After 25 plus years of evolving the basic system, our G-101 algorithms with their multidimensional platforms and subsystems can analyze rudimentary data into specialized ribbons with single function capabilities. G-101 relies on various methods for systematizing, organizing, and analyzing non-numeric data, mixed methods analysis, group discussions, discourse analysis, case and field studies to support and facilitate the process of sorting, structuring, and analyzing data material. To date, nine specific categories have been designed, and divided into 128 propriety subsets.
This article introduces the latest series of the G-101 SPM AI platform that was beta tested at Stocktwits from March 15, 2023, to March 31, 2024. Since then, G-101 is a fully functional platform in daily use at Stocktwists with a simple mission:
To prove that non-human intervention by an analytical chatbot
is far superior to the other kind.
G-101 applies qualitative data analysis software to provide insights into data sets while suggesting interpretations, known as “best guess,” analysis. Based on the data concentration, the G-101 algorithm can draw conclusions about the respective objective of underlying statistics. The subsystem possesses 128 units of applied data fields designed to deflect and predict values by converting anomalies into continuously compiled information and process them with algorithms to forecast directions of financial markets and individual investment targets. Once external vulnerabilities are identified that may affect investment values in the general market, the proprietary algorithm utilizes SPM Fast Checker Manager to verify the data within the subjective probability formula.
G-101 is an investment predictor that gathers data from sources and presents the values as a SPM matrix number. The database tracks over 5215 individual stocks with each one carrying a "floating" SPM tag. The higher the value the greater the subjective probability of the collective data being accurate, and culminates with the issuance of an auxiliary SPM tag
Rating of a 1/ SPM tag is judged on its subjective probability of increasing or decreasing in value in the future.
SPM 82.00 tag to 2/SPM 92.27 tag Superior Level One.
SPM 75.00 tag to SPM 81.99 tag Average Level Two
SPM 70.00 tag to SPM 74.99 tag Risk Level Three
SPM 55.00 tag to SPM 69.99 tag High Risk Level Four
1/ SPM are percentage calculated i.e. SPM 82.00 tag means 82% of the time the tag will be correct.
2/ SPM 92.27 tag is the highest value ever recorded on the last 5000 tag notices.
Daily abstracts are present as guides to supplement the SPM tags under the following titles: SPMNOTES: Presents clarifications on SPM tag values, current events and related issues to support our information disclosures.
FIRSTLOOK: Features the S&P 500 Index with commentaries during the trading day.
OPTIONACTION: Features specific put and call option candidates with commentaries that support SPM tags.
INSIDERS: Identifies individuals and groups who buy or sell large blocks of stocks from specific public companies.
DILUTION: Large liquidations or distribution of companies’ securities that dilution may affect ownership percentages.
SHOWTIME: Public and private events that showcase specific stock companies.
NOISE: “Heard on the Street” propaganda with a known or questionable purpose.
FLASHTRADE: Day trading stock or option declarations
SYMBOLBOX: A designated platform whereby a participant or follower can request the
valuation of an investment by a SPM tag declaration.
QUESTIONBOX: Any question by a participant relating to G-101.
Daily terms used in the managing of G-101 site content.
DAC means “dollar average cost” and determines the true cost of the investment position during the cycle of the trade.
EXIT means a declared SPM tag forecast when a specific stock or option should be sold.
What’s the stratagem behind the development of G-101 SPM series A1.4 algorithm?
To reinvent the participant as a “positional investor,” with a keen awareness of the symmetry between value and circumstances relative to each other at a single point in time. Sounds complicated. Not really.
Five factors are essential: Money, reliable information, discipline and timing. The last three are what a positional investor is all about. The algorithm is merely the roadmap within a game --- gets you where you want to go without getting lost. The contest creates a mental picture of subjective probability options that repeat again and again since the reliability of the data is known in advance. The result allows full awareness of a level playing field to execute without fear, knowing in advance that value and circumstance equals success.
Based on performance, G-101 is the only algorithm that tested superior to current AI contenders, Microsoft Copilot (powered by IOpenAI's Chat GPT-4), Google Gemini, and Anthropic's Claude 3. The conclusion is that these contenders cannot reliably calculate stock valuations and are technically worthless. Whether they close the learning gap is immaterial, our proprietary platform is constantly evolving by upgrading itself.
