def get_candles(self,
freq,
assets,
bar_count=1,
start_dt=None,
end_dt=None):
is_single = (isinstance(assets, TradingPair))
if is_single:
assets = [assets]
symbols = self.get_symbols(assets)
timeframe = CCXT.get_timeframe(freq)
if timeframe not in self.api.timeframes:
freqs = [CCXT.get_frequency(t) for t in self.api.timeframes]
raise UnsupportedHistoryFrequencyError(
exchange=self.name,
freq=freq,
freqs=freqs,
)
if start_dt is not None and end_dt is not None:
raise ValueError(
'Please provide either start_dt or end_dt, not both.')
if start_dt is None:
if end_dt is None:
end_dt = pd.Timestamp.utcnow()
dt_range = get_periods_range(
end_dt=end_dt,
periods=bar_count,
freq=freq,
)
start_dt = dt_range[0]
delta = start_dt - get_epoch()
since = int(delta.total_seconds()) * 1000
candles = dict()
for index, asset in enumerate(assets):
ohlcvs = self.api.fetch_ohlcv(symbol=symbols[index],
timeframe=timeframe,
since=since,
limit=bar_count,
params={})
candles[asset] = []
for ohlcv in ohlcvs:
candles[asset].append(
dict(last_traded=pd.to_datetime(ohlcv[0],
unit='ms',
utc=True),
open=ohlcv[1],
high=ohlcv[2],
low=ohlcv[3],
close=ohlcv[4],
volume=ohlcv[5]))
candles[asset] = sorted(candles[asset],
key=lambda c: c['last_traded'])
if is_single:
return six.next(six.itervalues(candles))
else:
return candles
def get_candles(self, freq, assets, bar_count=1, start_dt=None,
end_dt=None):
is_single = (isinstance(assets, TradingPair))
if is_single:
assets = [assets]
symbols = self.get_symbols(assets)
timeframe = CCXT.get_timeframe(freq)
if timeframe not in self.api.timeframes:
freqs = [CCXT.get_frequency(t) for t in self.api.timeframes]
raise UnsupportedHistoryFrequencyError(
exchange=self.name,
freq=freq,
freqs=freqs,
)
if start_dt is not None and end_dt is not None:
raise ValueError(
'Please provide either start_dt or end_dt, not both.'
)
elif end_dt is not None:
# Make sure that end_dt really wants data in the past
# if it's close to now, we skip the 'since' parameters to
# lower the probability of error
bars_to_now = pd.date_range(
end_dt, pd.Timestamp.utcnow(), freq=freq
)
# See: https://github.com/ccxt/ccxt/issues/1360
if len(bars_to_now) > 1 or self.name in ['poloniex']:
dt_range = get_periods_range(
end_dt=end_dt,
periods=bar_count,
freq=freq,
)
start_dt = dt_range[0]
since = None
if start_dt is not None:
delta = start_dt - get_epoch()
since = int(delta.total_seconds()) * 1000
candles = dict()
for index, asset in enumerate(assets):
ohlcvs = self.api.fetch_ohlcv(
symbol=symbols[index],
timeframe=timeframe,
since=since,
limit=bar_count,
params={}
)
candles[asset] = []
for ohlcv in ohlcvs:
candles[asset].append(dict(
last_traded=pd.to_datetime(
ohlcv[0], unit='ms', utc=True
),
open=ohlcv[1],
high=ohlcv[2],
low=ohlcv[3],
close=ohlcv[4],
volume=ohlcv[5]
))
candles[asset] = sorted(
candles[asset], key=lambda c: c['last_traded']
)
if is_single:
return six.next(six.itervalues(candles))
else:
return candles
def get_candles(self, freq, assets, bar_count=1, start_dt=None,
end_dt=None):
is_single = (isinstance(assets, TradingPair))
if is_single:
assets = [assets]
symbols = self.get_symbols(assets)
timeframe = CCXT.get_timeframe(freq)
if timeframe not in self.api.timeframes:
freqs = [CCXT.get_frequency(t) for t in self.api.timeframes]
raise UnsupportedHistoryFrequencyError(
exchange=self.name,
freq=freq,
freqs=freqs,
)
if start_dt is not None and end_dt is not None:
raise ValueError(
'Please provide either start_dt or end_dt, not both.'
)
if start_dt is None:
# TODO: determine why binance is failing
if end_dt is None and self.name not in ['binance']:
end_dt = pd.Timestamp.utcnow()
if end_dt is not None:
dt_range = get_periods_range(
end_dt=end_dt,
periods=bar_count,
freq=freq,
)
start_dt = dt_range[0]
if start_dt is not None:
# Convert out start date to a UNIX timestamp, then translate to
# milliseconds
delta = start_dt - get_epoch()
since = int(delta.total_seconds()) * 1000
else:
since = None
candles = dict()
for index, asset in enumerate(assets):
ohlcvs = self.api.fetch_ohlcv(
symbol=symbols[index],
timeframe=timeframe,
since=since,
limit=bar_count,
params={}
)
candles[asset] = []
for ohlcv in ohlcvs:
candles[asset].append(dict(
last_traded=pd.to_datetime(
ohlcv[0], unit='ms', utc=True
),
open=ohlcv[1],
high=ohlcv[2],
low=ohlcv[3],
close=ohlcv[4],
volume=ohlcv[5]
))
candles[asset] = sorted(
candles[asset], key=lambda c: c['last_traded']
)
if is_single:
return six.next(six.itervalues(candles))
else:
return candles