def treasury_yield(required_data):
'''
required data is an empty? dataframe that will be populated.
Returns a dataframe.
'''
treasury_data = zipline_treasuries.get_treasury_data()
treasury_yield = pd.DataFrame.from_records(data=treasury_data)
treasury_yield.set_index('date', inplace=True)
required_data.update(treasury_yield)
required_data = required_data * 10 # zipline has their yields in tenths' of a percent??
return required_data
def treasury_yield(required_data):
'''
required data is an empty? dataframe that will be populated.
Returns a dataframe.
'''
treasury_data = zipline_treasuries.get_treasury_data()
treasury_yield = pd.DataFrame.from_records(data=treasury_data)
treasury_yield.set_index('date', inplace=True)
required_data.update(treasury_yield)
required_data = required_data * 10 # zipline has their yields in tenths' of a percent??
return required_data
def load_market_data(self, bm_symbol='^GSPC'):
#TODO Parametric
#event_dt = datetime.today().replace(tzinfo=pytz.utc)
event_dt = datetime.now(pytz.utc)
# Getting today benchmark return
#NOTE Seems shit but later, previous days could be used to compute indicators
#last_bench_return = get_benchmark_returns(bm_symbol, start_date=(event_dt - pd.datetools.Day(self.loopback)))
#last_bench_return = last_bench_return[-1]
#print('Benchmark on {}: {}'.format(last_bench_return.date, last_bench_return.returns))
for exchange, infos in datautils.Exchange.iteritems():
if infos['index'] == bm_symbol:
code = datautils.Exchange[exchange]['code']
break
bm_returns = []
while event_dt < self.last_trading_day:
#TODO Current value to give
#TODO Append only if trading day and market hour
#bm_returns.append(DailyReturn(date=event_dt.replace(microsecond=0), returns=last_bench_return.returns))
bm_returns.append(DailyReturn(date=event_dt.replace(microsecond=0), returns=code))
#TODO Frequency control
event_dt += self.offset
bm_returns = sorted(bm_returns, key=attrgetter('date'))
tr_gen = get_treasury_data()
while True:
try:
last_tr = tr_gen.next()
except StopIteration:
break
tr_curves = {}
#tr_dt = datetime.today().replace(tzinfo=pytz.utc)
tr_dt = datetime.now(pytz.utc)
while tr_dt < self.last_trading_day:
#tr_dt = tr_dt.replace(hour=0, minute=0, second=0, tzinfo=pytz.utc)
tr_curves[tr_dt] = last_tr
tr_dt += self.offset
tr_curves = OrderedDict(sorted(
((dt, c) for dt, c in tr_curves.iteritems()),
key=lambda t: t[0]))
return bm_returns, tr_curves