Overall Statistics |
Total Orders
1258
Average Win
2.50%
Average Loss
-0.83%
Compounding Annual Return
28.946%
Drawdown
76.800%
Expectancy
1.577
Start Equity
100000
End Equity
56698861.15
Net Profit
56598.861%
Sharpe Ratio
0.819
Sortino Ratio
1.068
Probabilistic Sharpe Ratio
12.310%
Loss Rate
36%
Win Rate
64%
Profit-Loss Ratio
3.02
Alpha
0.17
Beta
0.86
Annual Standard Deviation
0.254
Annual Variance
0.064
Information Ratio
0.762
Tracking Error
0.215
Treynor Ratio
0.241
Total Fees
$15243.73
Estimated Strategy Capacity
$25000.00
Lowest Capacity Asset
XGTI VIFWXV9ZINVP
Portfolio Turnover
0.39%
|
# https://quantpedia.com/strategies/net-current-asset-value-effect/ # # The investment universe consists of all stocks on the London Exchange. Companies with more than one class of ordinary shares and foreign companies # are excluded. Also excluded are companies on the lightly regulated markets and companies which belong to the financial sector. The portfolio of # stocks is formed annually in July. Only those stocks with an NCAV/MV higher than 1.5 are included in the NCAV/MV portfolio. This Buy-and-hold # portfolio is held for one year. Stocks are weighted equally. # # QC implementation changes: # - Instead of all listed London stocks, we selected top 3000 US listed stocks by market cap from QC stock universe. from AlgorithmImports import * import numpy as np class NetCurrentAssetValueEffect(QCAlgorithm): def Initialize(self) -> None: self.SetStartDate(2000, 1, 1) self.SetCash(100_000) self.UniverseSettings.Leverage = 3 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.fundamental_count: int = 3_000 self.market: str = 'usa' self.country_id: str = 'USA' self.fin_sector_code: int = 103 self.ncav_threshold: float = 1.5 self.long_symbols: List[Symbol] = [] self.rebalance_month: int = 7 self.selection_flag: bool = True self.exchange: Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol self.Schedule.On(self.DateRules.MonthStart(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.Market == self.market and f.CompanyReference.CountryId == self.country_id and f.AssetClassification.MorningstarSectorCode != self.fin_sector_code and not np.isnan(f.EarningReports.BasicAverageShares.TwelveMonths) and f.EarningReports.BasicAverageShares.TwelveMonths != 0 and not np.isnan(f.MarketCap) and f.MarketCap != 0 and not np.isnan(f.ValuationRatios.WorkingCapitalPerShare) and f.ValuationRatios.WorkingCapitalPerShare != 0 ] sorted_by_market_cap: List[Fundamental] = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:self.fundamental_count] # Calculate NCAV/MV self.long_symbols = [x.Symbol for x in sorted_by_market_cap if ((x.ValuationRatios.WorkingCapitalPerShare * x.EarningReports.BasicAverageShares.TwelveMonths) / x.MarketCap) > self.ncav_threshold] 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.rebalance_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"))