### https://github.com/QuantConnect/Lean/blob/master/Algorithm.Framework/Selection/QC500UniverseSelectionModel.py

# 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 QC2000UniverseSelectionModel(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, numberCoarse=3000, numberFine=2000, by_week_or_month='month'):
        '''Initializes a new default instance of the QC500UniverseSelectionModel'''
        super().__init__(filterFineData, universeSettings, securityInitializer)
        self.numberOfSymbolsCoarse = numberCoarse
        self.numberOfSymbolsFine = numberFine
        self.dollarVolumeBySymbol = {}
        self.lastMonth = -1
        self.lastWeek=-1
        self.by_week_or_month=by_week_or_month
        

    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 self.by_week_or_month=='week':
            if algorithm.Time.isocalendar()[1] == self.lastWeek:
                return Universe.Unchanged
        elif self.by_week_or_month=='month':
            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 100 million'''
        
        algorithm.Debug('doing fine universe selection')
        if self.by_week_or_month=='week':
            self.lastWekk=algorithm.Time.isocalender()[1]
        elif self.by_week_or_month=='month':
            self.lastMonth = algorithm.Time.month

        sortedByDollarVolume = 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 > 120
                                        and x.MarketCap > 1e8],
                               key = lambda x: self.dollarVolumeBySymbol[x.Symbol], reverse=True)
         
        final=[x.Symbol for x in sortedByDollarVolume[:self.numberOfSymbolsFine]]                      
        algorithm.Debug('length of universe is {}'.format(len(final)))
        algorithm.Debug('top one is {}'.format(final[0]))

        return final

# Your New Python File

import statistics
from My_Universe_Selection import QC2000UniverseSelectionModel

class FocusedRedOrangeDragonfly(QCAlgorithm):

   def Initialize(self):
       self.SetStartDate(2021, 4, 1)  # Set Start Date
       #self.SetEndDate(2021,4,1)
       self.SetCash(100000)  # Set Strategy Cash
       self.SetExecution(ImmediateExecutionModel())
       self.UniverseSettings.Resolution=Resolution.Daily
       self.count = 0
       self.AddUniverseSelection(
          QC2000UniverseSelectionModel())
 

    #missing symbols in OnData for monday
       
       

   def OnData(self, data):
       
       self.Log(f"{self.Time}; Dividends: {data.Dividends.Count}; Splits: {data.Splits.Count}; Bars; {data.Bars.Count}")