wehub-resource-sync
86 lines
4.4 KiB
Python
86 lines
4.4 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 AlgorithmImports import *
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from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel
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from itertools import groupby
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from math import ceil
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class QC500UniverseSelectionModel(FundamentalUniverseSelectionModel):
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'''Defines the QC500 universe as a universe selection model for framework algorithm
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For details: https://github.com/QuantConnect/Lean/pull/1663'''
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def __init__(self, filterFineData = True, universeSettings = None):
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'''Initializes a new default instance of the QC500UniverseSelectionModel'''
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super().__init__(filterFineData, universeSettings)
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self.number_of_symbols_coarse = 1000
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self.number_of_symbols_fine = 500
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self.dollar_volume_by_symbol = {}
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self.last_month = -1
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def select_coarse(self, algorithm: QCAlgorithm, fundamental: list[Fundamental]):
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'''Performs coarse selection for the QC500 constituents.
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The stocks must have fundamental data
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The stock must have positive previous-day close price
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The stock must have positive volume on the previous trading day'''
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if algorithm.time.month == self.last_month:
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return Universe.UNCHANGED
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sorted_by_dollar_volume = sorted([x for x in fundamental if x.has_fundamental_data and x.volume > 0 and x.price > 0],
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key = lambda x: x.dollar_volume, reverse=True)[:self.number_of_symbols_coarse]
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self.dollar_volume_by_symbol = {x.Symbol:x.dollar_volume for x in sorted_by_dollar_volume}
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# If no security has met the QC500 criteria, the universe is unchanged.
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# A new selection will be attempted on the next trading day as self.lastMonth is not updated
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if len(self.dollar_volume_by_symbol) == 0:
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return Universe.UNCHANGED
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# return the symbol objects our sorted collection
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return list(self.dollar_volume_by_symbol.keys())
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def select_fine(self, algorithm: QCAlgorithm, fundamental: list[Fundamental]):
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'''Performs fine selection for the QC500 constituents
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The company's headquarter must in the U.S.
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The stock must be traded on either the NYSE or NASDAQ
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At least half a year since its initial public offering
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The stock's market cap must be greater than 500 million'''
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sorted_by_sector = sorted([x for x in fundamental if x.company_reference.country_id == "USA"
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and x.company_reference.primary_exchange_id in ["NYS","NAS"]
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and (algorithm.time - x.security_reference.ipo_date).days > 180
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and x.market_cap > 5e8],
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key = lambda x: x.company_reference.industry_template_code)
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count = len(sorted_by_sector)
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# If no security has met the QC500 criteria, the universe is unchanged.
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# A new selection will be attempted on the next trading day as self.lastMonth is not updated
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if count == 0:
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return Universe.UNCHANGED
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# Update self.lastMonth after all QC500 criteria checks passed
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self.last_month = algorithm.time.month
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percent = self.number_of_symbols_fine / count
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sorted_by_dollar_volume = []
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# select stocks with top dollar volume in every single sector
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for code, g in groupby(sorted_by_sector, lambda x: x.company_reference.industry_template_code):
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y = sorted(g, key = lambda x: self.dollar_volume_by_symbol[x.Symbol], reverse = True)
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c = ceil(len(y) * percent)
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sorted_by_dollar_volume.extend(y[:c])
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sorted_by_dollar_volume = sorted(sorted_by_dollar_volume, key = lambda x: self.dollar_volume_by_symbol[x.Symbol], reverse=True)
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return [x.Symbol for x in sorted_by_dollar_volume[:self.number_of_symbols_fine]]
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