Overall Statistics |
Total Trades
5304
Average Win
0.75%
Average Loss
-0.73%
Compounding Annual Return
5.943%
Drawdown
23.800%
Expectancy
0.089
Net Profit
404.067%
Sharpe Ratio
0.617
Loss Rate
46%
Win Rate
54%
Profit-Loss Ratio
1.03
Alpha
0.064
Beta
-0.012
Annual Standard Deviation
0.102
Annual Variance
0.01
Information Ratio
0.013
Tracking Error
0.195
Treynor Ratio
-5.066
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 class Skewness_Effect(QCAlgorithm): def Initialize(self): self.SetStartDate(1991, 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(2*self.lookup_period) # 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.Schedule.On(self.DateRules.MonthStart(self.symbols[0]), self.TimeRules.AfterMarketOpen(self.symbols[0]), self.Rebalance) def Rebalance(self): if self.IsWarmingUp: return # Skewness calculation skewness_data = {} for symbol in self.symbols: # NOTE: There's no need to exclude last day from history anymore, since we download data the right way -> with no Look-Ahead Bias. hist = self.History([symbol], 2*self.lookup_period, Resolution.Daily) if 'settle' in hist: hist = hist['settle'][-self.lookup_period:] prices = np.array(hist) 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) top_symbols = [x[0] for x in sorted_by_skewness[:quintile]] low_symbols = [x[0] for x in sorted_by_skewness[-quintile:]] # Trade execution self.Liquidate() for symbol in low_symbols: self.SetHoldings(symbol, 1/(2*quintile)) for symbol in top_symbols: self.SetHoldings(symbol, -1/(2*quintile)) # Quantpedia data class QuantpediaFutures(PythonData): def GetSource(self, config, date, isLiveMode): return SubscriptionDataSource("https://quantpedia.com/backtesting_data/futures/{0}.csv".format(config.Symbol), 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"