Financial Computing Numpy

objective

Implement vectorized numerical pipelines for finance with strict correctness and performance diagnostics.

workflow

1. define array shapes, broadcasting rules, and memory layout assumptions.
2. implement vectorized transforms for returns, covariances, and scenario matrices.
3. validate numerical accuracy against reference scalar implementations.
4. benchmark throughput and memory allocation under large workloads.
5. promote only after stability checks across edge-case numerical regimes.

required diagnostics

• broadcasting correctness and shape invariants across modules.
• floating-point error growth under long-horizon operations.
• covariance and factor decomposition stability diagnostics.
• vectorized-versus-scalar output parity checks.
• performance profile by array size and dtype.