Notebook

Alpha Vertex Precog 500 Alpha Testing¶

In [48]:
# Import necessary libraries 
from quantopian.pipeline.data.alpha_vertex import precog_top_500 as precog
from quantopian.pipeline import Pipeline
from quantopian.research import run_pipeline
from quantopian.pipeline.filters.morningstar import Q1500US
In [49]:
dir(precog)
Out[49]:
[u'asof_date',
 'domain',
 u'name',
 'ndim',
 u'predicted_five_day_log_return',
 u'symbol']
In [50]:
def make_pipeline():
    
    # Define the universe
    universe = (Q1500US() & precog.predicted_five_day_log_return.latest.notnull())
    
    # Define the pipeline
    pipe = Pipeline(
    columns = {
              'prediction':precog.predicted_five_day_log_return.latest,
              },
        screen = universe
    )
    
    return pipe
In [51]:
# Run pipeline
output = run_pipeline(make_pipeline(), start_date = '2012-06-09', end_date = '2017-06-09')
In [52]:
# Create a pandas dataframe of the output and display the first 20 values
output.head(20)
Out[52]:
prediction
2012-06-11 00:00:00+00:00 Equity(2 [ARNC]) 0.024
Equity(24 [AAPL]) 0.031
Equity(62 [ABT]) 0.035
Equity(67 [ADSK]) -0.015
Equity(76 [TAP]) 0.009
Equity(114 [ADBE]) 0.037
Equity(122 [ADI]) 0.051
Equity(128 [ADM]) 0.033
Equity(161 [AEP]) 0.043
Equity(166 [AES]) 0.035
Equity(168 [AET]) 0.021
Equity(185 [AFL]) 0.035
Equity(205 [AGN]) 0.027
Equity(216 [HES]) 0.107
Equity(239 [AIG]) -0.057
Equity(328 [ALTR]) 0.021
Equity(337 [AMAT]) 0.039
Equity(338 [BEAM]) 0.048
Equity(351 [AMD]) 0.077
Equity(353 [AME]) 0.044
In [ ]: