Hi Toan, thanks for bringing this up. I was unable to produce your error, but if you have your twitter import line in your initialize function, you should move it outside that function so that twitter is accessible globally.

If you have the import line in the right place and you're still seeing this issue, would you mind providing an example algo that demonstrates the error? If you're not comfortable sharing an algo here you could send it to Support. Thanks in advance for your help.

here's one that crashes.

from quantopian.algorithm import attach_pipeline, pipeline_output  
from quantopian.pipeline import Pipeline  
from quantopian.pipeline.factors import CustomFactor, SimpleMovingAverage, AverageDollarVolume, Latest, RSI  
from quantopian.pipeline.data.builtin import USEquityPricing  
from quantopian.pipeline.data import morningstar as mstar  
from quantopian.pipeline.filters.morningstar import IsPrimaryShare  
from quantopian.pipeline.data.psychsignal import stocktwits  
from quantopian.pipeline.data.psychsignal import twitter_noretweets as twitter  
from quantopian.pipeline.classifiers.morningstar import Sector  
from quantopian.pipeline.data import morningstar  
import numpy as np  
import pandas as pd  
import time

class twitter(CustomFactor):  
    inputs = [twitter.total_scanned_messages]  
    window_length = 50  
    def compute(self, today, assets, out, msgs):  
        out[:] =  np.nanmean(msgs[-5:], axis=0)/np.nanmean(msgs, axis=0)

class twitter_bull(CustomFactor):  
    inputs = [twitter.bullish_intensity]  
    window_length = 50  
    def compute(self, today, assets, out, msgs):  
        out[:] =  np.nanmean(msgs[-5:], axis=0)/np.nanmean(msgs, axis=0)

def make_pipeline():  
    pipe = Pipeline()  
    initial_screen = filter_universe()  
    pipe.add(twitter(mask=initial_screen), "twitter_momentum")  
    pipe.add(twitter_bull(mask=initial_screen), "twitter_bull")  
    return pipe

def initialize(context):  
    attach_pipeline(make_pipeline(), 'ranking_example')  
    context.dont_buy = security_lists.leveraged_etf_list  

def before_trading_start(context, data):  
    output = pipeline_output('ranking_example')  
def filter_universe():  
    common_stock = mstar.share_class_reference.security_type.latest.eq('ST00000001')  
    not_lp_name = ~mstar.company_reference.standard_name.latest.matches('.* L[\\. ]?P\.?$')  
    not_lp_balance_sheet = mstar.balance_sheet.limited_partnership.latest.isnull()  
    have_data = mstar.valuation.market_cap.latest.notnull()  
    not_otc = ~mstar.share_class_reference.exchange_id.latest.startswith('OTC')  
    not_wi = ~mstar.share_class_reference.symbol.latest.endswith('.WI')  
    not_depository = ~mstar.share_class_reference.is_depositary_receipt.latest  
    primary_share = IsPrimaryShare()  
    # Combine the above filters.  
    tradable_filter = (common_stock & not_lp_name & not_lp_balance_sheet &  
                       have_data & not_otc & not_wi & not_depository & primary_share)  
    high_volume_tradable = AverageDollarVolume(  
            window_length=21,  
            mask=tradable_filter  
        ).rank(ascending=False) < 1000

    mask = high_volume_tradable  
    return mask  

Hi Toan, this is happening because of the custom factor you named twitter, which is overriding the dataset imported under the same name. If you change the name of the custom factor this will work fine.

thanks!