Overall Statistics 
Total Trades
1041
Average Win
0.49%
Average Loss
0.55%
Compounding Annual Return
6.778%
Drawdown
54.100%
Expectancy
0.438
Net Profit
347.521%
Sharpe Ratio
0.383
Probabilistic Sharpe Ratio
0.016%
Loss Rate
24%
Win Rate
76%
ProfitLoss Ratio
0.89
Alpha
0.008
Beta
0.892
Annual Standard Deviation
0.153
Annual Variance
0.023
Information Ratio
0.04
Tracking Error
0.053
Treynor Ratio
0.066
Total Fees
$136.48
Estimated Strategy Capacity
$94000000.00
Lowest Capacity Asset
NVS RULY784EQ6AT

# https://quantpedia.com/strategies/netpayoutyieldeffect/ # # The investment universe consists of all stocks on NYSE, AMEX, and NASDAQ. At the end of June of each year t, ten portfolios are formed based on ranked # values net payout yield. The net payout yield is the ratio of dividends plus repurchases minus common share issuances in year t to yearend market # capitalization. There are two measures of payout yield, one based on the statement of cash flows, the other based on the change in Treasury stocks. # For the net payout yield, we use the cash flowbased measure of repurchases. The portfolio with the highest net payout yield is bought and held for # one year, after which it is rebalanced. # # QC implementation changes: #  Instead of all listed stock, we select 500 most liquid stocks traded on NYSE, AMEX, or NASDAQ. from AlgorithmImports import * class NetPayoutYieldEffect(QCAlgorithm): def Initialize(self): self.SetStartDate(2000, 1, 1) self.SetCash(100000) self.symbol = self.AddEquity('SPY', Resolution.Daily).Symbol self.coarse_count = 500 self.long = [] 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(10) 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'], key=lambda x: x.DollarVolume, reverse=True) return [x.Symbol for x in selected[:self.coarse_count]] def FineSelectionFunction(self, fine): fine = [x for x in fine if x.MarketCap !=0 and x.ValuationRatios.TotalYield != 0 and x.FinancialStatements.CashFlowStatement.CommonStockIssuance.TwelveMonths != 0 and ((x.SecurityReference.ExchangeId == "NYS") or (x.SecurityReference.ExchangeId == "NAS") or (x.SecurityReference.ExchangeId == "ASE"))] # Sorting by net payout. sorted_by_payout = sorted(fine, key = lambda x: ( (x.ValuationRatios.TotalYield * (x.MarketCap))  \ (x.FinancialStatements.CashFlowStatement.CommonStockIssuance.TwelveMonths / (x.MarketCap))), reverse=True) decile = int(len(sorted_by_payout) / 10) self.long = [x.Symbol for x in sorted_by_payout[:decile]] return self.long def OnData(self, data): if not self.selection_flag: return self.selection_flag = False 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.Liquidate(symbol) for symbol in self.long: self.SetHoldings(symbol, 1 / len(self.long)) self.long.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"))