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# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from clr import AddReference
AddReference("System.Core")
AddReference("QuantConnect.Common")
AddReference("QuantConnect.Algorithm")
from System import *
from QuantConnect import *
from QuantConnect.Algorithm import QCAlgorithm
from QuantConnect.Data.UniverseSelection import *
from datetime import date
### <summary>
### Demonstration of how to define a universe as a combination of use the coarse fundamental data and fine fundamental data
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="universes" />
### <meta name="tag" content="coarse universes" />
### <meta name="tag" content="regression test" />
class CoarseFineFundamentalRegressionAlgorithm(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2014,3,24) #Set Start Date
self.SetEndDate(2014,4,7) #Set End Date
self.SetCash(50000) #Set Strategy Cash
self.UniverseSettings.Resolution = Resolution.Daily
# this add universe method accepts two parameters:
# - coarse selection function: accepts an IEnumerable<CoarseFundamental> and returns an IEnumerable<Symbol>
# - fine selection function: accepts an IEnumerable<FineFundamental> and returns an IEnumerable<Symbol>
self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
self.changes = None
self.numberOfSymbolsFine = 2
# return a list of three fixed symbol objects
def CoarseSelectionFunction(self, coarse):
tickers = [ "GOOG", "BAC", "SPY" ]
if self.Time.date() < date(2014, 4, 1):
tickers = [ "AAPL", "AIG", "IBM" ]
return [ Symbol.Create(x, SecurityType.Equity, Market.USA) for x in tickers ]
# sort the data by P/E ratio and take the top 'NumberOfSymbolsFine'
def FineSelectionFunction(self, fine):
# sort descending by P/E ratio
sortedByPeRatio = sorted(fine, key=lambda x: x.ValuationRatios.PERatio, reverse=True)
# take the top entries from our sorted collection
return [ x.Symbol for x in sortedByPeRatio[:self.numberOfSymbolsFine] ]
def OnData(self, data):
# if we have no changes, do nothing
if self.changes is None: return
# liquidate removed securities
for security in self.changes.RemovedSecurities:
if security.Invested:
self.Liquidate(security.Symbol)
self.Debug("Liquidated Stock: " + str(security.Symbol.Value))
# we want 50% allocation in each security in our universe
for security in self.changes.AddedSecurities:
if (security.Fundamentals.EarningRatios.EquityPerShareGrowth.OneYear > 0.25):
self.SetHoldings(security.Symbol, 0.5)
self.Debug("Purchased Stock: " + str(security.Symbol.Value))
self.changes = None
# this event fires whenever we have changes to our universe
def OnSecuritiesChanged(self, changes):
self.changes = changes
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