def make_pipeline():
fd = Fundamentals()
td = TransactionData()
sectors = NASDAQSectorCodes()
ipos = NASDAQIPO()
return Pipeline(
columns={
'marketcap': fd.marketcap,
'liabilities': fd.liabilities,
'revenue': fd.revenue,
'eps': fd.eps,
'rnd': fd.rnd,
'netinc': fd.netinc,
'pe': fd.pe,
'ipoyear': ipos,
'yoy_sales': fd.yoy_sales,
'qoq_earnings': fd.qoq_earnings,
'sector': sectors,
# 'filingdate': td.Date,
'sharesownedbeforetransaction': td.sharesownedbeforetransaction,
'transactionshares': td.transactionshares,
'sharesownedfollowingtransaction':
td.sharesownedfollowingtransaction,
# 'pctSharesBotSld': td.pctSharesBotSld,
# 'dDiffInt': td.dDiffInt
}, )
def make_pipeline():
rsi = RSI()
fd = Fundamentals()
sectors = NASDAQSectorCodes()
return Pipeline(
columns={
'longs': rsi.top(3),
'shorts': rsi.bottom(3),
'ROE': fd.ROE_ART,
# 'CAPEX': fd.CAPEX_MRQ,
'sector': sectors,
}, )
def make_pipeline():
"""Sets up the pipeline"""
dollar_volume = AverageDollarVolume(window_length=20)
adv1000 = dollar_volume.top(1000)
fd = Fundamentals(mask=adv1000)
market_cap = fd.cshoq * fd.prccq # this is how to calculate market cap with Computstat fields
book_equity = fd.seqq - fd.PS # this is a quick way to calculate book_equity
book_to_price = book_equity / market_cap
biggest = market_cap.top(500, mask=adv1000)
smallest = market_cap.bottom(500, mask=adv1000)
highpb = book_to_price.top(500, mask=adv1000)
lowpb = book_to_price.bottom(500, mask=adv1000)
momentum = Momentum(mask=adv1000) # momentum
high_momentum = momentum.top(500, mask=adv1000)
low_momentum = momentum.bottom(500, mask=adv1000)
volatility = Volatility(mask=adv1000)
highvol = volatility.top(500, mask=adv1000)
lowvol = volatility.bottom(500, mask=adv1000)
streversal = RSI(window_length=14, mask=adv1000)
high_streversal = streversal.top(500, mask=adv1000)
low_streversal = streversal.bottom(500, mask=adv1000)
universe = biggest | smallest | highpb | lowpb | low_momentum | high_momentum
return Pipeline(
columns={
'returns': Returns(window_length=2),
# 'market_cap': market_cap, # not needed
# 'book_to_price': book_to_price, # not needed
'biggest': biggest,
'smallest': smallest,
'highpb': highpb,
'lowpb': lowpb,
# 'momentum': momentum, # not needed
'low_momentum': low_momentum,
'high_momentum': high_momentum,
# 'volatility': volatility, # not needed
'highvol': highvol,
'lowvol': lowvol,
# 'streversal': streversal, # not needed
'high_streversal': high_streversal,
'low_streversal': low_streversal
},
screen=universe)
def make_pipeline():
fd = Fundamentals()
sectors = NASDAQSectorCodes()
ipos = NASDAQIPO()
return Pipeline(columns={
'marketcap': fd.marketcap,
'liabilities': fd.liabilities,
'revenue': fd.revenue,
'eps': fd.eps,
'rnd': fd.rnd,
'netinc': fd.netinc,
'pe': fd.pe,
'ipoyear': ipos,
'yoy_sales': fd.yoy_sales,
'qoq_earnings': fd.qoq_earnings,
'sector': sectors
}, )
def make_pipeline():
fd = Fundamentals()
sectors = NASDAQSectorCodes()
ipos = NASDAQIPO()
return Pipeline(
columns={
'marketcap': fd.marketcap,
'liabilities': fd.liabilities,
'revenue': fd.revenue,
'eps': fd.eps,
'rnd': fd.rnd,
'netinc': fd.netinc,
'pe': fd.pe,
'ipoyear': ipos,
'yoy_sales': fd.yoy_sales,
'qoq_earnings': fd.qoq_earnings,
'sector': sectors,
'fcf': fd.fcf,
'pb': fd.pb,
'assets': fd.assets,
'cor': fd.cor,
'currentratio': fd.currentratio,
'de': fd.de,
'ebitda': fd.ebitda,
'ebt': fd.ebt,
'grossmargin': fd.grossmargin,
'inventory': fd.inventory,
'ncf': fd.ncf,
'netmargin': fd.netmargin,
'opex': fd.opex,
'payables': fd.payables,
'payoutratio': fd.payoutratio,
'receivables': fd.receivables,
'roa': fd.roa,
'roe': fd.roe,
'sgna': fd.sgna,
'taxassets': fd.taxassets,
'taxliabilities': fd.taxliabilities,
'workingcapital': fd.workingcapital,
'capex': fd.capex
},
)
