The Thesis
RSI Snap-Back is built on a straightforward premise: the largest US tech companies — Apple, Microsoft, Nvidia, Alphabet, Amazon, Meta, and Tesla — tend to recover sharply from short-term oversold conditions. The strategy enters when RSI drops below 35 and exits when it crosses above 70, rotating through a deliberately tight four-slot book. That slot limit isn't cosmetic; it enforces concentration discipline and caps the number of simultaneous drawdowns the portfolio can absorb at any one time.
Backtest Performance
Over 451 days of backtested history, RSI Snap-Back produced a total return of 20.95% (roughly $12,095 on a $10,000 starting equity), with a CAGR of 11.21%. Those are respectable numbers for a strategy that only executed 37 trades across the period — an average of one new position roughly every 12 days.
The 66.67% win rate is the headline strength: two out of every three trades closed profitably. In mean-reversion frameworks, that consistency matters more than raw return magnitude, because the strategy depends on having enough winning trades to offset the occasional sharp loss.
The Sharpe ratio of 0.61 is modest but not alarming for an equity mean-reversion approach. It reflects the reality that snap-back strategies carry genuine volatility — the 23.73% max drawdown confirms this. A portfolio that draws down nearly a quarter of its value at some point during a 15-month window is asking for real conviction from its holder.
Turnover clocked in at 773% — high in absolute terms, but not surprising given the strategy's design. Frequent rotation through a concentrated book naturally churns. At $1 per trade in fees, total costs were a manageable $37.
Recent Activity: Cash Preservation
The live paper-trading log tells a quieter story. Every scheduled run from June 17 through June 24 — six consecutive sessions — returned zero executions and zero rejections, leaving the full $10,000 book in cash. RSI Snap-Back simply hasn't seen any of its seven names drop into oversold territory (RSI < 35) during this stretch.
This is the strategy working as designed, not failing. Discipline in entry conditions is what makes the backtest win rate credible. A strategy that chases entries when its signal isn't present would erode the edge entirely.
Strengths and Risks
Strengths:
- Clean, rules-based logic with no ambiguity in execution
- Concentrated universe limits data noise and forces selectivity
- Consistent win rate suggests the RSI threshold calibration is sound
- Hard slot limit provides built-in risk management without complex position sizing
Risks:
- The 23.73% max drawdown is real and would test patience in a live account; mean-reversion strategies can get caught leaning into a trend that doesn't reverse
- No validation data is available yet — the backtest metrics are the only evidence of edge, and backtests on a 7-stock universe over 15 months leave limited room for statistical confidence
- Extended periods of inactivity (as seen now) are structurally fine but create pressure to lower entry thresholds — a temptation the strategy's rules rightly resist
- High turnover may become a tax or cost concern if ever moved from paper to live trading
Outlook
RSI Snap-Back is in a holding pattern that reflects market conditions rather than any strategy failure. The backtest suggests a workable edge when entries materialize. The more pressing open question is validation: with no out-of-sample test data yet recorded, it remains difficult to distinguish genuine alpha from curve-fitting on a narrow universe. As live paper-trading history accumulates, that picture will sharpen.