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 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