Overall Statistics |
Total Orders
1554
Average Win
0.40%
Average Loss
-0.46%
Compounding Annual Return
9.330%
Drawdown
52.400%
Expectancy
0.540
Start Equity
100000
End Equity
924923.54
Net Profit
824.924%
Sharpe Ratio
0.361
Sortino Ratio
0.377
Probabilistic Sharpe Ratio
0.128%
Loss Rate
18%
Win Rate
82%
Profit-Loss Ratio
0.89
Alpha
0.014
Beta
0.88
Annual Standard Deviation
0.146
Annual Variance
0.021
Information Ratio
0.189
Tracking Error
0.046
Treynor Ratio
0.06
Total Fees
$215.43
Estimated Strategy Capacity
$210000000.00
Lowest Capacity Asset
CED RWTPESR2XAAT
Portfolio Turnover
0.17%
|
# https://quantpedia.com/strategies/net-payout-yield-effect/ # # 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 year-end 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 flow-based 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 stocks, we selected 500 most liquid stocks traded on NYSE, AMEX, or NASDAQ. from AlgorithmImports import * import numpy as np class NetPayoutYieldEffect(QCAlgorithm): def Initialize(self) -> None: self.SetStartDate(2000, 1, 1) self.SetCash(100_000) self.UniverseSettings.Leverage = 5 self.UniverseSettings.Resolution = Resolution.Daily self.AddUniverse(self.FundamentalFunction) self.Settings.MinimumOrderMarginPortfolioPercentage = 0.0 self.settings.daily_precise_end_time = False # Fundamental Filter Parameters self.exchange_codes: List[str] = ['NYS', 'NAS', 'ASE'] self.fundamental_count: int = 500 self.quantile: int = 10 self.long_symbols: List[Symbol] = [] self.rebalancing_month: int = 6 self.selection_flag: bool = True self.exchange: Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol self.Schedule.On(self.DateRules.MonthEnd(self.exchange), self.TimeRules.AfterMarketOpen(self.exchange), self.Selection) def FundamentalFunction(self, fundamental: List[Fundamental]) -> List[Symbol]: if not self.selection_flag: return Universe.Unchanged filtered: List[Fundamental] = [f for f in fundamental if f.HasFundamentalData and f.SecurityReference.ExchangeId in self.exchange_codes and not np.isnan(f.MarketCap) and f.MarketCap !=0 and not np.isnan(f.ValuationRatios.TotalYield) and f.ValuationRatios.TotalYield != 0 and not np.isnan(f.FinancialStatements.CashFlowStatement.CommonStockIssuance.TwelveMonths) and f.FinancialStatements.CashFlowStatement.CommonStockIssuance.TwelveMonths != 0] top_by_dollar_volume: List[Fundamental] = sorted(filtered, key=lambda x: x.DollarVolume, reverse=True)[:self.fundamental_count] payout_yield = lambda x: ((x.ValuationRatios.TotalYield * (x.MarketCap)) - \ (x.FinancialStatements.CashFlowStatement.CommonStockIssuance.TwelveMonths / (x.MarketCap))) sorted_by_payout: List[Fundamental] = sorted(top_by_dollar_volume, key = payout_yield, reverse=True) if len(sorted_by_payout) >= self.quantile: quantile: int = int(len(sorted_by_payout) / self.quantile) self.long_symbols = [x.Symbol for x in sorted_by_payout[:quantile]] return self.long_symbols def OnData(self, slice: Slice) -> None: if not self.selection_flag: return self.selection_flag = False # Trade Execution portfolio: List[PortfolioTarget] = [PortfolioTarget(symbol, 1 / len(self.long_symbols)) for symbol in self.long_symbols if slice.ContainsKey(symbol) and slice[symbol] is not None] self.SetHoldings(portfolio, True) self.long_symbols.clear() def Selection(self) -> None: if self.Time.month == self.rebalancing_month: self.selection_flag = True def OnSecuritiesChanged(self, changes: SecurityChanges) -> None: for security in changes.AddedSecurities: security.SetFeeModel(CustomFeeModel()) # Custom fee model class CustomFeeModel(FeeModel): def GetOrderFee(self, parameters: OrderFeeParameters) -> OrderFee: fee: float = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005 return OrderFee(CashAmount(fee, "USD"))