How I Ran the Same Strategy Across Three Leveraged ETFs and Walked Away With a 96% Win Rate

· Source: Data Science on Medium · Field: Finance & Economics — Capital Markets & Investment Management · Depth: Intermediate, medium

Summary

An hourly trading strategy, refined over six months from December 26, 2025, to June 18, 2026, achieved a 96.43% win rate and a 60.22% compound return on the S&P 500 (SPY). This system utilizes an hourly chart for directional bias (v1) and a 15-minute chart for counter-trend moves (v2). When the identical signals were applied to 3x leveraged ETFs, the strategy demonstrated substantial return amplification driven by the underlying index Beta. SPXL (3x S&P 500) generated a 294.99% compound return, TQQQ (3x Nasdaq 100) yielded 723.19%, and SOXL (3x Semiconductors) produced an impressive 5,693.63%. While SPXL and TQQQ maintained a 96.43% win rate, SOXL's win rate dropped to 85.71% but delivered significantly higher average returns. The author identifies the system as a "volatility harvester" and suggests an optimal "triangle" architecture combining TQQQ and SPXL for win-rate stability and a smaller SOXL position for return elasticity, acknowledging the limited sample size and inherent risks of leveraged ETFs.

Key takeaway

For quantitative traders and portfolio managers evaluating high-frequency strategies, this analysis demonstrates how a simple hourly/15-minute counter-trend system can act as a powerful volatility harvester. You should consider diversifying across leveraged ETFs with varying Betas, such as TQQQ and SPXL for stability, and a smaller SOXL position for asymmetric return elasticity, while carefully managing the inherent structural risks of 3x leveraged products.

Key insights

A trading strategy's performance on leveraged ETFs scales exponentially with underlying Beta, acting as a volatility harvester.

Principles

Method

Use an hourly chart for trend bias (v1) and a 15-minute chart for counter-trend overlays (v2), triggering only when v2 opposes v1's direction.

In practice

Topics

Best for: Domain Expert, Consultant, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Data Science on Medium.