Despite a heated debate on the perceived increasing complexity of financial regulation, we are lacking an analytical framework to study regulatory complexity. To fill this gap, we propose to apply simple measures from the computer science literature by treating regulation like an algorithm - a fixed set of rules that determine how an input (e.g., a bank balance sheet) leads to an output (a regulatory decision). We can apply our measures to stylized regulations as well as actual regulatory texts. Our measures capture dimensions of complexity beyond the mere length of a regulation. In particular, shorter regulations are not necessarily less complex, as they can also use more "high-level" language and concepts. We validate our measures by running experiments involving the computation of risk-weighted assets under various rules.