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lib/dynamics/alligator/Alligator.md
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# ALLIGATOR: Williams Alligator
# ALLIGATOR: Williams Alligator
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Dynamic |
| **Inputs** | OHLCV bar (TBar) |
| **Parameters** | `jawPeriod`, `jawOffset`, `teethPeriod`, `teethOffset`, `lipsPeriod`, `lipsOffset` |
| **Outputs** | Multiple series (Jaw, Teeth, Lips) |
| **Output range** | Varies (see docs) |
| **Warmup** | `Math.Max(Math.Max(jawPeriod, teethPeriod), lipsPeriod)` bars |
### TL;DR
- The Williams Alligator is a trend-following system that uses three Smoothed Moving Averages (SMMA/RMA) with different periods and forward display o...
- Parameterized by `jawperiod`, `jawoffset`, `teethperiod`, `teethoffset`, `lipsperiod`, `lipsoffset`.
- Output range: Varies (see docs).
- Requires `Math.Max(Math.Max(jawPeriod, teethPeriod), lipsPeriod)` bars of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
The Williams Alligator is a trend-following system that uses three Smoothed Moving Averages (SMMA/RMA) with different periods and forward display offsets to visualize market phases. The Jaw (13-period, offset 8), Teeth (8-period, offset 5), and Lips (5-period, offset 3) create a layered structure where intertwined lines indicate consolidation ("sleeping") and separated, aligned lines indicate trending conditions ("eating"). The metaphor maps directly to position management: stay out when the alligator sleeps, ride when it eats. Each line uses Wilder's smoothing ($\alpha = 1/N$), which is heavier than standard EMA, providing superior noise rejection at the cost of additional lag.