Overall Statistics |
Total Trades 24 Average Win 2.78% Average Loss -2.24% Compounding Annual Return -2.757% Drawdown 6.200% Expectancy -0.067 Net Profit -1.981% Sharpe Ratio -0.307 Probabilistic Sharpe Ratio 9.316% Loss Rate 58% Win Rate 42% Profit-Loss Ratio 1.24 Alpha -0.02 Beta 0.016 Annual Standard Deviation 0.058 Annual Variance 0.003 Information Ratio -0.894 Tracking Error 0.132 Treynor Ratio -1.122 Total Fees $0.00 |
import numpy as np import pandas as pd import datetime as datetime import statsmodels.formula.api as sm from pandas.core import datetools import statsmodels.tsa.stattools as ts from pair import * class PairsTrading(QCAlgorithm): def __init__(self): self.symbols = [ 'EURUSD','AUDUSD','USDCHF','NZDUSD', 'USDCAD', 'USDJPY', 'AUDJPY','EURAUD','AUDCHF','AUDNZD','AUDCAD', 'GBPAUD','GBPUSD', 'EURCHF','EURNZD','EURCAD','EURJPY','EURGBP', 'CADCHF', 'NZDCHF','NZDCAD','NZDJPY','GBPNZD', 'GBPCAD','GBPJPY','GBPCHF','CADJPY', ] self.data_resolution = 5 self.num_bar = 6.5*60*60/(self.data_resolution) self.one_month = 6.5*20*60/(self.data_resolution) self.selected_num = 10 self.pair_num = 120 self.pair_threshod = 0.88 self.BIC = -3.34 self.count = 0 self.pair_list = [] self.selected_pair = [] self.trading_pairs = [] self.generate_count = 0 self.open_size = 2.32 self.close_size = 0.5 self.stop_loss = 4 self.data_count = 0 def Initialize(self): self.SetStartDate(2017,12,16) self.SetEndDate(2019,12,16) self.SetCash(50000) for i in range(len(self.symbols)): equity = self.AddForex(self.symbols[i], Resolution.Minute).Symbol self.symbols[i] = equity self.symbols[i].prices = [] self.symbols[i].dates = [] def generate_pairs(self): for i in range(len(self.symbols)): for j in range(i+1,len(self.symbols)): self.pair_list.append(pairs(self.symbols[i],self.symbols[j])) self.pair_list = [x for x in self.pair_list if x.cor > self.pair_threshod] self.pair_list.sort(key = lambda x: x.cor, reverse = True) if len(self.pair_list) > self.pair_num: self.pair_list = self.pair_list[:self.pair_num] def pair_clean(self,list): l = [] l.append(list[0]) for i in list: symbols = [x.a for x in l] + [x.b for x in l] if i.a not in symbols and i.b not in symbols: l.append(i) else: pass return l def OnData(self,data): if not self.Securities[self.symbols[0]].Exchange.ExchangeOpen: return #data aggregation if self.data_count < self.data_resolution: self.data_count +=1 return # refill the initial df if len(self.symbols[0].prices) < self.num_bar: for i in self.symbols: if data.ContainsKey(i) is True: i.prices.append(float(data[i].Close)) i.dates.append(data[i].EndTime) else: self.Log('%s is missing'%str(i)) self.symbols.remove(i) self.data_count = 0 return # generate paris if self.count == 0 and len(self.symbols[0].prices) == self.num_bar: if self.generate_count == 0: for i in self.symbols: i.df = pd.DataFrame(i.prices, index = i.dates, columns = ['%s'%str(i)]) self.generate_pairs() self.generate_count +=1 self.Log('pair list length:'+str(len(self.pair_list))) # correlation selection for i in self.pair_list: i.cor_update() # updatet the dataframe and correlation selection #elf.pair_list[0].a_price = list(dict.fromkeys(mylist) self.Debug("pairlist: " + str(len(self.pair_list[0].a_price))) if len(self.pair_list[0].a_price) != 0: for i in self.pair_list: i.df_update() i.cor_update() self.selected_pair = [x for x in self.pair_list if x.cor > 0.9] # cointegration selection for i in self.selected_pair: i.cointegration_test() self.selected_pair = [x for x in self.selected_pair if x.adf < self.BIC] self.selected_pair.sort(key = lambda x: x.adf) if len(self.selected_pair) == 0: self.Log('no selected pair') self.count += 1 return self.selected_pair = self.pair_clean(self.selected_pair) for i in self.selected_pair: i.touch = 0 self.Log(str(i.adf) + i.name) if len(self.selected_pair) > self.selected_num: self.selected_pair = self.selected_pair[:self.selected_num] self.count +=1 self.data_count = 0 return #update the pairs if self.count != 0 and self.count < self.one_month: num_select = len(self.selected_pair) for i in self.pair_list: if data.ContainsKey(i.a) is True and