Original paper

SSRN-id3271841.pdf2073.5KB

Abstract

This paper investigates the performance of three different trading strategies – Jegadeesh and Titman (1993), George and Hwang (2004) and Gatev, Goetzmann and Rouwenhorst (2006) – in 29 commodity futures from January 1979 to October 2017. We find there is no significant reversal profit across 189 formation-holding windows for all the three strategies. However, there are statistical and economically significant momentum profits, and the profitability increases with the rising of formation-holding periods. The strategy of inversing the conventional Gatev, Goetzmann and Rouwenhorst (2006) is more profitable than the other two momentum strategies on a risk-adjusted basis; but the superiority declines sharply since 1998. Momentum returns are quite sensitive to market conditions but the crash of momentum returns are partly predictable. Return seasonality, risk and herding also provide partial explanation of the momentum profits.

Keywords: Commodity futures, Momentum, Reversal, Formation-holding windows, Herding

Trading rules

Choose a set of commodity futures to trade.
Review the 12-month performance of each future.
Group futures into five categories using their performance metrics.
Take a long position on the quintile showcasing the best results (strongest momentum).
Take a short position on the quintile displaying the weakest results (least momentum).
Adjust the portfolio 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.

Do Momentum and Reversal Strategies Work in Commodity Futures? A Comprehensive Study

Original paper

Abstract

Trading rules

Python code

Backtrader