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CustomBenchmarkAlgorithm.py
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CustomBenchmarkAlgorithm.py
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# 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 *
### <summary>
### Shows how to set a custom benchmark for you algorithms
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="benchmarks" />
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)
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")