| Overall Statistics |
|
Total Trades 111 Average Win 0.70% Average Loss -0.38% Compounding Annual Return 5.184% Drawdown 29.900% Expectancy 1.237 Net Profit 11.669% Sharpe Ratio 0.318 Probabilistic Sharpe Ratio 13.045% Loss Rate 21% Win Rate 79% Profit-Loss Ratio 1.83 Alpha 0.075 Beta -0.072 Annual Standard Deviation 0.201 Annual Variance 0.04 Information Ratio -0.359 Tracking Error 0.27 Treynor Ratio -0.89 Total Fees $438.34 |
class SiegfriedsRework(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2010, 7, 1)
self.SetEndDate(2012, 9, 4)
self.SetCash(100000)
self.UniverseSettings.Resolution = Resolution.Daily
self.SetUniverseSelection(FineFundamentalUniverseSelectionModel(self.SelectCoarse, self.SelectFine))
self.AddEquity("SPY")
self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.AfterMarketOpen("SPY", 120), self.Rebalance)
self.num_symb_coarse = 8000
self.monthly_rebalance = False
self.new_symbols = []
self.invested_stocks = []
self.min_market_cap = 5
self.min_roic = 0.7
self.max_roic = 20 # max ROIC at purcahse only. if stock has roic intially below this threshold, but rises while in holdings, stock won't be liquidated
self.min_volume = 200000 # minimum trading volume
self.max_eveb = 100
# coarse/fine universe selection runs everyday at midnight
def SelectCoarse(self, coarse):
if self.monthly_rebalance == False:
return Universe.Unchanged # if selectcoarse returns universe.unchanged, selectfine is not called
self.monthly_rebalance = False
filtered_coarse = [x for x in coarse if x.HasFundamentalData] # removing ETFs, ETNs
sorted_coarse = sorted(filtered_coarse, key=lambda k:k.DollarVolume, reverse=True)
min_vol_coarse = [x for x in sorted_coarse if x.DollarVolume > self.min_volume]
top_liquid_coarse = min_vol_coarse[:self.num_symb_coarse]
return [i.Symbol for i in top_liquid_coarse]
def SelectFine(self, fine):
filtered_fine = [x for x in fine if x.CompanyReference.CountryId == "USA"
and x.AssetClassification.MorningstarSectorCode != MorningstarSectorCode.Energy
and x.AssetClassification.MorningstarSectorCode != MorningstarSectorCode.FinancialServices
and x.MarketCap/1000000 > self.min_market_cap
and x.FinancialStatements.BalanceSheet.InvestedCapital.TwelveMonths > 0
and x.FinancialStatements.IncomeStatement.EBIT.TwelveMonths/x.FinancialStatements.BalanceSheet.InvestedCapital.TwelveMonths > self.min_roic
and x.FinancialStatements.IncomeStatement.EBIT.TwelveMonths/x.FinancialStatements.BalanceSheet.InvestedCapital.TwelveMonths < self.max_roic
and x.ValuationRatios.EVToEBIT3YrAvg > 0
and x.ValuationRatios.EVToEBIT3YrAvg < self.max_eveb]
sorted_fine = sorted(filtered_fine, key=lambda x:x.ValuationRatios.EVToEBIT3YrAvg, reverse = False)
triptile = 1 if len(sorted_fine)/4 <= 0 else int(round((len(sorted_fine)/4)))
bottom_triptile = sorted_fine[:triptile]
# self.Debug(f"filtered_fine: {[str(i.Symbol) for i in filtered_fine]}")
self.new_symbols = [i.Symbol for i in bottom_triptile]
# self.Debug(f"chosen_fine: {[str(i) for i in self.new_symbols]}")
return self.new_symbols # new symbols to add to our watchlist
# this is called each time a security is added or removed from our universe
def OnSecuritiesChanged(self, changes):
# liquidate securities based on their fundamentals, remove from watchlist
for symbol in self.Portfolio.Keys:
# we only care about securities we are invested in
if not self.Portfolio[symbol].Invested:
continue
security = self.Securities[symbol]
# liquidate stocks that go below min ROIC
if security.Fundamentals.FinancialStatements.IncomeStatement.EBIT.TwelveMonths/security.Fundamentals.FinancialStatements.BalanceSheet.InvestedCapital.TwelveMonths < self.min_roic \
or security.Fundamentals.MarketCap/1000000 < self.min_market_cap:
