| 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 = Nonefrom 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]]