Writing 89 Tests for a Quantitative Trading Framework: Strategy and Trade-offs

Adding a test suite to an existing codebase is a different exercise than writing tests alongside new code. You're reverse-engineering the implicit contracts, discovering which behaviors are intentional and which are accidental, and deciding what's worth testing versus what's noise. This article covers how we built the test suite for QuantFlow, an open-source quantitative trading framework in Python, and the decisions behind each layer of tests. The Testing Problem in Quant Systems Quantitative trading code has a testing problem that most software doesn't: the outputs are floating-point numbers derived from financial time series, and "correct" is often a matter of degree. An SMA of [1, 2, 3, 4, 5] with period 5 should be exactly 3.0 — that's easy. But what should the RSI of a 50-bar synthetic price series be? You can't hardcode an expected value without coupling the test to the random seed and the exact implementation. This creates a tension between two testing philosophies: Test agains