Type Hints and Static Checking

Python normally does not care what type of object a variable holds until the moment the code actually runs and tries to use it, passing a string where a function expects a list of prices will not raise any error until that exact line executes, which might be deep inside a backtest that only reaches it once a month. Type hints let a developer write, next to a function's arguments and return value, what types are expected, for example def sharpe(returns: list[float], rf: float = 0.0) -> float:. Python itself still ignores these annotations at runtime, but a separate static checker such as mypy reads the whole codebase without running it and flags any place where the annotated types don't actually line up, a mismatched argument, a function that sometimes forgets to return a value, a dictionary used as if it always has a key it doesn't guarantee.

This matters especially in quant research code, which is frequently copied, modified, and handed off between researchers who did not write the original version; a caught type mismatch during a code review or a pre-commit check is far cheaper than a wrong number surfacing in a live P&L report weeks later.

The cost is upfront discipline: hints must be kept honest as code changes, or a checker that trusts stale annotations becomes worse than no checking at all.