Posted this video on X/Twitter under @vecvizanalytics pre-market on the morning of 5/19/25 with the comment $TLT as of the 5/16/25 close, reviewed using systematic channel analysis, machine learning, and narrative in vide below. Pre-market on 5/19 TLT is at the 0.382 level of Nov2019/ Dec2023/Jan2025 anchored vs10. #stocks #analytics #quantfinance #charts We intend to […]
TLT as of 5/16/25, followed by methodology Explanations Read More »
Posted this video on X/Twitter under @vecvizanalytics on the morning of 5/16/25 with the comment “Do you agree that 593 is likely tough resistance?”. We intend to post case studies on X/Twitter first, to establish a “time-stamp”, then add the content here. If you are interested in methodological linkages to established quant finance principles, a
SPY at the 5/15/25 close, Followed by methodology Explanations Read More »
Posted this video on X/Twitter under @vecvizanalytics pre-market on the morning of 5/15/25 with the comment “$UNH chart looks like TDG circa March 2020 as per Vector Model chart shape metrics. That would be a good thing bulls over 3m-6m. Conversely, if UNH has truly “leveled down”, we show the case for settling in at
UNH at the 5/14/25 close, followed by methodology Explanations Read More »
Posted this video on X/Twitter under @vecvizanalytics on the afternoon of 5/13/25 with the comment “$NVDA strong today. Video below explores the near /intermediate term upside. Shows potential gap in resistance between $135 and $141, but Vector Model indicates absent an unusual bullish lift or catalyst getting past $143 probably takes a month.” We intend to
NVDA as of 5/12/25 close, followed by methodology Explanations Read More »
Key issues confronting anyone reviewing a ticker’s merits as an investment, particularly one they are unfamiliar with: Vector Sets systematically select and frame the price history that matters most, given the most recent price. Vector Sets are the channels that VecViz systematically identifies from all combinations of tops and bottoms and then ranks according to
VecViz provides “Support and Resistance Based Investment Analytics”. Support and Resistance are long standing, popular concepts in technical analysis. However, the definition of neither “Support” nor “Resistance” has ever been well settled, let alone the methodology for identifying and measuring it. In the table below we summarize four papers from academia that focus on Support
Support and Resistance’s Evolving Definition Read More »
Mean Variance Optimization is primarily the concern of institutional investors and quants, but this blog can still be of interest to individual investors who actively consider their personal asset allocation. With month end, quarter end, and fiscal year end approaching, many institutional investment teams are in the late innings of portfolio strategy and asset allocation
Mitigating Common Mean Variance Optimization Process Challenges Read More »
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
An LLM’s Comparison of VecViz to Established Vol Models Read More »
“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
Realistic Vol Estimates through Support, Resistance and Machine Learning Read More »