AMG Kernel - Virtual Nature of Changing Value and Sentiment
AMG Kernel originates calculable 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 assets, and projects changing sentiment visualizations on an 'etch-a-sketch' personal dashboard canvas.
The Financial sentiment dashboard extends and scales actionable templates of value to non-Financial dimensional structures where changing sentiment is also relevant (media, tech, social, rideshare, determinative....) and matters.
The dashboard interface generates qualitative projections that signal truthful interactive visualizations of how the relevant 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 and infer qualitative notions of sentiment: How much? How many? How fast?
The interactive dashboard visualizes the magnitude and direction of the Financial data vectors, as well as the speed of the changing values, and projects templates that infer how common change is uniquely and personally understood.
The projections derive true insights that are learned from common understandings about the changing nature of securitized dollar-driven capital market asset values; to train mathematical insights 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 the user to visualize the regulated data and interactively train the continuous integer values to learn a unique, cogent, and truthful awareness of changing sentiment in any measured topic.
The analytics template trusted and regulated Financial data to train true sentiment for insights about change that signal opportunity. (more...).
