Overall Statistics Total Trades 7221 Average Win 1.12% Average Loss -0.87% Compounding Annual Return -0.956% Drawdown 49.000% Expectancy 0.066 Net Profit -18.675% Sharpe Ratio -0.007 Probabilistic Sharpe Ratio 0.000% Loss Rate 53% Win Rate 47% Profit-Loss Ratio 1.28 Alpha -0 Beta -0.011 Annual Standard Deviation 0.118 Annual Variance 0.014 Information Ratio -0.351 Tracking Error 0.214 Treynor Ratio 0.078 Total Fees $8008.12 Estimated Strategy Capacity$0 Lowest Capacity Asset ICE_WT1.QuantpediaFutures 2S
# https://quantpedia.com/strategies/trading-wti-brent-spread/
#
# A 20-day moving average of WTI/Brent spread is calculated each day. If the current spread value is above SMA 20 then we enter a short position
# in the spread on close (betting that the spread will decrease to the fair value represented by SMA 20). The trade is closed at the close of the
# trading day when the spread crosses below fair value. If the current spread value is below SMA 20 then we enter a long position betting that
# the spread will increase and the trade is closed at the close of the trading day when the spread crosses above fair value.

class WTIBRENTSpread(QCAlgorithm):

def Initialize(self):
self.SetStartDate(2000, 1, 1)
self.SetCash(100000)

self.symbols = [
"ICE_WT1",  # WTI Crude Futures, Continuous Contract
"ICE_B1"    # Brent Crude Oil Futures, Continuous Contract
]

self.spread = RollingWindow[float](20)

for symbol in self.symbols:
data = self.AddData(QuantpediaFutures, symbol, Resolution.Daily)
data.SetLeverage(5)
data.SetFeeModel(CustomFeeModel(self))

def OnData(self, data):
symbol1 = self.Symbol(self.symbols[0])
symbol2 = self.Symbol(self.symbols[1])

if symbol1 in data.Keys and symbol2 in data.Keys and data[symbol1] and data[symbol2]:
price1 = data[symbol1].Price
price2 = data[symbol2].Price

if price1 != 0 and price2 != 0:
spread = price1 - price2
self.spread.Add(spread)

# MA calculation.
if self.spread.IsReady:
spreads = [x for x in self.spread]
spread_ma20 = sum(spreads) / len(spreads)

current_spread = spreads[0]

if current_spread > spread_ma20:
self.SetHoldings(symbol1, -1)
self.SetHoldings(symbol2, 1)
elif current_spread < spread_ma20:
self.SetHoldings(symbol1, 1)
self.SetHoldings(symbol2, -1)

# Quantpedia data.
# NOTE: IMPORTANT: Data order must be ascending (datewise)
class QuantpediaFutures(PythonData):
def GetSource(self, config, date, isLiveMode):
return SubscriptionDataSource("data.quantpedia.com/backtesting_data/futures/{0}.csv".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv)

def Reader(self, config, line, date, isLiveMode):
data = QuantpediaFutures()
data.Symbol = config.Symbol

if not line[0].isdigit(): return None
split = line.split(';')

data.Time = datetime.strptime(split[0], "%d.%m.%Y") + timedelta(days=1)
data['back_adjusted'] = float(split[1])
data['spliced'] = float(split[2])
data.Value = float(split[1])

return data

# Custom fee model.
class CustomFeeModel(FeeModel):
def GetOrderFee(self, parameters):
fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
return OrderFee(CashAmount(fee, "USD"))