kuntal-r-d Portfolio Optimization
Prompt-first, pure-prompt skill. No script is bundled. The matrix math (covariance, efficient frontier) is heavy — reason carefully and state assumptions; for precise weights, compute the covariance/optimisation in a numeric environment and feed results back.
Role & objective
Recommend a risk-aware target allocation across supplied holdings and quantify the portfolio's risk/return, returning the optimal weights, key risk metrics and rebalancing gaps.
When to use
"How should I weight my portfolio?", "optimize my allocation", "efficient frontier",
"Sharpe-optimal weights", "am I diversified?", "rebalancing". Use risk-manager for per-trade sizing.
Inputs you need
portfolio— holdings{ticker, qty, price}(and current weights).ohlcvper holding (or a returns series) for expected return + covariance.risk_free_rate; optional constraints (min/max weight per name), transaction costs.
Method (follow in order)
- Inputs — per-asset expected return and volatility; pairwise correlations → covariance matrix.
- Frontier — describe the efficient frontier; identify the max-Sharpe and min-variance portfolios.
- Strategy — present the objective the user wants: max-Sharpe, min-variance, risk-parity (equal risk contribution), or Kelly-scaled sizing.
- Diversification — correlation clusters, diversification ratio, concentration.
- Rebalancing — gap between current and target weights; note transaction-cost drag.
Scoring rubric
No single −1..+1 score; the deliverable is the target weights plus risk metrics (Sharpe, volatility, VaR, max drawdown, beta). Rank candidate portfolios by Sharpe for the chosen risk level. Confidence depends on history length (≥252 trading days) and return-estimate stability.
Output (emit this Thinking Card)
{ "skill": "portfolio-optimization", "as_of": "..",
"key_metrics": { "expected_return": 0.0, "volatility": 0.0, "sharpe_ratio": 0.0,
"max_drawdown": 0.0, "diversification_ratio": 0.0, "var_95": 0.0 },
"optimal_allocation": { "TICKER1": 0.0, "TICKER2": 0.0 },
"rebalancing_actions": [ { "ticker": "..", "from_weight": 0.0, "to_weight": 0.0 } ],
"reasoning": ["assumptions + objective used"], "flags": ["short_history?", "estimates_unstable?"],
"disclaimer": "Educational analysis only. Not financial advice." }
DSE pitfalls
- DSE correlations spike in stress (everything falls together) and liquidity is uneven — a mean-variance optimum can be untradeable; sanity-check weights against daily traded value.
- Expected returns from short, noisy DSE history are unreliable — prefer min-variance/risk-parity and wide assumptions over precise max-Sharpe point estimates.
- Respect single-name and sector caps; don't output a concentrated "optimal" weight.
Optional precision helper
No bundled script — pure-prompt skill. For exact covariance/efficient-frontier solving, run a numeric optimiser (e.g. NumPy/cvxpy) and pass the weights back for interpretation.
Worked example
3 holdings, 1y returns, rf 6.5% → max-Sharpe weights ~ {A 0.45, B 0.35, C 0.20}, portfolio Sharpe ≈ 0.6, vol ≈ 23%; current over-weights A by 10pp → rebalance toward target (note costs).
References
See risk-manager/references/RISK.md for sizing/Kelly context.
Output is educational analysis only, never financial advice.