Alexandre Catarino
58f0caf647
Some python algorithms suffered corrections to run under the new python framework (pythonnet). Others were deleted because some features will be supported in futures implementations. Adds a method in AlgorithmPythonUtil to transform C# DateTime into Python datetime
96 lines
3.8 KiB
Python
96 lines
3.8 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.Core")
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AddReference("System.Collections")
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AddReference("QuantConnect.Common")
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AddReference("QuantConnect.Algorithm")
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from System import *
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from System.Collections.Generic import List
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from QuantConnect import *
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from QuantConnect.Algorithm import QCAlgorithm
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from QuantConnect.Data.UniverseSelection import *
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class CoarseFineFundamentalComboAlgorithm(QCAlgorithm):
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'''In this algorithm we demonstrate how to define a universe as a combination of use the coarse fundamental data and fine fundamental data'''
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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(2014,01,01) #Set Start Date
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self.SetEndDate(2015,01,01) #Set End Date
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self.SetCash(50000) #Set Strategy Cash
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self.UniverseSettings.Resolution = Resolution.Daily
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# this add universe method accepts two parameters:
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# - coarse selection function: accepts an IEnumerable<CoarseFundamental> and returns an IEnumerable<Symbol>
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# - fine selection function: accepts an IEnumerable<FineFundamental> and returns an IEnumerable<Symbol>
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self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
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self.__numberOfSymbols = 5
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self.__numberOfSymbolsFine = 2
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self._changes = SecurityChanges.None
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# sort the data by daily dollar volume and take the top 'NumberOfSymbols'
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def CoarseSelectionFunction(self, coarse):
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# sort descending by daily dollar volume
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sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
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# return the symbol objects of the top entries from our sorted collection
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top5 = sortedByDollarVolume[:self.__numberOfSymbols]
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# we need to return only the symbol objects
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list = List[Symbol]()
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for x in top5:
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list.Add(x.Symbol)
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return list
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# sort the data by P/E ratio and take the top 'NumberOfSymbolsFine'
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def FineSelectionFunction(self, fine):
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# sort descending by P/E ratio
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sortedByPeRatio = sorted(fine, key=lambda x: x.ValuationRatios.PERatio, reverse=True)
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# take the top entries from our sorted collection
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topFine = sortedByPeRatio[:self.__numberOfSymbolsFine]
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list = List[Symbol]()
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for x in topFine:
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list.Add(x.Symbol)
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return list
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def OnData(self, data):
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# if we have no changes, do nothing
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if self._changes == SecurityChanges.None: return
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# liquidate removed securities
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for security in self._changes.RemovedSecurities:
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if security.Invested:
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self.Liquidate(security.Symbol)
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# we want 20% allocation in each security in our universe
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for security in self._changes.AddedSecurities:
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self.SetHoldings(security.Symbol, 0.2)
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self._changes = SecurityChanges.None;
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# this event fires whenever we have changes to our universe
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def OnSecuritiesChanged(self, changes):
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self._changes = changes |