| Overall Statistics |
|
Total Trades 1296 Average Win 0.41% Average Loss -0.44% Compounding Annual Return 0.769% Drawdown 35.000% Expectancy 0.013 Net Profit 5.989% Sharpe Ratio 0.115 Probabilistic Sharpe Ratio 0.357% Loss Rate 48% Win Rate 52% Profit-Loss Ratio 0.94 Alpha 0.068 Beta -0.448 Annual Standard Deviation 0.132 Annual Variance 0.017 Information Ratio -0.467 Tracking Error 0.222 Treynor Ratio -0.034 Total Fees $5240.47 Estimated Strategy Capacity $0.00054 Lowest Capacity Asset ADRU SJNVOV70T9B9 |
# https://quantpedia.com/Screener/Details/55
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 PairsTradingwithCountryETFsAlgorithm(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2011, 1, 1)
self.SetEndDate(2018, 8, 1)
self.SetCash(100000)
# choose ten sector ETFs
tickers = ["GAF", # SPDR S&P Emerging Middle East & Africa ETF 2007.4
"ENZL", # iShares MSCI New Zealand Investable Market Index Fund 2010.9
"NORW", # Global X FTSE Norway 30 ETF 2011
"EWY", # iShares MSCI South Korea Index ETF 2000.6
"EWP", # iShares MSCI Spain Index ETF 1996
"EWD", # iShares MSCI Sweden Index ETF 1996
"EWL", # iShares MSCI Switzerland Index ETF 1996
"GXC", # SPDR S&P China ETF 2007.4
"EWC", # iShares MSCI Canada Index ETF 1996
"EWZ", # iShares MSCI Brazil Index ETF 2000.8
# "AND", # Global X FTSE Andean 40 ETF 2011.3
"AIA", # iShares S&P Asia 50 Index ETF 1996
"EWO", # iShares MSCI Austria Investable Mkt Index ETF 1996
"EWK", # iShares MSCI Belgium Investable Market Index ETF 1996
"ECH", # iShares MSCI Chile Investable Market Index ETF 2018 2008
# "EGPT", # Market Vectors Egypt Index ETF 2011
"EWJ", # iShares MSCI Japan Index ETF 1999
"EZU", # iShares MSCI Eurozone ETF 2000
"EWW", # iShares MSCI Mexico Inv. Mt. Idx 2000
# "ERUS", # iShares MSCI Russia ETF 2011
"IVV", # iShares S&P 500 Index 2001
"AAXJ", # iShares MSCI All Country Asia ex Japan Index ETF 2008.8
"EWQ", # iShares MSCI France Index ETF 2000
"EWH", # iShares MSCI Hong Kong Index ETF 1999
# "EPI", # WisdomTree India Earnings ETF 2008.3
"EIDO", # iShares MSCI Indonesia Investable Market Index ETF 2008.3
"EWI", # iShares MSCI Italy Index ETF 1996
"ADRU"] # BLDRS Europe 100 ADR Index ETF 2003
self.threshold = 0.5
self.symbols = []
for i in tickers:
self.symbols.append(self.AddEquity(i, Resolution.Hour).Symbol)
self.pairs = {}
self.formation_period = 121
self.history_price = {}
for symbol in self.symbols:
hist = self.History([symbol.Value], self.formation_period+1, Resolution.Hour)
if hist.empty:
self.symbols.remove(symbol)
else:
self.history_price[symbol.Value] = deque(maxlen=self.formation_period)
for tuple in hist.loc[str(symbol)].itertuples():
self.history_price[symbol.Value].append(float(tuple.close))
if len(self.history_price[symbol.Value]) < self.formation_period:
self.symbols.remove(symbol)
self.history_price.pop(symbol.Value)
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.sorted_pairs = None
def OnData(self, data):
# Update the price series everyday
for symbol in self.symbols:
if data.Bars.ContainsKey(symbol) and symbol.Value in self.history_price:
self.history_price[symbol.Value].append(float(data[symbol].Close))
if self.sorted_pairs is None: return
for i in self.sorted_pairs:
pair = Pair(i[0], i[1], self.history_price[i[0].Value], self.history_price[i[1].Value])
index_a = pair.index_a[-1]
index_b = pair.index_b[-1]
delta = pair.distance()
if index_a - index_b > self.threshold*delta:
if not self.Portfolio[pair.symbol_a].Invested and not self.Portfolio[pair.symbol_b].Invested:
ratio = self.Portfolio[pair.symbol_a].Price / self.Portfolio[pair.symbol_b].Price
quantity = int(self.CalculateOrderQuantity(pair.symbol_a, 0.2))
self.Sell(pair.symbol_a, quantity)
self.Buy(pair.symbol_b, floor(ratio*quantity))
elif index_a - index_b < -self.threshold*delta:
if not self.Portfolio[pair.symbol_a].Invested and not self.Portfolio[pair.symbol_b].Invested:
ratio = self.Portfolio[pair.symbol_b].Price / self.Portfolio[pair.symbol_a].Price
quantity = int(self.CalculateOrderQuantity(pair.symbol_b, 0.2))
self.Sell(pair.symbol_b, quantity)
self.Buy(pair.symbol_a, 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(pair.symbol_a)
self.Liquidate(pair.symbol_b)
def Rebalance(self):
# schedule the event to fire every half year to select pairs with the smallest historical distance
distances = {}
for i in self.symbol_pairs:
if i[0].Value in self.history_price and i[1].Value in self.history_price:
distances[i] = Pair(i[0], i[1], self.history_price[i[0].Value], self.history_price[i[1].Value]).distance()
self.sorted_pairs = sorted(distances, key = lambda x: distances[x])[:5]
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 = np.array(price_a)
self.price_b = np.array(price_b)
# compute normalized cumulative price indices
self.index_a = np.cumprod(self.price_a[1:]/self.price_a[:-1])
self.index_b = np.cumprod(self.price_b[1:]/self.price_b[:-1])
def distance(self):
return 1/120*sum(abs(self.index_a -self.index_b))