In response to the global financial crisis of 2008, the Federal Reserve decided to develop and implement stress tests to assess the soundness of the financial system. Each stress test involves crafting a potential real-world scenario and then quantifying the scenario's effect on both financial actors in the economy and the financial system as a whole. There currently exist two weaknesses in the Federal Reserve's stress testing approach. First, the number of stress tests faced by each financial institution is quite small, with many such stress test scenarios mimicking past historical events that are not necessarily reflective of future situations. Second, the Federal Reserve's toolkit is not sufficiently macroprudential in nature, even though the financial crisis did cause many central banks to nominally transition from a microprudential regulatory approach to a macroprudential regulatory approach. In this work, we tackle these two issues. We show how to massively increase the number and types of possible stress tests without increasing the computational burden. To do this, we generate classes of stress tests with potentially very large cardinalities. For each class of stress tests, we then construct in closed form probability distributions that capture the range of possible balance sheet effects both for each individual financial institution and for the entire financial system. The approach that we take towards increasing the number of stress tests is fundamentally macroprudential. We moreover show how the topologies of the bipartite networks linking financial institutions to assets shape stress tests' effects on the financial system.