As for conventional “stock-picking” services, i.e. Motley Fool Stock Advisor, Alpha Picks, Barbell Investor, Moby, Ticker Nerd, Zacks Home Run Investor, their “best” performance for any given year was never better than 61%. At present, these advisory services do not utilize AI chatbot platforms.
OVERVIEW
Computer algorithms work based on input and output. They take the input and apply each step of the algorithm to specific information to generate an output. For example, a search engine is an algorithm that takes a search query as an input and searches its database for items relevant to the words in the query. Common examples include: the support of analytics to determine investment values, the means we use to solve a long division problem, the functionality of a search engine, and even the process of doing laundry are all examples of an algorithm. Indeed, the evolution of life is a full display of algorithms directing the actions of cells that make up our bodies. This means that below the level of consciousness there are trillions of algorithmic processes constantly occurring within our bodies. There are six steps to creating an algorithm: (1) Determine the goal of a set of defined rules, (2) Access historic and current data, (3) Choose the right models, (4) Fine-tuning, (5) Visualize your results, and (6) Running your algorithm continuously. In this framework, there are four types: (i) Brute Force algorithm. (ii) Greedy algorithm. (iii) Recursive algorithm and (iv) Subjective Probability algorithm.
The Subjective Probability algorithm (“SPA”) and a proprietary rule are what G-101 is based on. The term and definition behind SPA were conceived by an affiliate of Northridge Corporation more than 25 years ago and at the time a pioneer in the field under the nomenclature Beat-the-Dart.
Our version of SPA G-101 under the SPA format is the closest approximation to consciousness. However, no computer or artificial intelligence will ever become conscious. But a form of consciousness under a “best guess” scenario can be created by the execution of software or other factors with an exclusive data pool or rhythms of quantifiable statistics to answer a specifically defined question.
How G-101 works: Through accommodation, G-101 may generate information that is initially conceived as unreliable, false, unorthodox, or misplaced (identified as “novel data”) only to be unified by cognitive dissonance to mitigate the conflicting data by either updating G-101's interpretation of the model to disqualify the variable or assimilation as factual. Thus, the new model within the G-101 matrix perceives the added information as usable or as a discarded sequence for further interpretation. When the model is updated, the novel information is no longer an anomaly but a new chain of data. By expanding the data fields, G-101 literally establishes a new category that is able to redirect the program, in essence, to change its mind, keeping what is known as facts but updating the interpretation. Thus, settled viewpoints and new values create classified estimations that rate the new data with a confidence factor whether it is trustworthy or falsity. Within the subjective probabilities, matrices are sets of “attitudes” which are combined to generate a neoteric equivalent of thinking. Therefore, the underlying factor of the accumulative data generates a perception, which when applied with the support statistics creates a conclusion, which was not available in the initial analysis. It’s like saying, “the G-101 algorithm can change its mind.” Indeed, calculated facts and persuasion become entangled with the corrupted data. Therefore, typical Recursive Subjective Probability algorithms used for evaluating investment values are only reliable 47% of the time. You would be better served by flipping a coin than applying an expensive algorithm to answer investment-based inquiries. This fact further illustrates that Wall Street is a high-class betting parlor since perception is more important than fact.
There is no truth only perception.
The reason for this chaos is persuasion. When data is revised in stages it allows for conventional thinking to be modified, which usually produces novel results. There are computer systems that actively guide and systems that actively misguide. G-101 treats persuasion as a presentment proficiency tool (“PPT”) to establish a more coercive standard. The conclusion behind this element is that data is only useful when its success rates can be measured. Allowing an algorithm to change its mind sounds impossible, but its intention to understand the higher truth increases the chance for success. G-101 is a mind-bender and within this context, its level of reliability has no equal.
LEARN MORE ABOUT G-101 ALGORITHM.
Go to https://stocktwits.com/G101SPM and sign up as a “follower’ and begin phantom trading a hypothetical account and use the “suggestions” to buy and sell investment positions. According to the historical data from the last 5000 ideas your phantom portfolio will grow at least 84%.
Oh, by the way, signing and using the G-101 platform is free.
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