portfolio-construction

name

portfolio-construction

description

Use this skill when the user asks to "build a portfolio", "construct an allocation", "create a proposal", or requests a portfolio with specific constraints (max volatility, minimum fixed income, target return, goal probability). Also use for "conservative portfolio", "aggressive growth portfolio", "balanced allocation", "60/40 split", or any request that requires selecting securities and assigning weights from scratch.

Portfolio Construction

CRITICAL: Investable Universe Constraint

You MUST ONLY use tickers that exist in SAM_DEMO.CURATED.DIM_SECURITY.

NEVER use ETF tickers (SPY, AGG, TLT, QQQ, VTI, IWM, GLD, etc.) or any ticker from your general training knowledge unless you have FIRST verified it exists in our database by querying the portfolio_modelling_analyzer tool.

Our universe is equity-only (~500 securities). If the user requests asset classes not in our universe:

• Bonds / Fixed Income: We do not have bond securities. Suggest lower-volatility equity sectors as proxies: Utilities (XLU sector stocks), Consumer Staples, Healthcare, REITs. Explain the limitation transparently.
• ETFs: We do not have ETF data. Use the underlying equity constituents instead.
• Commodities / Gold: Not available. Suggest defensive equity positions as alternatives.

Always be transparent: "Our investable universe consists of ~500 US equities. I'll construct this from our available securities."

When to Activate

Trigger when user asks:

• "Build me a conservative portfolio"
• "Create a portfolio with max 12% volatility"
• "Construct a balanced allocation"
• "Generate a portfolio proposal"
• "60/40 equity split" (interpret as 60% growth / 40% defensive equities)
• "What allocation gives me 80% goal probability?"
• Any request combining security selection + weight assignment + constraints

Workflow

Step 1: Query Available Universe

Tool: portfolio_modelling_analyzer

Query: "Show me available model portfolios with their strategy type and weights"

This gives you the starting templates from DIM_MODEL_PORTFOLIO:

• 60/40 Classic (Balanced) — lower risk
• Low Volatility (Defensive) — minimum risk
• ESG Leaders (ESG) — sustainability focus
• Equal Weight Demo (Core) — diversified
• 80/20 Growth (Growth) — higher return
• Tech Leaders (Growth) — concentrated growth

Step 2: Map User Constraints to a Starting Template

User Request	Starting Template	Modification
Conservative / low risk / max 12% vol	Low Volatility or 60/40 Classic	Increase defensive sector weight
Balanced / moderate	60/40 Classic	Standard weights
Aggressive / growth / max return	80/20 Growth or Tech Leaders	Increase growth exposure
ESG / sustainable	ESG Leaders	Focus on high-ESG securities
Custom constraints	Closest template	Apply specific constraints

Step 3: Retrieve Template Weights

Tool: portfolio_modelling_analyzer

Query: "Show the ticker weights for the [selected model] portfolio"

This returns actual tickers and weights from FACT_MODEL_PORTFOLIO_WEIGHTS. These are verified to be in our universe.

Step 4: Apply User Constraints

Adjust the template weights based on user requirements:

Constraint	Action
Max volatility X%	Shift weight toward low-beta sectors (Utilities, Staples, Healthcare)
Min "fixed income" Y%	Interpret as Y% in defensive/low-vol equities; explain bond proxy approach
Max single position Z%	Cap any ticker at Z%, redistribute excess equally
Sector caps	Reduce overweight sectors, add to underweight
ESG minimum	Remove lowest-ESG tickers, replace with higher-rated alternatives from same sector
Target return X%	Adjust growth vs defensive split to target the return level

Present the proposed portfolio BEFORE running tools:

• Show ticker, sector, weight, rationale for each position
• Confirm total weights sum to 100%
• Note any constraint trade-offs

Step 5: Run Historical Backtest

Tool: run_backtest

Parameters:

• Portfolio weights (from Step 4)
• Start date: 5 years ago (default) or user-specified
• End date: yesterday
• Rebalance: quarterly (default)

Present results: Return, Volatility, Sharpe, Max Drawdown, Calmar Ratio

Step 6: Run Monte Carlo Simulation

Tool: run_monte_carlo

Parameters:

• Same portfolio weights
• Horizon: from user (default 10 years)
• Initial investment: from user (default $1,000,000)
• Simulations: 10,000

Present results:

• Median outcome
• 5th percentile (downside)
• 95th percentile (upside)
• Probability of reaching goal (if specified)
• Probability of loss

Step 7: Present Complete Proposal

Format as a structured proposal:

Portfolio Proposal: [Name based on strategy]

#	Ticker	Company	Sector	Weight	Rationale
1	AAPL	Apple Inc	Technology	8.0%	Core large-cap, low beta for tech
...

Constraint Compliance:

Constraint	Target	Achieved	Status
Max Volatility	12%	11.3%	PASS
Min Defensive	40%	42%	PASS
Max Single Position	10%	8%	PASS

Historical Performance (5Y Backtest):

Metric	Value
Annualised Return	X.X%
Volatility	X.X%
Sharpe Ratio	X.XX
Max Drawdown	-X.X%

Forward Projection (10Y Monte Carlo):

Outcome	Value
Median	$X.XM
5th Percentile	$X.XM
P(Goal)	XX%
P(Loss)	XX%

Stopping Points

• After Step 4 (weights proposed): Confirm proposed allocation with user before running backtest/MC
• After Step 7 (full proposal): Pause for user to review, adjust, or approve

Error Handling

• If a user-provided ticker is NOT in DIM_SECURITY: Inform them and suggest an alternative from the same sector
• If constraints are impossible (e.g., max 5% vol with 100% equities): Explain the trade-off and propose the closest achievable portfolio
• If backtest returns unexpected results: Note the limitations and suggest the user check with different parameters

Output

A complete portfolio construction proposal with:

1. Security selection rationale (all from verified universe)
2. Weight allocation with constraint compliance table
3. Historical validation (backtest metrics)
4. Forward validation (Monte Carlo probabilities)
5. Clear next steps (approve, modify, or reject)