the formation of DayTraderAlgorithm.com an information platform for short term traders and investors to have unrestricted access to our proprietary Subjective Probability + Game Theory Model (SP+GTM) algorithm without cost to our clients.

SP+GTM is a multilevel set of rules secured by evolving data to evaluate trading or investment opportunity within specific moments in time. The statistical analysis function within database establishes a probability number value based on the likelihood that an event or circumstance will happen within the timeframe anticipated. The threshold of accuracy concluded from historical norms requires a score of 85 percent for a short-term stock trader to break even on the trade. Therefore, a score of SP+GTM=84 means the participator will probably lose money on the trade.

In most endeavors being 85 percent right, especially in sports, you would be considered a superstar. Performing at that level as a trader means you risk with a few mistakes to lose all your money. Believe this conclusion:

How SP+GTM works. (1) A business cycle is established by evaluating on a technical basis all the indexes and related categories that track “clusters of stocks” by direction and volumes, conditions or events affecting the economy and consumer sentimentality. (2) Once a factor number is determined by the algorithm, the direction of the primary stock index.SPX(The S&P 500, or just the S&P, is an American stock market index based on the market capitalizations of 500 large companies having common stock listed on the NYSE, NASDAQ, or the Cboe BZX Exchange of, i.e. the U.S. traded companies) is established for the “probability of direction” within a 10-day and 30-day cycle. And (3) From a list of 4500 individual’s stocks, the algorithm rates each one and isolates all scores above SP+GTM=86 (long) and SP+GTM=75 (short).

Each Monday before the Market opens, DayTraderAlgorithm will broadcast the likely direction of the S&P 500 Index for the week, and to suggest one stock with a rating of SP+GTM=86 or better. We decided on a long side play to demonstrate the accuracy of the algorithm.