Overall Statistics 
Total Trades
27468
Average Win
0.09%
Average Loss
0.07%
Compounding Annual Return
9.376%
Drawdown
27.200%
Expectancy
0.164
Net Profit
646.537%
Sharpe Ratio
0.805
Probabilistic Sharpe Ratio
10.371%
Loss Rate
49%
Win Rate
51%
ProfitLoss Ratio
1.29
Alpha
0.068
Beta
0.001
Annual Standard Deviation
0.084
Annual Variance
0.007
Information Ratio
0.047
Tracking Error
0.182
Treynor Ratio
89.983
Total Fees
$1038.81
Estimated Strategy Capacity
$86000.00
Lowest Capacity Asset
WSOB R735QTJ8XC9X

# https://quantpedia.com/strategies/investmentfactor/ # # The investment universe consists of all NYSE, Amex, and NASDAQ stocks. Firstly, stocks are allocated to five Size groups (Small to Big) at the end of each June # using NYSE market cap breakpoints. Stocks are allocated independently to five Investment (Inv) groups (Low to High) still using NYSE breakpoints. # The intersections of the two sorts produce 25 SizeInv portfolios. For portfolios formed in June of year t, Inv is the growth of total assets for # the fiscal year ending in t1 divided by total assets at the end of t1. Long portfolio with the highest Size and simultaneously with the lowest # Investment. Short portfolio with the highest Size and simultaneously with the highest Investment. The portfolios are valueweighted. # # QC implementation changes: #  Universe consists of top 3000 US stock by market cap from NYSE, AMEX and NASDAQ. #  Equally weighting is used. class InvestmentFactor(QCAlgorithm): def Initialize(self): self.SetStartDate(2000, 1, 1) self.SetCash(100000) self.symbol = self.AddEquity('SPY', Resolution.Daily).Symbol self.coarse_count = 3000 self.weight = {} self.last_year_data = {} 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(self)) security.SetLeverage(5) def CoarseSelectionFunction(self, coarse): if not self.selection_flag: return Universe.Unchanged selected = [x.Symbol for x in coarse if x.HasFundamentalData and x.Market == 'usa'] return selected def FineSelectionFunction(self, fine): fine = [x for x in fine if x.MarketCap != 0 and x.FinancialStatements.BalanceSheet.TotalAssets.TwelveMonths != 0 and x.OperationRatios.TotalAssetsGrowth.OneYear and \ ((x.SecurityReference.ExchangeId == "NYS") or (x.SecurityReference.ExchangeId == "NAS") or (x.SecurityReference.ExchangeId == "ASE"))] if len(fine) > self.coarse_count: sorted_by_market_cap = sorted(fine, key = lambda x: x.MarketCap, reverse=True) top_by_market_cap = sorted_by_market_cap[:self.coarse_count] else: top_by_market_cap = fine # Sorting by investment factor. sorted_by_inv_factor = sorted(top_by_market_cap, key = lambda x: (x.OperationRatios.TotalAssetsGrowth.OneYear / x.FinancialStatements.BalanceSheet.TotalAssets.TwelveMonths), reverse=True) if len(sorted_by_inv_factor) >= 5: quintile = int(len(sorted_by_inv_factor) / 5) self.long = [x.Symbol for x in sorted_by_inv_factor[quintile:]] self.short = [x.Symbol for x in sorted_by_inv_factor[:quintile]] return self.long + self.short def OnData(self, data): if not self.selection_flag: return self.selection_flag = False # Trade execution. long_count = len(self.long) short_count = len(self.short) 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) 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): if self.Time.month == 6: 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"))