Not supported · Patterns · 2026-09-05 (initial commit); timeframe gap fill 2026-10-11
Are Bat (100%) and Deep Crab (91.7%) 'S-tier' patterns?
What this means
The 'S-tier: 100%' label for Bat and '91.7%' for Deep Crab have no saved source, and they came from a hindsight-prone Python sim. On TradingView fills, Bat earns +0.25R on 1h but about 0R on 15m, and no pattern difference is statistically significant. The labels should not be cited; they are still in the indicator's input names.
The explanation is our best reading; the data shows the result but not the cause directly.
By timeframe
| Timeframe | Verdict | n | Result |
|---|---|---|---|
| 15-minute | Not supported | 664 | Bat +0.00R (n=664) |
| 1-hour | Not supported | 843 | Bat +0.25R, Deep Crab +0.22R; no pattern significant |
| 4-hour | Not supported | 235 | Bat +0.13 vs rest (q=0.42), Deep Crab +0.26 (q=0.14): no significant difference gap fill G8, 2026-10-11 (trade logs) |
| Daily | Not supported | 116 | Bat +0.01 (q=0.98), Deep Crab +0.23 (q=0.43) vs rest gap fill G8, 2026-10-11 (trade logs) |
- Hypothesis
- Bat and Deep Crab win far more often than other patterns (labels 'S-Tier: 100% / 91.7%', Shark 'A-Tier: 63.1%').
- Method
- Python deep dive on 50+ large caps, daily/weekly, 15 years (analytics/pattern_type_deep_dive.py).
- Result
- No saved output behind the labels. TradingView sweeps: Bat mean R +0.25 (1h, n=491), +0.00 (15m, n=664); Deep Crab +0.22 (1h, n=352); no pattern difference is significant. Gap fill: 4h Bat +0.13R vs other patterns (q = 0.42), Deep Crab +0.26R (q = 0.14); daily Bat +0.01R (q = 0.98), Deep Crab +0.23R (q = 0.43).
- How much to trust it
- Low: labels have no saved source; Python zigzag family with look-ahead
- Did it change the live rule?
- the labels are still in the Pine input names; all patterns enabled
- Notes
- Do not cite the labels.
Scripts and data for this study will be linked here when the research code is published on GitHub.
Education and research only. Not financial advice. Backtests use today's index members (survivorship bias).