ML Systems

objective

Execute ml systems work with reproducible research, explicit controls, and deployable outputs.

workflow

1. •define hypothesis, trade horizon, and capital-allocation constraints.
2. •build leak-safe features and align targets to executable decision times.
3. •estimate signal edge, turnover impact, and capacity limits.
4. •stress performance across volatility, liquidity, and crowding regimes.
5. •promote only when net performance remains robust after full trading costs.

required diagnostics

• •signal monotonicity, decay profile, and hit-rate stability.
• •capacity stress from participation growth and liquidity depletion.
• •regime dependency and edge persistence after parameter shifts.
• •cost-adjusted performance versus naive and benchmark alternatives.

risk controls

• •enforce gross and net exposure ceilings by strategy and instrument.
• •enforce concentration and turnover caps to prevent capacity overload.
• •enforce deactivation triggers for edge decay and drawdown breaches.

outputs

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

resources

• •use scripts/ml_systems_diagnostics.py for deterministic diagnostics.
• •use references/ml-systems-playbook.md for the domain-specific checklist and delivery structure.