class TimestampOps:
params = [None, "US/Eastern", pytz.UTC, dateutil.tz.tzutc()]
param_names = ["tz"]
def setup(self, tz):
self.ts = Timestamp("2017-08-25 08:16:14", tz=tz)
def time_replace_tz(self, tz):
self.ts.replace(tzinfo=pytz.timezone("US/Eastern"))
def time_replace_None(self, tz):
self.ts.replace(tzinfo=None)
def time_to_pydatetime(self, tz):
self.ts.to_pydatetime()
def time_normalize(self, tz):
self.ts.normalize()
def time_tz_convert(self, tz):
if self.ts.tz is not None:
self.ts.tz_convert(tz)
def time_tz_localize(self, tz):
if self.ts.tz is None:
self.ts.tz_localize(tz)
def time_to_julian_date(self, tz):
self.ts.to_julian_date()
def time_floor(self, tz):
self.ts.floor("5T")
def time_ceil(self, tz):
self.ts.ceil("5T")
class TimestampOps(object):
params = [None, 'US/Eastern', pytz.UTC, dateutil.tz.tzutc()]
param_names = ['tz']
def setup(self, tz):
self.ts = Timestamp('2017-08-25 08:16:14', tz=tz)
def time_replace_tz(self, tz):
self.ts.replace(tzinfo=pytz.timezone('US/Eastern'))
def time_replace_None(self, tz):
self.ts.replace(tzinfo=None)
def time_to_pydatetime(self, tz):
self.ts.to_pydatetime()
def time_normalize(self, tz):
self.ts.normalize()
def time_tz_convert(self, tz):
if self.ts.tz is not None:
self.ts.tz_convert(tz)
def time_tz_localize(self, tz):
if self.ts.tz is None:
self.ts.tz_localize(tz)
def time_to_julian_date(self, tz):
self.ts.to_julian_date()
def time_floor(self, tz):
self.ts.floor('5T')
def time_ceil(self, tz):
self.ts.ceil('5T')
class TimestampOps(object):
params = [None, 'US/Eastern', pytz.UTC,
dateutil.tz.tzutc()]
param_names = ['tz']
def setup(self, tz):
self.ts = Timestamp('2017-08-25 08:16:14', tz=tz)
def time_replace_tz(self, tz):
self.ts.replace(tzinfo=pytz.timezone('US/Eastern'))
def time_replace_None(self, tz):
self.ts.replace(tzinfo=None)
def time_to_pydatetime(self, tz):
self.ts.to_pydatetime()
def time_normalize(self, tz):
self.ts.normalize()
def time_tz_convert(self, tz):
if self.ts.tz is not None:
self.ts.tz_convert(tz)
def time_tz_localize(self, tz):
if self.ts.tz is None:
self.ts.tz_localize(tz)
def set_datetime(self, dt: pd.Timestamp) -> None:
self._current_prices_col = None
self._current_prices = None
if self._current_dt is not None:
# make up events for skipped days for update splits and divs.
current_date = self._current_dt.normalize()
target_date = dt.normalize()
if current_date != target_date:
for date in pd.bdate_range(current_date, target_date):
if date == current_date or date == target_date:
continue
stop = date + pd.DateOffset(days=1, seconds=-1)
table = self._prices.get_as_dict(date, stop)
bar_times = table.get_datetime_index()
if len(bar_times) == 0:
continue
open_time = bar_times[0]
super().set_datetime(open_time)
self.set_price('open')
self.market_open(self)
close_time = bar_times[-1]
super().set_datetime(close_time)
self.set_price('close')
self.update_portfolio_value()
self.market_close(self)
super().set_datetime(dt)
def merge_date_time(x:pd.Timestamp,y:pd.Timestamp):
'''
This function takes in a date part (as naive timestamp) and a
time part (as Timestamp to today's date), and return a combined
timestamp.
