Notebook
In [20]:
import numpy as np

from quantopian.pipeline import Pipeline
from quantopian.pipeline import CustomFactor
from quantopian.pipeline.classifiers.morningstar import Sector
from quantopian.pipeline.data import factset
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.pipeline.filters import QTradableStocksUS
from quantopian.research import run_pipeline

period_start = '2018-01-01'
period_end = '2018-01-01'

market_cap = factset.Fundamentals.mkt_val.latest

# Add the surprise factor to the pipeline.  
pipe = Pipeline(  
    columns={  
        'sector': Sector(),
        'market_cap': market_cap,
    },  
    #domain=US_EQUITIES,
    screen=QTradableStocksUS(),  
)

# Run the pipeline over a year and print the result.  
data_output = run_pipeline(pipe, period_start, period_end)  











    






Pipeline Execution Time: 0.25 Seconds


















In [21]:



    


data_output.head()



    















    
Out[21]:










  
    

      
      
      market_cap
      sector
    

  
  
    

      2018-01-02 00:00:00+00:00
      Equity(2 [ARNC])
      1.197530e+10
      310
    

    

      Equity(24 [AAPL])
      7.900500e+11
      311
    

    

      Equity(31 [ABAX])
      1.013150e+09
      206
    

    

      Equity(41 [ARCB])
      8.572090e+08
      310
    

    

      Equity(52 [ABM])
      2.749140e+09
      310
    

  






















In [25]:



    


data_mkt_cap_by_sector = data_output.groupby('sector').sum().dropna()/data_output['market_cap'].sum()
data_mkt_cap_by_sector.sort(columns=['market_cap'], ascending=False, inplace=True)
data_mkt_cap_by_sector



    















    






/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:2: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)
  










    
Out[25]:










  
    

      
      market_cap
    

    

      sector
      
    

  
  
    

      311
      0.202036
    

    

      103
      0.178771
    

    

      206
      0.125206
    

    

      102
      0.110135
    

    

      310
      0.110008
    

    

      205
      0.079613
    

    

      309
      0.062756
    

    

      308
      0.038574
    

    

      104
      0.035422
    

    

      101
      0.029876
    

    

      207
      0.027604
    

  

















            





In [38]:



    


import matplotlib.pyplot as plt

y = data_mkt_cap_by_sector['market_cap']
x = range(1, len(data_mkt_cap_by_sector.index)+1)

LABELS = data_mkt_cap_by_sector.index

plt.bar(x, y, width=0.5, align='center', color='navy')
plt.xticks(x, LABELS)
plt.legend(['Market Cap. by sector'], loc='upper left')



    















    
Out[38]:








<matplotlib.legend.Legend at 0x7ff43d942510>










    





















        






In [ ]:



    


 



    













In [41]:



    


import numpy as np
import pandas as pd

from quantopian.pipeline import Pipeline
from quantopian.pipeline import CustomFactor
from quantopian.pipeline.classifiers.morningstar import Sector
from quantopian.pipeline.data import factset
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.pipeline.filters import QTradableStocksUS
from quantopian.research import run_pipeline

period_start = '2018-01-01'
period_end = '2018-01-01'

class IndustryAvg(CustomFactor):  
    def compute(self, today, assets, out, fdmtl, sector):  
        df = pd.DataFrame(index=assets, data={"fdmtl": fdmtl[-1],  
                                              "sector": sector[-1]})  
        out[:] = df.groupby("sector").transform(np.mean).values.flatten()

universe = QTradableStocksUS()
        
pe_ratio = factset.Fundamentals.pe_af.latest
pe_ratio_sector_avg = IndustryAvg(inputs=[factset.Fundamentals.pe_af, Sector()], window_length=1, mask=universe)

pipe = Pipeline(  
    columns={  
        'pe_ratio': pe_ratio,
        'pe_ratio_sector_avg': pe_ratio_sector_avg,
    },  
    screen=universe,  
)

data_output = run_pipeline(pipe, period_start, period_end)  



    















    


















    






Pipeline Execution Time: 0.25 Seconds


















In [43]:



    


data_output.head(10)



    















    
Out[43]:










  
    

      
      
      pe_ratio
      pe_ratio_sector_avg
    

  
  
    

      2018-01-02 00:00:00+00:00
      Equity(2 [ARNC])
      75.1905
      36.742390
    

    

      Equity(24 [AAPL])
      16.7340
      101.717171
    

    

      Equity(31 [ABAX])
      33.7956
      67.440669
    

    

      Equity(41 [ARCB])
      38.9437
      36.742390
    

    

      Equity(52 [ABM])
      643.7120
      36.742390
    

    

      Equity(53 [ABMD])
      107.0090
      67.440669
    

    

      Equity(62 [ABT])
      41.3678
      67.440669
    

    

      Equity(64 [GOLD])
      28.6156
      75.303361
    

    

      Equity(67 [ADSK])
      154.3000
      101.717171
    

    

      Equity(76 [TAP])
      10.4186
      56.272595
    

  






















In [ ]: