elliott-wave

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

1. Wave 2 cannot retrace beyond the start of wave 1
2. Wave 3 cannot be the shortest impulse wave
3. 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