def test_all(self):
data = np.array(
[
[1, 1, 1, 1, 1, 1],
[0, 1, 1, 1, 1, 1],
[1, 0, 1, 1, 1, 1],
[1, 1, 0, 1, 1, 1],
[1, 1, 1, 0, 1, 1],
[1, 1, 1, 1, 0, 1],
[1, 1, 1, 1, 1, 0],
],
dtype=bool,
)
# With a window_length of N, 0's should be "sticky" for the (N - 1)
# days after the 0 in the base data.
# Note that, the way ``self.run_graph`` works, we compute the same
# number of output rows for all inputs, so we only get the last 4
# outputs for expected_3 even though we have enought input data to
# compute 5 rows.
expected_3 = np.array(
[
[0, 0, 0, 1, 1, 1],
[1, 0, 0, 0, 1, 1],
[1, 1, 0, 0, 0, 1],
[1, 1, 1, 0, 0, 0],
],
dtype=bool,
)
expected_4 = np.array(
[
[0, 0, 0, 1, 1, 1],
[0, 0, 0, 0, 1, 1],
[1, 0, 0, 0, 0, 1],
[1, 1, 0, 0, 0, 0],
],
dtype=bool,
)
class Input(Filter):
inputs = ()
window_length = 0
self.check_terms(
terms={
"3": All(inputs=[Input()], window_length=3),
"4": All(inputs=[Input()], window_length=4),
},
expected={
"3": expected_3,
"4": expected_4,
},
initial_workspace={Input(): data},
mask=self.build_mask(np.ones(shape=data.shape)),
)
def test_at_least_N(self):
# With a window_length of K, AtLeastN should return 1
# if N or more 1's exist in the lookback window
# This smoothing filter gives customizable "stickiness"
data = array(
[[1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 0, 0], [1, 1, 1, 0, 0, 0], [1, 1, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0]],
dtype=bool)
expected_1 = array([[1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 0, 0]],
dtype=bool)
expected_2 = array([[1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 0, 0], [1, 1, 1, 0, 0, 0]],
dtype=bool)
expected_3 = array([[1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 0, 0],
[1, 1, 1, 0, 0, 0], [1, 1, 0, 0, 0, 0]],
dtype=bool)
expected_4 = array([[1, 1, 1, 1, 0, 0], [1, 1, 1, 0, 0, 0],
[1, 1, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0]],
dtype=bool)
class Input(Filter):
inputs = ()
window_length = 0
all_but_one = AtLeastN(inputs=[Input()], window_length=4, N=3)
all_but_two = AtLeastN(inputs=[Input()], window_length=4, N=2)
any_equiv = AtLeastN(inputs=[Input()], window_length=4, N=1)
all_equiv = AtLeastN(inputs=[Input()], window_length=4, N=4)
results = self.run_graph(
TermGraph({
'AllButOne': all_but_one,
'AllButTwo': all_but_two,
'AnyEquiv': any_equiv,
'AllEquiv': all_equiv,
'Any': Any(inputs=[Input()], window_length=4),
'All': All(inputs=[Input()], window_length=4)
}),
initial_workspace={Input(): data},
mask=self.build_mask(ones(shape=data.shape)),
)
check_arrays(results['Any'], expected_1)
check_arrays(results['AnyEquiv'], expected_1)
check_arrays(results['AllButTwo'], expected_2)
check_arrays(results['AllButOne'], expected_3)
check_arrays(results['All'], expected_4)
check_arrays(results['AllEquiv'], expected_4)
def TradableStocksUS(sector):
# Equities listed as common stock (not preferred stock, ETF, ADR, LP, etc)
common_stock = master.SecuritiesMaster.usstock_SecurityType2.latest.eq(
'Common Stock')
# Filter for primary share equities; primary shares can be identified by a
# null usstock_PrimaryShareSid field (i.e. no pointer to a primary share)
is_primary_share = master.SecuritiesMaster.usstock_PrimaryShareSid.latest.isnull(
)
in_sector = master.SecuritiesMaster.usstock_Sector.latest.eq(sector)
# combine the security type filters to begin forming our universe
tradable_stocks = common_stock & is_primary_share & in_sector
# also require high dollar volume
tradable_stocks = AverageDollarVolume(
window_length=200, mask=tradable_stocks).percentile_between(90, 100)
# also require price > $5. Note that we use Latest(...) instead of EquityPricing.close.latest
# so that we can pass a mask
tradable_stocks = Latest([USEquityPricing.close],
mask=tradable_stocks) > 10
# also require no missing data for 200 days
tradable_stocks = AllPresent(inputs=[USEquityPricing.close],
window_length=200,
mask=tradable_stocks)
tradable_stocks = All([USEquityPricing.volume.latest > 0],
window_length=200,
mask=tradable_stocks)
return tradable_stocks
def test_all(self):
data = array(
[[1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1], [1, 0, 1, 1, 1, 1],
[1, 1, 0, 1, 1, 1], [1, 1, 1, 0, 1, 1], [1, 1, 1, 1, 0, 1],
[1, 1, 1, 1, 1, 0]],
dtype=bool)
