Just testing translating some code from iPython to Quantopian. I'm trying to calculate the linear regression slope for 5-day price movement for each security by creating a custom factor in a pipeline, but keep getting "error: "all the input array dimensions except for the concatenation axis must match exactly"" when I try to call pipeline_output.

Does anyone know how to fix this? Thank you!

This is how I'm computing the slope:

# Create custom factor #1: 5-day price movement slope  
class Momentum(CustomFactor):  
    inputs = [USEquityPricing.close]  
    window_length = 5  
    def compute(self, today, assets, out, close):  
        slope, intercept, r_value, p_value, std_err = stats.linregress(list(range(0,4)),close[0:4])  
        out[:] = slope  

This is the full code:

from quantopian.algorithm import attach_pipeline, pipeline_output  
from quantopian.pipeline import Pipeline  
from quantopian.pipeline import CustomFactor  
from quantopian.pipeline.data.builtin import USEquityPricing  
from quantopian.pipeline.data import morningstar  
from scipy import stats  
import pandas as pd



# Create custom factor #1: 5-day price movement slope  
class Momentum(CustomFactor):  
    inputs = [USEquityPricing.close]  
    window_length = 5  
    def compute(self, today, assets, out, close):  
        slope, intercept, r_value, p_value, std_err = stats.linregress(list(range(0,4)),close[0:4])  
        out[:] = slope  


# initialize pipeline and add any custom factors  
def initialize(context):  
    pipe = Pipeline()  
    attach_pipeline(pipe, 'ranked_momentum')  
    # Add the custom factor to the pipeline  
    momentum = Momentum()  
    pipe.add(momentum, 'momentum')  


#Rank momentum factor and add the rank to our pipeline  
    momentum_rank = momentum.rank(ascending=False)  
    pipe.add(momentum_rank, 'mom_rank')  

    # Schedule my rebalance function  
    schedule_function(func=rebalance,  
                      date_rule=date_rules.every_day(),  
               time_rule=time_rules.market_open(hours=0,minutes=30),  
                      half_days=True)  
    # set my leverage  
    context.long_leverage = 0.50  
    context.short_leverage = -0.50  
def before_trading_start(context, data):  
    # Call pipeline_output to get the output  
    context.output = pipeline_output('ranked_momentum')  
    # Narrow down the securities to only the top 100 and bottom 100 & update my universe  
    context.long_list = context.output.sort(  
        ['mom_rank'],  
        ascending=True  
    ).iloc[:100]

    context.short_list = context.output.sort(  
        ['mom_rank'],  
        ascending=True  
    ).iloc[-100:]  
    update_universe(context.long_list.index.union(context.short_list.index)) 


def handle_data(context, data):  
     # Record and plot the leverage of our portfolio over time.  
    record(leverage=context.account.leverage)  
    print "Long List"  
    log.info("\n" + str(context.long_list.sort(['mom_rank'], ascending=True).head(10)))  
    print "Short List"  
    log.info("\n" + str(context.short_list.sort(['mom_rank'], ascending=True).head(10)))

# This rebalancing is called according to our schedule_function settings.  
def rebalance(context,data):  
    long_weight = context.long_leverage / float(len(context.long_list))  
    short_weight = context.short_leverage / float(len(context.short_list))

    for long_stock in context.long_list.index:  
        if long_stock in data:  
            #log.info("ordering longs")  
            #log.info("weight is %s" % (long_weight))  
            order_target_percent(long_stock, long_weight)  
    for short_stock in context.short_list.index:  
        if short_stock in data:  
            #log.info("ordering shorts")  
            #log.info("weight is %s" % (short_weight))  
            order_target_percent(short_stock, short_weight)  
    for stock in context.portfolio.positions:  
        if stock not in context.long_list.index and stock not in context.short_list.index:  
            order_target(stock, 0)