def StocksUS():
return ((USEquityPricing.close.latest > 3)
& Exchange().element_of(['NYSE', 'NASDAQ', 'NYSEMKT']) &
(Sector().notnull()) & (~Sector().element_of(
['Financial Services', 'Real Estate', 'Energy', 'Utilities']))
& (IsDomesticCommonStock().eq(1)) &
(Fundamentals(field='revenue_arq') > 0) &
(Fundamentals(field='assets_arq') > 0) &
(Fundamentals(field='equity_arq') > 0) & (EV() > 0))
class EarningYield(CustomFactor):
inputs = [Fundamentals(field='netinccmnusd_art'), MarketCap()]
window_safe = True
window_length = 1
def compute(self, today, assets, out, earnings, mkt_cap):
l = self.window_length
out[:] = earnings[-l] / mkt_cap[-l]
class SalesYieldEV(CustomFactor):
inputs = [
Fundamentals(field='revenueusd_arq'),
EV(),
InterestRate(),
InflationRate()
]
window_safe = True
window_length = 1
def compute(self, today, assets, out, sales, ev, rateint, rateinf):
l = self.window_length
cf_yield_ev = sales[-l] / ev[-l]
out[:] = adjust_for_inflation(cf_yield_ev, l, rateint, rateinf)
class EarningYieldEV(CustomFactor):
inputs = [
Fundamentals(field='netinccmnusd_arq'),
EV(),
InterestRate(),
InflationRate()
]
window_safe = True
window_length = 1
def compute(self, today, assets, out, earning, ev, rateint, rateinf):
l = self.window_length
earning_yield_ev = earning[-l] / ev[-l]
out[:] = adjust_for_inflation(earning_yield_ev, l, rateint, rateinf)
class CashFlowYieldEV(CustomFactor):
inputs = [
Fundamentals(field='ncfo_arq'),
EV(),
InterestRate(),
InflationRate()
]
window_safe = True
window_length = 1
def compute(self, today, assets, out, cf, ev, rateint, rateinf):
l = self.window_length
cf_yield_ev = cf[-l] / ev[-l]
out[:] = adjust_for_inflation(cf_yield_ev, l, rateint, rateinf)
class BookValueYieldEV(CustomFactor):
inputs = [
Fundamentals(field='equityusd_arq'),
EV(),
InterestRate(),
InflationRate()
]
window_safe = True
window_length = 1
def compute(self, today, assets, out, bv, ev, rateint, rateinf):
l = self.window_length
bv_yield_ev = bv[-l] / ev[-l]
out[:] = adjust_for_inflation(bv_yield_ev, l, rateint, rateinf)
def make_pipeline():
rsi = RSI()
return Pipeline(
columns={
'longs': rsi.top(3),
'shorts': rsi.bottom(3),
'revenue': Fundamentals(field='revenue_art'),
},
# no screen: 1 mo -> ca. 4 minutes;
# 1 yr -> 20m
# base_universe: ca. 1 mo. -> 3 minutes
#screen = base_universe()
# tradable_stocks(): 1 mo. -> ca 1h
# 1yr -< 1h 40m
#screen = TradableStocksUS()
# NamedUniverse('tradable_stocks_us'): 1yr 7m
screen=NamedUniverse('tradable_stocks_us'))
def base_universe():
#return TradableStocksUS()
return ((Fundamentals(field='revenue_art') > 0) &
(Fundamentals(field='assets_arq') > 0) &
(Fundamentals(field='equity_arq') > 0) & (EV() > 0))
screen = StaticAssets(symbols(['IBM', 'F', 'AAPL']))
)
stocks = spe.run_pipeline(pipe, pipe_start, pipe_end)
print("stocks.shape [close]", stocks)
from sharadar.pipeline.factors import MarketCap, EV, Fundamentals
pipe_mkt_cap = Pipeline(columns={
'mkt_cap': MarketCap()
},
)
start_time = time.time()
stocks = spe.run_pipeline(pipe_mkt_cap, pipe_start, pipe_end)
print("stocks.shape [mkt cap]", stocks.shape)
print("--- %s ---" % datetime.timedelta(seconds=(time.time() - start_time)))
pipe_mkt_cap_ev = Pipeline(columns={
'mkt_cap': MarketCap(),
'ev': EV(),
'debt': Fundamentals(field='debtusd_arq'),
'cash': Fundamentals(field='cashnequsd_arq')
},
screen = StaticAssets(symbols(['IBM', 'F', 'AAPL']))
)
stocks = spe.run_pipeline(pipe_mkt_cap_ev, pipe_start, pipe_end)
print(stocks)
window_safe = True
window_length = 1
def compute(self, today, assets, out, earnings, mkt_cap):
l = self.window_length
out[:] = earnings[-l] / mkt_cap[-l]
month_length = 21
monthly = tuple(
np.append(np.arange(-11 * month_length, 0, step=month_length), -1))
universe = StaticAssets(symbols(['IBM', 'F', 'AAPL']))
pipe = Pipeline(columns={
'revenue':
Fundamentals(field='revenue', window_length=1),
'revenue_trend':
LogFundamentalsTrend(field='revenue', mask=universe).trend,
'ey':
EarningYield(),
'ey_trend':
LogTimeTrend([EarningYield()], periodic=monthly, mask=universe).trend,
},
screen=universe)
engine = make_pipeline_engine()
pipe_date = pd.to_datetime('2017-09-07', utc=True)
stocks = engine.run_pipeline(pipe, pipe_date)
print(stocks)
#NO PERIODIC