Overall Statistics |
Total Trades 5 Average Win 4.96% Average Loss -1.12% Compounding Annual Return 10.629% Drawdown 6.400% Expectancy 1.724 Net Profit 9.139% Sharpe Ratio 1.096 Loss Rate 50% Win Rate 50% Profit-Loss Ratio 4.45 Alpha 0.081 Beta -0.018 Annual Standard Deviation 0.072 Annual Variance 0.005 Information Ratio -0.167 Tracking Error 0.148 Treynor Ratio -4.458 Total Fees $9.25 |
# 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 ### <summary> ### EMA cross with SP500 E-mini futures ### In this example, we demostrate how to trade futures contracts using ### a equity to generate the trading signals ### It also shows how you can prefilter contracts easily based on expirations. ### It also shows how you can inspect the futures chain to pick a specific contract to trade. ### </summary> ### <meta name="tag" content="using data" /> ### <meta name="tag" content="futures" /> ### <meta name="tag" content="indicators" /> ### <meta name="tag" content="strategy example" /> class FuturesMomentumAlgorithm(QCAlgorithm): def Initialize(self): self.SetStartDate(2016, 1, 1) self.SetEndDate(2016, 8, 18) self.SetCash(100000) fastPeriod = 20 slowPeriod = 60 self._tolerance = 0.001 self.SetWarmUp(max(fastPeriod, slowPeriod)) # Adds SPY to be used in our EMA indicators equity = self.AddEquity("SPY", Resolution.Daily) self._fast = self.EMA(equity.Symbol, fastPeriod, Resolution.Daily) self._slow = self.EMA(equity.Symbol, slowPeriod, Resolution.Daily) # Adds the future that will be traded and # set our expiry filter for this futures chain future = self.AddFuture(Futures.Indices.SP500EMini) future.SetFilter(timedelta(0), timedelta(182)) def OnData(self, slice): 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: for chain in slice.FuturesChains: # find the front contract expiring no earlier than in 90 days contracts = filter(lambda x: x.Expiry > self.Time + timedelta(90), 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=True)[0] self.MarketOrder(contract.Symbol , 1) if self.Portfolio.Invested and self.IsDownTrend: self.Liquidate() def OnEndOfDay(self): if self.IsUpTrend: self.Plot("Indicator Signal", "EOD",1) elif self.IsDownTrend: self.Plot("Indicator Signal", "EOD",-1) else: self.Plot("Indicator Signal", "EOD",0) def OnOrderEvent(self, orderEvent): self.Log(str(orderEvent))