Engineer fixes AI bot timeout by building a resumable, self-healing data cache

A software engineer discovered his AI algorithmic trading bot was silently failing because fetching data for 250 stock tickers took roughly 700 seconds, exceeding his execution environment's 600-second hard timeout. The root cause traced back to a scaling oversight: during development he tested with only 10 tickers, never calculating the total runtime needed for full production load. Standard caching could not solve the problem alone, since any crash before completion wiped partially fetched data from memory, forcing a full restart each time. His fix was a self-healing cache that writes progress to disk every 50 tickers using Python's pickle format, so subsequent runs skip already-fetched tickers and only call the API for missing ones. By repeating this incremental approach across multiple runs, the complete cache for all 250 tickers is eventually built regardless of how many timeouts occur.