Original paper
Abstract
We compare the performance of two volatility scaling methods in momentum strategies: (i) the constant volatility scaling approach of Barroso and Santa-Clara (2015), and (ii) the dynamic volatility scaling method of Daniel and Moskowitz (2016). We perform momentum strategies based on these two approaches in an asset pool consisting of 55 global liquid futures contracts, and further compare these results to the time series momentum and buy-and-hold strategies. We find that the momentum strategy based on the constant volatility scaling method is the most efficient approach with an annual return of 15.3%.
Keywords:Â Cross-sectional momentum, Time series momentum, Momentum crashes, Volatility scaling
Trading rules
- Consider an assortment of 18 futures contracts comprising 7 currencies, 6 stock indices, and 5 fixed-income instruments.
- For currency futures, examine: Euro to USD, Yen to USD, Canadian Dollar to USD, British Pound to USD, Australian Dollar to USD, Swiss Franc to USD, and the USD index.
- For stock index futures, look at: France's CAC 40, UK's FTSE 100, Japan's Nikkei 225, USA's S&P 500, Swiss Market Index, and the Dow Jones EURO STOXX 50.
- For fixed income futures, focus on: 10-year Euro Bund, 10-year Canadian Government Bond, 2-year U.S. Treasury Note, 5-year U.S. Treasury Note, and 10-year U.S. Treasury Note.
- Biannually evaluate and order these futures based on their performance over the past half-year.
- Commit to the top six performing futures while betting against the bottom six.
- Distribute investments uniformly across the portfolio.
- Adjust portfolio composition every 6 months.
Python code
Backtrader
import backtrader as bt
class MomentumFutures(bt.Strategy):
params = (
('momentum_period', 126), # 6 months momentum period
('rebalance_days', 126), # Rebalance every 6 months
)
def __init__(self):
self.future_names = [
# Currency futures
'EURUSD', 'JPYUSD', 'CADUSD', 'GBPUSD', 'AUDUSD', 'CHFUSD', 'USDIDX',
# Stock index futures
'CAC40', 'FTSE100', 'NIKKEI225', 'SP500', 'SMI', 'DJEUROSTOXX50',
# Fixed income futures
'EUROBUND10Y', 'CGB10Y', 'USTNOTE2Y', 'USTNOTE5Y', 'USTNOTE10Y',
]
self.momentum = {data: bt.indicators.Momentum(data, period=self.params.momentum_period) for data in self.datas}
self.rebalance_countdown = self.params.rebalance_days
def next(self):
if self.rebalance_countdown > 0:
self.rebalance_countdown -= 1
else:
self.rebalance_countdown = self.params.rebalance_days
# Rank futures based on 6-month performance
rankings = list(self.datas)
rankings.sort(key=lambda data: self.momentum[data][0], reverse=True)
# Long top 6 and short bottom 6 performers
for i, data in enumerate(rankings):
position_size = 1 / 12
if i < 6:
self.order_target_percent(data, target=position_size)
elif i >= len(rankings) - 6:
self.order_target_percent(data, target=-position_size)
else:
self.order_target_percent(data, target=0)
if __name__ == '__main__':
cerebro = bt.Cerebro()
cerebro.addstrategy(MomentumFutures)
# Add futures data feeds
for future_name in MomentumFutures.future_names:
data = bt.feeds.YourDataFeed(dataname=future_name) # Replace 'YourDataFeed' with the actual data feed class
cerebro.adddata(data)
cerebro.run()Please replace ‘YourDataFeed’ with the actual data feed class that you are using for your backtesting.