self.invested_stocks.remove(security.Symbol)
self.Liquidate(security.Symbol)
# self.Debug(f"!!!Selling {security} @ {security.Price}," \
# + f" ROIC is {security.Fundamentals.FinancialStatements.IncomeStatement.EBIT.TwelveMonths/security.Fundamentals.FinancialStatements.BalanceSheet.InvestedCapital.TwelveMonths}," \
# + f" EBITDAEV is {security.Fundamentals.ValuationRatios.EVToEBIT3YrAvg}," \
# + f" MarketCap is {security.Fundamentals.MarketCap/1000000}" \
# + f" and Volume is {security.Fundamentals.DollarVolume/1000000}")
if len(self.new_symbols) > 0:
for security in changes.AddedSecurities:
if security.Fundamentals == None: # i.e. is an ETF/is SPY
continue
# prevent duplicate entry in invested_stocks
if security.Symbol not in self.invested_stocks and security.Symbol.Value is not str("PDLI R735QTJ8XC9X"):
self.invested_stocks.append(security.Symbol) # add new securities to watchlist
if len(self.invested_stocks) > 0:
# for symbol in self.invested_stocks:
# security = self.Securities[symbol]
# self.SetHoldings(security.Symbol, 0.9/len(self.invested_stocks)) #0.9 to keep cash buffer
# self.Debug(f"Buying {security.Symbol} @ {security.Price}," \
# + f" ROIC is {security.Fundamentals.FinancialStatements.IncomeStatement.EBIT.TwelveMonths/security.Fundamentals.FinancialStatements.BalanceSheet.InvestedCapital.TwelveMonths}," \
# + f" EBITEV is {security.Fundamentals.ValuationRatios.EVToEBIT3YrAvg}," \
# + f" MarketCap is {security.Fundamentals.MarketCap/1000000}" \
# + f" and Volume is {security.Fundamentals.DollarVolume/1000000}")
for symbol in self.invested_stocks:
security = self.Securities[symbol]
self.SetHoldings([PortfolioTarget(security.Symbol, (0.9/len(self.invested_stocks)))]) #0.9 to keep cash buffer
# self.Debug(f"Buying {security.Symbol} @ {security.Price}," \
# + f" ROIC is {security.Fundamentals.FinancialStatements.IncomeStatement.EBIT.TwelveMonths/security.Fundamentals.FinancialStatements.BalanceSheet.InvestedCapital.TwelveMonths}," \
# + f" EBITEV is {security.Fundamentals.ValuationRatios.EVToEBIT3YrAvg}," \
# + f" MarketCap is {security.Fundamentals.MarketCap/1000000}" \
# + f" and Volume is {security.Fundamentals.DollarVolume/1000000}")
if len(self.new_symbols) > 0:
self.Debug(f"Invested Stocks List: {[str(symbol) for symbol in self.invested_stocks]}")
self.Debug(f"Portfolio Invested Stocks: {[str(symbol) for symbol in self.Portfolio.Keys if self.Portfolio[symbol].Invested]}")
self.Debug(f"Value of Stocks: {[float(self.Portfolio[symbol].Quantity * self.Portfolio[symbol].Price) for symbol in self.Portfolio.Keys if self.Portfolio[symbol].Invested]}")
self.Debug(f"Portfolio % Invested: {(self.Portfolio.TotalPortfolioValue - self.Portfolio.Cash)/self.Portfolio.TotalPortfolioValue}")
# daily data is received at midnight
def OnData(self, data):
pass
# this is called according to Schedule.On in Initialize
def Rebalance(self):
self.monthly_rebalance = True
# def OnEndOfDay(self):
# # If there's a bug in QC data (e.g., PDLI currently showing incorrect stock price of $0),
# # symbols could end up in self.invested_stocks but not actually invested in portfolio
# self.invested_stocks = [symbol for symbol in self.Portfolio.Keys if self.Portfolio[symbol].Invested]
# self.Debug(f"Invested Stocks List: {[str(symbol) for symbol in self.invested_stocks]}")
# self.Debug(f"Portfolio Invested Stocks: {[str(symbol) for symbol in self.Portfolio.Keys if self.Portfolio[symbol].Invested]}")
# self.Debug(f"Value of Stocks: {[float(self.Portfolio[symbol].Quantity * self.Portfolio[symbol].Price) for symbol in self.Portfolio.Keys if self.Portfolio[symbol].Invested]}")
# self.Debug(f"Portfolio % Invested: {(self.Portfolio.TotalPortfolioValue - self.Portfolio.Cash)/self.Portfolio.TotalPortfolioValue}")