Simple Passive Momentum Trading with Bollinger Band

Below, you can see a simple trading algorithm based on momentum and bollinger band on Quantopian.com

# Simple Passive Momentum Trading with Bollinger Band
import numpy as np
import statsmodels.api as stat
import statsmodels.tsa.stattools as ts

# globals for batch transform decorator
R_P = 1 # refresh period in days
W_L = 30 # window length in days
lookback=22
def initialize(context):
    context.stock = sid(24) # Apple (ignoring look-ahead bias)
    # for long and shorting 
    context.max_notional = 1000000
    context.min_notional = -1000000.0
    # set a fixed slippage
    set_slippage(slippage.FixedSlippage(spread=0.01))
                
def handle_data(context, data):
    # find moving average 
    rVal=getMeanStd(data)

    # lets dont do anything if we dont have enough data yet    
    if rVal is None:
        return    
    
    meanPrice,stdPrice = rVal
    price=data[context.stock].price
    notional = context.portfolio.positions[context.stock].amount * price
    
    # Passive momentum trading where for trading signal, Z-score is estimated
    h=((price-meanPrice)/stdPrice)
    # Bollinger band, if price is out of 2 std of moving mean, than lets trade     
    if h>2 and notional < context.max_notional  :
       # long
       order(context.stock,h*1000)
    if h<-2 and notional > context.min_notional:
       # short
       order(context.stock,h*1000)
     
@batch_transform(window_length=W_L, refresh_period=R_P) 
def getMeanStd(datapanel):
    prices = datapanel['price']
    meanPrice=prices.mean()
    stdPrice=prices.std()
    if meanPrice is not None and stdPrice is not None :
        return (meanPrice, stdPrice)
    else:
        return None

Screen shot of the back testing result is:

Click here to run algorithm on Quantopian.com.