DeployQuantFirst-to-Last Day of Month vs RSI(2) Dip Snapback
Two rule sets, 59 ETFs, one engine and one window.
| monthly cycle | RSI(2) snapback | |
|---|---|---|
| Median CAGR (59 ETFs) | 5.5% | 4.7% |
| Median max drawdown | −36.6% | −17.5% |
| ETFs won (by CAGR) | 17 | 42 |
| Style | measuring how much of an asset's return accrues inside the month versus across month boundaries | liquid index ETFs with strong long-term drift; turnover is high so per-trade edges are small |
Where the gap was biggest
| ETF | monthly cycle | RSI(2) snapback | gap |
|---|---|---|---|
| SOXS | −77.5% | −29.9% | 47.6% |
| UVXY | −68.7% | −30.3% | 38.4% |
| SQQQ | −44.6% | −9.7% | 34.9% |
| TECS | −55.6% | −26.6% | 29.0% |
| TMF | −32.2% | −4.0% | 28.2% |
| QID | −28.8% | −1.9% | 27.0% |
| REW | −37.2% | −10.9% | 26.2% |
| TQQQ | 21.5% | 39.3% | 17.8% |
| SDS | −20.5% | −3.6% | 17.0% |
| VIXM | −14.8% | 0.1% | 15.0% |
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Frequently asked questions
Which is better: monthly cycle or RSI(2) snapback?
On this 2021-01-04 to 2026-10-02 window, RSI(2) snapback produced the higher CAGR on 42 of 59 ETFs. Median CAGR: monthly cycle 5.5% vs RSI(2) snapback 4.7%; median max drawdown: 36.6% vs 17.5%. 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.