DeployQuant200-Day SMA Regime Filter vs SMA-200 Trend + 15% Trailing Stop
Two rule sets, 59 ETFs, one engine and one window.
| 200-day regime filter | trend + trailing stop | |
|---|---|---|
| Median CAGR (59 ETFs) | 1.8% | 2.6% |
| Median max drawdown | −22.2% | −31.1% |
| ETFs won (by CAGR) | 30 | 29 |
| Style | a first systematic strategy, simple enough to audit every trade | long trends with moderate pullbacks; the 15% trail is wide enough to survive normal corrections |
Where the gap was biggest
| ETF | 200-day regime filter | trend + trailing stop | gap |
|---|---|---|---|
| SOXS | −19.7% | −4.9% | 14.8% |
| CTA | −3.0% | 5.5% | 8.5% |
| FAS | −2.7% | −11.1% | 8.3% |
| TBF | 4.3% | 11.3% | 7.0% |
| RINF | −2.1% | 4.0% | 6.1% |
| REW | −13.0% | −7.1% | 5.9% |
| SOXL | 23.4% | 17.8% | 5.6% |
| EEV | −5.6% | −0.9% | 4.7% |
| QLD | 21.6% | 17.2% | 4.4% |
| UVXY | −29.8% | −25.4% | 4.4% |
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: 200-day regime filter or trend + trailing stop?
On this 2021-01-04 to 2026-10-02 window, 200-day regime filter produced the higher CAGR on 30 of 59 ETFs. Median CAGR: 200-day regime filter 1.8% vs trend + trailing stop 2.6%; median max drawdown: 22.2% vs 31.1%. 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.