elliott-wave

To detect Elliott Wave patterns in a pandas DataFrame, the Skill analyzes datetime-indexed OHLCV high/low/close columns to identify 5-wave impulses and ABC corrections. It outputs timestamped discrete trading signals (1, -1, 0) suitable for backtesting or live execution.

Zigzag swing detection identifies alternating local highs and lows to build a consistent sequence for wave counting. It validates Elliott Wave impulses by enforcing iron rules, minimum bar counts, and configurable Fibonacci ratio tolerances without requiring external wave libraries.

Yes, you can validate ABC corrections in daily price series using configurable Fibonacci ratio tolerance checks. The Skill matches 3-wave corrections and applies these Fibonacci constraints to filter invalid patterns and generate reliable trading signals.

No, you do not need external technical analysis libraries to generate Elliott Wave signals. The Skill operates independently using built-in logic for Zigzag swing detection, wave recognition, and Fibonacci validation on standard pandas DataFrame inputs.

The best way to automate Elliott Wave counting for intraday or daily OHLC data is applying algorithmic pattern detection to pandas DataFrames. This replaces manual wave counting with objective, timestamped buy and sell signals validated by Fibonacci ratios.

Elliott Wave signal output returns 0 for neutral conditions when price series fail to match 5-wave impulses or ABC corrections, or when detected patterns violate minimum bar counts, iron rules, or configured Fibonacci ratio tolerances.