The strategy involves 30 commodity futures, ranking them daily based on normalized prices. The investor goes long on the lowest-ranked (L) commodities and shorts the highest-ranked (H) commodities, rebalancing daily.
ASSET CLASS: CFDs, futures | REGION: Global | FREQUENCY:
Daily | MARKET: commodities | KEYWORD: Rank Effect, Commodities
I. STRATEGY IN A NUTSHELL
Constructs a daily long-short portfolio using 30 commodity futures, going long the lowest-ranked and short the highest-ranked commodities based on normalized prices.
II. ECONOMIC RATIONALE
Lower-ranked commodities tend to appreciate faster than higher-ranked ones if normalized price distributions are stationary, creating a predictable rank effect.
III. SOURCE PAPER
The Rank Effect for Commodities [Click to Open PDF]
Ricardo T. Fernholz, Claremont McKenna College – Robert Day School of Economics and Finance; Christoffer Koch, Federal Reserve Bank of Dallas
<Abstract>
We uncover a large and significant low-minus-high rank effect for commodities across two centuries. There is nothing anomalous about this anomaly, nor is it clear how it can be arbitraged away. Using nonparametric econometric methods, we demonstrate that such a rank effect is a necessary consequence of a stationary relative asset price distribution. We confirm this prediction using daily commodity futures prices and show that a portfolio consisting of lower-ranked, lower-priced commodities yields 23% higher annual returns than a portfolio consisting of higher-ranked, higher-priced commodities. These excess returns have a Sharpe ratio nearly twice as high as the U.S. stock market yet are uncorrelated with market risk. In contrast to the extensive literature on asset pricing factors and anomalies, our results are structural and rely on minimal and realistic assumptions for the long-run properties of relative asset prices
IV. BACKTEST PERFORMANCE
| Annualised Return | 23.2% |
| Volatility | N/A |
| Beta | 0.146 |
| Sharpe Ratio | N/A |
| Sortino Ratio | 0.194 |
| Maximum Drawdown | N/A |
| Win Rate | 45% |
V. FULL PYTHON CODE
from AlgorithmImports import *
class RankEffectForCommodities(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2010, 1, 1)
self.SetCash(100000)
self.SetMaximumOrders(100000)
self.quantile: int = 5
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_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_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
]
for symbol in self.symbols:
data = self.AddData(QuantpediaFutures, symbol, Resolution.Daily)
data.SetFeeModel(CustomFeeModel())
data.SetLeverage(10)
self.base_price = {}
self.settings.daily_precise_end_time = False
def OnData(self, data):
# base
if self.Time.year == 2010 and self.Time.month == 1 and self.Time.day == 5:
for symbol in self.symbols:
if symbol in data and data[symbol]:
self.base_price[symbol] = data[symbol].Value
performance = {}
for symbol in self.symbols:
# Check if data is still coming.
if self.securities[symbol].get_last_data() and self.time.date() > QuantpediaFutures.get_last_update_date()[symbol]:
self.liquidate(symbol)
continue
symbol_obj = self.Symbol(symbol)
if symbol_obj in data and data[symbol_obj]:
price = data[symbol_obj].Value
if price != 0 and symbol in self.base_price:
base_price = self.base_price[symbol]
ret = (price / base_price) - 1
performance[symbol] = ret
if len(performance) < self.quantile:
return
sorted_perf = sorted(performance.items(), key = lambda x: x[1], reverse = True)
quintile = int(len(sorted_perf) / self.quantile)
long = [x[0] for x in sorted_perf[-quintile:]]
short = [x[0] for x in sorted_perf[:quintile]]
targets: List[PortfolioTarget] = []
for i, portfolio in enumerate([long, short]):
for symbol in portfolio:
if symbol in data and data[symbol]:
targets.append(PortfolioTarget(symbol, ((-1) ** i) / len(portfolio)))
self.SetHoldings(targets, True)
# 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):
_last_update_date:Dict[Symbol, datetime.date] = {}
@staticmethod
def get_last_update_date() -> Dict[Symbol, datetime.date]:
return QuantpediaFutures._last_update_date
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])
if config.Symbol.Value not in QuantpediaFutures._last_update_date:
QuantpediaFutures._last_update_date[config.Symbol.Value] = datetime(1,1,1).date()
if data.Time.date() > QuantpediaFutures._last_update_date[config.Symbol.Value]:
QuantpediaFutures._last_update_date[config.Symbol.Value] = data.Time.date()
return data
# Custom fee model.
class CustomFeeModel(FeeModel):
def GetOrderFee(self, parameters):
fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
return OrderFee(CashAmount(fee, "USD"))VI. Backtest Performance
