def get_exchange_history_window(self,
exchange_name,
assets,
end_dt,
bar_count,
frequency,
field,
data_frequency,
ffill=True):
"""
Fetching price history window from the exchange bundle.
Parameters
----------
exchange: Exchange
assets: list[TradingPair]
end_dt: datetime
bar_count: int
frequency: str
field: str
data_frequency: str
ffill: bool
Returns
-------
DataFrame
"""
# TODO: verify that the exchange supports the timeframe
bundle = self.exchange_bundles[exchange_name] # type: ExchangeBundle
freq, candle_size, unit, adj_data_frequency = get_frequency(
frequency, data_frequency)
adj_bar_count = candle_size * bar_count
trailing_bar_count = candle_size - 1
if data_frequency == 'minute' and adj_data_frequency == 'daily':
end_dt = end_dt.floor('1D')
series = bundle.get_history_window_series_and_load(
assets=assets,
end_dt=end_dt,
bar_count=adj_bar_count,
field=field,
data_frequency=adj_data_frequency,
algo_end_dt=self._last_available_session,
trailing_bar_count=trailing_bar_count,
)
df = resample_history_df(pd.DataFrame(series), freq, field)
return df
def get_history_window_with_bundle(self,
assets,
end_dt,
bar_count,
frequency,
field,
data_frequency=None,
ffill=True,
force_auto_ingest=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.
# TODO: fill how?
ffill: boolean
Forward-fill missing values. Only has effect if field
is 'price'.
Returns
-------
DataFrame
A dataframe containing the requested data.
"""
# TODO: this function needs some work,
# we're currently using it just for benchmark data
freq, candle_size, unit, data_frequency = get_frequency(
frequency, data_frequency, supported_freqs=['T', 'D'])
adj_bar_count = candle_size * bar_count
try:
series = self.bundle.get_history_window_series_and_load(
assets=assets,
end_dt=end_dt,
bar_count=adj_bar_count,
field=field,
data_frequency=data_frequency,
force_auto_ingest=force_auto_ingest)
except (PricingDataNotLoadedError, NoDataAvailableOnExchange):
series = dict()
for asset in assets:
if asset not in series or series[asset].index[-1] < end_dt:
# Adding bars too recent to be contained in the consolidated
# exchanges bundles. We go directly against the exchange
# to retrieve the candles.
start_dt = get_start_dt(end_dt, adj_bar_count, data_frequency)
trailing_dt = \
series[asset].index[-1] + get_delta(1, data_frequency) \
if asset in series else start_dt
# The get_history method supports multiple asset
# Use the original frequency to let each api optimize
# the size of result sets
trailing_bars = get_periods(trailing_dt, end_dt, freq)
candles = self.get_candles(
freq=freq,
assets=asset,
end_dt=end_dt,
bar_count=trailing_bars if trailing_bars < 500 else 500,
)
last_value = series[asset].iloc(0) if asset in series \
else np.nan
# Create a series with the common data_frequency, ffill
# missing values
candle_series = self.get_series_from_candles(
candles=candles,
start_dt=trailing_dt,
end_dt=end_dt,
data_frequency=data_frequency,
field=field,
previous_value=last_value)
if asset in series:
series[asset].append(candle_series)
else:
series[asset] = candle_series
df = resample_history_df(pd.DataFrame(series), freq, field)
# TODO: consider this more carefully
df.dropna(inplace=True)
return df
def get_exchange_history_window(self,
exchange_name,
assets,
end_dt,
bar_count,
frequency,
field,
data_frequency,
ffill=True):
"""
Fetching price history window from the exchange bundle.
Parameters
----------
exchange: Exchange
assets: list[TradingPair]
end_dt: datetime
bar_count: int
frequency: str
field: str
data_frequency: str
ffill: bool
Returns
-------
DataFrame
"""
# TODO: verify that the exchange supports the timeframe
bundle = self.exchange_bundles[exchange_name] # type: ExchangeBundle
freq, candle_size, unit, adj_data_frequency = get_frequency(
frequency, data_frequency, supported_freqs=['T', 'D'])
adj_bar_count = candle_size * bar_count
if data_frequency == "minute":
# for minute frequency always request data until the
# current minute (do not include the current minute)
last_dt_for_series = end_dt - datetime.timedelta(minutes=1)
# read the minute bundles for daily frequency to
# support last partial candle
# TODO: optimize this by applying this logic only for the last day
if adj_data_frequency == 'daily':
adj_data_frequency = 'minute'
adj_bar_count = adj_bar_count * 1440
else: # data_frequency == "daily":
last_dt_for_series = end_dt
series = bundle.get_history_window_series_and_load(
assets=assets,
end_dt=last_dt_for_series,
bar_count=adj_bar_count,
field=field,
data_frequency=adj_data_frequency,
algo_end_dt=self._last_available_session,
)
start_dt = get_start_dt(last_dt_for_series, adj_bar_count,
adj_data_frequency, False)
df = resample_history_df(pd.DataFrame(series), freq, field, start_dt)
return df
def get_history_window_with_bundle(self,
assets,
end_dt,
bar_count,
frequency,
field,
data_frequency=None,
ffill=True,
force_auto_ingest=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.
# TODO: fill how?
ffill: boolean
Forward-fill missing values. Only has effect if field
is 'price'.
Returns
-------
DataFrame
A dataframe containing the requested data.
"""
# TODO: this function needs some work, we're currently using it just for benchmark data
freq, candle_size, unit, data_frequency = get_frequency(
frequency, data_frequency
)
adj_bar_count = candle_size * bar_count
try:
series = self.bundle.get_history_window_series_and_load(
assets=assets,
end_dt=end_dt,
bar_count=adj_bar_count,
field=field,
data_frequency=data_frequency,
force_auto_ingest=force_auto_ingest
)
except (PricingDataNotLoadedError, NoDataAvailableOnExchange):
series = dict()
for asset in assets:
if asset not in series or series[asset].index[-1] < end_dt:
# Adding bars too recent to be contained in the consolidated
# exchanges bundles. We go directly against the exchange
# to retrieve the candles.
start_dt = get_start_dt(end_dt, adj_bar_count, data_frequency)
trailing_dt = \
series[asset].index[-1] + get_delta(1, data_frequency) \
if asset in series else start_dt
# The get_history method supports multiple asset
# Use the original frequency to let each api optimize
# the size of result sets
trailing_bars = get_periods(
trailing_dt, end_dt, freq
)
candles = self.get_candles(
freq=freq,
assets=asset,
end_dt=end_dt,
bar_count=trailing_bars if trailing_bars < 500 else 500,
)
last_value = series[asset].iloc(0) if asset in series \
else np.nan
# Create a series with the common data_frequency, ffill
# missing values
candle_series = self.get_series_from_candles(
candles=candles,
start_dt=trailing_dt,
end_dt=end_dt,
data_frequency=data_frequency,
field=field,
previous_value=last_value
)
if asset in series:
series[asset].append(candle_series)
else:
series[asset] = candle_series
df = resample_history_df(pd.DataFrame(series), freq, field)
# TODO: consider this more carefully
df.dropna(inplace=True)
return df
