Overall Statistics |
Total Trades
26573
Average Win
0.08%
Average Loss
-0.06%
Compounding Annual Return
7.379%
Drawdown
63.200%
Expectancy
0.201
Net Profit
418.015%
Sharpe Ratio
0.494
Probabilistic Sharpe Ratio
0.126%
Loss Rate
48%
Win Rate
52%
Profit-Loss Ratio
1.30
Alpha
0.067
Beta
-0.166
Annual Standard Deviation
0.117
Annual Variance
0.014
Information Ratio
-0.001
Tracking Error
0.22
Treynor Ratio
-0.347
Total Fees
$1006.38
Estimated Strategy Capacity
$36000.00
Lowest Capacity Asset
KELYB R735QTJ8XC9X
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# https://quantpedia.com/strategies/value-book-to-market-factor/ # # The investment universe contains all NYSE, AMEX, and NASDAQ stocks. To represent “value” investing, HML portfolio goes long high book-to-price stocks and short, # low book-to-price stocks. In this strategy, we show the results for regular HML which is simply the average of the portfolio returns of HML small (which goes long # cheap and short expensive only among small stocks) and HML large (which goes long cheap and short expensive only among large caps). The portfolio is equal-weighted # and rebalanced monthly. # # QC implementation changes: # - Instead of all listed stock, we select top 3000 stocks by market cap from QC stock universe. from AlgorithmImports import * class Value(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.quantile = 5 self.long = [] self.short = [] self.month = 12 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 = [x.Symbol for x in coarse if x.HasFundamentalData and x.Market == 'usa'] return selected def FineSelectionFunction(self, fine): sorted_by_market_cap = sorted([x for x in fine if x.ValuationRatios.PBRatio != 0 and \ ((x.SecurityReference.ExchangeId == "NYS") or (x.SecurityReference.ExchangeId == "NAS") or (x.SecurityReference.ExchangeId == "ASE"))], key = lambda x:x.MarketCap, reverse=True) top_by_market_cap = [x for x in sorted_by_market_cap[:self.coarse_count]] if len(top_by_market_cap) >= self.quantile: sorted_by_pb = sorted(top_by_market_cap, key = lambda x:(x.ValuationRatios.PBRatio), reverse=False) quantile = int(len(sorted_by_pb) / self.quantile) self.long = [i.Symbol for i in sorted_by_pb[:quantile]] self.short = [i.Symbol for i in sorted_by_pb[-quantile:]] 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) # Leveraged portfolio - 100% long, 100% short. for symbol in self.long: if symbol in data and data[symbol]: self.SetHoldings(symbol, 1 / len(self.long)) for symbol in self.short: if symbol in data and data[symbol]: self.SetHoldings(symbol, -1 / len(self.short)) self.long.clear() self.short.clear() def Selection(self): if self.month == 12: self.selection_flag = True self.month += 1 if self.month > 12: self.month = 1 # Custom fee model. class CustomFeeModel(FeeModel): def GetOrderFee(self, parameters): fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005 return OrderFee(CashAmount(fee, "USD"))