Overall Statistics |

Total Trades 7 Average Win 1.41% Average Loss -1.18% Compounding Annual Return 5.990% Drawdown 4.000% Expectancy -0.271 Net Profit 6.037% Sharpe Ratio 0.814 Loss Rate 67% Win Rate 33% Profit-Loss Ratio 1.19 Alpha 0.018 Beta 0.347 Annual Standard Deviation 0.06 Annual Variance 0.004 Information Ratio -0.504 Tracking Error 0.081 Treynor Ratio 0.14 Total Fees $0.00 |

# 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.Indicators") AddReference("QuantConnect.Common") from System import * from QuantConnect import * from QuantConnect.Algorithm import * from QuantConnect.Indicators import * from datetime import datetime ### <summary> ### Simple indicator demonstration algorithm of MACD ### </summary> ### <meta name="tag" content="indicators" /> ### <meta name="tag" content="indicator classes" /> ### <meta name="tag" content="plotting indicators" /> class MACDTrendAlgorithm(QCAlgorithm): def Initialize(self): '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.''' self.SetStartDate(2004, 1, 1) #Set Start Date self.SetEndDate(2005, 1, 1) #Set End Date self.SetCash(100000) #Set Strategy Cash # Find more symbols here: http://quantconnect.com/data self.AddCfd("SPX500USD", Resolution.Daily, Market.Oanda) # define our daily macd(12,26) with a 9 day signal self.__macd = self.MACD("SPX500USD", 12, 26, 9, MovingAverageType.Exponential, Resolution.Daily) self.__previous = datetime.min self.PlotIndicator("MACD", True, self.__macd, self.__macd.Signal) self.PlotIndicator("SPX500USD", self.__macd.Fast, self.__macd.Slow) def OnData(self, data): '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.''' # wait for our macd to fully initialize if not self.__macd.IsReady: return # only once per day if self.__previous.date() == self.Time.date(): return # define a small tolerance on our checks to avoid bouncing tolerance = 0.0025 holdings = self.Portfolio["SPX500USD"].Quantity signalDeltaPercent = (self.__macd.Current.Value - self.__macd.Signal.Current.Value)/self.__macd.Fast.Current.Value # if our macd is greater than our signal, then let's go long if holdings <= 0 and signalDeltaPercent > tolerance: # 0.01% # longterm says buy as well self.SetHoldings("SPX500USD", 1.0) # of our macd is less than our signal, then let's go short elif holdings >= 0 and signalDeltaPercent < -tolerance: self.Liquidate("SPX500USD") self.__previous = self.Time