AlexCatarino
a18bd953ac
- Adding new `ConfidenceWeightedPortfolioConstructionModel` (C# / Py) that will generate percent `Targets` based on the latest active `Insight` `Confidence` per `Symbol`. - Will ignore `Insights` that have no `Confidence`.(unit tested) - If the sum of all the last active `Insight` per `Symbol` is bigger than 1, it will factor down each target percent holdings proportionally so the sum is 1. (unit tested) - Adding unit tests - Adding a new regression test framework algorithm (C#/Py) -**Note**: `ConfidenceWeightedPortfolioConstructionModel` inherits from the `InsightWeightingPortfolioConstructionModel`. Protect method `GetValue` was implemented in `IWPCM` to enable the choice of `Insight` member.
60 lines
2.8 KiB
Python
60 lines
2.8 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 clr import AddReference
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AddReference("System")
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AddReference("QuantConnect.Algorithm")
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AddReference("QuantConnect.Algorithm.Framework")
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AddReference("QuantConnect.Common")
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from System import *
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from QuantConnect import *
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from QuantConnect.Orders import *
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from QuantConnect.Algorithm import *
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from QuantConnect.Algorithm.Framework import *
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from QuantConnect.Algorithm.Framework.Alphas import *
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from QuantConnect.Algorithm.Framework.Execution import *
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from QuantConnect.Algorithm.Framework.Portfolio import *
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from QuantConnect.Algorithm.Framework.Risk import *
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from QuantConnect.Algorithm.Framework.Selection import *
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from datetime import timedelta
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### <summary>
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### Test algorithm using 'ConfidenceWeightedPortfolioConstructionModel' and 'ConstantAlphaModel'
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### generating a constant 'Insight' with a 0.25 confidence
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### </summary>
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class ConfidenceWeightedFrameworkAlgorithm(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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# Set requested data resolution
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self.UniverseSettings.Resolution = Resolution.Minute
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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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symbols = [ Symbol.Create("SPY", SecurityType.Equity, Market.USA) ]
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# set algorithm framework models
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self.SetUniverseSelection(ManualUniverseSelectionModel(symbols))
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self.SetAlpha(ConstantAlphaModel(InsightType.Price, InsightDirection.Up, timedelta(minutes = 20), 0.025, 0.25))
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self.SetPortfolioConstruction(ConfidenceWeightedPortfolioConstructionModel())
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self.SetExecution(ImmediateExecutionModel())
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def OnEndOfAlgorithm(self):
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# holdings value should be 0.25 - to avoid price fluctuation issue we compare with 0.28 and 0.23
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if (self.Portfolio.TotalHoldingsValue > self.Portfolio.TotalPortfolioValue * 0.28
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or self.Portfolio.TotalHoldingsValue < self.Portfolio.TotalPortfolioValue * 0.23):
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raise ValueError("Unexpected Total Holdings Value: " + str(self.Portfolio.TotalHoldingsValue))
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