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.
83 lines
3.7 KiB
Python
83 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.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 QuantConnect.Orders import OrderStatus
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from QuantConnect.Orders.Fees import ConstantFeeModel
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### <summary>
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### In this algorithm we demonstrate how to use the coarse fundamental data to define a universe as the top dollar volume and set the algorithm to use raw prices
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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 RawPricesCoarseUniverseAlgorithm(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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# what resolution should the data *added* to the universe be?
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self.UniverseSettings.Resolution = Resolution.Daily
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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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# Set the security initializer with the characteristics defined in CustomSecurityInitializer
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self.SetSecurityInitializer(self.CustomSecurityInitializer)
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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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def CustomSecurityInitializer(self, security):
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'''Initialize the security with raw prices and zero fees
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Args:
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security: Security which characteristics we want to change'''
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security.SetDataNormalizationMode(DataNormalizationMode.Raw)
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security.SetFeeModel(ConstantFeeModel(0))
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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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# this event fires whenever we have changes to our universe
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def OnSecuritiesChanged(self, changes):
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# liquidate removed securities
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for security in 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 changes.AddedSecurities:
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self.SetHoldings(security.Symbol, 0.2)
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def OnOrderEvent(self, orderEvent):
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if orderEvent.Status == OrderStatus.Filled:
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self.Log(f"OnOrderEvent({self.UtcTime}):: {orderEvent}") |