _historical_ticker_bitfinex(
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source=source, symbol=symbol, period=period,
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start_time=start_time, end_time=end_time,
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**kwargs)
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else:
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raise ValueError("Source (%s [%s]) does not support historical ticker"
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% (source, source_name))
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def _historical_ticker_poloniex(source, symbol, period, start_time, end_time,
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**kwargs):
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"""Return historical ticker in Poloniex.
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:param source: Source, an Exchange API object.
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:param symbol: Symbol, string object.
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:param period: Period or frequency, followed with exchange protocol, string
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object.
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:param start_time: Start time, datetime object.
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:param end_time: Start time, datetime object.
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"""
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# Exchange validation
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if start_time is None and end_time is None:
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raise ValueError("Start time and end time cannot be both None.")
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request_func = partial(source.return_chart_data, currencyPair=symbol,
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period=period)
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if start_time is not None:
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request_func = partial(request_func,
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start=start_time.timestamp())
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if end_time is not None:
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request_func = partial(request_func,
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end=end_time.timestamp())
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data = request_func()
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data.raise_for_status()
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data = pd.DataFrame(data.json())
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data['date'] = data['date'].apply(
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lambda x: pd.to_datetime(x, unit='s'))
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data = data.set_index(['date'])
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data.index.name = 'datetime'
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data.columns.name = symbol
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return data
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def _historical_ticker_gdax(source, symbol, period, start_time, end_time,
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**kwargs):
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"""Return historical ticker in GDAX.
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:param source: Source, an Exchange API object.
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:param symbol: Symbol, string object.
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:param period: Period or frequency, followed with exchange protocol, string
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object.
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:param start_time: Start time, datetime object.
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:param end_time: Start time, datetime object.
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:param wait_sec: Seconds to wait between queries, int. Optional.
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"""
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# Exchange validation
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if (start_time is None) + (end_time is None) not in [0, 2]:
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# Both start and end time must be provided
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raise ValueError("Start and end time must be both provided")
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request_func = partial(source.products_candles, product_id=symbol,
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granularity=period)
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if start_time is None and end_time is None:
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# Just get the latest 300 ticks
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data = request_func()
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data.raise_for_status()
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data = pd.DataFrame(data.json())
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else:
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# Safety net
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last_datetime = start_time.timestamp()
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data = []
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while start_time <= end_time:
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tmp_data = request_func(
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start=start_time.isoformat(),
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end=(start_time +
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timedelta(seconds=period * 200)).isoformat())
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tmp_data.raise_for_status()
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tmp_data = pd.DataFrame(tmp_data.json())
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# Append into data list
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data.append(tmp_data)
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# Check to exit
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if last_datetime >= tmp_data.iloc[0, 0]:
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# Same as the previous query
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break
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else:
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last_datetime = tmp_data.iloc[0, 0]
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start_time = datetime.fromtimestamp(last_datetime)
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sleep(kwargs.get("wait_sec", 0.33))
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if len(data) > 1:
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data = pd.concat(data, axis=0)
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else:
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data = data[0]
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data.columns = ['datetime', 'low', 'high', 'open', 'close', 'volume']
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data['datetime'] = pd.to_datetime(data['datetime'], unit='s')
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data = data.set_index('datetime').sort_index()
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data = data[~data.index.duplicated(keep='first')]
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return data
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def _historical_ticker_bitfinex(source, symbol, period, start_time, end_time,
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**kwargs):
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"""Return historical ticker in Bitfinex.
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:param source: Source, an Exchange API object.
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:param symbol: Symbol, string object.
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:param period: Period or frequency, followed with exchange protocol, string
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object.
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:param start_time: Start time, datetime object.
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:param end_time: Start time, datetime object.
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:param wait_sec: Seconds to wait between queries, int. Optional.
