See https://www.quantopian.com/help#api-get-datetime . --Grant

Thanks, Grant! I can see the information in Quantopian documenation on get_datetime, but there is no information on that from http://www.zipline.io/search.html?q=get_datetime&check_keywords=yes&area=default

I improted the method from zipline.api import get_datetime and got the following error when trying to make_pipeline

Am I using it wrong?

In [12]:

my_pipe = make_pipeline()

---------------------------------------------------------------------------  
AttributeError                            Traceback (most recent call last)  
<ipython-input-12-dcf250cf5490> in <module>()  
----> 1 my_pipe = make_pipeline()

<ipython-input-11-e086084992bc> in make_pipeline()  
     13     #Add time discount from last filing date  
     14     last_filing_date = morningstar.financial_statement_filing.file_date.latest  
---> 15     today = get_datetime()  
     16     #time_penalty = (today - last_filing_date - 30)*0.01  
     17 

/build/src/qexec_repo/zipline_repo/zipline/utils/api_support.pyc in wrapped(*args, **kwargs)
     49     def wrapped(*args, **kwargs):  
     50         # Get the instance and call the method  
---> 51         return getattr(get_algo_instance(), f.__name__)(*args, **kwargs)  
     52     # Add functor to zipline.api  
     53     setattr(zipline.api, f.__name__, wrapped)

AttributeError: 'NoneType' object has no attribute 'get_datetime'

Not sure about that one. I'm not sure how to apply get_datetime() (if at all) in the research platform. It does work, however, in the backtester.

This is really causing headaches. I cannot construct the right variables. I've ran out of ideas. Any ideas where to look next?

You could use a Custom Factor which has the datetime representing today available in the method.

This example below calculates the difference in business days between the current date and the most recent buyback authorization. You can change the input to file date, or whatever date is relevant to your strategy.

from quantopian.pipeline.data.eventvestor import BuybackAuthorizations  
from quantopian.pipeline import Pipeline, CustomFactor  
from quantopian.pipeline.data.builtin import USEquityPricing  
from quantopian.research import run_pipeline  
from zipline.utils import tradingcalendar  
from quantopian.pipeline.factors.eventvestor import (  
    BusinessDaysUntilNextEarnings,  
    BusinessDaysSincePreviousEarnings,  
    BusinessDaysSinceBuybackAuth,  
)
import pandas as pd  
import numpy as np


def make_pipeline():  
    bbfactor = BuybackEventFactor()  
    return Pipeline(columns={"BuybackEventFactor": bbfactor })

# add whatever scale/penalty you want here  
def scale_buyback_factor(x):  
    x = x - np.nanmean(x)  
    return x / np.nansum(abs(x))  


class BuybackEventFactor(CustomFactor):  
    inputs=[BuybackAuthorizations.previous_date]  
    window_length = 1  
    def compute(self, today, assets, out, prev_date):  
        prev_date = prev_date[-1] # only interested in the latest buyback data  
        dates = []  
        for bb in prev_date:  
            try:  
                bb = pd.to_datetime(bb)  
                days = np.busday_count(bb, today)  
                dates.append(days)  
            except:  
                dates.append(np.nan)  
        dates = np.array(dates)  
        dates = scale_buyback_factor(dates)  
        out[:] = dates



out = run_pipeline(make_pipeline(), "01-01-2012", "11-02-2012")  
out.dropna(inplace=True)  
out.head()  

Let me know if this helps :)