Marco Grassi
e823dfdfb7
Regression Tests / build (push) Has been cancelled
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57 lines
2.2 KiB
Python
57 lines
2.2 KiB
Python
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from AlgorithmImports import *
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### <summary>
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### Test algorithm using 'QCAlgorithm.AddAlphaModel()'
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### </summary>
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class AddAlphaModelAlgorithm(QCAlgorithm):
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def Initialize(self):
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''' Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
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self.SetStartDate(2013,10,7) #Set Start Date
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self.SetEndDate(2013,10,11) #Set End Date
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self.SetCash(100000) #Set Strategy Cash
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self.UniverseSettings.Resolution = Resolution.Daily
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spy = Symbol.Create("SPY", SecurityType.Equity, Market.USA)
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fb = Symbol.Create("FB", SecurityType.Equity, Market.USA)
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ibm = Symbol.Create("IBM", SecurityType.Equity, Market.USA)
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# set algorithm framework models
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self.SetUniverseSelection(ManualUniverseSelectionModel([ spy, fb, ibm ]))
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self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
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self.SetExecution(ImmediateExecutionModel())
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self.AddAlpha(OneTimeAlphaModel(spy))
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self.AddAlpha(OneTimeAlphaModel(fb))
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self.AddAlpha(OneTimeAlphaModel(ibm))
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class OneTimeAlphaModel(AlphaModel):
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def __init__(self, symbol):
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self.symbol = symbol
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self.triggered = False
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def Update(self, algorithm, data):
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insights = []
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if not self.triggered:
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self.triggered = True
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insights.append(Insight.Price(
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self.symbol,
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Resolution.Daily,
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1,
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InsightDirection.Down))
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return insights
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