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))