Disapointing June 2026 filter performance, weighted down by pre-Agentic review VNA_Expected Return If purchased at the closing price of 6/12/26, the average of the 6 tickers in our 6/11/2026 close-based filter returned -3.45% through the 7/13/2026 close. This lagged the average price return of the 129 tickers in the screened universe by 171bps (they averaged -1.74%) and […]
VecViz on OpenBB Filter: Jun2026 Perf/ Jul2026 Update Read More »
Analyst “X-Factor” as a Quant Differentiator and How VecViz Can Help You Capture It June 2, 2026 A recent Bloomberg Opinion column by a computational hydrologist1 highlighted a looming risk in AI-driven markets: convergence. The author built an AI trading platform in just six days, demonstrating how rapidly modern tooling closes the expertise gap. When
Analyst “X-Factor” as a Quant Differentiator and How VecViz Can Help You Capture It Read More »
From Cognition to Context and Compute: VecViz’s Path to its VNA Target Price and OpenBB App ✕ From Cognition to Context and Compute:VecViz’s Path to its VNA Target Price and OpenBB App May 2026 The Founding Mission: Chart / Fundamental / Quant Synergy Price is truth. Fundamentals reveal intrinsic value. Quant brings objectivity and scale.
From Cognition to Context and Compute Read More »
VecViz(charts, narrative, quant) = valuation, risk, timing VecViz(charts, narrative, quant) = valuation, risk, timing VecViz is a framework for bridging charts, fundamental narrative, and quant to derive ticker price targets, performance rankings, and price probability percentiles. Deployed with MCP tools and structured prompts, the VecViz app for the OpenBB Workspace supports your exploration and scenario
VecViz(charts, narrative, quant) = valuation, risk, timing Read More »
April 28, 2026 I wrote “Quantifying the Kindling” in mid March, after hearing Lloyd Blankfein on a podcast comparing market conditions to an abundance of kindling on a forest floor. I knew then that it was a precursor to something more comprehensive I wanted to share on the subject of macro risk, which is the
VIX and Yield Signaling with VecViz INTERN-als Read More »
March 18, 2026 I heard former Goldman Sachs CEO, Lloyd Blankfein, say the following on Preet Bharara’s “Stay Tuned with Preet” podcast this last weekend: ““We’ve just gotten through a market with equities on record high levels with a very, very favorable financing environment and a lot still hasn’t been sold, really? So is it
Quantifying the kindling Read More »
Considering GAF-Trained CNNs in relation to and in combination with VecViz’s Vector Model and V-Score Our thanks to Tyler (Foster) Shelbert for his contributions to this post. Convolutional Neural Networks (CNNs) can excel at time series forecasting As a form of “Deep Learning”, CNNs can automatically extract complex, non-linear features from raw data without manual
Stripes or Plaid? Or Both? Read More »
2/2/2026 Our thanks to Jessie Li for her assistance with key elements of the analysis discussed below. TL/DR: They do, despite the fact that volume is not a direct input to any VecViz model. More explicit consideration of volume leads to improvements in baseline VecViz analytic performance, particularly for the V-Score. Volume is a well-regarded
Do The Virtues of Volume Extend to VecViz? Read More »
December 2, 2025 Each month we publish a 100+ page report for each individual VecViz volatility and expected return related features so that we can readily answer any of the following common concerns about summary performance stats: Quant Feature Quant Feature Eval Criteria Common Concerns: Expected forward return 1) correlation to forward returns2) interquartile range
December 2, 2025 Regimes help model builders differentiate market behavior into more stationary (and hence predictable) groupings. Regimes are commonly defined in terms of levels or growth rates in macroeconomic variables and market indices. Research on regime based models dates back at least to 1989. Here we discuss a regime framework built around the thesis
Breaking Good: VaR & OaR Breakage Rate Regimes Read More »