The thesis
RSI Snap-Back is a mean-reversion strategy that plays the Mag-7 — AAPL, MSFT, NVDA, GOOGL, AMZN, META and TSLA. The premise is simple and testable: large-cap tech tends to overshoot on short-term momentum, then snap back. The rules encode that directly — buy names when RSI falls below 35 (oversold), exit when RSI pushes above 70 (overbought), and hold no more than four names at once. That hard 4-slot book is the discipline mechanism: it caps concurrent exposure and forces the strategy to rotate capital into the most oversold candidates rather than sprawling across the whole universe.
Recent activity
Here is the honest picture: nothing has happened lately. The last six scheduled runs — from 2026-08-18 through 2026-08-25 — each report 0 executed, 0 rejected, with the book flat at $10,000 cash and $10,000 total. No open positions, no rejected orders.
That is not a malfunction; it is the strategy behaving as designed. If none of the seven names are printing RSI below 35, there is nothing to buy, and a mean-reversion system with no oversold signal correctly does nothing. The flip side is that RSI Snap-Back is entirely on the sidelines during a stretch where its universe evidently hasn't reached the extremes it hunts. It earns nothing while it waits.
Backtest and validation
Over a 451-day backtest the strategy returned 20.95%, lifting a $10k book to $12,095 — an 11.21% CAGR. The win rate is a healthy 66.67% across 37 trades, and total fees were a trivial $37 with no FX cost.
The risk numbers deserve equal billing. The Sharpe of 0.61 is modest — this is a bumpy ride relative to its return. The max drawdown of 23.73% is steep for a strategy meant to be defensive and mean-reverting; a trader would have watched nearly a quarter of the book evaporate at the worst point. Turnover of 773% confirms this is an active rotator, not a buy-and-hold — fees stayed low here, but that churn is a real drag under wider spreads or higher costs.
The verdict
The strengths are genuine: a clear, mechanical edge, a high win rate, and built-in position discipline. But two flags stand out. First, validation is null — there is no walk-forward or out-of-sample result on record, so the 20.95% is an in-sample fit until proven otherwise, and mean-reversion rules are especially prone to curve-fitting. Second, the drawdown-to-Sharpe profile says this strategy pays for its returns with volatility.
RSI Snap-Back is a credible, well-constructed idea that has yet to clear the bar that matters most on this platform: robust out-of-sample validation. Until it does — and until its signals actually fire again — treat the live book's current flat line as neither good news nor bad, just patience.