Historical performance simulation — 60 stocks, 252 trading days, updated nightly.
| Quartile | N | Win % | Avg Ret | Score Range |
|---|---|---|---|---|
| Q1 (Low) | 63 | 54.0% | +0.307% | 0.524–0.768 |
| Q2 | 64 | 51.6% | +0.499% | 0.769–0.8 |
| Q3 | 62 | 56.5% | +0.637% | 0.8–0.824 |
| Q4 (High) | 63 | 50.8% | +0.082% | 0.824–0.861 |
The backtest period split into 4 equal windows. Consistent performance across all periods suggests genuine edge rather than luck in one stretch.
| Period | Dates | N | Win % | Avg Ret | Total | SPY Total |
|---|---|---|---|---|---|---|
| Period 1 | 2025-08-13 – 2025-11-10 | 63 | 47.6% | +0.151% | +9.5% | +6.3% |
| Period 2 | 2025-11-11 – 2026-02-11 | 63 | 63.5% | +0.521% | +32.8% | +2.0% |
| Period 3 | 2026-02-12 – 2026-05-13 | 63 | 55.6% | +0.588% | +37.0% | +7.6% |
| Period 4 | 2026-05-14 – 2026-08-13 | 63 | 55.6% | +0.263% | +16.6% | +5.2% |
2,000 simulations randomly resampling the same return pool. If the actual total return falls in a high percentile, the pick order (skill) contributed meaningfully — not just the underlying stock returns.