Hello everyone, I'm new to Quantopian and I'm testing my first algo, it's purpose is to find stocktwits sentiment for a stock, give it a percentile value and invest a percentage of our portfolio based on that percentile.

My versión of stocktwits sentiment will be made by two factors: Bull minus bear and bull bear ratio, these are multiplied to add/lessen importance of the first ratio (which has the sentiment score). My code gives me the following error:

ValueError: cannot copy sequence with size 100 to array axis with dimension 8343

Which I know comes from the out[:] line, but don't understand why, since [stats.percentileofscore(a[x]*b[x], a[x,-1:]*b[x,-1:]) for x in range(len(a))] Works on normal Python with any given numpy array.

# This section is only importable in the backtester  
from quantopian.algorithm import attach_pipeline, pipeline_output

# General pipeline imports  
from quantopian.pipeline import Pipeline, CustomFilter  
from quantopian.pipeline.factors import AverageDollarVolume  
from quantopian.pipeline.filters import StaticAssets


from quantopian.pipeline.data.psychsignal import stocktwits_free  
from quantopian.pipeline import CustomFactor, CustomFilter

import numpy as np  
from scipy import stats

class ponderated(CustomFactor):  
    inputs = [stocktwits_free.bull_minus_bear, stocktwits_free.bull_bear_msg_ratio]  
    window_length = 100  
    def compute(self, today, assets, out, a, b):  
        out[:] = [stats.percentileofscore(a[x]*b[x], a[x,-1:]*b[x,-1:]) for x in range(len(a))]

def make_pipeline():  
    # Create our pipeline  
    pipe = Pipeline()  
    # Screen out penny stocks and low liquidity securities.  
    dollar_volume = AverageDollarVolume(window_length=20)  
    is_liquid = dollar_volume.rank(ascending=False) < 1000  
    #Our made class  
    ponderated_list = ponderated()  

    # Add pipeline factors  
    pipe.add(ponderated_list, 'ponderated')

    # Set our pipeline screens  
    pipe.set_screen(is_liquid)  
    return pipe

def initialize(context):  
    attach_pipeline(make_pipeline(), "pipeline")  
def portfolio_percentage():  
    results = pipeline_output('pipeline')  
    a = []  
    for each in results:  
        a.append(results)  
    return a

def handle_data(context, data):  
    results = portfolio_percentage()  
    for each in results:  
        order_target_percent(sid(24), float(each.iloc[-1]))