Overall Statistics |
Total Trades
24417
Average Win
0.08%
Average Loss
-0.03%
Compounding Annual Return
12.179%
Drawdown
37.200%
Expectancy
0.793
Net Profit
1096.107%
Sharpe Ratio
0.871
Probabilistic Sharpe Ratio
18.815%
Loss Rate
54%
Win Rate
46%
Profit-Loss Ratio
2.89
Alpha
0.106
Beta
0.024
Annual Standard Deviation
0.124
Annual Variance
0.015
Information Ratio
0.154
Tracking Error
0.212
Treynor Ratio
4.419
Total Fees
$1002.30
Estimated Strategy Capacity
$160000.00
Lowest Capacity Asset
PTNR RP7Z4T25NJOL
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# https://quantpedia.com/strategies/small-capitalization-stocks-premium-anomaly/ # # The investment universe contains all NYSE, AMEX, and NASDAQ stocks. Decile portfolios are formed based on the market capitalization # of stocks. To capture “size” effect, SMB portfolio goes long small stocks (lowest decile) and short big stocks (highest decile). # # QC implementation changes: # - Instead of all listed stock, we select top 3000 stocks by market cap from QC stock universe. class ValueBooktoMarketFactor(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.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(self)) security.SetLeverage(10) 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.MarketCap != 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]] quintile = int(len(top_by_market_cap) / 5) self.long = [i.Symbol for i in top_by_market_cap[-quintile:]] self.short = [i.Symbol for i in top_by_market_cap[: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) # Leveraged portfolio - 100% long, 100% short. 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.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"))