Options Feed Filtering Reduces Data Volume

The patent segments the options data feed into multiple categorical groups (e.g., new options, price changes, size changes, cancellations, exercises) and applies different filtering rules to each category. This segmentation allows the system to manage complexity by handling different types of data separately rather than processing all quotes uniformly, thus resolving the contradiction between providing comprehensive information and managing processing complexity.

The patent changes parameters such as filtering thresholds, category definitions, and data selection criteria to optimize the balance between information completeness and processing efficiency. By adjusting these parameters, the system can adapt to different market conditions and user needs while maintaining manageable data volumes, thus resolving the contradiction between providing comprehensive investment information and reducing processing complexity.

The patent extracts and removes redundant or less important quote data from the feed while retaining critical information necessary for market transparency. By taking out unnecessary data points (such as duplicate quotes or quotes below certain significance thresholds), the system maintains market transparency for important price movements while reducing the overall data volume that degrades platform throughput.

The patent applies partial filtering by selectively processing only certain types of quotes based on predefined criteria rather than processing all quotes equally. This partial action approach ensures that critical market information is transmitted in full detail while less critical data is filtered or aggregated, thus maintaining market transparency for important information while improving throughput by reducing unnecessary processing of excessive data.

The patent performs preliminary filtering and categorization of quote data before it reaches the trader, organizing information in advance according to relevance and importance. This preliminary action reduces the time traders need to spend analyzing data by presenting pre-processed, organized information that highlights the most significant market movements, thus maintaining decision-making accuracy while reducing data analysis time.

The patent applies different levels of detail and filtering to different types of data based on their local importance to trading decisions. Critical price changes receive full detail and immediate attention, while less important data is filtered or aggregated. This local quality approach ensures that traders receive comprehensive information where needed (maintaining decision accuracy) while reducing unnecessary data elsewhere (reducing analysis time).