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CoarseFundamentalTop3Algorithm.py
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85 lines (64 loc) · 3.4 KB
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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 *
### <summary>
### Demonstration of using coarse and fine universe selection together to filter down a smaller universe of stocks.
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="universes" />
### <meta name="tag" content="coarse universes" />
### <meta name="tag" content="fine universes" />
class CoarseFundamentalTop3Algorithm(QCAlgorithm):
def Initialize(self):
'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
self.SetStartDate(2014,3,24) #Set Start Date
self.SetEndDate(2014,4,7) #Set End Date
self.SetCash(50000) #Set Strategy Cash
# what resolution should the data *added* to the universe be?
self.UniverseSettings.Resolution = Resolution.Daily
# this add universe method accepts a single parameter that is a function that
# accepts an IEnumerable<CoarseFundamental> and returns IEnumerable<Symbol>
self.AddUniverse(self.CoarseSelectionFunction)
self.__numberOfSymbols = 3
self._changes = None
# sort the data by daily dollar volume and take the top 'NumberOfSymbols'
def CoarseSelectionFunction(self, coarse):
# sort descending by daily dollar volume
sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
# return the symbol objects of the top entries from our sorted collection
return [ x.Symbol for x in sortedByDollarVolume[:self.__numberOfSymbols] ]
def OnData(self, data):
self.Log(f"OnData({self.UtcTime}): Keys: {', '.join([key.Value for key in data.Keys])}")
# 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)
# we want 1/N allocation in each security in our universe
for security in self._changes.AddedSecurities:
self.SetHoldings(security.Symbol, 1 / self.__numberOfSymbols)
self._changes = None
# this event fires whenever we have changes to our universe
def OnSecuritiesChanged(self, changes):
self._changes = changes
self.Log(f"OnSecuritiesChanged({self.UtcTime}):: {changes}")
def OnOrderEvent(self, fill):
self.Log(f"OnOrderEvent({self.UtcTime}):: {fill}")