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)), )