HalldorAndersen
248 lines
11 KiB
Python
248 lines
11 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 clr import AddReference
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AddReference("System")
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AddReference("QuantConnect.Common")
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AddReference("QuantConnect.Algorithm")
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AddReference("QuantConnect.Indicators")
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AddReference("QuantConnect.Algorithm.Framework")
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from System import *
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from QuantConnect import *
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from QuantConnect.Orders.Fees import ConstantFeeModel
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from QuantConnect.Data.UniverseSelection import *
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from QuantConnect.Indicators import *
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from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel
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from datetime import timedelta, datetime
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from math import ceil
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from itertools import chain
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#
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# This alpha picks stocks according to Joel Greenblatt's Magic Formula.
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# First, each stock is ranked depending on the relative value of the ratio EV/EBITDA. For example, a stock
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# that has the lowest EV/EBITDA ratio in the security universe receives a score of one while a stock that has
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# the tenth lowest EV/EBITDA score would be assigned 10 points.
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#
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# Then, each stock is ranked and given a score for the second valuation ratio, Return on Capital (ROC).
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# Similarly, a stock that has the highest ROC value in the universe gets one score point.
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# The stocks that receive the lowest combined score are chosen for insights.
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#
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# Source: Greenblatt, J. (2010) The Little Book That Beats the Market
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#
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# This alpha is part of the Benchmark Alpha Series created by QuantConnect which are open sourced so the community and client funds can see an example of an alpha.
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#
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class MagicFormulaAlpha(QCAlgorithmFramework):
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''' Alpha Streams: Benchmark Alpha: Pick stocks according to Joel Greenblatt's Magic Formula'''
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def Initialize(self):
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self.SetStartDate(2018, 1, 1)
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self.SetCash(100000)
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#Set zero transaction fees
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self.SetSecurityInitializer(lambda security: security.SetFeeModel(ConstantFeeModel(0)))
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# select stocks using MagicFormulaUniverseSelectionModel
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self.SetUniverseSelection(GreenBlattMagicFormulaUniverseSelectionModel())
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# Use MagicFormulaAlphaModel to establish insights
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self.SetAlpha(RateOfChangeAlphaModel())
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# Equally weigh securities in portfolio, based on insights
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self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
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class RateOfChangeAlphaModel(AlphaModel):
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'''Uses Rate of Change (ROC) to create magnitude prediction for insights.'''
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def __init__(self, *args, **kwargs):
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self.lookback = kwargs['lookback'] if 'lookback' in kwargs else 1
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self.resolution = kwargs['resolution'] if 'resolution' in kwargs else Resolution.Daily
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self.predictionInterval = Time.Multiply(Extensions.ToTimeSpan(self.resolution), self.lookback)
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self.symbolDataBySymbol = {}
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def Update(self, algorithm, data):
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insights = []
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for symbol, symbolData in self.symbolDataBySymbol.items():
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if symbolData.CanEmit:
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insights.append(Insight.Price(symbol, self.predictionInterval, InsightDirection.Up, symbolData.Return, None))
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return insights
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def OnSecuritiesChanged(self, algorithm, changes):
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# clean up data for removed securities
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for removed in changes.RemovedSecurities:
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symbolData = self.symbolDataBySymbol.pop(removed.Symbol, None)
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if symbolData is not None:
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symbolData.RemoveConsolidators(algorithm)
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# initialize data for added securities
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symbols = [ x.Symbol for x in changes.AddedSecurities ]
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history = algorithm.History(symbols, self.lookback, self.resolution)
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if history.empty: return
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tickers = history.index.levels[0]
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for ticker in tickers:
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symbol = SymbolCache.GetSymbol(ticker)
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if symbol not in self.symbolDataBySymbol:
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symbolData = SymbolData(symbol, self.lookback)
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self.symbolDataBySymbol[symbol] = symbolData
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symbolData.RegisterIndicators(algorithm, self.resolution)
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symbolData.WarmUpIndicators(history.loc[ticker])
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class SymbolData:
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'''Contains data specific to a symbol required by this model'''
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def __init__(self, symbol, lookback):
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self.Symbol = symbol
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self.ROC = RateOfChange('{}.ROC({})'.format(symbol, lookback), lookback)
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self.Consolidator = None
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self.previous = 0
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def RegisterIndicators(self, algorithm, resolution):
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self.Consolidator = algorithm.ResolveConsolidator(self.Symbol, resolution)
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algorithm.RegisterIndicator(self.Symbol, self.ROC, self.Consolidator)
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def RemoveConsolidators(self, algorithm):
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if self.Consolidator is not None:
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algorithm.SubscriptionManager.RemoveConsolidator(self.Symbol, self.Consolidator)
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def WarmUpIndicators(self, history):
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for tuple in history.itertuples():
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self.ROC.Update(tuple.Index, tuple.close)
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@property
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def Return(self):
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return float(self.ROC.Current.Value)
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@property
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def CanEmit(self):
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if self.previous == self.ROC.Samples:
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return False
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self.previous = self.ROC.Samples
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return self.ROC.IsReady
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def __str__(self, **kwargs):
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return '{}: {:.2%}'.format(self.ROC.Name, (1 + self.Return)**252 - 1)
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class GreenBlattMagicFormulaUniverseSelectionModel(FundamentalUniverseSelectionModel):
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'''Defines a universe according to Joel Greenblatt's Magic Formula, as a universe selection model for the framework algorithm.
