elliott-wave

Elliott Wave signal detection works by validating Zigzag swing points in OHLCV market data against strict Fibonacci ratios and wave theory rules. It filters low-probability setups to identify completed ABC pullbacks and 5-wave impulse structures for high-probability trend reversals.

Automated technical analysis uses Fibonacci ratios to enforce strict Elliott Wave rules and filter out low-probability setups. Validating swing points against these mathematical relationships ensures that detected impulse and corrective structures meet deterministic criteria for reliable trading signals.

You can scan multiple assets for completed ABC pullbacks by processing their financial time-series data through the rule-based signal engine. The skill validates 3-wave corrective structures using Zigzag swing analysis to identify high-probability entry points for trend reversals.

The signal engine works with OHLCV market data across asset classes, using pandas and numpy to process time-series arrays. It identifies Elliott Wave impulse and corrective structures deterministically, independent of the specific financial instrument being analyzed.

Rule-based Elliott Wave detection relies on strict validation of Fibonacci ratios and wave theory rules, which may filter out valid but non-standard market structures. It provides deterministic signals but cannot account for subjective pattern variations that human analysts might recognize.