| Overall Statistics |
|
Total Trades 455 Average Win 0.44% Average Loss -0.32% Compounding Annual Return 12.586% Drawdown 12.700% Expectancy 0.133 Net Profit 9.276% Sharpe Ratio 0.991 Loss Rate 52% Win Rate 48% Profit-Loss Ratio 1.38 Alpha 0.103 Beta -0.024 Annual Standard Deviation 0.101 Annual Variance 0.01 Information Ratio -0.351 Tracking Error 0.119 Treynor Ratio -4.143 Total Fees $841.75 |
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from clr import AddReference
AddReference("System")
AddReference("QuantConnect.Algorithm")
AddReference("QuantConnect.Common")
from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Securities import *
from datetime import timedelta
import decimal as d
import numpy as np
class FuturesMomentumAlgorithm(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2017, 1, 1)
self.SetEndDate(2017, 9, 30)
self.SetCash(100000)
fastPeriod = 50
slowPeriod = 200
self._tolerance = 0.001
self.SetWarmUp(max(fastPeriod, slowPeriod))
# Adds OIL to be used in the SMA indicators
equity = self.AddEquity("USO", Resolution.Minute)
self._fast = self.SMA(equity.Symbol, fastPeriod, Resolution.Minute)
self._slow = self.SMA(equity.Symbol, slowPeriod, Resolution.Minute)
# Adds the future that will be traded and
# set our expiry filter for this futures chain
future = self.AddFuture(Futures.Energies.CrudeOilWTI)
future.SetFilter(timedelta(0), timedelta(60))
def OnData(self, slice):
for chain in slice.FuturesChains:
# find the front contract expiring no earlier than in 3 days
contracts = filter(lambda x: x.Expiry > self.Time + timedelta(3), chain.Value)
# if there is any contract, trade the front contract
if len(contracts) == 0: continue
contract = sorted(contracts, key = lambda x: x.Expiry, reverse=False)[0]
if self._slow.IsReady and self._fast.IsReady:
self.IsUpTrend = self._fast.Current.Value > self._slow.Current.Value * d.Decimal(1 + self._tolerance)
self.IsDownTrend = self._fast.Current.Value < self._slow.Current.Value * d.Decimal(1 + self._tolerance)
if (not self.Portfolio.Invested) and self.IsUpTrend:
self.MarketOrder(contract.Symbol , 1)
elif (not self.Portfolio.Invested) and self.IsDownTrend:
self.MarketOrder(contract.Symbol , -1)
elif self.Portfolio[contract.Symbol].IsLong and self.IsDownTrend:
self.MarketOrder(contract.Symbol , -1)
elif self.Portfolio[contract.Symbol].IsShort and self.IsUpTrend:
self.MarketOrder(contract.Symbol , 1)
def OnOrderEvent(self, orderEvent):
self.Log(str(orderEvent))