Make money doing the work you believe in
Tomorrow, I will be sharing my implementation of the paper “Applying Deep Learning to Enhance Momentum Trading Strategies in Stocks”, by Lawrence Takeuchi and Yu-Ying (Albert) Lee:
The reported results are striking: the deep learning model delivers an annualized return of 45.9% over the 1990–2009 test period, vastly outperforming the basic momentum strategy, which returned just 10.5%. It also achieved significantly higher Sharpe ratios and outperformance across deciles ranked by model confidence.
While our implementation uses a more modern approach (end-to-end training, no RBMs), and our dataset differs significantly in scope and period, the core motivation remains the same: to test whether deep learning can extract meaningful alpha from raw price signals, validating the broader claim of Richard Sutton’s Bitter Lesson—that general-purpose, computation-heavy methods outperform manually engineered ones over time.
