Commodity Futures Strategy Based on Return Skewness

Summary

This strategy ranks a universe of commodity futures by the skewness of their daily returns over a trailing 12-month window. At each monthly rebalance, it goes long the quintile with the lowest skewness and short the quintile with the highest, with equal weights within each side. The code uses continuous futures data, waits for a warm-up history, and checks that recent observations are available before calculating rankings.

The document provides implementation details for monthly scheduling, position updates, leverage, and a fee model, but it does not report backtest performance or establish that the signal is profitable after realistic costs. The example's universe and data source are fixed, and practical results would depend on contract construction, roll treatment, execution, financing, and the treatment of missing or stale prices. It is best read as a strategy specification and coding example rather than evidence of an enduring skewness premium.

Key ideas

• The strategy measures each commodity future's skewness using roughly one year of daily returns.
• It buys the lowest-skewness quintile and sells the highest-skewness quintile.
• Positions are equally weighted within each side and rebalanced monthly.
• The example uses continuous futures data and includes leverage and transaction fees in its implementation.
• No performance results are supplied, so profitability and robustness remain unestablished.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.