Overall Statistics Total Trades 2799 Average Win 1.28% Average Loss -1.02% Compounding Annual Return 16.645% Drawdown 40.000% Expectancy 0.253 Net Profit 2541.530% Sharpe Ratio 0.795 Probabilistic Sharpe Ratio 9.420% Loss Rate 44% Win Rate 56% Profit-Loss Ratio 1.26 Alpha 0.157 Beta 0.005 Annual Standard Deviation 0.198 Annual Variance 0.039 Information Ratio 0.323 Tracking Error 0.265 Treynor Ratio 31.029 Total Fees $6840.58 Estimated Strategy Capacity$0
# 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 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_CU1",  # Chicago Ethanol (Platts) Futures
"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.SetWarmup(self.period)
self.data = {}

for symbol in self.symbols:
data.SetFeeModel(CustomFeeModel(self))
data.SetLeverage(5)

self.data[symbol] = RollingWindow[float](self.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:
symbol_obj = self.Symbol(symbol)
if symbol_obj in data.Keys:
price = data[symbol_obj].Value
if price != 0:

def Rebalance(self):
if self.IsWarmingUp: return

# Skewness calculation
skewness_data = {}
for symbol in self.symbols:
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)

# 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]]

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)
return OrderFee(CashAmount(fee, "USD"))