# 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 * ### ### Shows how to set a custom benchmark for you algorithms ### ### ### class CustomBenchmarkAlgorithm(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(2013,10,7) #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 self.AddEquity("SPY", Resolution.Second) # Disabling the benchmark / setting to a fixed value # self.SetBenchmark(lambda x: 0) # Set the benchmark to AAPL US Equity self.SetBenchmark(Symbol.Create("AAPL", SecurityType.Equity, Market.USA)) def OnData(self, data): '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.''' if not self.Portfolio.Invested: self.SetHoldings("SPY", 1) self.Debug("Purchased Stock") tupleResult = SymbolCache.TryGetSymbol("AAPL", None) if tupleResult[0]: raise Exception("Benchmark Symbol is not expected to be added to the Symbol cache")