risk-balancing

name	risk-balancing
description	Balance portfolio risk and return using Markowitz mean-variance, CVaR, Risk parity, and Kelly criterion methods.
dependencies	{"tactics":["pareto-frontier-construction","scenario-stress-testing"],"sops":["objective-definition","optimization-run","pareto-visualization","portfolio-evaluation-per-scenario","scenario-construction","selection-from-frontier"]}

Risk Balancing

Purpose

Construct a portfolio that achieves acceptable returns while managing downside risk, correlation between failures, and tail events. Applies Markowitz-style thinking beyond finance to any domain with uncertain outcomes.

When to use

• Candidates have uncertain outcomes with estimable distributions
• Correlation between candidate failures matters
• Downside protection is as important as upside
• Stakeholders have explicit risk tolerance levels

Budget

Dimension	Target
Candidates evaluated	8-20
Risk factors modeled	2-5
Correlation pairs assessed	key pairs
Efficient frontier points	>=5

State Ledger

Field	Type	Description
candidates	list	Candidates with expected return and risk estimates
correlation_matrix	matrix	Pairwise correlation of candidate outcomes
risk_tolerance	number	Stakeholder risk appetite parameter
efficient_frontier	list	Risk-return trade-off curve
selected_portfolio	list	Final allocation balancing risk and return

Available Tactics

Tactic	When
pareto-frontier-construction	Building the efficient frontier (risk vs return)
scenario-stress-testing	Testing portfolio under adverse scenarios

Available SOPs

SOP	Purpose
objective-definition	Define risk and return metrics
optimization-run	Compute efficient frontier
pareto-visualization	Visualize risk-return trade-off
selection-from-frontier	Select portfolio matching risk tolerance
scenario-construction	Define stress scenarios
portfolio-evaluation-per-scenario	Test portfolio under stress

Execution Guidance

1. Define risk and return metrics via objective-definition
2. Estimate expected returns, variances, and correlations for candidates
3. Construct efficient frontier via optimization-run
4. Visualize risk-return trade-off via pareto-visualization
5. Select portfolio matching risk tolerance via selection-from-frontier
6. Optionally stress-test via scenario-stress-testing tactic

Output Format

strategy: risk-balancing
selected_portfolio:
  - candidate: <name>
    allocation_weight: <0-1>
    expected_return: <value>
    risk_contribution: <value>
portfolio_expected_return: <aggregate>
portfolio_risk: <variance or CVaR>
sharpe_ratio: <risk-adjusted return>
method_used: <mean-variance|CVaR|risk-parity|Kelly>

Available Tactics

Optional, no fixed order; the final leaf is always a sop.

Tactic	When to use
pareto-frontier-construction	Build the Pareto frontier from multi-objective optimization, visualize trade-offs, and select a portfolio from non-dominated solutions.
scenario-stress-testing	Construct distinct future scenarios, evaluate portfolio performance under each, and identify vulnerabilities and robustness characteristics.

Available SOPs

Optional, no fixed order; the final leaf is always a sop.

SOP	When to use
objective-definition	Define optimization objectives, constraints, and trade-off preferences from context and candidate information.
optimization-run	Execute multi-objective optimization on candidates to produce a Pareto front of non-dominated solutions.
pareto-visualization	Create visual representation of the Pareto frontier showing trade-offs between objectives with narrative explanation.
portfolio-evaluation-per-scenario	Evaluate a specific portfolio's performance metrics and vulnerabilities under a given scenario.
scenario-construction	Construct distinct future scenarios spanning key uncertainties for portfolio stress testing.
selection-from-frontier	Select the final portfolio from the Pareto front by applying stakeholder preferences and decision criteria.