Historical performance simulation — 60 stocks, 252 trading days, updated nightly.
| Quartile | N | Win % | Avg Ret | Score Range |
|---|---|---|---|---|
| Q1 (Low) | 63 | 79.4% | +1.175% | 0.593–0.689 |
| Q2 | 64 | 81.2% | +1.854% | 0.691–0.713 |
| Q3 | 62 | 93.5% | +4.233% | 0.713–0.743 |
| Q4 (High) | 63 | 98.4% | +4.931% | 0.743–0.852 |
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 | 2025-04-16 – 2025-07-17 | 63 | 87.3% | +2.247% | +141.6% | +16.1% |
| Period 2 | 2025-07-18 – 2025-10-15 | 63 | 87.3% | +3.956% | +249.3% | +6.2% |
| Period 3 | 2025-10-16 – 2026-01-15 | 63 | 90.5% | +3.018% | +190.1% | +4.4% |
| Period 4 | 2026-01-16 – 2026-04-17 | 63 | 87.3% | +2.933% | +184.8% | +3.1% |
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.