Help! Weird timezone error in notebook using alphalens

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edited

Hello all,

First error (seen below) has been resolved. See the update via my replies below.

I am relatively new to coding, and even newer to Quantopian. I am trying to conduct some research on alpha factors using alphalens, but am unable to even get the notebook working.

I've also tried re-writing the code to create the pipeline in initialization but the same error occurred. Any help would be greatly appreciated!

I know you all can view in the attached notebook, but I'll copy+paste below for good measure. Here's the code:

# Normal imports  
from quantopian.pipeline import Pipeline  
from quantopian.research import run_pipeline  
from quantopian.pipeline.filters.morningstar import Q1500US  
from quantopian.pipeline import CustomFactor  
from quantopian.pipeline.data.builtin import USEquityPricing  
import pandas as pd  
import numpy as np

# Testing factor imports  
from quantopian.pipeline.data.morningstar import operation_ratios  
from quantopian.pipeline.data.morningstar import valuation_ratios  
from quantopian.pipeline.data.morningstar import asset_classification  
from quantopian.pipeline.data.morningstar import valuation  
from quantopian.pipeline.data.morningstar import earnings_ratios

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# Single factor  
def custom_factor(CustomFactor):  
    inputs = [morningstar.valuation_ratios.ps_ratio]  
    window_length = 1  
    def compute(self, today, assets, out, ps):  
        table = pd.DataFrame(index=assets)  
        table ["ps"] = ps[-1]  
        out[:] = table.fillna(table.max()).mean(axis=1)  
# Factor requiring industry comparison  
#def custom_factor(CustomFactor):  
# Factor requiring multiple factors  
#def custom_factor(CustomFactor):

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

def make_pipeline():  
    testing_factor = custom_factor()  
    universe = (Q1500US() & testing_factor.notnull())  
    testing_factor = testing_factor.rank(ascending=true, mask=universe, method='average')  
    pipe = Pipeline(columns={'testing_factor':testing_factor}, screen = universe)  
    return pipe

result = run_pipeline(make_pipeline(), start_date='2014-01-01', end_date='2018-01-01')  
result.head()

And here's the error:

TypeErrorTraceback (most recent call last)  
<ipython-input-18-fce5c027f49a> in <module>()  
     43     return pipe  
     44  
---> 45 result = run_pipeline(make_pipeline(), start_date='2014-01-01', end_date='2018-01-01')  
     46 result.head()

<ipython-input-18-fce5c027f49a> in make_pipeline()  
     37 def make_pipeline():  
     38  
---> 39     testing_factor = custom_factor()  
     40     universe = (Q1500US() & testing_factor.notnull())  
     41     testing_factor = testing_factor.rank(ascending=true, mask=universe, method='average')

TypeError: custom_factor() takes exactly 1 argument (0 given)

4 responses

Blue Seahawk

class instead of def

Joe Kiefner

.... I have no idea how I overlooked that. I probably have over 50 custom factors used in various algorithms and somehow glossed right over that mistake.

Thanks Blue!

Joe Kiefner

Update:

Moving forward with the notebook creation and I'm one step away from being done, at least I think, and being able to conduct alpha research.

The last hiccup, however, is an error I've not seen before and haven't seen much commentary on:

AttributeErrorTraceback (most recent call last)  
<ipython-input-4-25ec6be0d7e3> in <module>()  
      4                                                                    prices = pricing,  
      5                                                                    quantiles = 5,  
----> 6                                                                    periods = (1,5,10))  
      7  
      8 alphalens.tears.create_full_tear_sheet(factor_data, by_group=True)

/usr/local/lib/python2.7/dist-packages/alphalens/utils.pyc in get_clean_factor_and_forward_returns(factor, prices, groupby, by_group, quantiles, bins, periods, filter_zscore, groupby_labels)
    296     """  
    297  
--> 298     if factor.index.levels[0].tz != prices.index.tz:  
    299         raise NonMatchingTimezoneError("The timezone of 'factor' is not the "  
    300                                        "same as the timezone of 'prices'. See "

AttributeError: 'builtin_function_or_method' object has no attribute 'levels'

The error is occurring between these two steps, apparently:

assets = result.index.levels[1].unique()

#start and end must be far enough back to collect data properly!!!  
pricing = get_pricing(assets, start_date='2014-12-01', end_date='2018-02-01', fields='close_price')  
len(assets)

import alphalens  
factor_data = alphalens.utils.get_clean_factor_and_forward_returns(factor = 'testing_factor',  
                                                                   prices = pricing,  
                                                                   quantiles = 5,  
                                                                   periods = (1,5,10))

alphalens.tears.create_full_tear_sheet(factor_data, by_group=True)

Luca

The error is that you are passing a string instead of the result of pipeline to alphalens.utils.get_clean_factor_and_forward_returns.

factor_data = alphalens.utils.get_clean_factor_and_forward_returns(factor = 'testing_factor', <-- ERROR

Try:

factor_data = alphalens.utils.get_clean_factor_and_forward_returns(factor = result['testing_factor'],

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