AlexCatarino
90 lines
4.5 KiB
Python
90 lines
4.5 KiB
Python
# 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")
|
|
AddReference("QuantConnect.Common")
|
|
AddReference("QuantConnect.Algorithm.Framework")
|
|
|
|
from QuantConnect.Data.UniverseSelection import *
|
|
from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel
|
|
from itertools import groupby
|
|
from math import ceil
|
|
|
|
class QC500UniverseSelectionModel(FundamentalUniverseSelectionModel):
|
|
'''Defines the QC500 universe as a universe selection model for framework algorithm
|
|
For details: https://github.com/QuantConnect/Lean/pull/1663'''
|
|
|
|
def __init__(self, filterFineData = True, universeSettings = None, securityInitializer = None):
|
|
'''Initializes a new default instance of the QC500UniverseSelectionModel'''
|
|
super().__init__(filterFineData, universeSettings, securityInitializer)
|
|
self.numberOfSymbolsCoarse = 1000
|
|
self.numberOfSymbolsFine = 500
|
|
self.dollarVolumeBySymbol = {}
|
|
self.lastMonth = -1
|
|
|
|
def SelectCoarse(self, algorithm, coarse):
|
|
'''Performs coarse selection for the QC500 constituents.
|
|
The stocks must have fundamental data
|
|
The stock must have positive previous-day close price
|
|
The stock must have positive volume on the previous trading day'''
|
|
if algorithm.Time.month == self.lastMonth:
|
|
return Universe.Unchanged
|
|
|
|
sortedByDollarVolume = sorted([x for x in coarse if x.HasFundamentalData and x.Volume > 0 and x.Price > 0],
|
|
key = lambda x: x.DollarVolume, reverse=True)[:self.numberOfSymbolsCoarse]
|
|
|
|
self.dollarVolumeBySymbol = {x.Symbol:x.DollarVolume for x in sortedByDollarVolume}
|
|
|
|
# If no security has met the QC500 criteria, the universe is unchanged.
|
|
# A new selection will be attempted on the next trading day as self.lastMonth is not updated
|
|
if len(self.dollarVolumeBySymbol) == 0:
|
|
return Universe.Unchanged
|
|
|
|
# return the symbol objects our sorted collection
|
|
return list(self.dollarVolumeBySymbol.keys())
|
|
|
|
def SelectFine(self, algorithm, fine):
|
|
'''Performs fine selection for the QC500 constituents
|
|
The company's headquarter must in the U.S.
|
|
The stock must be traded on either the NYSE or NASDAQ
|
|
At least half a year since its initial public offering
|
|
The stock's market cap must be greater than 500 million'''
|
|
|
|
sortedBySector = sorted([x for x in fine if x.CompanyReference.CountryId == "USA"
|
|
and x.CompanyReference.PrimaryExchangeID in ["NYS","NAS"]
|
|
and (algorithm.Time - x.SecurityReference.IPODate).days > 180
|
|
and x.MarketCap > 5e8],
|
|
key = lambda x: x.CompanyReference.IndustryTemplateCode)
|
|
|
|
count = len(sortedBySector)
|
|
|
|
# If no security has met the QC500 criteria, the universe is unchanged.
|
|
# A new selection will be attempted on the next trading day as self.lastMonth is not updated
|
|
if count == 0:
|
|
return Universe.Unchanged
|
|
|
|
# Update self.lastMonth after all QC500 criteria checks passed
|
|
self.lastMonth = algorithm.Time.month
|
|
|
|
percent = self.numberOfSymbolsFine / count
|
|
sortedByDollarVolume = []
|
|
|
|
# select stocks with top dollar volume in every single sector
|
|
for code, g in groupby(sortedBySector, lambda x: x.CompanyReference.IndustryTemplateCode):
|
|
y = sorted(g, key = lambda x: self.dollarVolumeBySymbol[x.Symbol], reverse = True)
|
|
c = ceil(len(y) * percent)
|
|
sortedByDollarVolume.extend(y[:c])
|
|
|
|
sortedByDollarVolume = sorted(sortedByDollarVolume, key = lambda x: self.dollarVolumeBySymbol[x.Symbol], reverse=True)
|
|
return [x.Symbol for x in sortedByDollarVolume[:self.numberOfSymbolsFine]] |