I have several years’ worth of raw Bloomberg CSV exports and supporting Excel sheets that must be transformed into clear, actionable trend insights. The work revolves around Python-based data analysis only—no scraping or automation tasks this time. The job I will hand over a structured folder of historical price, volume, and macro-indicator files. Your task is to import, clean, merge, and explore this data to spot medium- and long-term patterns that matter to portfolio allocation decisions. Accuracy and transparency are critical, so every step of the workflow should be well commented and reproducible. Preferred stack Pandas, NumPy, Jupyter (or a clean .py script), plus Matplotlib/Seaborn or Plotly for the charts. If you favour another mainstream analytical library that speeds things up, feel free to use it—just keep the final environment easy to replicate with a requirements.txt file. Deliverables • Annotated Python script or notebook • Cleaned and merged master dataset (CSV or Parquet) • At least three trend visualisations with short explanatory captions • One-page summary explaining methodology and key findings Acceptance criteria The code must run end-to-end on my machine with a single command, reproduce the figures exactly, and highlight any assumptions made during preprocessing. Once everything checks out, I’ll sign off and release the milestone.