My strategy outperformed the S&P 500 over 3 years with 1/3 of the drawdown

Kevinthetrader · 2026-05-12T22:21:19+00:00

=== SUMMARY === - The post describes a volume-based trading system (VRT Levels) that achieved +92.56% returns over ~3 years with a 7.46% max drawdown, outperforming the S&P 500 with ~1/3 of the drawdown. - The author’s thesis is that a high-frequency, low-win-rate (35.7%) strategy with tight risk management (3 ATR stop, 2R target) can compound effectively despite a low win rate. - Quality assessment: This is a backtest showcase with limited forward-looking validation; community comments strongly indicate potential overfitting and lack of live results, making it speculative noise rather than well-researched DD. === SENTIMENT === MIXED === TRADE IDEAS === No actionable trade ideas in this post. The author does not specify any ticker, asset class, or market direction. The strategy is presented as a generic system without a concrete symbol or current trade recommendation. All commentary is about the methodology and backtest results, not a live or explicit trade setup. === COMMENTS SUMMARY === The top community comments are highly skeptical, warning that outperforming SPY in a backtest is a common “rite of passage” and that real-world implementation often fails due to overfitting and execution challenges. One comment suggests running rolling walk‑forward optimization tests to validate robustness. No additional data or counter‑arguments supporting a specific trade idea were provided.

u/Kevinthetrader · Reddit — r/algotrading · May 12, 2026 at 22:21 · ⬆ 19 pts · 💬 54 comments | View on Reddit ↗

AI Summary

Summary

The post describes a volume-based trading system (VRT Levels) that achieved +92.56% returns over ~3 years with a 7.46% max drawdown, outperforming the S&P 500 with ~1/3 of the drawdown.
The author’s thesis is that a high-frequency, low-win-rate (35.7%) strategy with tight risk management (3 ATR stop, 2R target) can compound effectively despite a low win rate.
Quality assessment: This is a backtest showcase with limited forward-looking validation; community comments strongly indicate potential overfitting and lack of live results, making it speculative noise rather than well-researched DD.

Score 19

Comments 54

Upvote % 68%