Overall Statistics
Total Trades
36
Average Win
0.15%
Average Loss
-0.09%
Compounding Annual Return
38.220%
Drawdown
1.400%
Expectancy
0.182
Net Profit
1.070%
Sharpe Ratio
3.348
Probabilistic Sharpe Ratio
64.383%
Loss Rate
57%
Win Rate
43%
Profit-Loss Ratio
1.76
Alpha
-0.027
Beta
0.465
Annual Standard Deviation
0.084
Annual Variance
0.007
Information Ratio
-4.279
Tracking Error
0.089
Treynor Ratio
0.602
Total Fees
$39.50
from universe_selection_model import MyUniverseModel
class TestAlgo(QCAlgorithm):

    def Initialize(self):

        self.SetStartDate(2018, 5, 28)
        self.SetEndDate(2018, 6, 9)
        self.SetCash(100000)
        # Universe selection settings
        self.UniverseSettings.Resolution = Resolution.Minute
        self.SetUniverseSelection(MyUniverseModel())
        self.day = 0
        
    def OnSecuritiesChanged(self, changes):
        self.changes = changes
        for security in changes.RemovedSecurities:
            if security.Invested:
                self.Liquidate(security.Symbol, 'Removed from Universe')
        
    def OnData(self, data):
        if data.Time.day == self.day:
            return
        self.day = data.Time.day
        
        if self.changes is not None:
            for security in self.changes.AddedSecurities:
                if self.CurrentSlice.ContainsKey(security.Symbol):
                    self.SetHoldings(security.Symbol, 0.1)
        self.changes = None
from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel

class MyUniverseModel(FundamentalUniverseSelectionModel):

    def __init__(self):
        super().__init__(False)

    def SelectCoarse(self, algorithm, coarse):
        
        sortedByDollarVolume = sorted(coarse, key=lambda c: c.DollarVolume, reverse=True)
        symbols_by_price = [c.Symbol for c in sortedByDollarVolume if c.Price > 10]
        algorithm.filteredByPrice = symbols_by_price[:8]
        return algorithm.filteredByPrice

    def SelectFine(self, algorithm, fine):
        return [f.Symbol for f in fine]
# 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]]