OBSERVATION: “The best information always wins.”… Only the G-101 SPM AI algorithm has it.
- Greenmark 101
- 4 days ago
- 10 min read
Wall Street never had it; their collective poor performance proves the point.
The phrase "having the best information always wins" highlights the significant advantage and power derived from possessing superior data in decision-making and competitive situations. It emphasizes that informed choices, whether in business, warfare, or personal life, are more likely to lead to desired outcomes. This fundamental truth is crucial for success, particularly in business or competitive situations. It emphasizes that making well-informed decisions leads to a greater advantage and ultimately, big money.
On Wall Street it is the only truth. Yet, Wall Street struggles to consistently pick winning stocks. The abundance of information available today should make it easy to find undervalued or overpriced stocks, depending on which side is being picked. The underlying fact is a simple phrase: “best information.” Wall Street never had it; their collective poor performance proves the point.
Information comes in different flavors and sizes. Before AI predicting the reliability of good information was predicated upon fundamental analysis, technical analysis, and sentiment analysis, all too subjective and prone to bias and misinformation. Today’s investment research is not designed to predict good information because it is trained on massive amounts of dated text and compromised code data. This compromised dataset allows the AI to learn faulty patterns, flawed structures, and misleading relationships, which creates misinterpretation and generate unreliability. Current AI models predominantly learn from past data, but the stock market is dynamic and influenced by countless unpredictable factors beyond historical trends. Indeed, AI struggles to account for unforeseen events like geopolitical shifts, regulatory changes, or investment and trading anomalies, making it difficult to predict sudden market movements. Most AI models specialize in historical data, leading to inaccurate predictions in new market conditions.
Except for G-101 SPM AI algorithm, AI formats have difficulty interpreting qualitative factors: Other AI programs cannot fully grasp the human emotions and sentiment that significantly influence market movements.
G-101 SPM AI algorithm is a Master Tool, the closes thing to a Crystal Ball: G-101 SPM AI can analyze data and identify trends. It's not a guarantee of 100 percent success, but 86.14 percent on 3012 picks since March 2023 qualifies the algorithm as the best there is. Nothing is close. As for the unverifiable noise – buy reward.
G-101 SPM AI’s prediction accuracy and transparency are fully documented with each pick declared by date and time, and posted on an independent, credible third party’s database. See: Stocktwits.
ABSTRACTS
G-101 SPM AI algorithm
G-101 SPM AI PORTFOLIO REPORT CARD:
STOCKTWITS
NORTHRIDGE CORPORATION
LINKEDIN
G-101 SPM AI algorithm
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.
G-101 SPM AI PORTFOLIO REPORT CARD (March 2023 to June 2025)
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.
G-101 SPM AI PORTFOLIO REPORT CARD:
For the trading month ending, May 30, 2025, at 4:00 PM, 65 trades were posted of which 23 trades were closed out with 22 GAINS and one EVEN.
Our Accuracy Percentage Rating (APR) for the month ending May 30, 2025, was 100.00% versus 100% for the prior month.
From March 9, 2023, to May 30, 2025, a total of 2939 trades were posted with an APR of 89.05% versus 83.19% of the prior month.
The net results are based on liquidated values and net gains (losses) of unrealized positions as of May 30, 2025, 4:00 EST.
G-101 SPM AI PORTFOLIO REPORT CARD:
For the trading month ending, April 30, 2025, at 4:00 PM, 40 trades were posted of which 14 trades were closed out with 13 GAINS and one LOSS.
Accuracy Percentage Rating (APR) for the month ending April 30, 2025, was 92.86% versus 100.00 % for the prior month.
From March 9, 2023, to April 30, 2025, a total of 2874 trades were posted with an APR of 83.19% versus 79.04% of the prior month.
The net results are based on liquidated values and net gains (losses) of unrealized positions as of April 30, 2025, 4:00 EST.
G-101 SPM AI PORTFOLIO REPORT CARD:
For the trading month ending, March 31, 2025, at 4:00 PM, 57 trades were posted of which all 19 trades were closed out with 19 GAINS and no LOSSES.
Accuracy Percentage Rating (APR) for the month ending March 31, 2025, was 100.00 % versus 95.83% for the prior month.
From March 9, 2023, to March 2025, 2834 trades were posted with APR of 76.83%; the lowest value to date.
