The Thesis
RSI Snap-Back is a pure mean-reversion play on the Magnificent Seven — AAPL, MSFT, NVDA, GOOGL, AMZN, META, and TSLA. The logic is straightforward: large-cap tech names tend to snap back sharply after short-term momentum extremes, so the strategy enters when RSI drops below 35 (oversold) and exits when it climbs above 70 (overbought). A hard cap of four concurrent positions enforces capital discipline and bounds drawdown exposure at any single moment.
The universe choice is deliberate. These names carry deep liquidity, heavy institutional attention, and a tendency to revert rather than trend indefinitely — all properties that favour a mean-reversion approach over a momentum one.
Recent Activity: The Patience Test
The six most recent scheduled runs — spanning June 15 through June 22 — all returned zero executed trades. The book sits fully in cash at $10,000. This is not a malfunction; it is the strategy working exactly as designed. RSI Snap-Back only enters when at least one Mag-7 name is genuinely oversold. If the market is grinding sideways or drifting higher without a sharp sell-off in any of the seven names, the entry condition simply never fires.
Prolonged cash periods are the honest cost of a disciplined entry filter. A looser RSI threshold might generate more activity but would also degrade the quality of each signal.
Backtest Performance
Over 451 days, the strategy compounded $10,000 into $12,095, a 20.95% total return and an annualised CAGR of 11.21%. The win rate across 37 trades was 66.67% — two out of every three trades closed green, a solid base rate for a systematic strategy.
The Sharpe ratio of 0.61 is modest. It reflects meaningful volatility relative to the returns generated, which is partly structural: mean-reversion strategies on individual equities tend to absorb drawdowns while waiting for reversion, even when the ultimate outcome is profitable.
The most important risk metric here is the max drawdown of 23.73%. That figure deserves attention. A portfolio that drops nearly a quarter of its value at some point in the test period will test real-world conviction, especially during momentum-driven sell-offs where oversold conditions can keep getting more oversold before they snap back.
Turnover of 773% over the period reflects active rotation through the four-slot book — each slot was recycled roughly 1.75 times per year on average.
Strengths and Risks
What works: The entry filter is strict enough to produce a high win rate. The four-slot book prevents over-concentration. The universe — liquid mega-caps — reduces the risk of a single name blowing up the strategy with an idiosyncratic event.
What to watch: A 23.73% drawdown is non-trivial for a strategy running on a $10,000 paper account. In a genuine market dislocation — say, a broad tech sector re-rating — RSI could stay depressed across multiple names simultaneously, filling all four slots at once and leaving nowhere to rotate. The strategy has no macro filter, no stop-loss below the RSI exit, and no sector-correlation guard.
The absence of a formal validation run (validation: null) also means backtest results have not yet been stress-tested against out-of-sample data. The 20.95% return looks attractive, but without a walk-forward or hold-out period, curve-fitting risk remains an open question.
Outlook
RSI Snap-Back is doing what it should: waiting. The next interesting data point will be what happens when the first entry fires — whether the strategy exits cleanly at RSI > 70 or gets trapped in a prolonged drawdown will tell us more about its live-market behaviour than any backtest figure.