| name | elliott-wave |
| description | Elliott Wave Theory signal engine. Detects swing points through Zigzag, matches 5-wave impulse and 3-wave corrective structures, validates them with Fibonacci wave relationships, and generates trend-top / correction-complete signals. Pure in-house pandas implementation. |
| category | strategy |
Elliott Wave Theory
Purpose
Classic wave theory based on the core assumption that markets move in fractal wave structures:
| Structure | Wave Count | Direction | Meaning |
|---|
| Impulse wave | 5 waves (1-2-3-4-5) | Trend-following | Main trend direction |
| Corrective wave | 3 waves (A-B-C) | Counter-trend | Pullback correction |
Core Rules
Three Iron Rules for Impulse Waves
- Wave 2 cannot retrace beyond the start of wave 1
- Wave 3 cannot be the shortest impulse wave
- Wave 4 cannot enter the price territory of wave 1
Fibonacci Relationships Between Waves
- Wave 2 retraces 0.5-0.618 of wave 1
- Wave 3 = wave 1 × 1.618 (most common)
- Wave 4 retraces 0.382 of wave 3
- Wave 5 ≈ the length of wave 1
Signal Logic
- 5-wave advance completed → sell (trend top)
- ABC pullback completed → buy (correction finished)
- Wave 3 in progress → stay with the trend (no reversal signal is generated)
Parameters
| Parameter | Default | Description |
|---|
| swing_window | 10 | Rolling window for swing-point detection |
| fib_tolerance | 0.15 | Tolerance for Fibonacci ratios |
| min_wave_bars | 5 | Minimum number of candles per wave |
Notes
Wave theory is highly subjective, and automatic counting can yield multiple interpretations. This implementation uses a "simplest effective single interpretation" strategy and would rather miss signals than misclassify them.
Dependencies
pip install pandas numpy requests
Signal Convention
1 = long, -1 = short, 0 = stand aside
