def test_compute_lifetimes(self):
num_assets = 4
env = TradingEnvironment()
trading_day = env.trading_day
first_start = pd.Timestamp('2015-04-01', tz='UTC')
frame = make_rotating_asset_info(
num_assets=num_assets,
first_start=first_start,
frequency=env.trading_day,
periods_between_starts=3,
asset_lifetime=5
)
env.write_data(equities_df=frame)
finder = env.asset_finder
all_dates = pd.date_range(
start=first_start,
end=frame.end_date.max(),
freq=trading_day,
)
for dates in all_subindices(all_dates):
expected_with_start_raw = full(
shape=(len(dates), num_assets),
fill_value=False,
dtype=bool,
)
expected_no_start_raw = full(
shape=(len(dates), num_assets),
fill_value=False,
dtype=bool,
)
for i, date in enumerate(dates):
it = frame[['start_date', 'end_date']].itertuples()
for j, start, end in it:
# This way of doing the checks is redundant, but very
# clear.
if start <= date <= end:
expected_with_start_raw[i, j] = True
if start < date:
expected_no_start_raw[i, j] = True
expected_with_start = pd.DataFrame(
data=expected_with_start_raw,
index=dates,
columns=frame.index.values,
)
result = finder.lifetimes(dates, include_start_date=True)
assert_frame_equal(result, expected_with_start)
expected_no_start = pd.DataFrame(
data=expected_no_start_raw,
index=dates,
columns=frame.index.values,
)
result = finder.lifetimes(dates, include_start_date=False)
assert_frame_equal(result, expected_no_start)
def test_compute_lifetimes(self):
num_assets = 4
trading_day = self.env.trading_day
first_start = pd.Timestamp('2015-04-01', tz='UTC')
frame = make_rotating_equity_info(
num_assets=num_assets,
first_start=first_start,
frequency=self.env.trading_day,
periods_between_starts=3,
asset_lifetime=5
)
self.env.write_data(equities_df=frame)
finder = self.env.asset_finder
all_dates = pd.date_range(
start=first_start,
end=frame.end_date.max(),
freq=trading_day,
)
for dates in all_subindices(all_dates):
expected_with_start_raw = full(
shape=(len(dates), num_assets),
fill_value=False,
dtype=bool,
)
expected_no_start_raw = full(
shape=(len(dates), num_assets),
fill_value=False,
dtype=bool,
)
for i, date in enumerate(dates):
it = frame[['start_date', 'end_date']].itertuples()
for j, start, end in it:
# This way of doing the checks is redundant, but very
# clear.
if start <= date <= end:
expected_with_start_raw[i, j] = True
if start < date:
expected_no_start_raw[i, j] = True
expected_with_start = pd.DataFrame(
data=expected_with_start_raw,
index=dates,
columns=frame.index.values,
)
result = finder.lifetimes(dates, include_start_date=True)
assert_frame_equal(result, expected_with_start)
expected_no_start = pd.DataFrame(
data=expected_no_start_raw,
index=dates,
columns=frame.index.values,
)
result = finder.lifetimes(dates, include_start_date=False)
assert_frame_equal(result, expected_no_start)
def test_compute_lifetimes(self):
num_assets = 4
env = TradingEnvironment()
trading_day = env.trading_day
first_start = pd.Timestamp('2015-04-01', tz='UTC')
frame = make_rotating_asset_info(
num_assets=num_assets,
first_start=first_start,
frequency=env.trading_day,
periods_between_starts=3,
asset_lifetime=5
)
env.write_data(equities_df=frame)
finder = env.asset_finder
all_dates = pd.date_range(
start=first_start,
end=frame.end_date.max(),
freq=trading_day,
)
for dates in all_subindices(all_dates):
expected_mask = full(
shape=(len(dates), num_assets),
fill_value=False,
dtype=bool,
)
for i, date in enumerate(dates):
it = frame[['start_date', 'end_date']].itertuples()
for j, start, end in it:
if start <= date <= end:
expected_mask[i, j] = True
# Filter out columns with all-empty columns.
expected_result = pd.DataFrame(
data=expected_mask,
index=dates,
columns=frame.index.values,
)
actual_result = finder.lifetimes(dates)
assert_frame_equal(actual_result, expected_result)
def test_compute_lifetimes(self, env=None):
num_assets = 4
trading_day = env.trading_day
first_start = pd.Timestamp('2015-04-01', tz='UTC')
frame = make_rotating_asset_info(num_assets=num_assets,
first_start=first_start,
frequency=env.trading_day,
periods_between_starts=3,
asset_lifetime=5)
finder = AssetFinder(frame)
all_dates = pd.date_range(
start=first_start,
end=frame.end_date.max(),
freq=trading_day,
)
for dates in all_subindices(all_dates):
expected_mask = full(
shape=(len(dates), num_assets),
fill_value=False,
dtype=bool,
)
for i, date in enumerate(dates):
it = frame[['start_date', 'end_date']].itertuples()
for j, start, end in it:
if start <= date <= end:
expected_mask[i, j] = True
# Filter out columns with all-empty columns.
expected_result = pd.DataFrame(
data=expected_mask,
index=dates,
columns=frame.sid.values,
)
actual_result = finder.lifetimes(dates)
assert_frame_equal(actual_result, expected_result)
