SMT Divergences [LuxAlgo]

How it works

The SMT Divergences indicator operates by detecting swing points on both the chart symbol and selected external tickers, typically ES and YM by default. It uses a configurable swing lookback window to identify significant price swing points, comparing these points across different securities to detect divergences.

When swing points are simultaneously detected on multiple tickers, the indicator performs a comparative analysis to highlight potential divergence patterns. The detection process requires swing point confirmation over three candles, ensuring a more robust and reliable identification of divergence patterns between different financial instruments.

Why it may be useful

SMT Divergences provide traders with a nuanced view of price behavior across different securities, offering insights into potential market dynamics and correlations. By comparing swing points between tickers, traders can identify potential misalignments or divergent trends that might signal upcoming market movements or trading opportunities.

The accompanying dashboard offers statistical context, presenting the frequency and percentage of swing highs and lows that result in divergences. This quantitative approach helps traders understand the significance and frequency of divergence patterns, enabling more informed decision-making and strategic trading approaches.

When to explore it

The SMT Divergences indicator is most effective when analyzing markets with interconnected securities or those showing potential correlational behavior. It works best in markets with established relationships, such as index futures or closely related financial instruments where price movements might exhibit meaningful divergences.

Traders should use this indicator during periods of market transition or when seeking to understand complex price interactions between different securities. It is particularly useful for identifying potential trend reversals, confirming market sentiment, and exploring subtle price dynamics that might not be immediately apparent through traditional single-instrument analysis.

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