mtf-confluence-scorer

Multi-timeframe confluence is quantified by scoring directional alignment across several timeframes, generating a heat map to visualize agreement, and providing a recommended action for trade planning.

A confluence heat map visualizes multi-timeframe trend alignment by scoring directional agreement across timeframes, highlighting where data converges or diverges to support trading decisions.

To generate a multi-timeframe bias score, invoke the analysis with a target symbol and desired timeframes, which evaluates directional agreement and outputs a unified confluence score.

Multi-timeframe confluence scoring requires compatible time-series data formats to accurately analyze directional agreement across timeframes and produce the confluence heat map output.

Confluence heat maps identify high-confidence trade setups by systematically scoring multi-timeframe directional alignment, helping traders pinpoint scenarios where multiple timeframes agree.

Scoring multi-timeframe directional alignment is limited by the requirement for compatible time-series data, meaning the accuracy of the confluence heat map depends entirely on the input data quality.