Lecture 24

## Introduction

This algorithm was originally from an implementation by Ernest Chan from his books on algorithmic trading. It demonstrates how one might trade a pair of stocks that they are confident are cointegrated.

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

import quantopian.optimize as opt
import quantopian.algorithm as algo

def initialize(context):
# Quantopian backtester specific variables
set_symbol_lookup_date('2014-01-01')

context.stock_pairs = [(symbol('ABGB'), symbol('FSLR')),
(symbol('CSUN'), symbol('ASTI'))]

context.stocks = symbols('ABGB', 'FSLR', 'CSUN', 'ASTI')

context.num_pairs = len(context.stock_pairs)
# strategy specific variables
context.lookback = 20 # used for regression
context.z_window = 20 # used for zscore calculation, must be <= lookback

context.target_weights = pd.Series(index=context.stocks, data=0.25)

context.inLong = [False] * context.num_pairs
context.inShort = [False] * context.num_pairs

# Only do work 30 minutes before close
schedule_function(func=check_pair_status, date_rule=date_rules.every_day(), time_rule=time_rules.market_close(minutes=30))

# Will be called on every trade event for the securities you specify.
def handle_data(context, data):
# Our work is now scheduled in check_pair_status
pass

def check_pair_status(context, data):

prices = data.history(context.stocks, 'price', 35, '1d').iloc[-context.lookback::]

for i in range(context.num_pairs):

(stock_y, stock_x) = context.stock_pairs[i]

Y = prices[stock_y]
X = prices[stock_x]

# Comment explaining try block
try:
except ValueError as e:
log.debug(e)
return

context.target_weights = get_current_portfolio_weights(context, data)

new_spreads[i, :] = Y[-1] - hedge * X[-1]

# Keep only the z-score lookback period

if context.inShort[i] and zscore < 0.0:
context.target_weights[stock_y] = 0
context.target_weights[stock_x] = 0

context.inShort[i] = False
context.inLong[i] = False

record(X_pct=0, Y_pct=0)
allocate(context, data)
return

if context.inLong[i] and zscore > 0.0:
context.target_weights[stock_y] = 0
context.target_weights[stock_x] = 0

context.inShort[i] = False
context.inLong[i] = False

record(X_pct=0, Y_pct=0)
allocate(context, data)
return

if zscore < -1.0 and (not context.inLong[i]):
y_target_shares = 1
X_target_shares = -hedge
context.inLong[i] = True
context.inShort[i] = False

(y_target_pct, x_target_pct) = computeHoldingsPct(y_target_shares,X_target_shares, Y[-1], X[-1])

context.target_weights[stock_y] = y_target_pct * (1.0/context.num_pairs)
context.target_weights[stock_x] = x_target_pct * (1.0/context.num_pairs)

record(Y_pct=y_target_pct, X_pct=x_target_pct)
allocate(context, data)
return

if zscore > 1.0 and (not context.inShort[i]):
y_target_shares = -1
X_target_shares = hedge
context.inShort[i] = True
context.inLong[i] = False

(y_target_pct, x_target_pct) = computeHoldingsPct( y_target_shares, X_target_shares, Y[-1], X[-1] )

context.target_weights[stock_y] = y_target_pct * (1.0/context.num_pairs)
context.target_weights[stock_x] = x_target_pct * (1.0/context.num_pairs)

record(Y_pct=y_target_pct, X_pct=x_target_pct)
allocate(context, data)
return

model = sm.OLS(Y, X).fit()
return model.params[1]
model = sm.OLS(Y, X).fit()
return model.params.values

def computeHoldingsPct(yShares, xShares, yPrice, xPrice):
yDol = yShares * yPrice
xDol = xShares * xPrice
notionalDol =  abs(yDol) + abs(xDol)
y_target_pct = yDol / notionalDol
x_target_pct = xDol / notionalDol
return (y_target_pct, x_target_pct)

def get_current_portfolio_weights(context, data):
positions = context.portfolio.positions
positions_index = pd.Index(positions)
share_counts = pd.Series(
index=positions_index,
data=[positions[asset].amount for asset in positions]
)

current_prices = data.current(positions_index, 'price')
current_weights = share_counts * current_prices / context.portfolio.portfolio_value
return current_weights.reindex(positions_index.union(context.stocks), fill_value=0.0)

def allocate(context, data):
# Set objective to match target weights as closely as possible, given constraints
objective = opt.TargetWeights(context.target_weights)

# Define constraints
constraints = []
constraints.append(opt.MaxGrossExposure(1.0))

algo.order_optimal_portfolio(
objective=objective,
constraints=constraints,
)

```

The lectures on this website are provided for informational purposes only and do not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor do they constitute an offer to provide investment advisory services by Quantopian.

In addition, the lectures offer no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.