algorithm-selection-efficient-frontier

name	algorithm-selection-efficient-frontier
description	Algorithm selection efficient frontier workflows for choosing execution algorithms based on cost-risk tradeoffs and order difficulty. use when tasks involve selecting between execution algos under explicit utility constraints.

Algorithm Selection Efficient Frontier

objective

Select execution algorithms using a quantitative cost-risk frontier and order-context features.

workflow

1. define utility function and risk-aversion inputs for order classes.
2. estimate expected cost and risk for each candidate algorithm.
3. map algorithms onto a comparable efficient frontier.
4. apply selection rules by order difficulty and market regime.
5. promote policy only after post-trade validation against utility targets.

required diagnostics

• frontier stability across regimes and volatility states.
• selection hit rate versus ex-post best algorithm choice.
• utility regret distribution by order category.
• cost and risk forecast calibration errors.
• policy drift after structural market changes.

risk controls

• enforce conservative fallback algorithm on missing features.
• enforce guardrails for extreme volatility and illiquidity.
• enforce periodic model review and challenger testing.

outputs

• run python scripts/algorithm_selection_efficient_frontier_diagnostics.py input.csv --output diagnostics.json and keep the json artifact.
• write an implementation memo using references/algorithm-selection-efficient-frontier-playbook.md with assumptions, tests, limits, and rollout plan.

resources

• use scripts/algorithm_selection_efficient_frontier_diagnostics.py for deterministic diagnostics.
• use references/algorithm-selection-efficient-frontier-playbook.md for the domain checklist and delivery structure.