Ndtyjky

I am new to Quantopian and I am trying to obtain the ipo's dates from Morningstar. I found some code on Quantopian and embedded it into my pipline. (the rest of the code is copied for test purposes.

The error message I get is TypeError: object of type 'NoneType' has no len() on line xxx. I looked at other stockoverflow help where they analyzed the same error and given there should be data there, I'm not sure what's wrong.

The error is on the line : out[:] = np.apply_along_axis(get_delta_days, 0, ipo_dates)

The code is:
"""
sample test of morningstar.

"""





# Import the libraries we will use here



import pandas as pd



import numpy as np



from quantopian.algorithm import attach_pipeline, pipeline_output



from quantopian.pipeline import Pipeline



from quantopian.pipeline.data.builtin import USEquityPricing



from quantopian.pipeline.factors import AverageDollarVolume, Returns



from quantopian.pipeline.data import Fundamentals



#from quantopian.pipeline.data import morningstar as mstar



from quantopian.pipeline import  CustomFactor



def initialize(context):

"""

The initialize function is the place to create your pipeline (security 

selector),

and set trading conditions such as commission and slippage. It is called 

once

at the start of the simulation and also where context variables can be 

set.

"""



# Define context variables that can be accessed in other methods of

# the algorithm.

context.long_leverage = 0.5

context.short_leverage = -0.5

context.returns_lookback = 5



# Rebalance on the first trading day of each week at 11AM.

schedule_function(rebalance, 

                  date_rules.every_day(),#week_start(days_offset=0),

                  time_rules.market_open(hours = 1, minutes = 30))



# Record tracking variables at the end of each day.

schedule_function(record_vars,

                  date_rules.every_day(),

                  time_rules.market_close(minutes=1))



# Create and attach our pipeline (dynamic security selector), defined 

below.

attach_pipeline(make_pipeline(context), 'mean_reversion_example')





class StockAge(CustomFactor):  



inputs = [Fundamentals.ipo_date]  

window_length = 1  

def compute(self, today, assets, out, ipo_dates):  



    def get_delta_days(ipo_dates):  

        # Convert last known (ie -1) ipo_date to Timestamps  

        # Subtract ipo date from current date  

        # Return delta days  

        ipo = pd.Timestamp(ipo_dates[-1], tz='UTC', offset='C')  

        delta = today - ipo  

        print ('Hello, world!')

        return None if pd.isnull(delta) else float(delta.days) 





    # Apply the above function across each column and output the values  

    out[:] = np.apply_along_axis(get_delta_days, 0, ipo_dates)       



def make_pipeline(context):







# Create a pipeline object. 

    pipe = Pipeline()

    stk_age = StockAge()

    stk_top = stk_age.notnan()

    pipe.add(stk_top,'ipo')



    return pipe



def before_trading_start(context, data):

"""

Called every day before market open. This is where we get the securities

that made it through the pipeline.

"""



    context.myipo = context.output[context.output['ipo']]



# Keep a list reference and a set reference to all of our pipeline securities

# (set has much faster lookup)

    context.security_list = context.myipo.index.tolist()

    context.security_set = set(context.security_list)



def assign_weights(context):

"""

Assign weights to ou long and short target positions.

"""



# Set the allocations to even weights for each long position, and even weights

# for each short position.







def rebalance(context,data):

"""

This rebalancing function is called according to our schedule_function 

settings.  

"""



assign_weights(context)

for security in context.security_list:

    order_target_percent(security, context.short_weight)

for security in context.portfolio.positions:

    if security not in context.security_set and data.can_trade(security):

        order_target_percent(security, 0)

order_target_percent

# Log the long and short orders each week.

log.info("This week's ipos: "+", ".join([ipo_.symbol for ipo_ in context.ipo.index]))







def record_vars(context, data):

"""

This function is called at the end of each day and plots certain variables.

"""



# Check how many long and short positions we have.

    longs = shorts = 0

    for position in context.portfolio.positions.itervalues():

        if position.amount > 0:

            longs += 1

        if position.amount < 0:

            shorts += 1



# Record and plot the leverage of our portfolio over time as well as the 

# number of long and short positions. Even in minute mode, only the end-of-day 

# leverage is plotted.

    record(leverage = context.account.leverage, long_count=longs,     short_count=shorts)