Overall Statistics |
Total Trades
1629
Average Win
0.78%
Average Loss
-0.60%
Compounding Annual Return
6.768%
Drawdown
19.300%
Expectancy
0.288
Net Profit
247.385%
Sharpe Ratio
0.692
Probabilistic Sharpe Ratio
9.026%
Loss Rate
44%
Win Rate
56%
Profit-Loss Ratio
1.31
Alpha
0.07
Beta
0.01
Annual Standard Deviation
0.102
Annual Variance
0.01
Information Ratio
0.029
Tracking Error
0.213
Treynor Ratio
6.885
Total Fees
$0.00
|
# 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 scipy.stats import skew from collections import deque class Skewness_Effect(QCAlgorithm): def Initialize(self): self.SetStartDate(2000, 1, 1) self.SetEndDate(2019, 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_NG1", # Natural Gas (Henry Hub) Physical Futures, Continuous Contract "CME_PA1", # Palladium 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.lookup_period = 12*21 self.SetWarmup(self.lookup_period) self.data = {} # True -> Quantpedia data # False -> Quandl free data self.use_quantpedia_data = True if not self.use_quantpedia_data: self.symbols = ['CHRIS/' + x for x in self.symbols] for symbol in self.symbols: if self.use_quantpedia_data: self.AddData(QuantpediaFutures, symbol, Resolution.Daily) else: self.AddData(QuandlFutures, symbol, Resolution.Daily) self.data[symbol] = deque(maxlen=self.lookup_period) self.Schedule.On(self.DateRules.MonthStart(self.symbols[0]), self.TimeRules.AfterMarketOpen(self.symbols[0]), self.Rebalance) def OnData(self, data): for symbol in self.symbols: if self.Securities.ContainsKey(symbol): price = self.Securities[symbol].Price if price != 0: self.data[symbol].append(price) def Rebalance(self): if self.IsWarmingUp: return # Skewness calculation skewness_data = {} for symbol in self.symbols: if len(self.data[symbol]) == self.data[symbol].maxlen: prices = np.array([x for x in self.data[symbol]]) returns = (prices[1:]-prices[:-1])/prices[:-1] if len(returns) == self.lookup_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) # Skewness sorting sorted_by_skewness = sorted(skewness_data.items(), key = lambda x: x[1], reverse = True) quintile = int(len(sorted_by_skewness)/5) 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) count = len(long + short) if count == 0: return for symbol in long: self.SetHoldings(symbol, 1/count) for symbol in short: self.SetHoldings(symbol, -1/count) # Quantpedia data 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 try: if not line[0].isdigit(): return None split = line.split(';') data.Time = datetime.strptime(split[0], "%d.%m.%Y") + timedelta(days=1) data['settle'] = float(split[1]) data.Value = float(split[1]) except: return None return data # Quandl free data class QuandlFutures(PythonQuandl): def __init__(self): self.ValueColumnName = "settle"