I am attempting to write a custom factor to compute the Gain Percent of each stock's industry. I need to be able to offset the window so that I can return (for example) the 1 week gain for the industry for the previous week. I am getting the following error when I try to execute it:

TypeError: zipline.pipeline.pipeline.add() expected a value of type zipline.pipeline.term.Term for argument 'term', but got abc.ABCMeta
instead. There was a runtime error on line 68.

Any help in figuring out how to do this would be appreciated.

FYI: Line 68 is:

 pipe.add(gainpctind_off0, name='gainpctind_off0')  

Thanks.

# Import the libraries we will use here  
import numpy as np  
from numpy import ma  
import pandas as pd  
import talib as ta  
from quantopian.pipeline.factors import SimpleMovingAverage  
from quantopian.algorithm import attach_pipeline, pipeline_output  
from quantopian.pipeline import Pipeline, CustomFactor  
from quantopian.pipeline.data.builtin import USEquityPricing  
import quantopian.pipeline.data.morningstar as ms

def GainPct(offset=0, nbars=2):  
    class GainPctFact(CustomFactor):  
        window_length = nbars + offset  
        inputs = [USEquityPricing.close]  
        def compute(self, today, assets, out, close):  
            out[:] = (close[-1] - close[offset]) / close[offset] * 100  
    return GainPctFact

def GainPctInd(offset=0, nbars=2):  
    class GainPctIndFact(CustomFactor):  
        window_length = nbars + offset  
        inputs = [USEquityPricing.close, ms.asset_classification.morningstar_industry_code]  
        def compute(self, today, assets, out, close, industries):  
            # For each industry, build a list of the per-stock gains over the given window  
            gains_by_industry = {}  
            for i in range(0, len(industries)):  
                industry = industries[i]  
                asset_gainpct = (close[-1] - close[offset]) / close[offset] * 100  
                if industry in assets_by_industry:  
                    gains_by_industry[industry].append(asset_gainpct)  
                else:  
                    gains_by_industry[industry] = [asset_gainpct]

            # Loop through each stock's industry and compute a mean value for that  
            # industry (caching it for reuse) and return that industry mean for  
            # that stock  
            mean_cache = {}  
            for i in range(0, len(industries)):  
                industry = industries[i]  
                if not industry in mean_cache:  
                    mean_cache[industry] = np.mean(gains_by_industry[industry])  
                out[i] = mean_cache[industry]  
    return GainPctIndFact    

# The initialize function is the place to set your tradable universe and define any parameters.  
def initialize(context):  
    pipe = Pipeline()  
    attach_pipeline(pipe, name='my_pipeline')

    gainpctind_off0 = GainPctInd()  
    gainpctind_off1 = GainPctInd(1)  
    gainpctind1wk = GainPctInd(0, 5)  
    gainpctindprevwk = GainPctInd(5, 5)  
    pipe.add(gainpctind_off0, name='gainpctind_off0')  
    pipe.add(gainpctind_off1, name='gainpctind_off1')  
    pipe.add(gainpctind1wk, name='gainpctind1wk')  
    pipe.add(gainpctindprevwk, name='gainpctindprevwk')  

def before_trading_start(context, data):  
    results = pipeline_output('my_pipeline')  
    print results.head(5)  
    update_universe(results.sort('gainpctind1wk').index[:10])  

# The handle_data function is run every bar.  
def handle_data(context,data):  
    # Record and plot the leverage of our portfolio over time.  
    record(leverage = context.account.leverage)

    # We also want to monitor the number of long and short positions  
    # in our portfolio over time. This loop will check our positition sizes  
    # and add the count of longs and shorts to our plot.  
    longs = shorts = 0  
    for position in context.portfolio.positions.itervalues():  
        if position.amount > 0:  
            longs += 1  
        if position.amount < 0:  
            shorts += 1  
    record(long_count=longs, short_count=shorts)