def make_pipeline():
rsi = RSI()
return Pipeline(
columns={
'longs': rsi.top(3),
'shorts': rsi.bottom(3),
},
)
def make_pipeline():
rsi = RSI()
return Pipeline(
columns={
"longs": rsi.top(3),
"shorts": rsi.bottom(3),
},
)
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():
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 make_pipeline(underly_sid, shortable_sid):#,DAY_HACK_ENABLED=True,BB_WIN_LEN=126,WIN_LEN=20):
sid = SidFactor()
pipeline_underly_sid = sid.eq(underly_sid[0])
print("jzzz:", type(pipeline_underly_sid))
print("MNAME:", MNAME)
for i in underly_sid:
pipeline_underly_sid = pipeline_underly_sid | sid.eq(i)
print('jzzz:', pipeline_underly_sid)
pipeline_shortable_sid = sid.eq(shortable_sid[0])
for i in shortable_sid:
pipeline_shortable_sid = pipeline_shortable_sid | sid.eq(i)
#Filter for our choice of assets.
bbands = BollingerBands(window_length=BB_WIN_LEN, k=sqrt(BB_WIN_LEN/5),mask = pipeline_underly_sid)
lower, middle, upper = bbands
#cud = Cud()#mask(pipeline underly_sid)
returns_1D = Returns(window_length = 2, mask = pipeline_underly_sid)
returns_6M = Returns(window_length = WIN_LEN, mask = pipeline_underly_sid)
returns_6M = returns_6M if DAY_HACK_ENABLED else returns_6M - returns_1D
returns_3M = Returns(window_length = 60, mask = pipeline_underly_sid)
returns_3M = returns_3M if DAY_HACK_ENABLED else returns_3M - returns_1D
returns_5M = Returns(window_length = WIN_LEN, mask = pipeline_underly_sid)#for es only
returns_5M = returns_5M if DAY_HACK_ENABLED else returns_5M - returns_1D
returns_1M = Returns(window_length = 20, mask = pipeline_underly_sid)#for es only
returns_1M = returns_1M if DAY_HACK_ENABLED else returns_1M - returns_1D
returns_5D = Returns(window_length =W_WIN_LEN,mask = pipeline_underly_sid)
returns_5D = returns_5D if DAY_HACK_ENABLED else returns_5D - returns_1D
returns_6D = Returns(window_length =7,mask = pipeline_underly_sid) - returns_1D
annualizedVolatility_6M = AnnualizedVolatility(window_length = WIN_LEN,mask = pipeline_underly_sid)
annualizedVolatility_3M = AnnualizedVolatility(window_length = int(WIN_LEN/2), mask = pipeline_underly_sid)
annualizedVolatility_1M = AnnualizedVolatility(window_length = int(WIN_LEN/6), mask = pipeline_underly_sid)
#annualizedVolatility_6M = ComputeVol6M(mask = pipeline_underly_sid)
#annualizedVolatility_3M = ComputeVol3M(mask = pipeline_underly_sid)
#annualizedVolatility_1M = ComputeVol1M(mask = pipeline_underly_sid)
annualizedVolatility = max(annualizedVolatility_6M, annualizedVolatility_3M,annualizedVolatility_1M)
sharpes_6M = returns_6M/annualizedVolatility_6M
raw_signals = None
if SHARPE:
raw_signals = sharpes_6M
else:
raw_signals = returns_6M
signals = raw_signals if LONG_ONLY else (raw_signals*raw_signals)
#positive_return = (returns_6M > 0)
#trigger = (returns 5D > -trigger_level) if LONG_ONLY else ((returns_5D < trigger_level)
sigtop = signals.top(NUM_TOP_POSITIONS)
raw_sig_top = raw_signals.top(NUM_TOP_POSITIONS)
raw_sig_bot = raw_signals.bottom(NUM_TOP_POSITIONS)
alongs = sigtop & raw_sig_top & (raw_signals > 0) & (returns_5D > -TRIGGER_LEVEL) # & (cud >= 13)
ashorts = sigtop & raw_sig_bot &(raw_signals <= 0 ) & (returns_5D < TRIGGER_LEVEL) & pipeline_shortable_sid
#alongs = (annualizedVolatility_6M> 0) #shorts# signals.top(NUN TOP POSITIONS)
if not PIPE_ENABLED:
alongs = (annualizedVolatility_6M > 0)
ashorts = (annualizedVolatility_6M < 0)# if LONG ONLY else longs
long_short_screen = alongs if LONG_ONLY else (alongs | ashorts)
my_screen = (annualizedVolatility_6M > 0) if MNAME in ['es','bd','dm','ed','md'] else long_short_screen
#my_screen = long_short_screen if False else (annualizedVolatility_6M > 0)#hlw
print('finger1',MNAME)
rsi = RSI(window_length=10,mask = pipeline_underly_sid)
print('rsipipe',annualizedVolatility_6M)
print('fxxxDAY_HACK_ENABLED',DAY_HACK_ENABLED)
#Only compute volatilities for assets that satisfy the requirements of our rolling strategy.
return Pipeline(
columns= {
#'cud' : cud,
'volatility' : annualizedVolatility,
'sharpes_6W' : sharpes_6M,
'drets': returns_1D,
'wrets': returns_5D,
'returns_6D': returns_6D,
'returns_1M': returns_1M,
'returns_3M': returns_3M,
'returns_5M': returns_5M,
'returns_6M': returns_6M,
#'signals': (signals)*signals,
'shorts' : ashorts,
#'wrets' : returns_5D,
'lower' : lower,
'middle' :middle,
'upper' :upper,
#'rsi':rsi.top(3),
'rsi_longs': rsi.top(3),
'rsi_shorts': rsi.bottom(3),
#'annualizedVolatility :annualizedVolatility_6M,
},
screen = my_screen
)
