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)

 

Author