Deep Sector Rotation Swing Trading

Abstract

A system for sector rotation swing trading of exchange-traded funds (ETFs) using deep learning is presented. Weekly trades are made on funds representing 11 major sectors of the U.S. economy.
The trading system was backtested for the period January 2012 through December 2022. Annualized CAGR returns exceeded the benchmark buy-and-hold strategy by an average 12.63% (median 7.63%). Of particular note is the positive alpha (28.4%) achieved in trading for 2022, a difficult year for stocks in which the S&P 500 index experienced a CAGR loss of 18% . Over the studied period, Sharpe ratios averaged 1.39, and the mean maximum drawdown was 10%.
The deep model design is multiple-input, multiple output, and can be easily extended to include other factors that may influence predictability of future price movements. The results presented here are preliminary, and are exclusive of trading costs. Analysis of these costs is prerequisite to deployment as a semi-mechanical swing trading system.

Keywords: sector rotation, swing trading, deep learning