from AlgorithmImports import *
# endregion
class IntangiblesAdjustedProfitabilityFactor(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2000, 1, 1)
self.SetCash(100000)
self.market:Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
self.leverage:int = 3
self.quantile:int = 3
self.coarse_count:int = 1000
self.selection_month:int = 6
self.long:List[Symbol] = []
self.short:List[Symbol] = []
self.selection_flag:bool = False
self.Settings.MinimumOrderMarginPortfolioPercentage = 0.
self.UniverseSettings.Resolution = Resolution.Daily
self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
self.Schedule.On(self.DateRules.MonthEnd(self.market), self.TimeRules.BeforeMarketClose(self.market), self.Selection)
def OnSecuritiesChanged(self, changes:SecurityChanges) -> None:
for security in changes.AddedSecurities:
security.SetFeeModel(CustomFeeModel())
security.SetLeverage(self.leverage)
def CoarseSelectionFunction(self, coarse:List[CoarseFundamental]) -> List[Symbol]:
if not self.selection_flag:
return Universe.Unchanged
selected:List[Symbol] = [x.Symbol
for x in sorted([x for x in coarse if x.HasFundamentalData and x.Market == 'usa'],
key = lambda x: x.DollarVolume, reverse = True)[:self.coarse_count]]
# selected = [x.Symbol for x in coarse if x.HasFundamentalData and x.Market == 'usa']
return selected
def FineSelectionFunction(self, fine:List[FineFundamental]) -> List[Symbol]:
fine = [x for x in fine if x.MarketCap != 0 and \
(x.SecurityReference.ExchangeId == 'NYS') or (x.SecurityReference.ExchangeId == 'NAS') or (x.SecurityReference.ExchangeId == 'ASE')]
# if len(fine) > self.coarse_count:
# fine = sorted(fine, key=lambda x: x.MarketCap, reverse=True)[:self.coarse_count]
# profitability calculation
profitability:Dict[Symbol, float] = {
stock.Symbol : stock.FinancialStatements.IncomeStatement.GrossProfit.TwelveMonths - stock.FinancialStatements.IncomeStatement.InterestExpense.TwelveMonths - stock.FinancialStatements.IncomeStatement.GeneralAndAdministrativeExpense.TwelveMonths \
for stock in fine
}
# sort and divide into quantiles
if len(profitability) >= self.quantile:
sorted_roe:List[Symbol] = sorted(profitability, key=profitability.get, reverse=True)
quantile:int = len(sorted_roe) // self.quantile
self.long = sorted_roe[:quantile]
self.short = sorted_roe[-quantile:]
self.rebalance_flag = True
return self.long + self.short
def OnData(self, data: Slice):
# yearly rebalance
if not self.selection_flag:
return
self.selection_flag = False
invested:List[Symbol] = [x.Key for x in self.Portfolio if x.Value.Invested]
for symbol in invested:
if symbol not in self.long + self.short:
self.Liquidate(symbol)
# trade execution
for symbol in self.long:
if symbol in data and data[symbol]:
self.SetHoldings(symbol, 1/len(self.long))
for symbol in self.short:
if symbol in data and data[symbol]:
self.SetHoldings(symbol, -1/len(self.long))
def Selection(self) -> None:
# selection on end of June
if self.Time.month != self.selection_month:
return
self.selection_flag = True
# Custom fee model
class CustomFeeModel(FeeModel):
def GetOrderFee(self, parameters):
fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
return OrderFee(CashAmount(fee, "USD"))
