"Making big money in a rigged market is a science."

Greenmark 101
Jul 1
3 min read

"Learn all the rules --- then break some.'

CURRENT RESULTS OF THE G-101 SPM AI PORTFOLIO

G-101 SPM AI PORTFOLIO REPORT CARD:

For the trading month ending, June 30, 2025 at 4:00 PM, 73 trades were posted of which 24 trades were closed out with 23 GAINS and one LOSS. *

^^ The Accuracy Percentage Rating (APR) for the month ending June 30, 2025, was 95.83% versus 100.00% for the prior month.

^^ From March 9, 2023, to June 30, 2025, a total of 3012 trades were posted with an APR of 86.14% versus 89.05% of the prior month. The net results are based on liquidated values, which includes net gains (losses) of unrealized positions as of June 30, 2025, 4:00 EST.

*** Results are independently certified as declared by reviewing https://stocktwits.com/G101SPM

Prior to the development of the G-101 SPM AI algorithm, it was generally assumed that the equity market, where company shares are traded and safeguarded by anti-fraud regulations, was inherently fair. However, after extensive beta testing over many years, it has been observed that the stock market may not function on an entirely level playing field. This conclusion was reached during our latest iteration of the G-101 SPM AI algorithm when stock-picking accuracy ratings surpassed 85% on the last 2000 transactions.

These result cannot be true unless the “game is rigged”

--- not all of the time, but enough of the time to allow

our algorithm to fair out the truth. Considering

this single factor is unlikely that anyone

is "right" 50% of the time, let alone

85% of the time.

G-101 SPM algorithm is an investment/enterprise predictor that gathers data from 145 preset sources and presents the values as SPM TAGS. The database tracks over 5215 individual stock components and other data subsets with each one carrying a "floating" SPM tag. The higher the value the greater the subjective probability of the collective data being accurate. The performance-driven platform evaluates current and historical records; and is structured as a digital monitoring surveillance tool to solve and deter misinformation while managing a database to offer "best guess" solutions. Its main engine is the Primary Volume Locator (PVL) which continuously tracks cash flows of the S&P 500 Index components to establish a directional control grid. Once the algorithm is locked into that variable, the platform searches for stock anomalies and imbalances between supply and demand. Only a third of the subsets are dedicated to fundamental and technical indicators. The balance of subsets are programed to track industrial and general economic data, investor sentiment, including emotions like fear, greed, and optimism, and overall market trends.

G-101 SPM AI algorithm has 145 data subsets organized into a single column. Each data point is assigned to a value-weighted component that reflects its relative importance and accuracy. This means some data points contribute more significantly to the overall average or calculation than others. Once compiled the value-weighted data column is measured as a single entity within a proprietary validation matrix that creates a time-sensitive, subjective probability model (SPM) tag value.

The algorithm can identify and track stock manipulations through data irregularities; can follow variations in trading patterns of schemers with low-latency data or insider information. These anomalies are predictable due to their repetitive and consistent structure, allowing for anticipation of a stock’s direction. Such predictabilities enable our algorithm to follow guidelines that disclose pattern creations and tendencies. By recognizing collective changes and repetitions between elements in the patterns, the platform is able to rate the reliability of such formations by applying a subjective probability model (SPM) tag value as a “best guess” conclusion score. i.e. A higher SPM tag value indicates a more reliable source of data.