Artificial intelligence stock trading uses machine learning, sentiment analysis, and advanced algorithmic forecasts to examine millions of details and carry out transactions at the lowest price. To reduce risks and increase returns, artificial intelligence traders also accurately and effectively assess future markets. A beneficial and original method for assessing or presenting the valuable decision from the accumulation of valuable information is the weighted aggregated sum product assessment approach. The notion of picture fuzzy soft set is dominant to intuitionistic fuzzy soft because it uses extra information in the form of abstinence grade that makes this structure more valuable and superior as compared to intuitionistic fuzzy soft set. It means that when data contains abstinence grade then the idea of intuitionistic fuzzy soft set fails to handle such kind of information. Moreover, in this case, the decision-making approach becomes limited. In many decision-making situations, we have to utilize a more advanced structure to avoid any kind of data loss. Hence in the case of an intuitionistic fuzzy soft set the chance of data loss increases. To avoid this kind of situation in decision-making scenarios, in this framework, we have delivered the notion of weighted averaging, ordered weighted averaging, weighted geometric, and ordered weighted geometric aggregation operators under the environment of a picture fuzzy soft set. Moreover, we have delivered properties like Idempotency, Boundedness, Shift-invariance and Homogeneity of these developed notions. We have delivered the WASPAS method for picture fuzzy soft information and employed this technique for the classification of artificial intelligence trading systems. Additionally, we provide a few scenarios using the multi-attribute decision-making technique to confirm and demonstrate the utilization of the aforementioned information and try to find the best artificial intelligence trading system. Additionally, to increase the value of the evaluated information, we compare the derived operators with a variety of currently used or existing methods.