Hello Everyone,

Can you please tell me why I have a problem at line 55? with a zero division problem? z = (spread - meanPrice)/stdPrice

I can't seem to find why it does not work, please let me know if you can find the solution. I attached the code below.

Thank you very much!

Sincerely,
Terence

import statsmodels.api as sm
import numpy as np
import pandas as pd

def initialize(context):
context.notional=20000

context.pair_list=[  
           [sid(15634), sid(2637)], #utilities  
           [sid(22660),sid(9038)],#energy  
           [sid(3149),sid(20940)], #industrials  
           [sid(42700),sid(7239)],  
           [sid(4010),sid(24757)],  
           [sid(1335),sid(20088)], #financials  
           [sid(24491),sid(19177)], #basic materials  
           [sid(42596),sid(41149)], #communications  
           [sid(7687),sid(2754)], #consumer cyclical  
           [sid(2254),sid(4521)]  
           ]

pct_per_algo= 1.0 / len(context.pair_list)  

context.algos=pd.Series([Pair_Trade(context.pair_list[t][0], context.pair_list[t][1], pct_per_algo) for t in range(0, (len(context.pair_list)))])  

def handle_data(context, data):
context.algos.apply(lambda algo: algo.handle_data(context, data))

class Pair_Trade(object):

def __init__(self, stk0, stk1, pct_of_account):  
    self.stk0=stk0  
    self.stk1=stk1  
    self.pct=pct_of_account


def handle_data(self, context, data):  
    cash=context.notional*self.pct  
    cash=float(cash)  
    price_history = history(bar_count=200, frequency='1d', field='price')  

    beta = self.ols_transform(price_history, self.stk0, self.stk1)  
    beta=float(beta)  


    spread=data[self.stk0].price - beta*(data[self.stk1].price)  

    rVal = self.getMeanStd(price_history, beta, self.stk0, self.stk1)  
    meanPrice, stdPrice = rVal  

    z = (spread - meanPrice)/stdPrice

    if z>2 and context.portfolio.positions[self.stk0].amount==0 and context.portfolio.positions[self.stk1].amount==0:  
        order_value(self.stk1, cash)  
        order_value(self.stk0, -cash)

    elif z<-2 and context.portfolio.positions[self.stk0].amount==0 and context.portfolio.positions[self.stk1].amount==0 :  
        order_value(self.stk0, cash)  
        order_value(self.stk1, -cash)  

    elif abs(z)<0.5 and context.portfolio.positions[self.stk0].amount!=0 and context.portfolio.positions[self.stk1].amount!=0:  
        order_target(self.stk0, 0)  
        order_target(self.stk1, 0)  
    record(cash=cash)  

def getMeanStd(self, prices, beta, sid0, sid1):  
    meanPrice = np.mean(prices[sid0] - beta*prices[sid1])  
    stdPrice= np.std(prices[sid0] - beta*prices[sid1])  
    if meanPrice is not None and stdPrice is not None :  
        return (meanPrice, stdPrice)  
    else:  
        return None  


def ols_transform(self, prices, sid0, sid1):  
    """  
    Computes regression coefficient (slope and intercept)  
    via Ordinary Least Squares between two SIDs.  
    """  
    p0 = prices[sid0]  
    p1 = sm.add_constant(prices[sid1], prepend=True)  
    intercept, slope =sm.OLS(p0, p1).fit().params  
    return slope