Overall Statistics |
Total Trades 1 Average Win 0% Average Loss 0% Compounding Annual Return 264.548% Drawdown 2.200% Expectancy 0 Net Profit 1.668% Sharpe Ratio 4.41 Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0.007 Beta 76.342 Annual Standard Deviation 0.193 Annual Variance 0.037 Information Ratio 4.355 Tracking Error 0.193 Treynor Ratio 0.011 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.Orders import * from QuantConnect.Algorithm import * from QuantConnect.Algorithm.Framework import * from QuantConnect.Algorithm.Framework.Execution import * from QuantConnect.Algorithm.Framework.Portfolio import * from QuantConnect.Algorithm.Framework.Risk import * from QuantConnect.Algorithm.Framework.Selection import * from QuantConnect.Algorithm.Framework.Alphas import * import numpy as np ### <summary> ### Basic template framework algorithm uses framework components to define the algorithm. ### </summary> ### <meta name="tag" content="using data" /> ### <meta name="tag" content="using quantconnect" /> ### <meta name="tag" content="trading and orders" /> class BasicTemplateFrameworkAlgorithm(QCAlgorithmFramework): '''Basic template framework algorithm uses framework components to define the algorithm.''' 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.''' # Set requested data resolution self.UniverseSettings.Resolution = Resolution.Minute self.SetStartDate(2013,10,07) #Set Start Date self.SetEndDate(2013,10,11) #Set End Date self.SetCash(100000) #Set Strategy Cash # Find more symbols here: http://quantconnect.com/data # Forex, CFD, Equities Resolutions: Tick, Second, Minute, Hour, Daily. # Futures Resolution: Tick, Second, Minute # Options Resolution: Minute Only. symbols = [ Symbol.Create("SPY", SecurityType.Equity, Market.USA) ] # set algorithm framework models self.PortfolioSelection = ManualPortfolioSelectionModel(symbols) self.Alpha = ConstantAlphaModel(AlphaType.Price, AlphaDirection.Up, TimeSpan.FromMinutes(20), 0.025, None) self.PortfolioConstruction = SimplePortfolioConstructionModel() # these are the default values for Execution and RiskManagement models #self.Execution = ImmediateExecutionModel() #self.RiskManagement = NullRiskManagementModel() self.Debug("numpy test >>> print numpy.pi: " + str(np.pi)) def OnOrderEvent(self, orderEvent): if orderEvent.Status == OrderStatus.Filled: self.Debug("Purchased Stock: {0}".format(orderEvent.Symbol))