I have a financial dataset that needs to be turned into clear, actionable allocation recommendations. My focus is preskriptif analysis, so I’m not just after insights—I need an optimizer that suggests exactly how to rebalance a multi-asset portfolio for better risk-adjusted returns. The raw figures are already collected; what’s missing is the model that converts them into concrete decisions. You’re free to choose the most suitable stack—Python (Pandas, NumPy, SciPy, PyPortfolioOpt), R, or another reliable tool—as long as the results are reproducible and the code is well commented. Deliverables • A cleaned, well-structured dataset ready for modelling • A prescriptive optimisation model with parameter explanations • Recommended asset weights along with scenario stress-tests • A concise technical memo (or notebook) that maps data inputs to final recommendations and can be presented to stakeholders Acceptance criteria will be the model’s ability to run end-to-end on my machine and a set of allocation suggestions that materially improve the Sharpe ratio versus the current baseline.