DeployQuantDrawdown Dip Buyer + 8% Target vs RSI(2) Dip Snapback
Two rule sets, 59 ETFs, one engine and one window.
| dip buyer | RSI(2) snapback | |
|---|---|---|
| Median CAGR (59 ETFs) | 1.3% | 4.7% |
| Median max drawdown | −26.2% | −17.5% |
| ETFs won (by CAGR) | 4 | 55 |
| Style | assets that sell off hard and recover; it trades volatility without chasing strength | liquid index ETFs with strong long-term drift; turnover is high so per-trade edges are small |
Where the gap was biggest
| ETF | dip buyer | RSI(2) snapback | gap |
|---|---|---|---|
| SQQQ | −39.2% | −9.7% | 29.4% |
| TQQQ | 11.9% | 39.3% | 27.4% |
| TMF | −29.8% | −4.0% | 25.8% |
| QID | −27.1% | −1.9% | 25.3% |
| SOXL | 16.4% | 39.2% | 22.8% |
| REW | −33.5% | −10.9% | 22.6% |
| TECS | −45.3% | −26.6% | 18.7% |
| UVXY | −48.7% | −30.3% | 18.4% |
| VIXM | −16.7% | 0.1% | 16.8% |
| SDS | −19.9% | −3.6% | 16.3% |
Build it from blocks (or type it in English), backtest it on 5.7 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: dip buyer or RSI(2) snapback?
On this 2021-01-04 to 2026-10-02 window, RSI(2) snapback produced the higher CAGR on 55 of 59 ETFs. Median CAGR: dip buyer 1.3% vs RSI(2) snapback 4.7%; median max drawdown: 26.2% 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.