Portfolio Optimization

Select balanced combinations from candidate sets by optimizing across multiple objectives simultaneously. This campaign applies portfolio theory concepts — originally from finance but broadly applicable — to any selection problem where you must choose a subset from many candidates while balancing competing concerns.

Strategy Routing

| Signal | Strategy | |--------|----------| | maximize total value / ROI / impact within budget | value-maximization | | maximize coverage / diversity / avoid redundancy | diversity-maximization | | balance risk / hedge / diversify failure modes | risk-balancing | | sequence / phase / timeline / dependencies | temporal-sequencing | | robust under uncertainty / scenario-proof | robustness-under-uncertainty |

Manifest

Strategies

| Strategy | Description | |----------|-------------| | value-maximization | Maximize total value within constraints using Knapsack, LP, Cost-benefit, NPV ranking | | diversity-maximization | Maximize portfolio diversity using MAP-Elites, Niche coverage, Maximum dispersion | | risk-balancing | Balance risk-return using Markowitz mean-variance, CVaR, Risk parity, Kelly criterion | | temporal-sequencing | Optimal ordering using Real Options, Critical path, Dependency graph, Staged investment | | robustness-under-uncertainty | Perform well across futures using Minimax regret, Robust optimization, Scenario planning |

Tactics

| Tactic | Description | |--------|-------------| | pareto-frontier-construction | Build and visualize the Pareto frontier, then select from non-dominated solutions | | niche-coverage-analysis | Map candidates to niches, score coverage, identify gaps | | scenario-stress-testing | Evaluate portfolio performance across multiple future scenarios |

SOPs

| SOP | Description | |-----|-------------| | objective-definition | Define optimization objectives and constraints from context | | optimization-run | Execute multi-objective optimization to produce Pareto front | | pareto-visualization | Visualize trade-offs along the Pareto frontier | | selection-from-frontier | Select final portfolio from Pareto front given preferences | | niche-definition | Define niches within the solution space | | niche-mapping | Map candidates to defined niches | | coverage-scoring | Score coverage completeness and identify gaps | | scenario-construction | Construct distinct future scenarios from uncertainties | | portfolio-evaluation-per-scenario | Evaluate a portfolio under a specific scenario | | portfolio-synthesis | Synthesize evaluations into final robust portfolio recommendation |

Budget Table

| Dimension | M-tier Target | |-----------|---------------| | Candidates considered | 8-20 | | Objectives optimized | >=2 simultaneously | | Scenarios tested | >=3 distinct futures | | Pareto points generated | >=5 non-dominated solutions |

MCP Tools

• mcp__wiki-vault__vault_search — retrieve prior portfolio analyses and candidate data
• mcp__wiki-vault__vault_query_graph — traverse relationships between candidates
• mcp__wiki-vault__vault_add_edge — record portfolio decisions and rationale

Context Management

• Pass candidate list and objective weights between strategy and tactics
• Pareto front data flows from optimization-run to visualization and selection
• Scenario definitions are shared across all evaluation SOPs
• Final synthesis aggregates all per-scenario evaluations

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Available Strategies

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

| Strategy | When to use | | --- | --- | | diversity-maximization | Maximize portfolio diversity and coverage using MAP-Elites, Niche coverage, Maximum dispersion, and Anti-clustering methods. | | risk-balancing | Balance portfolio risk and return using Markowitz mean-variance, CVaR, Risk parity, and Kelly criterion methods. | | robustness-under-uncertainty | Select portfolios that perform well across multiple future scenarios using Minimax regret, Robust optimization, Scenario planning, and Info-gap methods. | | temporal-sequencing | Determine optimal ordering and phasing of portfolio investments using Real Options, Critical path, Dependency graph, and Staged investment methods. | | value-maximization | Maximize total portfolio value within constraints using Knapsack, Linear programming, Cost-benefit analysis, and NPV ranking methods. |

Available SOPs

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

| SOP | When to use | | --- | --- | | context-checkpoint | Append research process and results to the current Phase's context file. Covers both process and results with genuine substance. Use this skill at plan-designated checkpoint points — typically after each strategy completes or at key decision nodes within a research Phase. | | context-init | Create a new context file for a research Phase. Called once at Phase start to initialize the file that subsequent context-checkpoint calls will append to. Use this skill whenever a new research Phase begins and a fresh context file is needed. | | convergence-saturation-detection | Determines when to stop iterating — coverage threshold met or marginal returns diminishing. Shared across all campaigns. | | convergence-sensitivity-analysis | Tests conclusion robustness by perturbing parameters and observing rank changes. Shared across scoring, portfolio, and steel-manning campaigns. |

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