Editing Openai/693eaf5a-1494-8007-b1e3-00abefb11523 (section)

===== This volumetric value is not a physical quantity — it’s a shape-based metric combining time, price, slope, and curvature into one scalar. It can be used as a structural indicator of: =====

• how fast the price rose
• how long the window was
• how sharply the maximum occurred relative to the full time period

In a back-test such volumes often correlate with momentum bursts: steep increases over shorter windows produce larger volumes.

If you want, the next step is to turn this geometric “volume” into:

• a Bayesian feature
• a momentum-structure indicator
• a volatility-weighted signal
• a classification predictor for the probability of future price moves

We can also compute:

• full rolling tetrahedral volumes
• normalized volumes
• their cross-correlation with future returns
• ROC/AUC for predictive strength

Just say the word and I’ll run the next stage locally in Python: building a full volumetric back-tested signal from this geometry.