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