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An LLM’s Comparison of VecViz to Established Vol Models

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 […]

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Realistic Vol Estimates through Support, Resistance and Machine Learning

“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

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“Vector Strength” quantifies support and resistance

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

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“Let me warn you…” of the limitations of VecViz’s Analytics.

“Let me warn you, Icarus, to take the middle way, in case the moisture weighs down your wings, if you fly too low, or if you go too high, the sun scorches them. Travel between the extremes.” Ovid, Metamorpheses Similar to Daedelus’ warning to Icarus regarding his wings, VecViz advises you use the Vector Model

“Let me warn you…” of the limitations of VecViz’s Analytics. Read More »