A. Use a scalar_iter Pandas UDF with iterator-based processing, implementing state management through persistent storage (Delta tables) that gets updated after each batch to maintain continuity across iterator chunks.

B. Use a scalar Pandas UDF that processes the entire dataset at once, implementing custom partitioning logic within the UDF to group by stock symbol and maintain state using global variables shared across all executor processes.

C. Use applyInPandas on a Spark DataFrame so that each stock symbol group is received as a pandas DataFrame, allowing processing within each group while maintaining state variables local to each group's processing function.

D. Use a grouped-aggregate Pandas UDF that processes each stock symbol group independently, maintaining state through intermediate aggregation results that get passed between successive UDF calls via broadcast variables.