Template for implementing algo:

# Import Algorithm API  
import quantopian.algorithm as algo  
# Import Optimize API  
import quantopian.optimize as opt  
# Pipeline imports  
from quantopian.pipeline import Pipeline  
from quantopian.pipeline.filters import QTradableStocksUS  
from quantopian.pipeline.experimental import risk_loading_pipeline  
from quantopian.pipeline.classifiers.fundamentals import Sector

import pandas as pd  
import numpy as np  
import math


"""
IMPORT DUMP FROM RESEARCH NOTEBOOK  
"""
#########################################

### PIPELNE IMPORTS


###UNIVERSE  
from quantopian.pipeline.filters import QTradableStocksUS

###Price Data Class

from quantopian.pipeline.data import USEquityPricing as EquityPricing

#[EquityPricing.close, EquityPricing.volume]

###Classifiers (Sector)  
from quantopian.pipeline.classifiers.fundamentals import Sector


from quantopian.pipeline.factors import DailyReturns,Returns,PercentChange,VWAP,AverageDollarVolume,AnnualizedVolatility,SimpleBeta,SimpleMovingAverage,Latest,MaxDrawdown,RSI,ExponentialWeightedMovingAverage,ExponentialWeightedMovingStdDev,LinearWeightedMovingAverage,WeightedAverageValue,MovingAverageConvergenceDivergenceSignal,RollingPearsonOfReturns,RollingSpearmanOfReturns,BusinessDaysSincePreviousEvent,Aroon,FastStochasticOscillator,IchimokuKinkoHyo,TrueRange

#########################################

###Custom Factors  
#### COSTOM CLASSES

maximumLeverage = 1.1

maximumPositionSize = .05

maximumTurnover = 1.0

def initialize(context):  



    # Constraint parameters  
    context.max_leverage = maximumLeverage  
    context.max_pos_size = maximumPositionSize  
    context.max_turnover = maximumTurnover  
    # Attach data pipelines  
    algo.attach_pipeline(  
        make_pipeline(),  
        'data_pipe'  
    )  
    algo.attach_pipeline(  
        risk_loading_pipeline(),  
        'risk_pipe'  
    )  
    # Schedule rebalance function  
    algo.schedule_function(  
        rebalance,  
        algo.date_rules.week_start(),  
        algo.time_rules.market_open(),  
    )  
def before_trading_start(context, data):  
    # Get pipeline outputs and  
    # store them in context  
    context.pipeline_data = algo.pipeline_output('data_pipe')  
    context.risk_factor_betas = algo.pipeline_output('risk_pipe').dropna()  
def make_pipeline():  
    """  
    THIS IS WHERE ALL OF THE DATA FETCHING AND COMPUTATION HAPPENS  
    """  
    quarter_length = 65  


    universe = QTradableStocksUS()

    ###SOME CONSTANTS  
    #1 period is 1 day for window length  
    window_length_all = 14  
    decayRate = .5  
    closePrices = [EquityPricing.close]  
    targetTicker = symbols('SPY')  
#######Set up data to query  

#######Do some computations and design our factor to analyze


    _SimpleBeta = SimpleBeta(target = sid(8554), regression_length = 252)

    factor_to_analyze = _SimpleBeta

#######Define some screens and filters  
    bigScreen = factor_to_analyze.zscore() > 0  

######Initialize Sector Classifier  
    sector = Sector()  
    return Pipeline(  
    columns = {'factor_to_analyze': factor_to_analyze},  
    screen = QTradableStocksUS() & factor_to_analyze.notnull() & sector.notnull() & bigScreen  
    )

def rebalance(context, data):  
    # Retrieve alpha from pipeline output  
    alpha = context.pipeline_data.factor_to_analyze  
    print(alpha)  
    print(len(alpha))  
    if not alpha.empty:  
        # Create MaximizeAlpha objective  
        objective = opt.MaximizeAlpha(alpha)

        # Create position size constraint  
        constrain_pos_size = opt.PositionConcentration.with_equal_bounds(  
            -context.max_pos_size,  
            context.max_pos_size  
        )  
        # Constrain target portfolio's leverage  
        max_leverage = opt.MaxGrossExposure(context.max_leverage)

        # Ensure long and short books  
        # are roughly the same size  
        dollar_neutral = opt.DollarNeutral()

        # Constrain portfolio turnover  
        max_turnover = opt.MaxTurnover(context.max_turnover)  
        # Constrain target portfolio's risk exposure  
        # By default, max sector exposure is set at  
        # 0.2, and max style exposure is set at 0.4  
        factor_risk_constraints = opt.experimental.RiskModelExposure(  
            context.risk_factor_betas,  
            version=opt.Newest  
        )

        # Rebalance portfolio using objective  
        # and list of constraints  
        algo.order_optimal_portfolio(  
            objective=objective,  
            constraints=[  
                constrain_pos_size,  
                max_leverage,  
                dollar_neutral,  
                max_turnover,  
                factor_risk_constraints,  
            ]  
        )  

Notebook for doing factor analysis : (Click view notebook and clone it to play around with stuff!)