DeployQuantRSI(2) Dip Snapback vs 200-Day SMA Regime Filter
Two rule sets, 59 ETFs, one engine and one window.
| RSI(2) snapback | 200-day regime filter | |
|---|---|---|
| Median CAGR (59 ETFs) | 4.7% | 1.8% |
| Median max drawdown | −17.5% | −22.2% |
| ETFs won (by CAGR) | 44 | 15 |
| Style | liquid index ETFs with strong long-term drift; turnover is high so per-trade edges are small | a first systematic strategy, simple enough to audit every trade |
Where the gap was biggest
| ETF | RSI(2) snapback | 200-day regime filter | gap |
|---|---|---|---|
| FAS | 18.7% | −2.7% | 21.4% |
| TECL | 30.2% | 11.5% | 18.7% |
| SOXL | 39.2% | 23.4% | 15.8% |
| TQQQ | 39.3% | 24.9% | 14.4% |
| SPUU | 24.1% | 10.5% | 13.6% |
| TECS | −26.6% | −13.9% | 12.7% |
| VIXM | 0.1% | −12.2% | 12.3% |
| SSO | 24.6% | 12.4% | 12.2% |
| RINF | 8.5% | −2.1% | 10.6% |
| XLY | 8.2% | −2.2% | 10.4% |
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Frequently asked questions
Which is better: RSI(2) snapback or 200-day regime filter?
On this 2021-01-04 to 2026-10-02 window, RSI(2) snapback produced the higher CAGR on 44 of 59 ETFs. Median CAGR: RSI(2) snapback 4.7% vs 200-day regime filter 1.8%; median max drawdown: 17.5% vs 22.2%. Which is better depends on the asset and what you optimize for. 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 2026-10-02, $10,000 starting capital, no margin, no fees or slippage in the headline run; buy-and-hold puts 98% of the account in at the first open, as the templates do) and do not guarantee future results. Nothing on this page is investment advice. Live trading involves risk of loss.