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From the universe QC500, stocks are ranked using the valuation ratios, Enterprise Value to EBITDA (EV/EBITDA) and Return on Assets (ROA).
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'''
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def __init__(self,
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filterFineData = True,
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universeSettings = None,
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securityInitializer = None):
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'''Initializes a new default instance of the MagicFormulaUniverseSelectionModel'''
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super().__init__(filterFineData, universeSettings, securityInitializer)
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# Number of stocks in Coarse Universe
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self.NumberOfSymbolsCoarse = 500
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# Number of sorted stocks in the fine selection subset using the valuation ratio, EV to EBITDA (EV/EBITDA)
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self.NumberOfSymbolsFine = 20
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# Final number of stocks in security list, after sorted by the valuation ratio, Return on Assets (ROA)
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self.NumberOfSymbolsInPortfolio = 10
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self.lastMonth = -1
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self.dollarVolumeBySymbol = {}
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self.symbols = []
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def SelectCoarse(self, algorithm, coarse):
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'''Performs coarse selection for 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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month = algorithm.Time.month
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if month == self.lastMonth:
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return self.symbols
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self.lastMonth = month
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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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filtered = [x for x in coarse if x.HasFundamentalData
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and x.Volume > 0
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and x.Price > 0]
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# sort the stocks by dollar volume and take the top 1000
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top = sorted(filtered, key=lambda x: x.DollarVolume, reverse=True)[:self.NumberOfSymbolsCoarse]
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self.dollarVolumeBySymbol = { i.Symbol: i.DollarVolume for i in top }
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self.symbols = list(self.dollarVolumeBySymbol.keys())
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return self.symbols
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def SelectFine(self, algorithm, fine):
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'''QC500: Performs fine selection for the coarse selection 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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Magic Formula: Rank stocks by Enterprise Value to EBITDA (EV/EBITDA)
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Rank subset of previously ranked stocks (EV/EBITDA), using the valuation ratio Return on Assets (ROA)'''
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# QC500:
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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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filteredFine = [x for x in fine if x.CompanyReference.CountryId == "USA"
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and (x.CompanyReference.PrimaryExchangeID == "NYS" or x.CompanyReference.PrimaryExchangeID == "NAS")
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and (algorithm.Time - x.SecurityReference.IPODate).days > 180
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and x.EarningReports.BasicAverageShares.ThreeMonths * x.EarningReports.BasicEPS.TwelveMonths * x.ValuationRatios.PERatio > 5e8]
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count = len(filteredFine)
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if count == 0: return []
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myDict = dict()
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percent = float(self.NumberOfSymbolsFine / count)
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# select stocks with top dollar volume in every single sector
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for key in ["N", "M", "U", "T", "B", "I"]:
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value = [x for x in filteredFine if x.CompanyReference.IndustryTemplateCode == key]
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value = sorted(value, key=lambda x: self.dollarVolumeBySymbol[x.Symbol], reverse = True)
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myDict[key] = value[:ceil(len(value) * percent)]
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# stocks in QC500 universe
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topFine = list(chain.from_iterable(myDict.values()))[:self.NumberOfSymbolsCoarse]
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# Magic Formula:
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## Rank stocks by Enterprise Value to EBITDA (EV/EBITDA)
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## Rank subset of previously ranked stocks (EV/EBITDA), using the valuation ratio Return on Assets (ROA)
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# sort stocks in the security universe of QC500 based on Enterprise Value to EBITDA valuation ratio
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sortedByEVToEBITDA = sorted(topFine, key=lambda x: x.ValuationRatios.EVToEBITDA , reverse=True)
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# sort subset of stocks that have been sorted by Enterprise Value to EBITDA, based on the valuation ratio Return on Assets (ROA)
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sortedByROA = sorted(sortedByEVToEBITDA[:self.NumberOfSymbolsFine], key=lambda x: x.ValuationRatios.ForwardROA, reverse=False)
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# retrieve list of securites in portfolio
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top = sortedByROA[:self.NumberOfSymbolsInPortfolio]
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self.symbols = [f.Symbol for f in top]
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return self.symbols
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