def get_fx_cross_tick(self, start, end, cross,
cut = "NYC", source = "gain", cache_algo='cache_algo_return', type = 'spot'):
if isinstance(cross, str):
cross = [cross]
time_series_request = TimeSeriesRequest()
time_series_factory = self.time_series_factory
data_frame_agg = None
time_series_request.gran_freq = "tick" # tick
time_series_request.freq_mult = 1 # 1 min
time_series_request.cut = cut # NYC/BGN ticker
time_series_request.fields = ['bid', 'ask'] # bid/ask field only
time_series_request.cache_algo = cache_algo # cache_algo_only, cache_algo_return, internet_load
time_series_request.environment = 'backtest'
time_series_request.start_date = start
time_series_request.finish_date = end
time_series_request.data_source = source
time_series_request.category = 'fx'
for cr in cross:
if (type == 'spot'):
time_series_request.tickers = cr
cross_vals = time_series_factory.harvest_time_series(time_series_request)
cross_vals.columns = [cr + '.bid', cr + '.ask']
if data_frame_agg is None:
data_frame_agg = cross_vals
else:
data_frame_agg = data_frame_agg.join(cross_vals, how='outer')
# strip the nan elements
data_frame_agg = data_frame_agg.dropna()
return data_frame_agg
def get_fx_cross(self, start, end, cross,
cut = "NYC", source = "bloomberg", freq = "intraday", cache_algo='cache_algo_return', type = 'spot'):
if source == "gain" or source == 'dukascopy' or freq == 'tick':
return self.get_fx_cross_tick(start, end, cross,
cut = cut, source = source, cache_algo='cache_algo_return', type = 'spot')
if isinstance(cross, str):
cross = [cross]
time_series_request = TimeSeriesRequest()
time_series_factory = self.time_series_factory
time_series_calcs = TimeSeriesCalcs()
data_frame_agg = None
if freq == 'intraday':
time_series_request.gran_freq = "minute" # intraday
elif freq == 'daily':
time_series_request.gran_freq = "daily" # intraday
time_series_request.freq_mult = 1 # 1 min
time_series_request.cut = cut # NYC/BGN ticker
time_series_request.fields = 'close' # close field only
time_series_request.cache_algo = cache_algo # cache_algo_only, cache_algo_return, internet_load
time_series_request.environment = 'backtest'
time_series_request.start_date = start
time_series_request.finish_date = end
time_series_request.data_source = source
for cr in cross:
base = cr[0:3]
terms = cr[3:6]
if (type == 'spot'):
# non-USD crosses
if base != 'USD' and terms != 'USD':
base_USD = self.fxconv.correct_notation('USD' + base)
terms_USD = self.fxconv.correct_notation('USD' + terms)
# TODO check if the cross exists in the database
# download base USD cross
time_series_request.tickers = base_USD
time_series_request.category = self.fxconv.em_or_g10(base, freq)
base_vals = time_series_factory.harvest_time_series(time_series_request)
# download terms USD cross
time_series_request.tickers = terms_USD
time_series_request.category = self.fxconv.em_or_g10(terms, freq)
terms_vals = time_series_factory.harvest_time_series(time_series_request)
if (base_USD[0:3] == 'USD'):
base_vals = 1 / base_vals
if (terms_USD[0:3] == 'USD'):
terms_vals = 1 / terms_vals
base_vals.columns = ['temp']
terms_vals.columns = ['temp']
cross_vals = base_vals.div(terms_vals, axis = 'index')
cross_vals.columns = [cr + '.close']
else:
if base == 'USD': non_USD = terms
if terms == 'USD': non_USD = base
correct_cr = self.fxconv.correct_notation(cr)
time_series_request.tickers = correct_cr
time_series_request.category = self.fxconv.em_or_g10(non_USD, freq)
cross_vals = time_series_factory.harvest_time_series(time_series_request)
# flip if not convention
if(correct_cr != cr):
cross_vals = 1 / cross_vals
cross_vals.columns.names = [cr + '.close']
elif type[0:3] == "tot":
if freq == 'daily':
# download base USD cross
time_series_request.tickers = base + 'USD'
time_series_request.category = self.fxconv.em_or_g10(base, freq) + '-tot'
if type == "tot":
base_vals = time_series_factory.harvest_time_series(time_series_request)
else:
x = 0
# download terms USD cross
time_series_request.tickers = terms + 'USD'
time_series_request.category = self.fxconv.em_or_g10(terms, freq) + '-tot'
if type == "tot":
terms_vals = time_series_factory.harvest_time_series(time_series_request)
else:
x = 0
base_rets = time_series_calcs.calculate_returns(base_vals)
terms_rets = time_series_calcs.calculate_returns(terms_vals)
cross_rets = base_rets.sub(terms_rets.iloc[:,0],axis=0)
# first returns of a time series will by NaN, given we don't know previous point
cross_rets.iloc[0] = 0
cross_vals = time_series_calcs.create_mult_index(cross_rets)
cross_vals.columns = [cr + '-tot.close']
elif freq == 'intraday':
self.logger.info('Total calculated returns for intraday not implemented yet')
return None
if data_frame_agg is None:
data_frame_agg = cross_vals
else:
data_frame_agg = data_frame_agg.join(cross_vals, how='outer')
# strip the nan elements
data_frame_agg = data_frame_agg.dropna()
return data_frame_agg