def initialize(context):
# set_commission(commission.PerShare(cost=0, min_trade_cost=None))
# set_slippage(slippage.FixedSlippage(spread=0))
pipe = Pipeline()
attach_pipeline(pipe, 'ranked')
dollar_volume = AverageDollarVolume(window_length=1)
high_dollar_volume = dollar_volume.percentile_between(95, 100)
alpha41 = Alpha41(mask=high_dollar_volume)
vwap = VWAP(window_length=1)
alpha41 = alpha41**.5 - vwap
alpha41_rank = alpha41.rank(mask=high_dollar_volume)
roe = ROE(mask=high_dollar_volume)
combo_raw = (alpha41_rank)
pipe.add(combo_raw, 'combo_raw')
pipe.set_screen(roe > .005)
schedule_function(func=rebalance,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_open(hours=0, minutes=1))
context.long_leverage = .5
context.short_leverage = -.5
context.short_num = 20
context.long_num = 20
def make_pipeline():
"""
Make a VWAP pipeline.
"""
vwap = VWAP(inputs=[USEquityPricing.close, USEquityPricing.volume],
window_length=14)
short_sma = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=35)
long_sma = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=50)
symbol = SidInList(sid_list=(8554)) # Only show values for the S&P500
return Pipeline(
columns={
'vwap': vwap,
'close': USEquityPricing.close.latest,
'short_sma': short_sma,
'long_sma': long_sma,
'symbol': symbol,
},
screen=symbol,
)
result.head()
# factors can also be added to an existing Pipeline instance using the Pipeline.add method
my_pipe = Pipeline()
f1 = SomeFactor(...)
my_pipe.add(f1)
# The most commonly used built-in Factor is Latest
# Latest factor gets the most recent value of a given data column
def make_pipeline():
mean_close_10 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=10)
latest_close = USEquityPricing.close.latest
return Pipeline(columns={
'10_day_mean_close': mean_close_10,
'latest_close_price': latest_close
})
result = run_pipeline(make_pipeline(), '2015-05-05', '2015-05-05')
result.head()
# NB: .latest can sometimes return things other than Factors. We'll see examples of other possible return types in later lessons
# Some factors have default inputs that should never be changed.
# For example the VWAP built-in factor is always calculated from USEquityPricing.close and USEquityPricing.volume, so no need to specify these default BoundColumns
from quantopian.pipeline.factors import VWAP
vwap = VWAP(window_length=10)
mean_close_10 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=10)
# This add another column for pipeline
latest_close = USEquityPricing.close.latest
return Pipeline(columns={
'10_day_mean_close': mean_close_10,
'latest_close_price': latest_close
})
''' Default Inputs '''
# Some factors have default inputs that should never be changed.
# Volume Weighted Average Price
from quantopian.pipeline.factors import VWAP
vwap = VWAP(window_length=10) # don't need 'input=' here
'''
4. Combining Factors
'''
''' Combining Factors '''
# Using arithemetics
f1 = SomeFactor()
f2 = SomeOtherFactor()
average = (f1 + f2) / 2.0
# e.g. Percent_difference (by combining two factors)
def make_pipeline():
mean_close_10 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=10)