Constructing Price, Time, and Volume Bars with Adaptive Renko Metrics

Summary

The article proposes representing market activity with bars that combine price range, elapsed time, and volume distribution by price. It describes calculating a normalized volume profile, momentum from price velocity and volume intensity, and a volatility measure based on weighted tick changes. A weighted strength score combines normalized momentum, volatility, volume concentration, and spread. The implementation discussion also introduces adaptive price-brick sizing based on spread and ATR, alongside a volume threshold derived from historical median and standard deviation.

Python and MetaTrader 5 are presented as the tools for building and updating the bars, with Renko-style blocks and additional statistics used to summarize activity. The author reports visual observations of apparent reversals in historical examples but supplies no defined sample, benchmark, or measured predictive results. Several formulas and choices are presented heuristically, including the weighting scheme and normalization, so the proposed features require careful validation before being used as trading signals.

Key ideas

• The proposed visualization adds volume distribution by price to the usual price and time dimensions.
• Momentum, volatility, volume concentration, and spread are combined into a weighted bar-strength measure.
• Price-brick size adapts to spread and ATR, while volume thresholds use historical distribution statistics.
• The article reports visual examples but does not provide quantified or benchmarked evidence of predictive value.
• The weighting and normalization choices are heuristic and need validation for each instrument and data source.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.