def get_history_window(self,
assets,
end_dt,
bar_count,
frequency,
field,
data_frequency=None,
is_current=False):
"""
Public API method that returns a dataframe containing the requested
history window. Data is fully adjusted.
Parameters
----------
assets : list[TradingPair]
The assets whose data is desired.
end_dt: datetime
The date of the last bar
bar_count: int
The number of bars desired.
frequency: string
"1d" or "1m"
field: string
The desired field of the asset.
data_frequency: string
The frequency of the data to query; i.e. whether the data is
'daily' or 'minute' bars.
is_current: bool
Skip date filters when current data is requested (last few bars
until now).
Notes
-----
Catalysts requires an end data with bar count both CCXT wants a
start data with bar count. Since we have to make calculations here,
we ensure that the last candle match the end_dt parameter.
Returns
-------
DataFrame
A dataframe containing the requested data.
"""
freq, candle_size, unit, data_frequency = get_frequency(
frequency, data_frequency, supported_freqs=['T', 'D', 'H'])
# we want to avoid receiving empty candles
# so we request more than needed
# TODO: consider defining a const per asset
# and/or some retry mechanism (in each iteration request more data)
min_candles_number = get_candles_number_from_minutes(
unit, candle_size, self.MIN_MINUTES_REQUESTED)
requested_bar_count = bar_count
if requested_bar_count < min_candles_number:
requested_bar_count = min_candles_number
# The get_history method supports multiple asset
candles = self.get_candles(
freq=freq,
assets=assets,
bar_count=requested_bar_count,
end_dt=end_dt if not is_current else None,
)
# candles sanity check - verify no empty candles were received:
for asset in candles:
if not candles[asset]:
raise NoCandlesReceivedFromExchange(
bar_count=requested_bar_count,
end_dt=end_dt,
asset=asset,
exchange=self.name)
# for avoiding unnecessary forward fill end_dt is taken back one second
forward_fill_till_dt = end_dt - timedelta(seconds=1)
series = get_candles_df(candles=candles,
field=field,
freq=frequency,
bar_count=requested_bar_count,
end_dt=forward_fill_till_dt)
# TODO: consider how to approach this edge case
# delta_candle_size = candle_size * 60 if unit == 'H' else candle_size
# Checking to make sure that the dates match
# delta = get_delta(delta_candle_size, data_frequency)
# adj_end_dt = end_dt - delta
# last_traded = asset_series.index[-1]
# if last_traded < adj_end_dt:
# raise LastCandleTooEarlyError(
# last_traded=last_traded,
# end_dt=adj_end_dt,
# exchange=self.name,
# )
df = pd.DataFrame(series)
df.dropna(inplace=True)
return df.tail(bar_count)
def get_history_window_for_asset(self,
asset,
end_dt,
bar_count,
frequency,
fields,
data_frequency=None,
is_current=False):
"""
Public API method that returns a dataframe containing the requested
history window. Data is fully adjusted.
Parameters
----------
asset : TradingPair
The asset whose data is desired.
end_dt: datetime
The date of the last bar
bar_count: int
The number of bars desired.
frequency: string
"1d" or "1m"
fields: string
The desired fields of the asset.
data_frequency: string
The frequency of the data to query; i.e. whether the data is
'daily' or 'minute' bars.
is_current: bool
Skip date filters when current data is requested (last few bars
until now).
Notes
-----
Catalysts requires an end data with bar count both CCXT wants a
start data with bar count. Since we have to make calculations here,
we ensure that the last candle match the end_dt parameter.
Returns
-------
DataFrame
A dataframe containing the requested data.
"""
freq, candle_size, unit, data_frequency = get_frequency(
frequency, data_frequency, supported_freqs=['T', 'D', 'H'])
# we want to avoid receiving empty candles
# so we request more than needed
# TODO: consider defining a const per asset
# and/or some retry mechanism (in each iteration request more data)
min_candles_number = get_candles_number_from_minutes(
unit, candle_size, self.MIN_MINUTES_REQUESTED)
requested_bar_count = bar_count
if requested_bar_count < min_candles_number:
requested_bar_count = min_candles_number
candles = self.get_candles(
freq=freq,
assets=
asset, # when we pass a single asset, the response is a flat DataFrame with the fields as columns
bar_count=requested_bar_count,
end_dt=end_dt if not is_current else None,
)
# candles sanity check - verify no empty candles were received:
if len(candles) == 0:
raise NoCandlesReceivedFromExchange(bar_count=requested_bar_count,
end_dt=end_dt,
asset=asset,
exchange=self.name)
# for avoiding unnecessary forward fill end_dt is taken back one second
forward_fill_till_dt = end_dt - timedelta(seconds=1)
candles_df = get_candles_df_for_asset(candles=candles,
fields=fields,
freq=frequency,
bar_count=requested_bar_count,
end_dt=forward_fill_till_dt)
return candles_df.tail(bar_count)