def __init__(self):
self.logger = LoggerManager().getLogger(__name__)
self._all_econ_tickers = pandas.read_csv(Constants().all_econ_tickers)
self._econ_country_codes = pandas.read_csv(
Constants().econ_country_codes)
self._econ_country_groups = pandas.read_csv(
Constants().econ_country_groups)
self.time_series_factory = LightTimeSeriesFactory()
def __init__(self):
super(StrategyTemplate, self).__init__()
self.logger = LoggerManager().getLogger(__name__)
##### FILL IN WITH YOUR OWN PARAMETERS FOR display, dumping, TSF etc.
self.tsfactory = LightTimeSeriesFactory()
self.DUMP_CSV = 'output_data/'
self.DUMP_PATH = 'output_data/' + datetime.date.today().strftime("%Y%m%d") + ' '
self.FINAL_STRATEGY = 'Thalesians FX CTA'
self.SCALE_FACTOR = 3
return
data_source='bloomberg', # use Bloomberg as data source
tickers=[
'EURUSD', # ticker (Thalesians)
'GBPUSD',
'AUDUSD'
],
fields=['close'], # which fields to download
vendor_tickers=[
'EURUSD BGN Curncy', # ticker (Bloomberg)
'GBPUSD BGN Curncy',
'AUDUSD BGN Curncy'
],
vendor_fields=['PX_LAST'], # which Bloomberg fields to download
cache_algo='internet_load_return') # how to return data
ltsf = LightTimeSeriesFactory()
df = None
df = ltsf.harvest_time_series(time_series_request)
tsc = TimeSeriesCalcs()
df = tsc.calculate_returns(df)
df = tsc.rolling_corr(df['EURUSD.close'],
20,
data_frame2=df[['GBPUSD.close', 'AUDUSD.close']])
gp = GraphProperties()
gp.title = "1M FX rolling correlations"
gp.scale_factor = 3
pf = PlotFactory()
