Automating Wolfe Wave Detection with Swings and Fibonacci Levels
Summary
The document describes an MQL5 Expert Advisor that searches historical prices for bullish and bearish Wolfe Wave formations. It identifies swing highs and lows using a configurable neighborhood, then checks the sequence and relative placement of five wave points. Fibonacci expansions help constrain candidate waves, while proportionality between adjacent legs provides another pattern-quality condition. The EA also draws labels, expansion objects, and trend lines to make detected structures visible on the chart.
The proposed trading logic waits for a trend-line confirmation before entering and uses the line connecting Waves 1 and 4 as an exit objective. The article discusses changing Fibonacci thresholds to adjust detection sensitivity and reversing the wave rules for bullish formations. It offers an implementation framework and chart-based explanation, not evidence of predictive accuracy, profitability, or robustness across instruments and timeframes. Swing definitions depend on lookback settings, and pattern recognition may be sensitive to parameter choices; the document does not report a systematic out-of-sample evaluation.
Key ideas
- A Wolfe Wave detector can build candidate patterns from ordered swing highs and lows.
- Fibonacci expansion ranges and leg symmetry can be used to validate candidate wave points.
- Chart labels and trend lines help inspect patterns and define potential entry and exit conditions.
- The described entry waits for price confirmation around a pattern trend line.
- Parameter sensitivity and the absence of reported out-of-sample results limit conclusions about reliability.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.