yogsoth-ai 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
- Define risk and return metrics via objective-definition
- Estimate expected returns, variances, and correlations for candidates
- Construct efficient frontier via optimization-run
- Visualize risk-return trade-off via pareto-visualization
- Select portfolio matching risk tolerance via selection-from-frontier
- 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. |