Michael Handschuh
18a559943e
- Adds log to display the python version the algorithm is using. - Fixes python algorithms that were failing because of small subtleties like leading zeroes. - Updates pythonnet with a version compiled with python 3.6 flags - Changes in DockerfileFoundation: we now use miniconda to manage the python environment. - Took the opportunity to add NTLK (#1349), Tensorforce (#1369) and PyTorch/Pyro (#1385). - Changes readme in Algorithm.Python to show steps to install miniconda
78 lines
3.1 KiB
Python
78 lines
3.1 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("QuantConnect.Common")
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AddReference("QuantConnect.Algorithm")
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from System import *
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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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### <summary>
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### Demonstration of using coarse and fine universe selection together to filter down a smaller universe of stocks.
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### </summary>
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### <meta name="tag" content="using data" />
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### <meta name="tag" content="universes" />
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### <meta name="tag" content="coarse universes" />
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### <meta name="tag" content="fine universes" />
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class CoarseFundamentalTop5Algorithm(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(2014,1,1) #Set Start Date
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self.SetEndDate(2015,1,1) #Set End Date
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self.SetCash(50000) #Set Strategy Cash
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# what resolution should the data *added* to the universe be?
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self.UniverseSettings.Resolution = Resolution.Daily
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# this add universe method accepts a single parameter that is a function that
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# accepts an IEnumerable<CoarseFundamental> and returns IEnumerable<Symbol>
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self.AddUniverse(self.CoarseSelectionFunction)
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self.__numberOfSymbols = 5
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self._changes = 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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return [ x.Symbol for x in sortedByDollarVolume[:self.__numberOfSymbols] ]
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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 == 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 = 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 |