Hello guys!

Sorry if it is a dumb question, but I couldn't run the attached algo.

When I try do backtest, it returns nothing... but if I change the assets to equity, for exemple, the algo works fine.

Anyone could help me to find what I doing wrong, please?

 

Thanks in advance!

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 OilSpreadTrading(QCAlgorithm):

def Initialize(self):
self.SetTimeZone('America/Sao_Paulo')
self.SetCash(50000) # Set Strategy Cash

self.SetStartDate(2016, 1, 1) # Set Start Date
self.SetEndDate(2019, 9, 30) # Set Start Date

self.training_period = 250
self.threshold = 1.

# add used assets
tickers = ['BCO_USD', 'WTICO_USD']
self.assets = []

for i in tickers:
self.assets.append(self.AddSecurity(SecurityType.Cfd, i, Resolution.Daily).Symbol)

# data used to train de algo to generate signals
for i in self.assets:
i.hist_window = RollingWindow[QuoteBar](self.training_period)


def OnData(self, data):
if not (data.ContainsKey('BCO_USD') and data.ContainsKey('WTICO_USD')):
return

# add the new data to de history
for sym in self.assets:
sym.hist_window.Add(data[sym])

price_x = pd.Series([float(i.Close) for i in self.assets[0].hist_window], index = [i.Time for i in self.assets[0].hist_window])
#ret_x = np.log(price_x / price_x.shift(1))

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

if len(price_x) < 250:
return

spread_price = self.linreg(np.log(price_x), np.log(price_y))
mean_price = np.mean(spread_price)
std_price = np.std(mean_price)
ratio = floor(self.Portfolio[self.assets[1]].Price / self.Portfolio[self.assets[0]].Price)

if spread_price[-1] > mean_price + self.threshold * std_price:
#if not self.Portfolio[self.assets[0]].Invested and not self.Portfolio[self.assets[1]].Invested:
if not self.Portfolio[self.assets[0]].Quantity > 0 and not self.Portfolio[self.assets[0]].Quantity < 0:
#qtd = int(self.CalculateOrderQuantity(self.assets[0], 0.2))
qtd = 10
self.Sell(self.assets[1], qtd)
self.Buy(self.assets[0], floor(ratio * qtd))

elif spread_price[-1] < mean_price - self.threshold * std_price:
#qtd = int(self.CalculateOrderQuantity(self.assets[0], 0.2))
qtd = 10
#if not self.Portfolio[self.assets[0]].Invested and not self.Portfolio[self.assets[1]].Invested:
if not self.Portfolio[self.assets[0]].Quantity < 0 and not self.Portfolio[self.assets[0]].Quantity > 0:
self.Sell(self.assets[0], qtd)
self.Buy(self.assets[1], floor(ratio * qtd))

else:
self.Liquidate(self.assets[0])
self.Liquidate(self.assets[1])


def linreg(self, x, y):

linreg = linear_model.LinearRegression()
X1 = np.column_stack([np.ones(len(x)), x])
linreg.fit(X1, y)
beta = linreg.coef_[0]
alfa = linreg.intercept_
spread = y - x*beta - alfa

return spread