HalldorAndersen
76 lines
3.2 KiB
Python
76 lines
3.2 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 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.Portfolio import EqualWeightingPortfolioConstructionModel
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from QuantConnect.Algorithm.Framework.Execution import ImmediateExecutionModel
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from Selection.UncorrelatedSelectionModel import UncorrelatedSelectionModel
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from datetime import timedelta
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class UncorrelatedFrameworkAlgorithm(QCAlgorithmFramework):
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def Initialize(self):
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self.UniverseSettings.Resolution = Resolution.Daily
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self.SetStartDate(2018,1,1) # Set Start Date
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self.SetCash(1000000) # Set Strategy Cash
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benchmark = Symbol.Create("SPY", SecurityType.Equity, Market.USA)
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self.SetUniverseSelection(UncorrelatedUniverseSelectionModel(benchmark))
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self.SetAlpha(UncorrelatedUniverseSelectionAlphaModel())
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self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
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self.SetExecution(ImmediateExecutionModel())
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class UncorrelatedUniverseSelectionAlphaModel(AlphaModel):
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'''Uses ranking of intraday percentage difference between open price and close price to create magnitude and direction prediction for insights'''
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def __init__(self, numberOfStocks = 10, predictionInterval = timedelta(1)):
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self.predictionInterval = predictionInterval
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self.numberOfStocks = numberOfStocks
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def Update(self, algorithm, data):
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symbolsRet = dict()
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for kvp in algorithm.ActiveSecurities:
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security = kvp.Value
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if security.HasData:
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open = security.Open
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if open != 0:
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symbolsRet[security.Symbol] = security.Close / open - 1
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# Rank on the absolute value of price change
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symbolsRet = dict(sorted(symbolsRet.items(), key=lambda kvp: abs(kvp[1]),reverse=True)[:self.numberOfStocks])
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insights = []
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for symbol, price_change in symbolsRet.items():
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# Emit "up" insight if the price change is positive and "down" otherwise
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direction = InsightDirection.Up if price_change > 0 else InsightDirection.Down
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insights.append(Insight.Price(symbol, self.predictionInterval, direction, abs(price_change), None))
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return insights
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