def create_returns_from_range(trading_calendar):
current = trading_calendar.first_open
end = trading_calendar.last_close
one_day = timedelta(days=1)
test_range = []
while current <= end:
r = risk.DailyReturn(current, random.random())
test_range.append(r)
current = get_next_trading_dt(current, one_day, trading_calendar)
return test_range
def load_market_data():
try:
fp_bm = get_datafile('benchmark.msgpack', "rb")
except IOError:
print """
data msgpacks aren't distribute with source.
Fetching data from Yahoo Finance.
""".strip()
dump_benchmarks()
fp_bm = get_datafile('benchmark.msgpack', "rb")
bm_list = msgpack.loads(fp_bm.read())
bm_returns = []
for packed_date, returns in bm_list:
event_dt = tuple_to_date(packed_date)
#event_dt = event_dt.replace(
# hour=0,
# minute=0,
# second=0,
# tzinfo=pytz.utc
#)
daily_return = risk.DailyReturn(date=event_dt, returns=returns)
bm_returns.append(daily_return)
fp_bm.close()
bm_returns = sorted(bm_returns, key=attrgetter('date'))
try:
fp_tr = get_datafile('treasury_curves.msgpack', "rb")
except IOError:
print """
data msgpacks aren't distribute with source.
Fetching data from data.treasury.gov
""".strip()
dump_treasury_curves()
fp_tr = get_datafile('treasury_curves.msgpack', "rb")
tr_list = msgpack.loads(fp_tr.read())
tr_curves = {}
for packed_date, curve in tr_list:
tr_dt = tuple_to_date(packed_date)
#tr_dt = tr_dt.replace(hour=0, minute=0, second=0, tzinfo=pytz.utc)
tr_curves[tr_dt] = curve
fp_tr.close()
return bm_returns, tr_curves
def create_returns_from_list(returns, trading_calendar):
current = trading_calendar.first_open
one_day = timedelta(days=1)
test_range = []
#sometimes the range starts with a non-trading day.
if not trading_calendar.is_trading_day(current):
current = get_next_trading_dt(current, one_day, trading_calendar)
for return_val in returns:
r = risk.DailyReturn(current, return_val)
test_range.append(r)
current = get_next_trading_dt(current, one_day, trading_calendar)
return test_range
def create_returns(daycount, trading_calendar):
"""
For the given number of calendar (not trading) days return all the trading
days between start and start + daycount.
"""
test_range = []
current = trading_calendar.first_open
one_day = timedelta(days=1)
for day in range(daycount):
current = current + one_day
if trading_calendar.is_trading_day(current):
r = risk.DailyReturn(current, random.random())
test_range.append(r)
return test_range
def handle_market_close(self):
# add the return results from today to the list of DailyReturn objects.
todays_date = self.market_close.replace(hour=0, minute=0, second=0)
todays_return_obj = risk.DailyReturn(todays_date,
self.todays_performance.returns)
self.returns.append(todays_return_obj)
#update risk metrics for cumulative performance
self.cumulative_risk_metrics.update(self.todays_performance.returns)
# increment the day counter before we move markers forward.
self.day_count += 1.0
# calculate progress of test
self.progress = self.day_count / self.total_days
# Take a snapshot of our current peformance to return to the
# browser.
daily_update = self.to_dict()
#move the market day markers forward
self.market_open = self.market_open + self.calendar_day
while not self.trading_environment.is_trading_day(self.market_open):
if self.market_open > self.trading_environment.trading_days[-1]:
raise Exception(
"Attempt to backtest beyond available history.")
self.market_open = self.market_open + self.calendar_day
self.market_close = self.market_open + self.trading_day
# Roll over positions to current day.
self.todays_performance = PerformancePeriod(
self.todays_performance.positions,
self.todays_performance.ending_value,
self.todays_performance.ending_cash,
self.market_open,
self.market_close,
keep_transactions=True)
return daily_update
def test_risk_metrics_returns(self):
risk_metrics_refactor = risk.RiskMetricsIterative(
self.start_date, self.trading_env)
todays_date = self.start_date
cur_returns = []
for i, ret in enumerate(RETURNS):
todays_return_obj = risk.DailyReturn(
todays_date,
ret
)
cur_returns.append(todays_return_obj)
# Move forward day counter to next trading day
todays_date += self.oneday
while not self.trading_env.is_trading_day(todays_date):
todays_date += self.oneday
try:
risk_metrics_original = risk.RiskMetricsBatch(
start_date=self.start_date,
end_date=todays_date,
returns=cur_returns,
trading_environment=self.trading_env
)
except Exception as e:
#assert that when original raises exception, same
#exception is raised by risk_metrics_refactor
np.testing.assert_raises(
type(e), risk_metrics_refactor.update, ret)
continue
risk_metrics_refactor.update(ret)
self.assertEqual(
risk_metrics_original.start_date,
risk_metrics_refactor.start_date)
self.assertEqual(
risk_metrics_original.end_date,
risk_metrics_refactor.end_date)
self.assertEqual(
risk_metrics_original.treasury_duration,
risk_metrics_refactor.treasury_duration)
self.assertEqual(
risk_metrics_original.treasury_curve,
risk_metrics_refactor.treasury_curve)
self.assertEqual(
risk_metrics_original.treasury_period_return,
risk_metrics_refactor.treasury_period_return)
self.assertEqual(
risk_metrics_original.benchmark_returns,
risk_metrics_refactor.benchmark_returns)
self.assertEqual(
risk_metrics_original.algorithm_returns,
risk_metrics_refactor.algorithm_returns)
risk_original_dict = risk_metrics_original.to_dict()
risk_refactor_dict = risk_metrics_refactor.to_dict()
self.assertEqual(set(risk_original_dict.keys()),
set(risk_refactor_dict.keys()))
err_msg_format = """\
"In update step {iter}: {measure} should be {truth} but is {returned}!"""
for measure in risk_original_dict.iterkeys():
if measure == 'max_drawdown':
np.testing.assert_almost_equal(
risk_refactor_dict[measure],
risk_original_dict[measure],
err_msg=err_msg_format.format(
iter=i,
measure=measure,
truth=risk_original_dict[measure],
returned=risk_refactor_dict[measure]))
else:
np.testing.assert_equal(
risk_original_dict[measure],
risk_refactor_dict[measure],
err_msg_format.format(
iter=i,
measure=measure,
truth=risk_original_dict[measure],
returned=risk_refactor_dict[measure])
)