The Thesis
RSI Snap-Back operates on a deceptively simple premise: the seven largest US technology names — AAPL, MSFT, NVDA, GOOGL, AMZN, META, and TSLA — don't stay oversold for long. When one of them drops to an RSI below 35, institutional support and fundamental gravity tend to pull it back. The strategy enters on that signal and exits when RSI crosses above 70, capturing the reversion rather than riding the trend.
The four-slot book is the structural spine of the approach. By capping concurrent positions at four names, the strategy avoids the scenario where a sector-wide drawdown hits all seven names simultaneously. It's a practical way to enforce diversification without complicating the entry logic.
Backtest Performance
Over 451 days of backtesting, RSI Snap-Back generated a 20.95% total return on a $10,000 starting equity, compounding to $12,095. That works out to an 11.21% CAGR — comfortably above a simple cash position, though the headline number deserves some context.
The Sharpe ratio of 0.61 tells a more nuanced story. It's not a standout risk-adjusted figure; for every unit of volatility taken on, the strategy earns less than two-thirds of a unit of return. The maximum drawdown of 23.73% is the harder number to sit with — a peak-to-trough loss of nearly a quarter of the portfolio is a real test of conviction, particularly in a concentrated universe of high-beta tech names.
On the positive side, a 66.7% win rate across 37 trades is a meaningful edge for a mean-reversion system. Snap-back strategies often struggle with win rate because the trades that don't work tend to keep going against you. Two-thirds batting average over 37 trades is a credible signal, though the sample size remains modest.
Turnover at 773% is high relative to the trade count, which likely reflects the way positions are sized and rotated as the 4-slot book updates. This is worth watching as the strategy scales — friction compounds.
Recent Activity: Waiting for the Setup
The past week has been quiet — very quiet. Scheduled runs on June 10, 11, 12, 15, 16, and 17 all returned zero executed trades, with the portfolio sitting entirely in cash at $10,000. No positions were entered or rejected; the RSI thresholds simply haven't triggered across the Mag-7 universe.
This is the strategy behaving exactly as designed. If the seven largest tech names are in momentum or consolidating above oversold territory, RSI Snap-Back waits. Cash drag is real, but undisciplined entries would be worse. The stretch of inactivity also serves as a reminder that this strategy is inherently episodic — it earns in bursts, not steadily.
Risks and Considerations
A few flags worth tracking:
- No out-of-sample validation. The validation field is currently null. All performance figures come from backtested data, which carries curve-fitting risk — particularly with a small 37-trade sample and a universe of just seven names.
- Concentration in a correlated universe. The Mag-7 move together more often than not. A macro shock that clips all seven simultaneously could overwhelm the 4-slot discipline.
- Mean-reversion in a momentum regime. If large-cap tech enters a sustained uptrend, the RSI < 35 threshold may fire rarely or not at all — extending the current cash-heavy posture indefinitely.
- Drawdown tolerance. The 23.73% maximum drawdown means a live account running this strategy should be sized with the expectation of significant paper losses before recovery.
RSI Snap-Back is a coherent, well-structured strategy with a genuine statistical edge in its backtest period. The priority before increasing capital allocation should be running a formal out-of-sample validation pass to stress-test those numbers against data the strategy didn't train on.