Hi, I am looking at the sample algo here.

https://www.quantconnect.com/tutorials/strategy-library/pairs-trading-with-stocks

 

I am trying to determine what happens if a pair has an open position and the rebalance occurs. If that pair is no longer part of the actively traded pairs, does the open position ever exit?

If so, how is it managed?

 

Thanks

 

# https://quantpedia.com/Screener/Details/12

import numpy as np
import pandas as pd
from scipy import stats
from math import floor
from datetime import timedelta
from collections import deque
import itertools as it
from decimal import Decimal

class PairsTradingAlgorithm(QCAlgorithm):

def Initialize(self):

self.SetStartDate(2014,1,1)
self.SetEndDate(2018,1,1)
self.SetCash(100000)

tickers = [ 'XLK', 'QQQ', 'BANC', 'BBVA', 'BBD', 'BCH', 'BLX', 'BSBR', 'BSAC', 'SAN',
'CIB', 'BXS', 'BAC', 'BOH', 'BMO', 'BK', 'BNS', 'BKU', 'BBT','NBHC', 'OFG',
'BFR', 'CM', 'COF', 'C', 'VLY', 'WFC', 'WAL', 'WBK','RBS', 'SHG', 'STT', 'STL', 'SCNB', 'SMFG', 'STI']
# 'DKT', 'DB', 'EVER', 'KB', 'KEY', , 'MTB', 'BMA', 'MFCB', 'MSL', 'MTU', 'MFG',
# 'PVTD', 'PB', 'PFS', 'RF', 'RY', 'RBS', 'SHG', 'STT', 'STL', 'SCNB', 'SMFG', 'STI',
# 'SNV', 'TCB', 'TD', 'USB', 'UBS', 'VLY', 'WFC', 'WAL', 'WBK', 'WF', 'YDKN', 'ZBK']
self.threshold = 2
self.symbols = []
for i in tickers:
self.symbols.append(self.AddEquity(i, Resolution.Daily).Symbol)

self.pairs = {}
self.formation_period = 252

self.history_price = {}
for symbol in self.symbols:
hist = self.History([symbol], self.formation_period+1, Resolution.Daily)
if hist.empty:
self.symbols.remove(symbol)
else:
self.history_price[str(symbol)] = deque(maxlen=self.formation_period)
for tuple in hist.loc[str(symbol)].itertuples():
self.history_price[str(symbol)].append(float(tuple.close))
if len(self.history_price[str(symbol)]) < self.formation_period:
self.symbols.remove(symbol)
self.history_price.pop(str(symbol))

self.symbol_pairs = list(it.combinations(self.symbols, 2))
# Add the benchmark
self.AddEquity("SPY", Resolution.Daily)
self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.AfterMarketOpen("SPY"), self.Rebalance)
self.count = 0
self.sorted_pairs = None


def OnData(self, data):
# Update the price series everyday
for symbol in self.symbols:
if data.Bars.ContainsKey(symbol) and str(symbol) in self.history_price:
self.history_price[str(symbol)].append(float(data[symbol].Close))
if self.sorted_pairs is None: return

for i in self.sorted_pairs:
# calculate the spread of two price series
spread = np.array(self.history_price[str(i[0])]) - np.array(self.history_price[str(i[1])])
mean = np.mean(spread)
std = np.std(spread)
ratio = self.Portfolio[i[0]].Price / self.Portfolio[i[1]].Price
# long-short position is opened when pair prices have diverged by two standard deviations
if spread[-1] > mean + self.threshold * std:
if not self.Portfolio[i[0]].Invested and not self.Portfolio[i[1]].Invested:
quantity = int(self.CalculateOrderQuantity(i[0], 0.2))
self.Sell(i[0], quantity)
self.Buy(i[1], floor(ratio*quantity))

elif spread[-1] < mean - self.threshold * std:
quantity = int(self.CalculateOrderQuantity(i[0], 0.2))
if not self.Portfolio[i[0]].Invested and not self.Portfolio[i[1]].Invested:
self.Sell(i[1], quantity)
self.Buy(i[0], floor(ratio*quantity))

# the position is closed when prices revert back
elif self.Portfolio[i[0]].Invested and self.Portfolio[i[1]].Invested:
self.Liquidate(i[0])
self.Liquidate(i[1])


def Rebalance(self):
# schedule the event to fire every half year to select pairs with the smallest historical distance
if self.count % 6 == 0:
distances = {}
for i in self.symbol_pairs:
distances[i] = Pair(i[0], i[1], self.history_price[str(i[0])], self.history_price[str(i[1])]).distance()
self.sorted_pairs = sorted(distances, key = lambda x: distances[x])[:4]
self.count += 1

class Pair:
def __init__(self, symbol_a, symbol_b, price_a, price_b):
self.symbol_a = symbol_a
self.symbol_b = symbol_b
self.price_a = price_a
self.price_b = price_b

def distance(self):
# calculate the sum of squared deviations between two normalized price series
norm_a = np.array(self.price_a)/self.price_a[0]
norm_b = np.array(self.price_b)/self.price_b[0]
return sum((norm_a - norm_b)**2)