Overall Statistics |
Total Trades
2835
Average Win
1.45%
Average Loss
-1.40%
Compounding Annual Return
6.256%
Drawdown
44.900%
Expectancy
0.084
Net Profit
257.989%
Sharpe Ratio
0.38
Probabilistic Sharpe Ratio
0.069%
Loss Rate
47%
Win Rate
53%
Profit-Loss Ratio
1.03
Alpha
0.068
Beta
0.001
Annual Standard Deviation
0.18
Annual Variance
0.032
Information Ratio
-0.006
Tracking Error
0.252
Treynor Ratio
109.416
Total Fees
$6048.85
|
# https://quantpedia.com/strategies/term-structure-effect-in-commodities/ # # This simple strategy buys each month the 20% of commodities with the highest roll-returns and shorts the 20% of commodities with the lowest # roll-returns and holds the long-short positions for one month. The contracts in each quintile are equally-weighted. # The investment universe is all commodity futures contracts. class TermStructureCommodities(QCAlgorithm): def Initialize(self): self.SetStartDate(2000, 1, 1) self.SetCash(100000) self.symbols = [ "CME_S", # Soybean Futures, Continuous Contract "CME_W", # Wheat Futures, Continuous Contract "CME_SM", # Soybean Meal Futures, Continuous Contract "CME_BO", # Soybean Oil Futures, Continuous Contract "CME_C", # Corn Futures, Continuous Contract "CME_O", # Oats Futures, Continuous Contract "CME_LC", # Live Cattle Futures, Continuous Contract "CME_FC", # Feeder Cattle Futures, Continuous Contract "CME_LN", # Lean Hog Futures, Continuous Contract "CME_GC", # Gold Futures, Continuous Contract "CME_SI", # Silver Futures, Continuous Contract "CME_PL", # Platinum Futures, Continuous Contract "CME_CL", # Crude Oil Futures, Continuous Contract "CME_HG", # Copper Futures, Continuous Contract "CME_LB", # Random Length Lumber Futures, Continuous Contract "CME_NG", # Natural Gas (Henry Hub) Physical Futures, Continuous Contract "CME_PA", # Palladium Futures, Continuous Contract "CME_RR", # Rough Rice Futures, Continuous Contract "CME_CU", # Chicago Ethanol (Platts) Futures "CME_DA", # Class III Milk Futures "ICE_CC", # Cocoa Futures, Continuous Contract "ICE_CT", # Cotton No. 2 Futures, Continuous Contract "ICE_KC", # Coffee C Futures, Continuous Contract "ICE_O", # Heating Oil Futures, Continuous Contract "ICE_OJ", # Orange Juice Futures, Continuous Contract "ICE_SB", # Sugar No. 11 Futures, Continuous Contract ] # True -> Quantpedia data # False -> Quandl free data self.use_quantpedia_data = True if self.use_quantpedia_data: for symbol in self.symbols: sym = symbol + '1' data = self.AddData(QuantpediaFutures, sym, Resolution.Daily) data.SetLeverage(5) data.SetFeeModel(CustomFeeModel(self)) self.symbols2 = ['CHRIS/' + x for x in self.symbols] for symbol in self.symbols2: sym1 = symbol + '1' data = self.AddData(QuandlFutures, sym1, Resolution.Daily) if not self.use_quantpedia_data: data.SetLeverage(5) data.SetFeeModel(CustomFeeModel(self)) sym2 = symbol + '2' self.AddData(QuandlFutures, sym2, Resolution.Daily) self.Schedule.On(self.DateRules.MonthStart(self.symbols2[0] + '1'), self.TimeRules.AfterMarketOpen(self.symbols2[0] + '1'), self.Rebalance) def Rebalance(self): # Roll return calc. roll_return = {} for symbol_index in range(0, len(self.symbols2)): symbol = self.symbols2[symbol_index] sym1 = symbol + '1' sym2 = symbol + '2' traded_symbol = '' if self.use_quantpedia_data: traded_symbol = self.symbols[symbol_index] + '1' else: traded_symbol = sym1 price1 = self.Securities[sym1].Price price2 = self.Securities[sym2].Price if price1 != 0 and price2 != 0: roll_return[traded_symbol] = price1 / price2 - 1 # Roll return sorting. long = [] short = [] if len(roll_return) != 0: sorted_by_roll = sorted(roll_return.items(), key=lambda x: x[1], reverse = True) quintile = int(len(sorted_by_roll) / 5) long = [x[0] for x in sorted_by_roll[:quintile]] short = [x[0] for x in sorted_by_roll[-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 # Quandl free data class QuandlFutures(PythonQuandl): def __init__(self): self.ValueColumnName = "settle" # Custom fee model. class CustomFeeModel(FeeModel): def GetOrderFee(self, parameters): fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005 return OrderFee(CashAmount(fee, "USD"))