I’m expanding a Python-based financial data analysis project and need fresh, well-structured code written from scratch. The goal is to take raw market datasets (CSV and API pulls) and transform them into clear insights—cleaning, feature engineering, exploratory visualisations, then model-ready outputs. Here’s the flow I have in mind: pull historical prices, corporate actions and macro indicators; use pandas and NumPy to wrangle them into a unified time-series frame; surface descriptive statistics and charts with matplotlib or seaborn; finally hand off tidy dataframes for back-testing or predictive modelling. I’ll supply sample files, the API keys and an outline of the metrics I’m tracking; you turn that outline into reproducible Python modules and Jupyter notebooks with concise documentation. Deliverables • Well-commented .py modules and companion notebooks • Reusable functions for data ingestion, cleaning, and merging • Visual output (PNG or inline) illustrating key trends • A short README explaining environment setup and execution steps Acceptance criteria Code must run on Python 3.10, adhere to PEP 8, and reproduce the same results on a fresh virtualenv using only requirements.txt. If you prefer alternate libraries such as polars or plotly, let me know beforehand so we can keep dependencies tight. If you’ve built similar pipelines for equities, crypto, or macro data, I’d love to see a quick repo link or snippet when you reply.