RSI(2) Dip Snapback vs 200-Day SMA Regime Filter
Two rule sets, 59 ETFs, one engine and window — a genuinely like-for-like comparison.
| RSI(2) snapback | 200-day regime filter | |
|---|---|---|
| Median CAGR (59 ETFs) | 4.4% | 1.6% |
| Median max drawdown | −19.5% | −22.7% |
| ETFs won (by CAGR) | 45 | 14 |
| Style | liquid index ETFs with strong long-term drift; turnover is high so per-trade edges are small | a first systematic strategy — it's simple enough to fully understand and audit every trade |
Where the gap was biggest
| ETF | RSI(2) snapback | 200-day regime filter | gap |
|---|---|---|---|
| CLSE | 6.3793117942596455e+31% | −100.0% | 6.3793117942596455e+31% |
| KMLM | −100.0% | −0.7% | 99.3% |
| EEV | −100.0% | −5.8% | 94.2% |
| TECL | 29.4% | 5.6% | 23.8% |
| SOXS | −26.6% | −2.9% | 23.7% |
| FAS | 19.3% | −0.5% | 19.8% |
| TQQQ | 39.2% | 23.6% | 15.6% |
| SSO | 25.7% | 12.1% | 13.7% |
| VIXM | 1.0% | −12.6% | 13.6% |
| SPUU | 22.7% | 9.8% | 12.9% |
Build it from blocks (or type it in English), backtest it on 5.5 years of minute data in seconds, tweak any parameter, then paper trade it on live data. No card, no broker needed to start.
Frequently asked questions
Which is better: RSI(2) snapback or 200-day regime filter?
On this 2021-01-04–2026-07-17 window, RSI(2) snapback produced the higher CAGR on 45 of 59 ETFs. Median CAGR: RSI(2) snapback 4.4% vs 200-day regime filter 1.6%; median max drawdown: 19.5% vs 22.7%. "Better" depends on the asset and what you optimize — the per-ETF table shows where each wins.
Dig deeper
Backtests are hypothetical, computed by DeployQuant's engine on minute-resolution consolidated US market data (2021-01-04 to 2026-07-17, $10,000 starting capital, no margin, fees and slippage not modeled) and do not guarantee future results. Nothing on this page is investment advice. Live trading involves risk of loss.