Most crash detection systems rely on price thresholds, correlation breakdowns, or volatility spikes — all of which are lagging indicators by design. Topological Data Analysis (TDA) offers a fundamentally different lens: instead of measuring what the market is doing, it measures the shape of how returns are distributed over time. When that shape changes abruptly, it often signals a structural regime shift before traditional indicators catch up. In this article, we implement a TDA-based market crash detector using persistent homology to track the evolving structure of return distributions, and Wasserstein distance to quantify how dramatically that structure changes between rolling windows. The pipeline runs on real equity data fetched via yfinance , requires no proprietary data, and produces a single interpretable signal that can be used as a standalone filter or layered into an existing strategy. Most algo trading content gives you theory. This gives you the code. 3 Python strategies. Fully backtested.…