Notebook
In [76]:
from quantopian.pipeline import Pipeline
from quantopian.research import run_pipeline
from quantopian.pipeline.filters import QTradableStocksUS
from quantopian.pipeline.data import factset
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
In [77]:
zs = factset.Fundamentals.zscore_qf.latest

my_pipe = Pipeline(
    columns={'z_score': zs,
            },
    screen= QTradableStocksUS()
    )

start = '2002-11-01'
end = '2019-03-13'
results = run_pipeline(my_pipe, start, end)











    






Pipeline Execution Time: 27.39 Seconds


















In [78]:



    


results = results.dropna()
results



    















    
Out[78]:










  
    

      
      
      z_score
    

  
  
    

      2002-11-01 00:00:00+00:00
      Equity(2 [ARNC])
      1.863670
    

    

      Equity(24 [AAPL])
      3.601540
    

    

      Equity(41 [ARCB])
      3.457220
    

    

      Equity(60 [ABS])
      4.057910
    

    

      Equity(62 [ABT])
      4.733950
    

    

      Equity(64 [GOLD])
      3.363640
    

    

      Equity(67 [ADSK])
      4.371160
    

    

      Equity(76 [TAP])
      1.786940
    

    

      Equity(88 [ACI])
      1.010160
    

    

      Equity(107 [ACV])
      5.042170
    

    

      Equity(110 [RAMP])
      2.871670
    

    

      Equity(114 [ADBE])
      13.341800
    

    

      Equity(115 [ADCT])
      10.213800
    

    

      Equity(122 [ADI])
      4.026620
    

    

      Equity(128 [ADM])
      2.416930
    

    

      Equity(161 [AEP])
      1.324880
    

    

      Equity(166 [AES])
      0.468870
    

    

      Equity(197 [AGCO])
      2.873200
    

    

      Equity(205 [AGN])
      6.982590
    

    

      Equity(209 [AM])
      2.234460
    

    

      Equity(216 [HES])
      1.810700
    

    

      Equity(247 [AIN])
      2.587590
    

    

      Equity(266 [AJG])
      2.773690
    

    

      Equity(289 [MATX])
      2.384750
    

    

      Equity(300 [ALK])
      1.006120
    

    

      Equity(301 [ALKS])
      0.486815
    

    

      Equity(328 [ALTR])
      10.356300
    

    

      Equity(337 [AMAT])
      8.090430
    

    

      Equity(338 [BEAM])
      4.475960
    

    

      Equity(351 [AMD])
      1.149860
    

    

      ...
      ...
      ...
    

    

      2019-03-13 00:00:00+00:00
      Equity(51693 [GTES])
      1.551080
    

    

      Equity(51700 [AGS])
      1.110960
    

    

      Equity(51725 [FTSI])
      -3.372510
    

    

      Equity(51742 [WHD])
      5.892640
    

    

      Equity(51743 [EOLS])
      2.809310
    

    

      Equity(51815 [GLIB_A])
      0.761767
    

    

      Equity(51832 [ZS])
      15.135000
    

    

      Equity(51854 [DBX])
      3.676210
    

    

      Equity(51917 [ZUO])
      8.680390
    

    

      Equity(51928 [NVT])
      2.155580
    

    

      Equity(51934 [EAF])
      2.983640
    

    

      Equity(51937 [DOCU])
      3.900020
    

    

      Equity(51942 [PVTL])
      6.968810
    

    

      Equity(51955 [CDAY])
      0.902577
    

    

      Equity(51957 [LASR])
      11.281500
    

    

      Equity(51961 [SMAR])
      11.971400
    

    

      Equity(51966 [APY])
      2.394080
    

    

      Equity(51975 [INSP])
      16.458500
    

    

      Equity(51982 [CBLK])
      0.777320
    

    

      Equity(51987 [AMRX])
      0.981905
    

    

      Equity(51992 [PS])
      2.560410
    

    

      Equity(52028 [WH])
      1.445650
    

    

      Equity(52030 [EVOP])
      0.457920
    

    

      Equity(52038 [CGC])
      6.503450
    

    

      Equity(52045 [PRSP])
      1.200510
    

    

      Equity(52059 [CVIA])
      1.626980
    

    

      Equity(52064 [EVRG])
      0.795902
    

    

      Equity(52084 [VNE])
      2.637630
    

    

      Equity(52090 [USX])
      2.565680
    

    

      Equity(52100 [AVLR])
      3.963240
    

  



6294504 rows × 1 columns





















In [79]:



    


median_zs = results.groupby(level=0).median()



    













In [80]:



    


ax = median_zs.plot(title='Altman z score median for QTU')
ax.set_xlabel('Trade day')
ax.set_ylabel('Median Altman z score')



    















    
Out[80]:








<matplotlib.text.Text at 0x7f4972190d68>










    


























In [81]:



    


results_time = results.reset_index(level=1, drop=True).apply(np.log10)



    













In [82]:



    


times = pd.unique(results_time.index)



    













In [83]:



    


N_days = len(times)



    













In [84]:



    


N_days



    















    
Out[84]:








4117



















In [85]:



    


N_bins = 100



    













In [86]:



    


a_min = results_time['z_score'].min()
a_max = results_time['z_score'].max()



    













In [87]:



    


hist = np.empty([N_bins,N_days])



    













In [88]:



    


for k,index in enumerate(pd.unique(results_time.index)):
    (hist[:,k],bins) = np.histogram(results_time.loc[index,'z_score'].values, bins=N_bins, range=(a_min,a_max), density=True)



    








            





In [89]:



    


plt.imshow(hist, cmap='hot',aspect='auto')
plt.show()



    















    





















        






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