I need a computational-finance specialist who can dive straight into raw market datasets, clean and transform them, run the appropriate statistical tests or predictive models, and deliver practical, investor-ready insights. The work is time-sensitive, so an initial draft in just a few days—and a polished final package soon after—is essential. You’ll receive large price, volume, and fundamental data files in CSV and SQL formats. Feel free to use Python with pandas, NumPy, scikit-learn, R, MATLAB, or any similarly robust stack—the key is that every step is reproducible and clearly documented. Deliverables • Well-commented code or notebooks • A brief README with environment and execution instructions • Cleaned datasets in CSV or Parquet • Visualisations plus a concise interpretive report (PDF or Jupyter markdown) Please attach or link past work that shows your experience with comparable financial data projects; that alone will help me decide quickly. I’ll stay responsive throughout so we can keep momentum and wrap this up ASAP.