Notebook
In [10]:
from quantopian.pipeline.data.builtin import USEquityPricing  
import pandas as pd  
from quantopian.pipeline import CustomFactor, Pipeline  
from quantopian.research import run_pipeline  
from quantopian.pipeline.factors import SimpleMovingAverage
from quantopian.pipeline.filters import StaticSids

security = symbols(8554)  

def make_pipeline():  
    latest_close = USEquityPricing.close.latest  
    latest_open = USEquityPricing.open.latest  

    
    return Pipeline(  
        columns = { 'latest_close':latest_close,
                  'latest_open':latest_open, },  
        screen = StaticSids([security])
    )

result = run_pipeline(make_pipeline(), '2017-11-30', '2017-12-13')
result = result.reset_index().set_index('level_0')  

df=get_pricing(security, start_date='2017-11-30', end_date='2017-12-13',
            symbol_reference_date=None, frequency='daily', handle_missing='raise')

pd.concat([result["latest_open"], df["open_price"], result["latest_close"], df["close_price"], ], axis=1)
Out[10]:
latest_open open_price latest_close close_price
level_0
2017-11-30 00:00:00+00:00 263.02 263.76 262.73 264.88
2017-12-01 00:00:00+00:00 263.76 264.76 264.88 264.44
2017-12-04 00:00:00+00:00 264.76 266.31 264.44 264.14
2017-12-05 00:00:00+00:00 266.31 264.43 264.14 263.20
2017-12-06 00:00:00+00:00 264.43 262.87 263.20 263.20
2017-12-07 00:00:00+00:00 262.87 263.09 263.20 264.05
2017-12-08 00:00:00+00:00 263.09 264.99 264.05 265.49
2017-12-11 00:00:00+00:00 264.99 265.58 265.49 266.33
2017-12-12 00:00:00+00:00 265.58 266.57 266.33 266.79
2017-12-13 00:00:00+00:00 266.57 NaN 266.79 NaN