# With a window_length of N, 0's should be "sticky" for the (N - 1)
# days after the 0 in the base data.
# Note that, the way ``self.run_graph`` works, we compute the same
# number of output rows for all inputs, so we only get the last 4
# outputs for expected_3 even though we have enought input data to
# compute 5 rows.
expected_3 = array([[0, 0, 0, 1, 1, 1], [1, 0, 0, 0, 1, 1],
[1, 1, 0, 0, 0, 1], [1, 1, 1, 0, 0, 0]],
dtype=bool)
expected_4 = array([[0, 0, 0, 1, 1, 1], [0, 0, 0, 0, 1, 1],
[1, 0, 0, 0, 0, 1], [1, 1, 0, 0, 0, 0]],
dtype=bool)
class Input(Filter):
inputs = ()
window_length = 0
results = self.run_graph(
TermGraph({
'3': All(inputs=[Input()], window_length=3),
'4': All(inputs=[Input()], window_length=4),
}),
initial_workspace={Input(): data},
mask=self.build_mask(ones(shape=data.shape)),
)
check_arrays(results['3'], expected_3)
check_arrays(results['4'], expected_4)
def TradableStocksUS(market_cap_filter=False):
"""
Returns a Pipeline filter of tradable stocks, defined as:
- Common stocks only (no preferred stocks, ADRs, LPs, or ETFs)
- Primary shares only
- 200-day average dollar volume >= $2.5M
- price >= $5
- 200 continuous days of price and volume.
If market_cap_filter=True, also requires market cap > $500M.
"""
# Equities listed as common stock (not preferred stock, ETF, ADR, LP, etc)
common_stock = master.SecuritiesMaster.usstock_SecurityType2.latest.eq(
'Common Stock')
# Filter for primary share equities; primary shares can be identified by a
# null usstock_PrimaryShareSid field (i.e. no pointer to a primary share)
is_primary_share = master.SecuritiesMaster.usstock_PrimaryShareSid.latest.isnull(
)
# combine the security type filters to begin forming our universe
tradable_stocks = common_stock & is_primary_share
# also require high dollar volume
tradable_stocks = AverageDollarVolume(window_length=200,
mask=tradable_stocks) >= 2.5e6
# also require price > $5. Note that we use Latest(...) instead of EquityPricing.close.latest
# so that we can pass a mask
tradable_stocks = Latest([EquityPricing.close], mask=tradable_stocks) > 5
# also require no missing data for 200 days
tradable_stocks = AllPresent(inputs=[EquityPricing.close],
window_length=200,
mask=tradable_stocks)
tradable_stocks = All([EquityPricing.volume.latest > 0],
window_length=200,
mask=tradable_stocks)
if market_cap_filter:
# also require market cap over $500M
tradable_stocks = Latest([
sharadar.Fundamentals.slice(dimension='ARQ',
period_offset=0).MARKETCAP
],
mask=tradable_stocks) >= 500e6
return tradable_stocks
def test_at_least_N(self):
# With a window_length of K, AtLeastN should return 1
# if N or more 1's exist in the lookback window
# This smoothing filter gives customizable "stickiness"
data = np.array(
[
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 0, 0],
[1, 1, 1, 0, 0, 0],
[1, 1, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0],
],
dtype=bool,
)
expected_1 = np.array(
[
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 0, 0],
],
dtype=bool,
)
expected_2 = np.array(
[
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 0, 0],
[1, 1, 1, 0, 0, 0],
],
dtype=bool,
)
expected_3 = np.array(
[
[1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 0, 0],
[1, 1, 1, 0, 0, 0],
[1, 1, 0, 0, 0, 0],
],
dtype=bool,
)
expected_4 = np.array(
[
[1, 1, 1, 1, 0, 0],
[1, 1, 1, 0, 0, 0],
[1, 1, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0],
],
dtype=bool,
)
class Input(Filter):
inputs = ()
window_length = 0
all_but_one = AtLeastN(inputs=[Input()], window_length=4, N=3)
all_but_two = AtLeastN(inputs=[Input()], window_length=4, N=2)
any_equiv = AtLeastN(inputs=[Input()], window_length=4, N=1)
all_equiv = AtLeastN(inputs=[Input()], window_length=4, N=4)
self.check_terms(
terms={
"AllButOne": all_but_one,
"AllButTwo": all_but_two,
"AnyEquiv": any_equiv,
"AllEquiv": all_equiv,
"Any": Any(inputs=[Input()], window_length=4),
"All": All(inputs=[Input()], window_length=4),
},
expected={
"Any": expected_1,
"AnyEquiv": expected_1,
"AllButTwo": expected_2,
"AllButOne": expected_3,
"All": expected_4,
"AllEquiv": expected_4,
},
initial_workspace={Input(): data},
mask=self.build_mask(np.ones(shape=data.shape)),
)