Financial Computing Python

objective

Build Python tooling for quant workflows with reproducible data transformations and testable research code.

workflow

1. define data contracts, calendar conventions, and feature definitions before coding.
2. implement pandas pipelines for ingestion, cleaning, and time alignment.
3. build reusable modules for signals, portfolio logic, and analytics.
4. validate outputs with deterministic unit tests and replay datasets.
5. promote only after profiling runtime and memory behavior on production-size data.

required diagnostics

• feature leakage and look-ahead checks on timestamp alignment.
• missing-data handling consistency across symbols and sessions.
• run-time and memory cost by pipeline stage.
• signal reproducibility across repeated runs and seeds.
• backtest consistency between batch and incremental updates.