convergence-portfolio-optimization

Business

Portfolio Optimization Campaign — select balanced combinations from candidate sets optimizing value, diversity, risk, and robustness using Markowitz, Knapsack, Pareto, Real Options, MAP-Elites, and minimax regret methods.

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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

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. Each append MUST contain >=500 lines of markdown covering both process and results. 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.