portfolio-optimizer

name	portfolio-optimizer
description	Use this skill when the user wants to optimize portfolio weights, find the efficient frontier, construct a maximum Sharpe or minimum variance portfolio, or rebalance with specific constraints like position caps, sector limits, or ESG minimums. Also use when the user says "what's the optimal allocation" or "rebalance for minimum risk".

Portfolio Optimizer

When to Activate

Trigger when user asks: "optimize my portfolio", "maximum Sharpe portfolio", "efficient frontier", "optimal allocation with 5% position cap", "rebalance for minimum variance"

Workflow

Step 1: Get Input Data

Tool: quantitative_analyzer or portfolio_modelling_analyzer

Retrieve:

• Expected returns (from factor model or historical)
• Covariance matrix (from historical returns)
• Current weights (if rebalancing)

Step 2: Parse Constraints

Extract from user request:

• Position limits: "max 5% per stock" -> bounds = (0, 0.05)
• Sector caps: "max 30% in tech" -> linear constraint
• ESG minimum: "minimum BBB average" -> ESG score constraint
• Budget: weights must sum to 1.0
• Long-only: no short selling (bounds >= 0)

Step 3: Run Optimization via Code Execution

Tool: code_execution

You MUST use ONLY the code from optimize.py. Copy the relevant function(s) into the code_execution tool and call them with the data from Step 1. Do NOT write custom optimization code, do NOT import libraries other than numpy, and do NOT attempt alternative approaches. If the code fails or the function does not exist for the user's request, STOP and inform the user that this optimization objective is not supported.

Convert expected returns and covariance from Step 1 into numpy arrays, apply constraints from Step 2 as bounds, then call the appropriate function:

User Intent	Function
Maximum Sharpe / best risk-adjusted	optimize_max_sharpe(expected_returns, cov_matrix, bounds)
Minimum variance / lowest risk	optimize_min_variance(cov_matrix, bounds)
Risk parity / equal risk contribution	optimize_risk_parity(cov_matrix, bounds)
Target a specific return level	optimize_target_return(expected_returns, cov_matrix, target_return, bounds)
Compare risk-return tradeoffs	efficient_frontier(expected_returns, cov_matrix, n_points)

If the user requests an objective not in this table, inform them it is not currently supported.

Step 4: Present Results

Optimal Weights:

Security	Current Weight	Optimal Weight	Change	Sector
[Ticker]	X.X%	X.X%	+/-X.X%	[Sector]

Portfolio Metrics:

Metric	Current	Optimal	Improvement
Expected Return	X.X%	X.X%	+X.Xbps
Volatility	X.X%	X.X%	-X.Xbps
Sharpe Ratio	X.XX	X.XX	+X.XX
Max Position	X.X%	X.X%

Use data_to_chart for efficient frontier visualization.

Stopping Points

• After Step 2 (constraints parsed): confirm constraints and objective with user before running optimisation
• After Step 4 (results presented): pause for user to review weights and request adjustments

Output

Optimal weights table with current vs optimal comparison, portfolio metrics improvement summary, and efficient frontier chart.