Overall Statistics
```# 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:
else:

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:]]

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"```