Overall Statistics |
Total Orders
2875
Average Win
1.34%
Average Loss
-0.94%
Compounding Annual Return
8.827%
Drawdown
54.600%
Expectancy
0.183
Start Equity
100000
End Equity
807502.26
Net Profit
707.502%
Sharpe Ratio
0.311
Sortino Ratio
0.377
Probabilistic Sharpe Ratio
0.026%
Loss Rate
51%
Win Rate
49%
Profit-Loss Ratio
1.42
Alpha
0.056
Beta
-0.029
Annual Standard Deviation
0.176
Annual Variance
0.031
Information Ratio
0.053
Tracking Error
0.241
Treynor Ratio
-1.902
Total Fees
$3753.11
Estimated Strategy Capacity
$0
Lowest Capacity Asset
CME_LC1.QuantpediaFutures 2S
Portfolio Turnover
2.99%
|
# https://quantpedia.com/strategies/skewness-effect-in-commodities/ # # The investment universe consists of 27 futures contracts on commodities. Each month, investor calculates skewness (3rd moment of returns) # from daily returns from data going 12 months into the past for all futures. Commodities are then sorted into quintiles and investor goes # long quintile containing the commodities with the 20% lowest total skewness and short quintile containing the commodities with the 20% highest # total skewness (over a ranking period of 12 months). The resultant portfolio is equally weighted and rebalanced each month. import numpy as np from AlgorithmImports import * from scipy.stats import skew class SkewnessEffect(QCAlgorithm): def Initialize(self): self.SetStartDate(2000, 1, 1) self.SetCash(100000) self.symbols = [ "CME_S1", # Soybean Futures, Continuous Contract "CME_W1", # Wheat Futures, Continuous Contract "CME_SM1", # Soybean Meal Futures, Continuous Contract "CME_BO1", # Soybean Oil Futures, Continuous Contract "CME_C1", # Corn Futures, Continuous Contract "CME_O1", # Oats Futures, Continuous Contract "CME_LC1", # Live Cattle Futures, Continuous Contract "CME_FC1", # Feeder Cattle Futures, Continuous Contract "CME_LN1", # Lean Hog Futures, Continuous Contract "CME_GC1", # Gold Futures, Continuous Contract "CME_SI1", # Silver Futures, Continuous Contract "CME_PL1", # Platinum Futures, Continuous Contract "CME_CL1", # Crude Oil Futures, Continuous Contract "CME_HG1", # Copper Futures, Continuous Contract "CME_LB1", # Random Length Lumber Futures, Continuous Contract # "CME_NG1", # Natural Gas (Henry Hub) Physical Futures, Continuous Contract "CME_PA1", # Palladium Futures, Continuous Contract "CME_RR1", # Rough Rice Futures, Continuous Contract "CME_DA1", # Class III Milk Futures "ICE_RS1", # Canola Futures, Continuous Contract "ICE_GO1", # Gas Oil Futures, Continuous Contract "CME_RB2", # Gasoline Futures, Continuous Contract "CME_KW2", # Wheat Kansas, Continuous Contract "ICE_WT1", # WTI Crude Futures, Continuous Contract "ICE_CC1", # Cocoa Futures, Continuous Contract "ICE_CT1", # Cotton No. 2 Futures, Continuous Contract "ICE_KC1", # Coffee C Futures, Continuous Contract "ICE_O1", # Heating Oil Futures, Continuous Contract "ICE_OJ1", # Orange Juice Futures, Continuous Contract "ICE_SB1", # Sugar No. 11 Futures, Continuous Contract ] self.period = 12 * 21 self.quantile = 5 self.SetWarmup(self.period) self.data = {} for symbol in self.symbols: data = self.AddData(QuantpediaFutures, symbol, Resolution.Daily) data.SetFeeModel(CustomFeeModel()) data.SetLeverage(5) self.data[symbol] = RollingWindow[float](self.period) self.Schedule.On(self.DateRules.MonthStart(self.symbols[0]), self.TimeRules.At(0, 0), self.Rebalance) def OnData(self, data): for symbol in self.symbols: symbol_obj = self.Symbol(symbol) if symbol_obj in data.Keys: price = data[symbol_obj].Value if price != 0: self.data[symbol].Add(price) def Rebalance(self): if self.IsWarmingUp: return # Skewness calculation skewness_data = {} for symbol in self.symbols: if self.data[symbol].IsReady: if self.Securities[symbol].GetLastData() and (self.Time.date() - self.Securities[symbol].GetLastData().Time.date()).days < 5: prices = np.array([x for x in self.data[symbol]]) returns = (prices[:-1] / prices[1:]) - 1 if len(returns) == self.period - 1: # NOTE: Manual skewness calculation example # avg = np.average(returns) # std = np.std(returns) # skewness = (sum(np.power((x - avg), 3) for x in returns)) / ((self.return_history[symbol].maxlen-1) * np.power(std, 3)) skewness_data[symbol] = skew(returns) long = [] short = [] if len(skewness_data) >= self.quantile: # Skewness sorting sorted_by_skewness = sorted(skewness_data.items(), key = lambda x: x[1], reverse = True) quintile = int(len(sorted_by_skewness) / self.quantile) long = [x[0] for x in sorted_by_skewness[-quintile:]] short = [x[0] for x in sorted_by_skewness[:quintile]] # Trade execution invested = [x.Key.Value for x in self.Portfolio if x.Value.Invested] for symbol in invested: if symbol not in long + short: self.Liquidate(symbol) for symbol in long: self.SetHoldings(symbol, 1 / len(long)) for symbol in short: self.SetHoldings(symbol, -1 / len(short)) # Quantpedia data. # NOTE: IMPORTANT: Data order must be ascending (datewise) class QuantpediaFutures(PythonData): def GetSource(self, config, date, isLiveMode): return SubscriptionDataSource("data.quantpedia.com/backtesting_data/futures/{0}.csv".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv) def Reader(self, config, line, date, isLiveMode): data = QuantpediaFutures() data.Symbol = config.Symbol if not line[0].isdigit(): return None split = line.split(';') data.Time = datetime.strptime(split[0], "%d.%m.%Y") + timedelta(days=1) data['back_adjusted'] = float(split[1]) data['spliced'] = float(split[2]) data.Value = float(split[1]) return data # Custom fee model. class CustomFeeModel(FeeModel): def GetOrderFee(self, parameters): fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005 return OrderFee(CashAmount(fee, "USD"))