Overall Statistics |
Total Trades
77829
Average Win
0.04%
Average Loss
-0.04%
Compounding Annual Return
1.493%
Drawdown
47.000%
Expectancy
0.015
Net Profit
40.295%
Sharpe Ratio
0.162
Probabilistic Sharpe Ratio
0.000%
Loss Rate
49%
Win Rate
51%
Profit-Loss Ratio
1.00
Alpha
0.022
Beta
-0.145
Annual Standard Deviation
0.087
Annual Variance
0.008
Information Ratio
-0.208
Tracking Error
0.204
Treynor Ratio
-0.098
Total Fees
$1622.44
Estimated Strategy Capacity
$24000000.00
Lowest Capacity Asset
GBT W2ZDO2ZQZ9ET
|
# https://quantpedia.com/strategies/roa-effect-within-stocks/ # # The investment universe contains all stocks on NYSE and AMEX and Nasdaq with Sales greater than 10 million USD. Stocks are then sorted into # two halves based on market capitalization. Each half is then divided into deciles based on Return on assets (ROA) calculated as quarterly # earnings (Compustat quarterly item IBQ – income before extraordinary items) divided by one-quarter-lagged assets (item ATQ – total assets). # The investor then goes long the top three deciles from each market capitalization group and goes short bottom three deciles. The strategy is # rebalanced monthly, and stocks are equally weighted. # # QC implementation changes: # - Instead of all listed stock, we select 500 most liquid stocks traded on NYSE, AMEX, or NASDAQ. from AlgorithmImports import * class ROAEffectWithinStocks(QCAlgorithm): def Initialize(self): self.SetStartDate(2000, 1, 1) self.SetCash(100000) self.symbol = self.AddEquity('SPY', Resolution.Daily).Symbol self.course_count = 500 self.long = [] self.short = [] self.selection_flag = False self.UniverseSettings.Resolution = Resolution.Daily self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction) self.Schedule.On(self.DateRules.MonthEnd(self.symbol), self.TimeRules.AfterMarketOpen(self.symbol), self.Selection) def OnSecuritiesChanged(self, changes): for security in changes.AddedSecurities: security.SetFeeModel(CustomFeeModel()) security.SetLeverage(5) def CoarseSelectionFunction(self, coarse): if not self.selection_flag: return Universe.Unchanged selected = sorted([x for x in coarse if x.HasFundamentalData and x.Market == 'usa' and x.Price > 5], key=lambda x: x.DollarVolume, reverse=True) return [x.Symbol for x in selected[:self.course_count]] def FineSelectionFunction(self, fine): fine = [x for x in fine if x.MarketCap != 0 and x.ValuationRatios.SalesPerShare * x.EarningReports.DilutedAverageShares.Value > 10000000 and x.OperationRatios.ROA.ThreeMonths != 0 and ((x.SecurityReference.ExchangeId == "NYS") or (x.SecurityReference.ExchangeId == "NAS") or (x.SecurityReference.ExchangeId == "ASE"))] # Sorting by market cap. sorted_by_market_cap = sorted(fine, key = lambda x: x.MarketCap, reverse=True) half = int(len(sorted_by_market_cap) / 2) top_mc = [x for x in sorted_by_market_cap[:half]] bottom_mc = [x for x in sorted_by_market_cap[half:]] if len(top_mc) >= 10 and len(bottom_mc) >= 10: # Sorting by ROA. sorted_top_by_roa = sorted(top_mc, key = lambda x:(x.OperationRatios.ROA.Value), reverse=True) decile = int(len(sorted_top_by_roa) / 10) long_top = [x.Symbol for x in sorted_top_by_roa[:decile*3]] short_top = [x.Symbol for x in sorted_top_by_roa[-(decile*3):]] sorted_bottom_by_roa = sorted(bottom_mc, key = lambda x:(x.OperationRatios.ROA.Value), reverse=True) decile = int(len(sorted_bottom_by_roa) / 10) long_bottom = [x.Symbol for x in sorted_bottom_by_roa[:decile*3]] short_bottom = [x.Symbol for x in sorted_bottom_by_roa[-(decile*3):]] self.long = long_top + long_bottom self.short = short_top + short_bottom return self.long + self.short def OnData(self, data): if not self.selection_flag: return self.selection_flag = False # Trade execution. stocks_invested = [x.Key for x in self.Portfolio if x.Value.Invested] for symbol in stocks_invested: if symbol not in self.long + self.short: self.Liquidate(symbol) long_count = len(self.long) short_count = len(self.short) for symbol in self.long: self.SetHoldings(symbol, 1 / long_count) for symbol in self.short: self.SetHoldings(symbol, -1 / short_count) self.long.clear() self.short.clear() def Selection(self): 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"))