Overall Statistics |
Total Trades 1 Average Win 0% Average Loss 0% Compounding Annual Return 77.754% Drawdown 1.200% Expectancy 0 Net Profit 5.172% Sharpe Ratio 4.916 Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0.554 Beta -2.27 Annual Standard Deviation 0.105 Annual Variance 0.011 Information Ratio 4.747 Tracking Error 0.105 Treynor Ratio -0.226 Total Fees $3.24 |
# 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.Indicators import * from QuantConnect.Data.Market import TradeBar ### <summary> ### Using rolling windows for efficient storage of historical data; which automatically clears after a period of time. ### </summary> ### <meta name="tag" content="using data" /> ### <meta name="tag" content="history and warm up" /> ### <meta name="tag" content="history" /> ### <meta name="tag" content="warm up" /> ### <meta name="tag" content="indicators" /> ### <meta name="tag" content="rolling windows" /> class RollingWindowAlgorithm(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(2013,10,1) #Set Start Date self.SetEndDate(2013,11,1) #Set End Date self.SetCash(100000) #Set Strategy Cash # Find more symbols here: http://quantconnect.com/data self.AddEquity("SPY", Resolution.Daily) # Creates a Rolling Window indicator to keep the 2 TradeBar self.window = RollingWindow[TradeBar](2) # For other security types, use QuoteBar # Creates an indicator and adds to a rolling window when it is updated self.SMA("SPY", 5).Updated += self.SmaUpdated self.smaWin = RollingWindow[IndicatorDataPoint](5) def SmaUpdated(self, sender, updated): '''Adds updated values to rolling window''' self.smaWin.Add(updated) def OnData(self, data): '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.''' # Add SPY TradeBar in rollling window self.window.Add(data["SPY"]) # Wait for windows to be ready. if not (self.window.IsReady and self.smaWin.IsReady): return currBar = self.window[0] # Current bar had index zero. pastBar = self.window[1] # Past bar has index one. self.Log("Price: {0} -> {1} ... {2} -> {3}".format(pastBar.Time, pastBar.Close, currBar.Time, currBar.Close)) currSma = self.smaWin[0] # Current SMA had index zero. pastSma = self.smaWin[self.smaWin.Count-1] # Oldest SMA has index of window count minus 1. #good idea self.Log("Ethan Hammes " +str(self.smaWin[0].Value / self.smaWin[self.smaWin.Count-1].Value)) #bad idea #self.Log("Ethan Hammes " +str(self.smaWin[0] / self.smaWin[self.smaWin.Count-1])) self.Log("SMA: {0} -> {1} ... {2} -> {3}".format(pastSma.Time, pastSma.Value, currSma.Time, currSma.Value)) if not self.Portfolio.Invested and currSma.Value > pastSma.Value: self.SetHoldings("SPY", 1)