G-101 SPM AI PORTFOLIO REPORT CARD:
For the trading month ending, February 28, 2025, at 4:00 PM, 71 symbols were posted (as trades) of which 24 were closed out: 23 as GAINS, and 1 as LOSS.
Accuracy Percentage Rating (APR) for the month ending February 28, 2025, was 95.83% versus 80.00% for the prior month. ^^ From March 9, 2023, to February 28, 2025, 2777 symbols were posted with APR of 79.04%. The results were based on liquidated values and net gains (losses) of unrealized positions as of February 28, 2025, 4:00 EST. * First time since April 30, 2023, was the APR below 80% on the entire portfolio.
G-101 SPM AI PORTFOLIO REPORT CARD:
For the trading month ending, January 31, 2025, at 4:00 PM, 58 symbols were posted (as trades) of which 15 were closed out: 12 as GAINS, and 3 LOSSES.
Accuracy Percentage Rating (APR) for the month ending December 31, 2024, was 80.00 versus 92.31% for the prior month.
From March 9, 2023, to January 31, 2025, 2707 symbols were posted with APR of 84.92%.
The results were based on liquidated values and net gains (losses) of unrealized positions as of January 31, 2025, 4:00 EST.
* G-101 SPM AI PORTFOLIO REPORT CARD: For the trading month ending, December 31, 2024, at 4:00 PM, 85 symbols were posted (as trades) of which 26 were closed out: 22 as GAINS, 2 as LOSSES, and 2 as EVEN.
Accuracy percentage rating (APR) for the month ending December 31, 2024, was 92.31 versus 88.89% for the prior month.
From March 9, 2023, to December 31, 2024, 2649 symbols were posted and had an accuracy rating of 89.05%. The results were based on liquidated values and net gains (losses) of unrealized positions as of December 31, 2024, 4:00 EST.
*Click SEARCH (magnifying glass) icon on our landing page to review all trades.
G-101 SPM AI PORTFOLIO REPORT CARD:
For the trading month ending, November 29, 2024, at 1:00 PM, 18 symbols were posted of which 16 were liquidated at a gain and 2 were liquidated at a loss.
Accuracy percentage rating (APR) for the month ending November 29, 2024, of net liquidates positions was 88.89 % versus 93.05% for the prior month.
From March 9, 2023, to November 28, 2024, 2564 symbol were posted with an accuracy rating of 87.06% versus 97.05% (prior month).
The results were based on liquidated values and net gains (losses) of unrealized positions.
*G-101 SPM AI PORTFOLIO REPORT CARD:
For the month ending October 31, 2024, 94 symbols were posted, with twenty-nine [29] were liquidated at a gain and two [2] were liquidated at a loss.
Accuracy percentage rating for October 31, 2024, from the results of net position liquidation was 93.55 % versus 87.10% for the prior month.
From March 9, 2023, to October 31, 2024, 2508 symbol were posted with an accuracy rating of 97.05 versus 85.02% (prior month) at liquidation and net of unrealized positions.
____
* Modification of portfolio calculations and summaries.
Since commencing postings, we elected to present “performance” based on net statistical values. Using a quantified method as to the algorithms’ accuracy offers a more concise indicator.
Effective September 31, 2024, the new formula:
+ symbol +prediction = percentage result at liquidation.
G-101 SPM AI PORTFOLIO REPORT CARD:
For the month ending September 30, 2024, 92 symbols were posted, with twenty-seven [27] were liquidated at a gain while four [4] were liquidated at a loss.
The Accuracy Percentage Rating (APR) for the month ending March 28, 2024, was 87.10% versus 97.30%.
G-101 SPM AI PORTFOLIO REPORT CARD:
For the month ending August 30, 2024, 124 trades were executed, with thirty-six [36] being sold at a profit while one [1] was sold at a loss.
The Accuracy Percentage Rating (APR) for the month ending March 28, 2024, was 97.30 % versus 92.59%.
G-101 SPM AI PORTFOLIO REPORT CARD:
For the month ending July 31, 2024, 107 trades were executed of which [50] fifty were sold at a profit while [4] four were sold at a loss and [1] one was even.
The Accuracy Percentage Rating (APR) for the month ending March 28, 2024, was 92.59% versus 81.25%.
G-101 SPM AI PORTFOLIO REPORT CARD:
For the month ending June 28, 2024, 102 trades were executed of which 27 were sold at a profit while 5 were sold at a loss.
