Price mismatch at Pipeline and get

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Price mismatch at Pipeline and get_pricing

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Price mismatching at USEquityPricing and get_pricing

from quantopian.pipeline.data.builtin import USEquityPricing  
import pandas as pd  
from quantopian.pipeline import CustomFactor, Pipeline  
from quantopian.research import run_pipeline


security = symbols(45000)  
df=get_pricing(security, start_date='2016-06-30', end_date='2016-06-30',  
            symbol_reference_date=None, frequency='daily', fields= 'close_price', handle_missing='raise')

print 'get_pricing',df.tail(1).values[0]

def make_pipeline():  
    latest_close = USEquityPricing.close.latest  
    return Pipeline(  
        columns = {  
            'latest_close':latest_close,  
        },  
    )

result = run_pipeline(make_pipeline(), '2016-06-30', '2016-06-30')

result = result.reset_index()  
print 'USEquityPricing',result[result['level_1']==security][['latest_close']].values[0][0]

get_pricing 50.36
USEquityPricing 49.44

why is mismatching ? numbers should be equal.

5 responses

Ernesto Perez

Hello Volodymyr,

The get_pricing function actually uses a different data feed than the one used by the backtester and the pipeline API. We are working on migrating get_pricing to use the new data feed so the datasets match in both environments.

Sorry for any confusion this might have caused.

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Dan Dunn

get_pricing() now uses the same adjusted data source as pipeline and the backtester.

Disclaimer

Volodymyr Nepiuk

I tried experiment

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



security = symbols(45000)  

def make_pipeline():  
    latest_close = USEquityPricing.close.latest  
    sma = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=7)


    return Pipeline(  
        columns = {  
            'sma':sma,  
            'latest_close':latest_close  
        },  
    )

result = run_pipeline(make_pipeline(), '2016-01-30', '2016-06-30')

result = result.reset_index().set_index('level_0')  
print result[result['level_1']==security][['sma','latest_close']] #print result of pipeline

df=get_pricing(security, start_date='2016-01-30', end_date='2016-06-30',  
            symbol_reference_date=None, frequency='daily', handle_missing='raise')

print df #print get pricing output

No data are the same. At get_pricing return the price and close_price columns are the same.
Also they doesn't match with pipeline data.
Suppose I calculate average wrongly or make mistake with adjusted and close price. But if I change price to 'high' and 'low' at pipeline and get_pricing the also will don't match.

3.I also tried to calc different way of calculation average by hand(pandas rolling mean) and by pipeline.I friend to get the same result and also not successfully. (Can you give me code where return of SimpleMovingAverage, and pandas rolling on get_pricing will match ?)

Shahrouz Raeisi

I am also trying quandl data and comparing them to get_pricing and get different prices.

Is this also related to the same issue mentioned here on different numbers between get_pricing and pipeline?

Dan Whitnable

get_pricing and run_pipeline use the same data and will return the same results. See this post https://www.quantopian.com/posts/research-updates-get-pricing-and-jupyter-notebook-upgrade

If one is seeing a difference between the two it's likely one of these things...

the end date in get_pricing determines the date at which all previous data is split adjusted. Pipeline data is split adjusted each day. See https://www.quantopian.com/posts/usequitypricing-and-get-pricing-data-difference
the dates in get_pricing are the actual data dates (ie the HLCV data on that day). However, the dates of the dataframe index returned by run_pipeline are the dates that the pipeline is run (ie typically the day AFTER the actual data date). See https://www.quantopian.com/posts/pipeline-and-getpricing-price-shift
the high-low-close-volume (HLCV) data used by Quantopian may be a bit off from similar data from other resources. See the Q FAQs (https://www.quantopian.com/faq ) and this post https://www.quantopian.com/posts/pricing-differences

@Shahrouz Raeisi Not sure what 'differences' you are seeing but it could be one of these?

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