AMG Kernel - Virtual Nature of Changing Value and Sentiment
AMG Kernel originates market data visualizations of changing sentiment with Financial filings at SEC.gov; the primary integer data store for analytics of the causal manifestations at the forefront of change, real and expected, in the $50 Trillion goods and services array of regulated securities markets assets.
AMG Kernel integrates the SEC data source with a secure private connection to dashboard features that empower investors, advisors, exchange trading participants, and other subcribers with interactive control of unique and personal visualizations of changing sentiment.
AMG Kernel transforms densified EDGAR integer form data with analytics that visualize truthful continuous demand for securitized open-end fund (including ETF) assets, and projects changing sentiment visualizations on an 'etch-a-sketch' personal canvas.
The algorithmic interactive dashboard tool applies shared understandings of dollar-driven American capital market continuous demand data visualizations of asset levels and flows; that signal, template, and train truthful, cognitive, and solipsistic AI inferences of how sentiment is changing in Financial and non-Financial dimensions, where structured questions about change and value are also relevant and matter (media, tech, social, rideshare, determinative....).
The Financial sentiment dashboard extends and scales actionable templates of True Value.
The dashboard interface process generates qualitative projections that signal truthful interactive visualizations of how unique Financial values are changing.
Subscribers access continuous regulated asset data reported by open-end funds, and originate visualizations in two sentiment vectors of market demand: real investment cost (mutual fund and ETF net investment flow metadata), and expected market price action (portfolio data).
The analytics put three questions to the changing data values that infer qualitative notions of sentiment - to train AI: How much? How many? How fast?
The magnitude and direction of the Financial data vectors, as well as the speed of the changing values, are visualized on templates that infer from shared questions about how common values are changing; on unique and personally understood interactive dashboard visualizations.
The visualizations derive true templates that are learned from regulated data about the changing nature of securitized dollar-driven capital market asset values; and enable each subscriber to train quantitative inferences for AI that are as unbiased and trusted as the continuous data, itself.
The data 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 submitted by investment management companies.
s3://valuevector3,4,5.... - Learning visualization templates of value
The open-end fund asset price levels and cash investment flow information foot in the regulated data dashboard templates.
Example Dashboard Mechanisms: AWS QuickSight+AWS SageMaker Canvas Models; Tableau CRM 'Fund Flow Dashboard' with AWS SageMaker, or ad hoc connection.
The method empowers each user to visualize the regulated data and interactively train the continuous integer values to learn a cognitive awareness of changing sentiment in any measured topic.
This copyright describes a personal dashboard to originate visualizations that assert American dollar-driven capital market sentiment data analytics as the kernel of 'truth to value' - to monetize alternative vectors of change - or 'whatever' mediums of exchange.
The analytics template trusted and regulated Financial data to train true inferences for insights about change that signal opportunity. (more...).
