import numpy as np
import pandas as pd
import datetime as datetime
import statsmodels.formula.api as sm
import statsmodels.tsa.stattools as ts
from pair import *
class PairsTrading(QCAlgorithm):
def __init__(self):
self.symbols = ['BTCUSD','LTCUSD','ETHUSD','LTCBTC','ETHBTC']
self.data_resolution = 10
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 = 0.70
self.BIC = -3.00
self.count = 0
self.pair_list = []
self.selected_pair = []
self.trading_pairs = []
self.generate_count = 0
self.open_size = 1.32
self.close_size = 0.5
self.stop_loss = 6
self.data_count = 0
def Initialize(self):
self.SetStartDate(2016,10,7)
self.SetEndDate(2017,12,31)
self.SetCash(90000)
self.SetBrokerageModel(BrokerageName.GDAX, AccountType.Cash)
for i in range(len(self.symbols)):
crypto = self.AddCrypto(self.symbols[i],Resolution.Minute).Symbol
self.symbols[i] = crypto
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
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:
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
returnimport 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 = []