The foundational AMG Kernel originates in the integer source of Economic Truth - US Capital Asset Data
AMG Kernel subscribers visualize changing structured values and sentiment with trusted US dollar-driven market data to derive unique and personal analytic templates of value.
Proposition: Variable integer data fields of Capital Asset levels and flows enable subscribers to visualize, signal, and template time-series analytics of numerically reconciled sentiment values; to train unique and personal answers and gain intelligence from trusted information that is sourced, embedded, and inferred in the continuous EDGAR submission stream.
The visualizations are generated from shared questions and common understandings of real and expected continuous market data: AMGUI Market Sentiment Features Prototype Dash
The Financial analytics template how relevant and important values are changing in less regulated and non-Financial vectors of sentiment (media, tech, social, rideshare, determinative....).
The AMG Kernel is a virtual private PLATFORM of data and analytics that enable subscribers to visualize changing sentiment in regulated filings.
SECURE Fiduciary login using cloud services like AWS, visualization features like Tableau, and ad hoc tools.
Access to Analytic Value Vectors of Change: Professional Ownership Activity (Multiple Forms and Frequencies) - Investor Fund Flow Metadata
Subscribers interactively visualize how economic sentiment and market demand is changing in asset and share prices, portfolio action, and open-end fund flows in US capital assets.
Intuitive and Actionable Dashboard Research - Fiduciary's continuous market sentiment and unique research is applied to unique overlays of special-cause variations of demand for assets.
Filtered access is provided to the financial data source of 300+ form-types, by thousands of companies, and millions of securities positions reported in subsets of the types of filings; all connected with continuous and trusted asset, price, and share data.
The investor fund flow metadata foot with portfolio asset levels reported in SEC filings that form a dual-vector metaphor to gain intelligence about how sentiment values are changing.
The data are processed and prepped, populating a table per interval; quarter, month, day; and can be unified with intraday data:
The data files reside in object storage (AWS cloud buckets, eg.):
s3://valuevector1 - Retail and Professional cash demand for securities visualized in open-end mutual fund (including ETF) net flow metadata.
s3://valuevector2 - Professional market demand for securities visualized in portfolio asset, price, and share data filings submssions by investment management companies.
s3://valuevector3,4,5.... - Learning visualization templates of value
The analytics visualize trusted changing capital market values and template a cognitive awareness of sentiment between the Fiduciary and Customer - Always fixated on their shared tangible form of expression: Truthful numerically reconciled values that anchor a continuous learning awareness of how intentional sentiment is changing.
The US market asset data train LLM with truthful virtual inferences formed by, and sequentially empowered with, gained actionable intelligence from regulated submissions of EDGAR filings.
The dollar-driven EDGAR form integers in the non-reasoning agentic model define the cognitive anchor for the virtual truth about how intentional and actionable change is occuring.
The power of the EDGAR dataset accretes with each unbiased and trusted filing submission, and asserts the continuous database as the ground truth about the virtual nature of intentional change.
The outcome is intelligence gained from this dual-vector dollar-driven structure that templates integer data signals of quantitative vectors of trusted changing values to train qualitative intentions of sentiment.
The data form the foundational 'truth to value' kernel that serves to anchor and extend reasoning models for any measured topic...
Free from unintended or misaligned priorities and strategies, deployment omissions and trade-offs, noise, and other hallucinations that don't foot with the TNA levels and flows in s3://valuevectors 1 and 2.