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"""
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request_func = partial(source.candles, symbol=symbol, timeframe=period,
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section="hist", sort=1, limit=1000)
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if start_time is None and end_time is None:
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data = request_func()
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data.raise_for_status()
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data = pd.DataFrame(data.json())
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else:
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# Safety net
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last_datetime = (
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0 if start_time is None else start_time.timestamp() * 1000)
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data = []
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while start_time is None or end_time is None or start_time <= end_time:
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f = request_func
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if start_time is not None:
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f = partial(f, start=round(start_time.timestamp() * 1000))
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if end_time is not None:
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f = partial(f, end=round(end_time.timestamp() * 1000))
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tmp_data = f()
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tmp_data.raise_for_status()
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tmp_data = tmp_data.json()
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if len(tmp_data) == 0:
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break
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elif last_datetime >= tmp_data[-1][0]:
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print(last_datetime)
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print(tmp_data[-1][0])
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break
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else:
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last_datetime = tmp_data[-1][0]
|
||||
start_time = datetime.fromtimestamp(
|
||||
round(last_datetime / 1000 + 1))
|
||||
data.append(pd.DataFrame(tmp_data))
|
||||
|
||||
sleep(kwargs.get("wait_sec", 1))
|
||||
|
||||
data = pd.concat(data, axis=0)
|
||||
|
||||
data.columns = ["datetime", "open", "close", "high", "low", "volume"]
|
||||
data["datetime"] = pd.to_datetime(data["datetime"], unit="ms")
|
||||
data = data.set_index("datetime").sort_index()
|
||||
data = data[~data.index.duplicated(keep='first')]
|
||||
|
||||
return data
|
||||
@@ -0,0 +1,120 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2018-01-27T23:56:10.234256Z",
|
||||
"start_time": "2018-01-27T23:56:09.703438Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%load_ext autoreload\n",
|
||||
"%autoreload 2\n",
|
||||
"\n",
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"from libcryptomarket.core import candles, latest_candles"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Candles"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2018-01-28T00:06:20.147882Z",
|
||||
"start_time": "2018-01-28T00:06:14.812410Z"
|
||||
},
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Running exchange poloniex for instrument BTC_LTC\n",
|
||||
"Running exchange bitfinex for instrument tBTCUSD\n",
|
||||
"Running exchange gdax for instrument BTC-USD\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for source, symbol in [\n",
|
||||
" (\"poloniex\", \"BTC_LTC\"), \n",
|
||||
" (\"bitfinex\", \"tBTCUSD\"),\n",
|
||||
" (\"gdax\", \"BTC-USD\")]:\n",
|
||||
" print(\"Running exchange {} for instrument {}\".format(source, symbol))\n",
|
||||
" data = candles(source=source, symbol=symbol, \n",
|
||||
" start_time=pd.Timestamp(\"2017-12-15\"), end_time=pd.Timestamp(\"2017-12-31\"), frequency=\"1d\")\n",
|
||||
" assert data[\"start_time\"].iloc[0] == pd.Timestamp(\"2017-12-15\")\n",
|
||||
" assert data[\"end_time\"].iloc[-1] == pd.Timestamp(\"2017-12-31\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Latest candles"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"for source, symbols in [\n",
|
||||
" (\"poloniex\", [\"BTC_LTC\", \"BTC_ETH\"]), \n",
|
||||
" (\"bitfinex\", [\"tBTCUSD\", \"tETHUSD\"])]:\n",
|
||||
" print(\"Running exchange {} for instrument {}\".format(source, symbols))\n",
|
||||
" data = latest_candles(source=source, symbols=symbols, frequency=\"30m\", frequency_count=1)\n",
|
||||
" assert data.shape[0] == 1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2018-01-28T00:02:19.164726Z",
|
||||
"start_time": "2018-01-28T00:02:14.937305Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"data = candles(source=\"gdax\", symbol=\"BTC-USD\", \n",
|
||||
" start_time=pd.Timestamp(\"2017-12-15\"), end_time=pd.Timestamp(\"2017-12-31\"), frequency=\"1d\")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.5.2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -53,6 +53,8 @@ setup(
|
||||
'Programming Language :: Python :: 3.4',
|
||||
'Programming Language :: Python :: 3.5',
|
||||
],
|
||||
entry_points={'console_scripts': [
|
||||
'request-candles=libcryptomarket.cli.candles:main']},
|
||||
test_suite='tests',
|
||||
tests_require=test_requirements,
|
||||
setup_requires=setup_requirements,
|
||||
|
||||
Reference in New Issue
Block a user