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]]