Original paper
Abstract
While much is known about the financialization of commodities, less is known about how to profitably invest in commodities. We develop a four-factor asset pricing model of commodity returns. Our four-factor model prices both commodity spot and term risk premia in an intuitive manner related to investable portfolios. The straightforward construction of our factors is an improvement over previous models. Furthermore, our four-factor model prices commodity risk premia using both sorted portfolios and risk adjusted alphas as benchmarks. Thus, we feel it is an appropriate benchmark to evaluate commodity investment vehicles.
Keywords:Â commodity investments, risk premia, smart beta, exchange traded funds
Trading rules
- Determine a set of commodity futures for trading purposes
- Assess the historical performance of every future from the previous year
- Categorize futures into five groups depending on their performance
- Take a bullish position on the quintile with the strongest momentum
- Take a bearish position on the quintile with the weakest momentum
- Adjust positions on a monthly basis.
Python code
Backtrader
import backtrader as bt
import numpy as np
class CommodityMomentum(bt.Strategy):
params = (
('lookback', 12),
('rebalance_period', 30),
)
def __init__(self):
self.future_data = {}
self.inds = {}
def prenext(self):
self.next()
def nextstart(self):
self.next()
def next(self):
if len(self.data) % self.params.rebalance_period == 0:
self.rebalance_portfolio()
def add_future_data(self, future_name, future_data):
self.future_data[future_name] = future_data
self.inds[future_name] = {}
self.inds[future_name]['momentum'] = bt.indicators.Momentum(future_data.close, period=self.params.lookback)
def rebalance_portfolio(self):
momentums = []
for future_name, future_data in self.future_data.items():
if len(future_data) < self.params.lookback:
continue
momentum_value = self.inds[future_name]['momentum'][0]
momentums.append((future_name, momentum_value))
sorted_momentums = sorted(momentums, key=lambda x: x[1])
quintile_size = len(sorted_momentums) // 5
long_futures = [future_name for future_name, _ in sorted_momentums[-quintile_size:]]
short_futures = [future_name for future_name, _ in sorted_momentums[:quintile_size]]
for future_name, future_data in self.future_data.items():
if future_name in long_futures:
self.order_target_percent(future_data, target=1.0/len(long_futures))
elif future_name in short_futures:
self.order_target_percent(future_data, target=-1.0/len(short_futures))
else:
self.order_target_percent(future_data, target=0)
cerebro = bt.Cerebro()
# Add your data feeds and set the respective names
# Example: data_feed = bt.feeds.YourDataFeed(dataname="your_data_feed")
# cerebro.adddata(data_feed, name="future_name")
strategy = cerebro.addstrategy(CommodityMomentum)
for future_name, future_data in strategy.future_data.items():
strategy.add_future_data(future_name, future_data)
cerebro.run()Please note that you need to add your data feeds and set the respective names before running the code.