data.ContainsKey(i.b) is True: i.price_record(data[i.a],data[i.b]) else: self.Log('%s has no data'%str(i.name)) self.pair_list.remove(i) ## selected pairs for i in self.selected_pair: i.last_error = i.error for i in self.trading_pairs: i.last_error = i.error ## enter for i in self.selected_pair: price_a = float(data[i.a].Close) price_b = float(data[i.b].Close) i.error = price_a - (i.model.params[0] + i.model.params[1]*price_b) if (self.Portfolio[i.a].Quantity == 0 and self.Portfolio[i.b].Quantity == 0) and i not in self.trading_pairs: if i.touch == 0: if i.error < i.mean_error - self.open_size*i.sd and i.last_error > i.mean_error - self.open_size*i.sd: i.touch += -1 elif i.error > i.mean_error + self.open_size*i.sd and i.last_error < i.mean_error + self.open_size*i.sd: i.touch += 1 else: pass elif i.touch == -1: if i.error > i.mean_error - self.open_size*i.sd and i.last_error < i.mean_error - self.open_size*i.sd: self.Log('long %s and short %s'%(str(i.a),str(i.b))) i.record_model = i.model i.record_mean_error = i.mean_error i.record_sd = i.sd self.trading_pairs.append(i) self.SetHoldings(i.a, 5.0/(len(self.selected_pair))) self.SetHoldings(i.b, -5.0/(len(self.selected_pair))) i.touch = 0 elif i.touch == 1: if i.error < i.mean_error + self.open_size*i.sd and i.last_error > i.mean_error + self.open_size*i.sd: self.Log('long %s and short %s'%(str(i.b),str(i.a))) i.record_model = i.model i.record_mean_error = i.mean_error i.record_sd = i.sd self.trading_pairs.append(i) self.SetHoldings(i.b, 5.0/(len(self.selected_pair))) self.SetHoldings(i.a, -5.0/(len(self.selected_pair))) i.touch = 0 else: pass else: pass # close for i in self.trading_pairs: if data.ContainsKey(i.a) and data.ContainsKey(i.b): price_a = float(data[i.a].Close) price_b = float(data[i.b].Close) i.error = price_a - (i.record_model.params[0] + i.record_model.params[1]*price_b) if ((i.error < i.record_mean_error + self.close_size*i.record_sd and i.last_error >i.record_mean_error + self.close_size*i.record_sd) or (i.error > i.record_mean_error - self.close_size*i.record_sd and i.last_error <i.record_mean_error - self.close_size*i.record_sd)): self.Log('close %s'%str(i.name)) self.Liquidate(i.a) self.Liquidate(i.b) self.trading_pairs.remove(i) elif i.error < i.record_mean_error - self.stop_loss*i.record_sd or i.error > i.record_mean_error + self.stop_loss*i.record_sd: self.Log('close %s to stop loss'%str(i.name)) self.Liquidate(i.a) self.Liquidate(i.b) self.trading_pairs.remove(i) else: pass self.count +=1 self.data_count = 0 return if self.count == self.one_month: self.count = 0 self.data_count = 0 return # Override the base class event handler for order events def OnOrderEvent(self, orderEvent): order = self.Transactions.GetOrderById(orderEvent.OrderId) self.Debug(str(orderEvent.Symbol) + "{0}: {1}: {2}".format(self.Time, order.Type, orderEvent))
import numpy as np import pandas as pd import datetime as datetime import statsmodels.formula.api as sm import statsmodels.tsa.stattools as ts class pairs(object): def __init__(self,a,b): self.a = a self.b = b self.name = str(a) + ':' + str(b) self.df = pd.concat([a.df,b.df],axis = 1).dropna() self.num_bar = self.df.shape[0] self.cor = self.df.corr().ix[0][1] self.error = 0 self.last_error = 0 self.a_price = [] self.a_date = [] self.b_price = [] self.b_date = [] def cor_update(self): self.cor = self.df.corr().ix[0][1] def cointegration_test(self): self.model = sm.ols(formula = '%s ~ %s'%(str(self.a),str(self.b)), data = self.df).fit() self.adf = ts.adfuller(self.model.resid,autolag = 'BIC')[0] self.mean_error = np.mean(self.model.resid) self.sd = np.std(self.model.resid) def price_record(self,data_a,data_b): self.a_price.append(float(data_a.Close)) self.a_date.append(data_a.EndTime) self.b_price.append(float(data_b.Close)) self.b_date.append(data_b.EndTime) def df_update(self): new_df = pd.DataFrame({str(self.a):self.a_price,str(self.b):self.b_price},index = [self.a_date]).dropna() self.df = pd.concat([self.df,new_df]) self.df = self.df.tail(self.num_bar) for i in [self.a_price,self.a_date,self.b_price,self.b_date]: i = []