def is_trading_date(entity_type, exchange, timestamp: pd.Timestamp):
if type(timestamp) == str:
timestamp = to_pd_timestamp(timestamp)
# just ignore 00:00
# the_date = date_and_time(timestamp, '09:00')
if entity_type == 'stock':
return (timestamp.weekday() != 5) and (timestamp.weekday() != 6)
return True
def generate_date_feature(date: pd.Timestamp):
"""
Generate date features.
"""
result = pd.Series()
result['timestamp'] = date.timestamp()
format_string = '%Y-%m-%d'
dt_string = date.strftime(format_string)
result['holiday'] = int(
(dt_string in holiday)
or (date.weekday() in [5, 6] and dt_string not in work))
result = pd.concat(
[result, get_onehot(date.weekday(), 0, 6, name='weekday')])
return result
def test_non_nano_fields(self, dt64, ts):
alt = Timestamp(dt64)
assert ts.year == alt.year
assert ts.month == alt.month
assert ts.day == alt.day
assert ts.hour == ts.minute == ts.second == ts.microsecond == 0
assert ts.nanosecond == 0
assert ts.to_julian_date() == alt.to_julian_date()
assert ts.weekday() == alt.weekday()
assert ts.isoweekday() == alt.isoweekday()
def get_time_of_use_period(dt: pd.Timestamp) -> str:
"""Return a string corresponding to the time-of-use category for the
datetime."""
# 7PM - 7AM, weekends, and holidays are always off-peak
if (dt.weekday() >= 5 or not (7 <= dt.hour < 12 + 7)
or dt in ONTARIO_HOLIDAYS):
return "Off-peak"
# Nov - Apr is winter, versus May-Oct for summer
is_winter = dt.month in (11, 12, 1, 2, 3, 4)
# On-peak and off-peak switch between winter and summer
if (7 <= dt.hour < 11) or (12 + 5 <= dt.hour < 12 + 7):
return "On-peak" if is_winter else "Mid-peak"
elif 11 <= dt.hour < 12 + 5:
return "Mid-peak" if is_winter else "On-peak"
else:
assert False, "Time of use inference error." # should never occur!
def generate_requests(self, timestamp: pd.Timestamp):
month = timestamp.month if timestamp.month in self.monthly_vals else 0
seconds_per_time_unit = self.__class__.SECONDS_IN_DAY // len(
self.monthly_vals[month][timestamp.weekday()])
time_unit = (int(timestamp.timestamp()) %
self.__class__.SECONDS_IN_DAY) // seconds_per_time_unit
self._populate_split_across_seconds_if_needed(timestamp, month,
seconds_per_time_unit,
time_unit)
second_in_time_unit = int(timestamp.timestamp(
)) % seconds_per_time_unit # TODO: is it correct?
self._populate_simulation_steps_in_second_if_needed(
second_in_time_unit)
return self._generate_requests_on_current_simulation_step(timestamp)
def _populate_split_across_seconds_if_needed(self, timestamp: pd.Timestamp,
month: int,
seconds_per_time_unit: int,
time_unit: int):
if month != self.current_month and time_unit != self.current_time_unit:
# Generate the split if not available
self.current_month = month
self.current_time_unit = time_unit
self.current_means_split_across_seconds = {
s: 0
for s in range(seconds_per_time_unit)
}
avg_reqs_val = self.monthly_vals[month][
timestamp.weekday()][time_unit]
for _ in range(avg_reqs_val):
self.current_means_split_across_seconds[np.random.randint(
seconds_per_time_unit)] += 1
def find_weekday(date_str):
from pandas import Timestamp as ts
date = ts.strptime(date_str, "%Y-%m-%d")
return ts.weekday(date)
