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))