Michael Handschuh
fdc866fda0
We didn't experience the expected performance improvements. Locally under unit test there was aboout an order of magnitude throughput increase, but when run against the history benchmark, this new approach was 60% slower. We're reverting this for now to perform further analysis and better understand the performance profiling of the python history stack.
87 lines
3.6 KiB
Python
87 lines
3.6 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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from datetime import date
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### <summary>
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### Demonstration of how to define a universe as a combination of use the coarse fundamental data and fine fundamental data
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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="regression test" />
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class CoarseFineFundamentalRegressionAlgorithm(QCAlgorithm):
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def Initialize(self):
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self.SetStartDate(2014,3,24) #Set Start Date
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self.SetEndDate(2014,4,7) #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.changes = None
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self.numberOfSymbolsFine = 2
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# return a list of three fixed symbol objects
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def CoarseSelectionFunction(self, coarse):
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tickers = [ "GOOG", "BAC", "SPY" ]
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if self.Time.date() < date(2014, 4, 1):
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tickers = [ "AAPL", "AIG", "IBM" ]
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return [ Symbol.Create(x, SecurityType.Equity, Market.USA) for x in tickers ]
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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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return [ x.Symbol for x in sortedByPeRatio[:self.numberOfSymbolsFine] ]
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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 is 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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self.Debug("Liquidated Stock: " + str(security.Symbol.Value))
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# we want 50% allocation in each security in our universe
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for security in self.changes.AddedSecurities:
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if (security.Fundamentals.EarningRatios.EquityPerShareGrowth.OneYear > 0.25):
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self.SetHoldings(security.Symbol, 0.5)
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self.Debug("Purchased Stock: " + str(security.Symbol.Value))
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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 |