HalldorAndersen
89 lines
3.7 KiB
Python
89 lines
3.7 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 Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel
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from Selection.UncorrelatedToSPYUniverseSelectionModel import UncorrelatedToSPYUniverseSelectionModel
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from datetime import datetime, timedelta
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class UncorrelatedToSPYFrameworkAlgorithm(QCAlgorithmFramework):
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def Initialize(self):
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self.UniverseSettings.Resolution = Resolution.Daily
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self.SetStartDate(2019,2,2) # Set Start Date
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self.SetEndDate(2019,3,15) # Set End Date
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self.SetCash(100000) # Set Strategy Cash
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self.SetUniverseSelection(UncorrelatedToSPYUniverseSelectionModel())
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self.SetAlpha(UncorrelatedToSPYAlphaModel())
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self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
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self.SetExecution(ImmediateExecutionModel())
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class UncorrelatedToSPYAlphaModel(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, *args, **kwargs):
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self.lookback = kwargs['lookback'] if 'lookback' in kwargs else 1
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self.numberOfStocks = kwargs['numberOfStocks'] if 'numberOfStocks' in kwargs else 10
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self.resolution = kwargs['resolution'] if 'resolution' in kwargs else Resolution.Daily
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self.predictionInterval = Time.Multiply(Extensions.ToTimeSpan(self.resolution), self.lookback)
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self.symbolDataBySymbol = {}
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def Update(self, algorithm, data):
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insights = []
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ret = []
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symbols = []
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activeSec = [x.Key for x in algorithm.ActiveSecurities]
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for symbol in activeSec:
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if algorithm.ActiveSecurities[symbol].HasData:
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open = algorithm.Securities[symbol].Open
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close = algorithm.Securities[symbol].Close
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if open != 0:
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openCloseReturn = close/open - 1
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ret.append(openCloseReturn)
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symbols.append(symbol)
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# Intraday price change
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symbolsRet = dict(zip(symbols,ret))
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# Rank on price change
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symbolsRanked = dict(sorted(symbolsRet.items(), key=lambda kv: kv[1],reverse=False)[:self.numberOfStocks])
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# Emit "up" insight if the price change is positive and "down" insight if the price change is negative
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for key,value in symbolsRanked.items():
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if value > 0:
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insights.append(Insight.Price(key, self.predictionInterval, InsightDirection.Up, value, None))
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else:
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insights.append(Insight.Price(key, self.predictionInterval, InsightDirection.Down, value, None))
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return insights
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