def get_time_series_cached(self, time_series_request):
"""
get_time_series_cached - Loads time series from cache (if it exists)
Parameters
----------
time_series_request : TimeSeriesRequest
contains various properties describing time series to fetched, including ticker, start & finish date etc.
Returns
-------
pandas.DataFrame
"""
if (time_series_request.freq == "intraday"):
ticker = time_series_request.tickers
else:
ticker = None
fname = self.create_time_series_hash_key(time_series_request, ticker)
if (fname in self._time_series_cache):
data_frame = self._time_series_cache[fname]
tsf = TimeSeriesFilter()
return tsf.filter_time_series(time_series_request, data_frame)
return None
def get_time_series_cached(self, time_series_request):
"""
get_time_series_cached - Loads time series from cache (if it exists)
Parameters
----------
time_series_request : TimeSeriesRequest
contains various properties describing time series to fetched, including ticker, start & finish date etc.
Returns
-------
pandas.DataFrame
"""
if (time_series_request.freq == "intraday"):
ticker = time_series_request.tickers
else:
ticker = None
fname = self.create_time_series_hash_key(time_series_request, ticker)
if (fname in self._time_series_cache):
data_frame = self._time_series_cache[fname]
tsf = TimeSeriesFilter()
return tsf.filter_time_series(time_series_request, data_frame)
return None
def harvest_time_series(self, time_series_request, kill_session = True):
"""
havest_time_series - Loads time series from specified data provider
Parameters
----------
time_series_request : TimeSeriesRequest
contains various properties describing time series to fetched, including ticker, start & finish date etc.
Returns
-------
DataFrame
"""
tickers = time_series_request.tickers
loader = self.get_loader(time_series_request.data_source)
# check if tickers have been specified (if not load all of them for a category)
# also handle single tickers/list tickers
create_tickers = False
if tickers is None : create_tickers = True
elif isinstance(tickers, str):
if tickers == '': create_tickers = True
elif isinstance(tickers, list):
if tickers == []: create_tickers = True
if create_tickers:
time_series_request.tickers = self.config.get_tickers_list_for_category(
time_series_request.category, time_series_request.source, time_series_request.freq, time_series_request.cut)
# intraday or tick: only one ticker per cache file
if (time_series_request.freq in ['intraday', 'tick']):
data_frame_agg = self.download_intraday_tick(time_series_request, loader)
# daily: multiple tickers per cache file - assume we make one API call to vendor library
else: data_frame_agg = self.download_daily(time_series_request, loader)
if('internet_load' in time_series_request.cache_algo):
self.logger.debug("Internet loading.. ")
# signal to loader template to exit session
if loader is not None and kill_session == True: loader.kill_session()
if(time_series_request.cache_algo == 'cache_algo'):
self.logger.debug("Only caching data in memory, do not return any time series."); return
tsf = TimeSeriesFilter()
# only return time series if specified in the algo
if 'return' in time_series_request.cache_algo:
# special case for events/events-dt which is not indexed like other tables
if hasattr(time_series_request, 'category'):
if 'events' in time_series_request.category:
return data_frame_agg
try:
return tsf.filter_time_series(time_series_request, data_frame_agg)
except:
return None
def harvest_time_series(self, time_series_request, kill_session = True):
"""
havest_time_series - Loads time series from specified data provider
Parameters
----------
time_series_request : TimeSeriesRequest
contains various properties describing time series to fetched, including ticker, start & finish date etc.
Returns
-------
pandas.DataFrame
"""
tickers = time_series_request.tickers
loader = self.get_loader(time_series_request.data_source)
# check if tickers have been specified (if not load all of them for a category)
# also handle single tickers/list tickers
create_tickers = False
if tickers is None : create_tickers = True
elif isinstance(tickers, str):
if tickers == '': create_tickers = True
elif isinstance(tickers, list):
if tickers == []: create_tickers = True
if create_tickers:
time_series_request.tickers = self.config.get_tickers_list_for_category(
time_series_request.category, time_series_request.source, time_series_request.freq, time_series_request.cut)
# intraday or tick: only one ticker per cache file
if (time_series_request.freq in ['intraday', 'tick']):
data_frame_agg = self.download_intraday_tick(time_series_request, loader)
# daily: multiple tickers per cache file - assume we make one API call to vendor library
else: data_frame_agg = self.download_daily(time_series_request, loader)
if('internet_load' in time_series_request.cache_algo):
self.logger.debug("Internet loading.. ")
# signal to loader template to exit session
if loader is not None and kill_session == True: loader.kill_session()
if(time_series_request.cache_algo == 'cache_algo'):
self.logger.debug("Only caching data in memory, do not return any time series."); return
tsf = TimeSeriesFilter()
# only return time series if specified in the algo
if 'return' in time_series_request.cache_algo:
# special case for events/events-dt which is not indexed like other tables
if hasattr(time_series_request, 'category'):
if 'events' in time_series_request.category:
return data_frame_agg
try:
return tsf.filter_time_series(time_series_request, data_frame_agg)
except:
import traceback
self.logger.error(traceback.format_exc())
return None
