Notebook
In [107]:
# def initialize(context):
from quantopian.pipeline import Pipeline
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.pipeline.factors import ExponentialWeightedMovingAverage, EWMA
from quantopian.pipeline.factors.morningstar import MarketCap
from quantopian.research import run_pipeline
In [170]:
# def make_pipeline(context): context is optional and used only if make_pipeline will be used on an external variable

def make_pipeline():
    
    mktcap = MarketCap()
    mktcap_500 = mktcap.top(500)
    
    #Calculates EMA for security
    EMA20 = EWMA.from_span(inputs=[USEquityPricing.close], window_length=20, span=20, mask=mktcap_500)
    EMA50 = EWMA.from_span(inputs=[USEquityPricing.close], window_length=50, span=50, mask=mktcap_500)
    EMA200 = EWMA.from_span(inputs=[USEquityPricing.close], window_length=200, span=200, mask=mktcap_500)
    
    #Checks if EMA is in uptrend
    EMA2050_upcheck = (EMA20 > EMA50)
    EMA50200_upcheck = (EMA50 > EMA200)

    #Checks if EMA is in downtrend, could also use = ~ inversion filter
    EMA2050_downcheck = (EMA20 < EMA50)
    EMA50200_downcheck = (EMA50 < EMA200)
    
    EMA_uptrend = (EMA2050_upcheck & EMA50200_upcheck)
    
    #Create table and assign variables as columns   
    pipe_columns ={
            'EMA20': EMA20,
            'EMA50': EMA50,
            'EMA200': EMA200,
            'EMA2050_upcheck': EMA2050_upcheck,
            'EMA50200_upcheck': EMA50200_upcheck,
            'EMA2050_downcheck': EMA2050_downcheck,
            'EMA50200_downcheck': EMA50200_downcheck,
            'mktcap': mktcap
            }
      
    #outputs table with columns (can omit to remove columns), and screens to only show securities where EMA_uptrend is TRUE
    return Pipeline(pipe_columns, screen = EMA_uptrend)
In [ ]:
# def before_trading_start(context, data):
In [ ]:
# def compute_weights(context):
In [ ]:
# def rebalance(context,data):
In [ ]:
# def record_vars(context, data):
In [171]:
my_pipe = make_pipeline()
#mktCap = MarketCap()
#my_pipe.add(mktCap, 'mktCap')
result = run_pipeline(my_pipe, '2012-05-17', '2012-05-17')
result.head()

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-171-8935c9b6d4b4> in <module>()
      1 
----> 2 my_pipe = make_pipeline()
      3 #mktCap = MarketCap()
      4 #my_pipe.add(mktCap, 'mktCap')
      5 result = run_pipeline(my_pipe, '2012-05-17', '2012-05-17')

<ipython-input-170-45029229440b> in make_pipeline()
     32             'mktcap': mktcap
     33             }
---> 34     pipe_columns.sort(pipe_columns.columns[9], ascending=False)
     35 
     36     #outputs table with columns (can omit to remove columns), and screens to only show securities where EMA_uptrend is TRUE

AttributeError: 'dict' object has no attribute 'sort'
In [126]:
from quantopian.pipeline.factors.morningstar import MarketCap
mktcap = MarketCap()
print mktcap

MarketCap((valuation.market_cap::float64,), window_length=1)
In [ ]: