I already have reliable forecast data for an energy‐related commodity and need it converted into a robust profit-and-loss decision framework. Your assignment is to design a convex optimization model that ingests those forecasts, respects operational constraints (position limits, transaction costs, risk thresholds) and outputs an optimal trading or hedging schedule that maximises expected profit while controlling downside exposure. You are free to build in Python as long as the final model is reproducible and the code is clearly documented. A short technical note that walks through the mathematical formulation, explains the choice of solver, and shows back-test results against my sample data will complete the job. Please base your proposal on prior experience with convex programming—CVX, CVXPY, Gurobi, Mosek, or comparable toolkits—and energy market optimisation. Point me to one or two similar projects or publications so I can quickly confirm fit; detailed proposals are less important to me than demonstrated expertise.