The Accuracy Percentage Rating (APR) for the month ending March 28, 2024, was 81.25 versus 88.24%.
G-101 SPM AI PORTFOLIO REPORT CARD: For the month ending May 31, 2024, 129 trades were executed; 45 were sold at a profit while 6 were sold at a loss.
The Accuracy Percentage Rating (APR) for the month ending March 28, 2024, was 88.24 versus 82.35%.
G-101 SPM AI PORTFOLIO REPORT CARD:
For the month ending April 30, 2024, 98 trades were executed; 28 were sold at a profit while 6 were sold at a loss.
The Accuracy Percentage Rating (APR) for the month ending March 28, 2024, was 82.35 versus 88.46%.
G-101 SPM AI PORTFOLIO REPORT CARD:
For the month of March 28, 2024, 4:00 PM ET, 138 trades were executed of which 46 had gains and 6 had losses or even, 87% success rate.
The Accuracy Percentage Rating (APR) for the month ending March 28, 2024, was 88.46% versus 78.57%.
G-101 SPM AI PORTFOLIO REPORT CARD:
For the month of February 29, 2024, 4:00 PM ET, 87 trades were posted of which 33 were sold at a profit during the month, and 9 were sold at a loss.
The Accuracy Percentage Rating (APR) for the month ending February 29, 2024, was 78.57% versus 82.22.
G-101 PORTFOLIO REPORT CARD:
For the month of January 31, 2024, 4:00 PM ET, 69 trades were posted of which 37 were closed out with GAINS and 8 LOSSES.
The Accuracy Percentage Rating (APR) for the month ending January 31.2024, was 82.22%
STOCKTWITS MEDIA PLATFORM
STOCKTWISTS
On or about January 5, 2023, Northridge Company began its search to identify an independent social media platform with the ability to post stock picks and have a permanent record created to confirm such transactions by actual dates and times. The Stocktwist’s platform had these capabilities. On March 17, 2023, we began our attested beta cycle on *Stocktwists, as an independent platform with audience dynamics, to document each transaction.
As of July 6, 2025, 14,651 entries were posted on Stocktwists, which records all interaction with the platform.
See: G-101 PORTFOLIO REPORT CARD
Stocktwits is a social media platform designed for sharing ideas between investors, traders, and entrepreneurs. Founded in 2008 by Howard Lindzon and Soren McBeth, it introduced the use of the cashtag, a way to group discussions around a stock symbol preceded by a dollar sign. Stocktwits eventually became a standalone network where users share market sentiment, ideas, and strategies in real-time.
G-101 SPM COMPANY owns the G-101 SPM database architecture
NORTHRIDGE COMPANY and affiliates
Founded in 1987 as a financial systems research and development entity, it focused on data collection and management. A database was created to identify production of hemp and cannabis, their usage and related information. The company was a pioneer, published articles, conducted seminars and managed testing facilities in seed selection, hybrid methodology and related sciences, hydroponics and a variety of cultivation systems, including extraction methods and systems management. In the process the company collected and managed extensive data pools, designed systems from county/state licensing programs, rules and regulations and all levels on cultivation and production on a national basis. These activities were gradually terminated, while the database architecture and proprietary information collection system were retained and enhanced. After 2007, the company expanded its data processing capabilities to include specials data procurement, financial management and related services. These activities evolved into the creation of the G-101 SPM model. The latest version began beta testing in March 2023.
Whether it was serendipity or some other manifestation, how the database column evolved is radically different than anything known to science. Traditional row-based databases store data in rows, where all data for a single record is stored together. Columnar databases, in contrast, store all the values for a single column together. This difference in data organization has profound implications for how data is accessed and processed. Our proprietary G-101 SPM model appears impossible to duplicate unless one possesses the original data sequences.
Christopher Netelkos, creators and developer of the G-101 SPM algorithm
LinkedIn is used as posting source to confirm current events on matters containing G-101 SPM algorithm.
LinkedIn is a social media platform primarily focused on professional networking.
CONCLUSION
"The best information always wins."
There you have it; from soup to nuts.
G-101 SPM AI is the only stock picking algorithm that truly works.
Full transparency with unimpeachable documentation ---
From March 9, 2023, to June 30, 2025, a total of 3012 picks were posted on Stocktwists with an Accuracy Percentage Rating (APR) of 86.14%.
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