I'm trying to run Pairs trading strategy for BTCUSD and XMRUSD. Since data is not freely available, I have re-formatted my existing data (manually imported from API) to LEAN format, according to this - https://www.quantconnect.com/lean/documentation/topic16.html

But, I have only trade data, no quote. So, when I run my algo, it's throwing errors about missing "_quote" data:

`20190426 09:27:12.450 ERROR:: DefaultDataProvider.Fetch(): The specified file was not found: ../../../Data/crypto/bitfinex/minute/xmrusd/20190201_quote.zip`

To me it seems like this should be runnable with trade data only. Or am I wrong? Is there something I need to configure explicitly in my strategy for it to not look for quote data?

`from clr import AddReference`

AddReference("System")

AddReference("QuantConnect.Algorithm")

AddReference("QuantConnect.Indicators")

AddReference("QuantConnect.Common")

from System import *

from QuantConnect import *

from QuantConnect.Algorithm import *

from QuantConnect.Brokerages import *

from QuantConnect.Data import BaseData

from QuantConnect.Data.Market import *

from QuantConnect.Securities import *

from QuantConnect.Algorithm import *

from QuantConnect.Indicators import *

from sklearn import linear_model

import numpy as np

import pandas as pd

from scipy import stats

from math import floor

from datetime import timedelta

class Pairs1(QCAlgorithm):

def Initialize(self):

self.SetStartDate(2019,1,1)

self.SetEndDate(2019,1,31)

self.SetCash(10000)

self.numdays = 7 # set the length of training period

self.symbols = []

self.threshold = 1.

self.AddCrypto("BTCUSD", Resolution.Minute, Market.Bitfinex);

self.AddCrypto("XMRUSD", Resolution.Minute, Market.Bitfinex);

for i in self.symbols:

i.hist_window = RollingWindow[TradeBar](self.numdays)

def OnData(self, data):

if not (data.ContainsKey("BTCUSD") and data.ContainsKey("XMRUSD")): return

for symbol in self.symbols:

symbol.hist_window.Add(data[symbol])

price_x = pd.Series([float(i.Close) for i in self.symbols[0].hist_window],

index = [i.Time for i in self.symbols[0].hist_window])

price_y = pd.Series([float(i.Close) for i in self.symbols[1].hist_window],

index = [i.Time for i in self.symbols[1].hist_window])

if len(price_x) < 250: return

spread = self.regr(np.log(price_x), np.log(price_y))

mean = np.mean(spread)

std = np.std(spread)

ratio = floor(self.Portfolio[self.symbols[1]].Price / self.Portfolio[self.symbols[0]].Price)

# quantity = float(self.CalculateOrderQuantity(self.symbols[0],0.4))

if spread[-1] > mean + self.threshold * std:

if not self.Portfolio[self.symbols[0]].Quantity > 0 and not self.Portfolio[self.symbols[0]].Quantity < 0:

self.Sell(self.symbols[1], 100)

self.Buy(self.symbols[0], ratio * 100)

elif spread[-1] < mean - self.threshold * std:

if not self.Portfolio[self.symbols[0]].Quantity < 0 and not self.Portfolio[self.symbols[0]].Quantity > 0:

self.Sell(self.symbols[0], 100)

self.Buy(self.symbols[1], ratio * 100)

else:

self.Liquidate()

def regr(self,x,y):

regr = linear_model.LinearRegression()

x_constant = np.column_stack([np.ones(len(x)), x])

regr.fit(x_constant, y)

beta = regr.coef_[0]

alpha = regr.intercept_

spread = y - x*beta - alpha

return spread