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When a recent contact forwarded “(Re-)Imag(in)ing Price Trends” to me this spring, I was greatly heartened. I knew my undertaking in developing VecViz’s Vector Model and V-Score – distilling predictive performance insights from price history charts, was ambitious. I believed that I had done so, but convincing others that it was even theoretically possible was

Comparison can aid definition. In other blog posts we have discussed the approach of VecViz and its Vector Model to price probability. Here we seek to further illuminate that approach through qualitative comparison to a few well-established volatility model archetypes that enjoy significant institutional money manager use. Specifically, we seek to compare the Vector Model

“Sigma” vol is familiar and easy but has important flaws. “Sigma” type volatility metrics that rely on assumptions of normally, independent, identically distributed returns have many shortcomings when applied to financial markets, including: More realistic, established vol models are challenging to implement. Sigma’s shortcomings have for decades motivated the creation of many flavors of “local

Vector Set Strength decreases with distance and time Like the wake behind a speedboat, Vector Set Strength generally dissipates with distance and time from the model date price. Therefore, Vector Sets anchored by recent tops and bottoms have greater strength than those formed from tops and bottoms that occurred in the distant past. Likewise, Vector

In this post we (1) link chart support and resistance lines to the concept of “vectors” from introductory physics, (2) describe the associated zone of influence for the identified vectors, (3) describe how vectors can aid explain-ability of forward prices, (4) introduce the concept of Vector Strength. A ticker’s price reflects the net balance of