'''
x = pd.Timestamp(x)
y = pd.Timestamp(y)
return pd.Timestamp(x.value + y.value - y.normalize().value)
class TimestampOps(object):
params = [None, 'US/Eastern', 'UTC']
param_names = ['tz']
def setup(self, tz):
self.ts = Timestamp('2017-08-25 08:16:14', tz=tz)
def time_replace_tz(self, tz):
self.ts.replace(tzinfo=pytz.timezone('US/Eastern'))
def time_replace_None(self, tz):
self.ts.replace(tzinfo=None)
def time_to_pydatetime(self, tz):
self.ts.to_pydatetime()
def time_normalize(self, tz):
self.ts.normalize()
def _ts_floor(ts: pd.Timestamp, freq: str = 'month'):
d = {
'month': lambda: pd.Timedelta('1 day') * (ts.day - 1),
'week': lambda: pd.Timedelta('1 day') * ts.weekday,
'day': lambda: 0
}
f = d.get(freq)
if not f:
raise ValueError(
"""freq must be month, week, or day."""
)
return ts.normalize() - f()
def set_datetime(self, dt: pd.Timestamp) -> None:
if self._current_dt is not None:
# make up events for skipped days for update splits and divs.
current_date = self._current_dt.normalize()
target_date = dt.normalize()
for date in pd.bdate_range(current_date, target_date):
if date == current_date or date == target_date:
continue
super().set_datetime(date)
self._update_time()
self.market_open(self)
if self._current_row is not None:
self._current_prices = self._current_row[
self.dataloader.ohlcv[3]].to_dict()
self.update_portfolio_value()
self.market_close(self)
super().set_datetime(dt)
self._update_time()
def test_normalize(self, tz_naive_fixture, arg):
tz = tz_naive_fixture
ts = Timestamp(arg, tz=tz)
result = ts.normalize()
expected = Timestamp("2013-11-30", tz=tz)
assert result == expected
def test_normalize(self, dt64, ts):
alt = Timestamp(dt64)
result = ts.normalize()
assert result._reso == ts._reso
assert result == alt.normalize()
def test_normalize(self, tz_naive_fixture, arg):
tz = tz_naive_fixture
ts = Timestamp(arg, tz=tz)
result = ts.normalize()
expected = Timestamp('2013-11-30', tz=tz)
assert result == expected
def get_ists_claims(
date: pd.Timestamp,
ids: Optional[pd.Index] = None,
lr_flag: bool = True,
columns: Optional[List] = None,
) -> pd.DataFrame:
"""
Given a series of IDs and a date, return a df with ISTS claim info for each ID
Claim info is taken from last LR date before given date.
Parameters
----------
date: pd.Timestamp
Date to look up
ids: pd.Index
optional list of ids to select
lr_flag: bool = True
If True, just return records flagged as on the Live Register
columns: Optional[List] = None
Columns from the ISTS claim database to return. Default is all columns.
Returns
-------
df: pd.DataFrame
"""
col_list = unpack(
get_col_list(engine,
"ists_claims",
columns=columns,
required_columns=["lr_flag"]) +
get_col_list(engine,
"ists_personal",
columns=columns,
required_columns=["ppsn"]))
lookup_date = nearest_lr_date(date.normalize(), how="previous").date()
query_text = f"""\
SELECT {col_list}
FROM ists_claims c
JOIN ists_personal p
ON c.personal_id=p.id
WHERE lr_date = '{lookup_date}'
"""
if lr_flag:
query_text += """\
AND lr_flag is true
"""
query, params = get_parameterized_query(query_text, ids)
ists = pd.read_sql(
query,
con=engine,
params=params,
parse_dates=datetime_cols(engine, "ists_claims") +
datetime_cols(engine, "ists_personal"),
).drop_duplicates("ppsn", keep="first")
return (ists.dropna(axis=0, how="all").reset_index(drop=True).set_index(
"ppsn", verify_integrity=True))
