elliott-wave

Elliott Wave pattern identification from daily market data uses a pure-Pandas workflow to detect swing points via Zigzag, match 5-wave impulse and 3-wave corrective structures, and validate them with Fibonacci relationships.

Generating trading signals using Fibonacci wave relationships in Pandas applies rule-based validation to wave counts, producing trend-top and correction-complete signals from daily OHLCV data.

The Elliott Wave signal engine works with daily OHLCV data across assets, applying Zigzag swing detection and wave counting without requiring external dependencies beyond a pure in-house Pandas implementation.

Automating technical analysis for impulse and corrective waves is best handled by a pure-Pandas engine that detects Zigzag swing points, matches wave structures, and validates them with Fibonacci relationships.

Validating 5-wave impulse structures using technical analysis involves matching Zigzag swing points to the wave count and checking their Fibonacci relationships to confirm trend-top or correction-complete signals.