Portfolio Manager

Portfolio Manager

Overview

Analyze and manage investment portfolios by integrating with Alpaca MCP Server to fetch real-time holdings data, then performing comprehensive analysis covering asset allocation, diversification, risk metrics, individual position evaluation, and rebalancing recommendations. Generate detailed portfolio reports with actionable insights.

This skill leverages Alpaca's brokerage API through MCP (Model Context Protocol) to access live portfolio data, ensuring analysis is based on actual current positions rather than manually entered data.

When to Use

Invoke this skill when the user requests:

• "Analyze my portfolio"
• "Review my current positions"
• "What's my asset allocation?"
• "Check my portfolio risk"
• "Should I rebalance my portfolio?"
• "Evaluate my holdings"
• "Portfolio performance review"
• "What stocks should I buy or sell?"
• Any request involving portfolio-level analysis or management

Prerequisites

Alpaca MCP Server Setup

This skill requires Alpaca MCP Server to be configured and connected. The MCP server provides access to:

• Current portfolio positions
• Account equity and buying power
• Historical positions and transactions
• Market data for held securities

MCP Server Tools Used:

• get_account_info - Fetch account equity, buying power, cash balance
• get_positions - Retrieve all current positions with quantities, cost basis, market value
• get_portfolio_history - Historical portfolio performance data
• Market data tools for price quotes and fundamentals

If Alpaca MCP Server is not connected, inform the user and provide setup instructions from references/alpaca-mcp-setup.md.

REST Fallback Connection Check

Run the connection check from the repository root. Use paper credentials first; never paste credentials into a report or commit them to the repository.

export ALPACA_API_KEY="<alpaca-key-id>"
export ALPACA_SECRET_KEY="<alpaca-secret-key>"
export ALPACA_PAPER="true"
uv run python skills/portfolio-manager/scripts/check_alpaca_connection.py

The command writes a redacted diagnostic summary to stdout and returns zero only when the account and positions endpoints succeed. It does not create a report file or place orders.

Workflow

Step 1: Fetch Portfolio Data via Alpaca MCP or REST fallback

Use Alpaca MCP Server tools to gather current portfolio information when available. In scheduled Hermes jobs, MCP tools may not be exposed even when Alpaca credentials are present; in that case, use the Alpaca REST API directly with ALPACA_API_KEY, ALPACA_SECRET_KEY, and ALPACA_PAPER.

1.1 Get Account Information:

Preferred: use mcp__alpaca__get_account_info to fetch:
- Account equity (total portfolio value)
- Cash balance
- Buying power
- Account status

Fallback REST endpoints:
- paper: https://paper-api.alpaca.markets/v2/account
- live:  https://api.alpaca.markets/v2/account
Headers:
- APCA-API-KEY-ID=$ALPACA_API_KEY
- APCA-API-SECRET-KEY=$ALPACA_SECRET_KEY

1.2 Get Current Positions:

Preferred: use mcp__alpaca__get_positions to fetch all holdings:
- Symbol ticker
- Quantity held
- Average entry price (cost basis)
- Current market price
- Current market value
- Unrealized P&L ($ and %)
- Position size as % of portfolio

Fallback REST endpoints:
- paper: https://paper-api.alpaca.markets/v2/positions
- live:  https://api.alpaca.markets/v2/positions

1.3 Get Portfolio History (Optional):

Preferred: use mcp__alpaca__get_portfolio_history for performance analysis:
- Historical equity values
- Time-weighted return calculation
- Drawdown analysis

Fallback REST endpoint:
- /v2/account/portfolio/history

Scheduled-job fallback discipline:

• Clearly label the source as Alpaca REST fallback rather than MCP.
• Use ALPACA_PAPER=true to choose paper endpoint; otherwise use live endpoint.
• Still validate that long market value plus cash approximately reconciles to equity, and highlight margin/leverage if long_market_value > equity.
• For weekly core portfolio cron jobs, compute exposure using equity as the denominator as well as gross market value: gross_long_exposure = long_market_value / equity, cash_pct = cash / equity, and explicitly flag margin-funded portfolios when gross exposure is materially above 100% or cash is negative. Do not let sector weights look benign by using only gross-long denominator when the account is levered.
• When the request emphasizes dividend holdings or forced-review triggers, build normalized monitor input from the live holdings and hand it to kanchi-dividend-review-monitor rather than treating dividend review as a narrative-only section. Also build tax-planning input for kanchi-dividend-us-tax-accounting when account-location notes are requested; label it degraded if account type or holding-period windows are unavailable.

Data Validation:

• Verify all positions have valid ticker symbols
• Confirm market values sum to approximate account equity
• Check for any stale or inactive positions
• Handle edge cases (fractional shares, options, crypto if supported)

Step 2: Enrich Position Data

For each position in the portfolio, gather additional market data and fundamentals:

2.1 Current Market Data:

• Real-time or delayed price quotes
• Daily volume and liquidity metrics
• 52-week range
• Market capitalization

2.2 Fundamental Data: Use WebSearch or available market data APIs to fetch:

• Sector and industry classification
• Key valuation metrics (P/E, P/B, dividend yield)
• Recent earnings and financial health indicators
• Analyst ratings and price targets
• Recent news and material developments

2.3 Technical Analysis:

• Price trend (20-day, 50-day, 200-day moving averages)
• Relative strength
• Support and resistance levels
• Momentum indicators (RSI, MACD if available)

Step 3: Portfolio-Level Analysis

Perform comprehensive portfolio analysis using frameworks from reference files:

3.1 Asset Allocation Analysis

Read references/asset-allocation.md for allocation frameworks

Analyze current allocation across multiple dimensions:

By Asset Class:

• Equities vs Fixed Income vs Cash vs Alternatives
• Compare to target allocation for user's risk profile
• Assess if allocation matches investment goals

By Sector:

• Technology, Healthcare, Financials, Consumer, etc.
• Identify sector concentration risks
• Compare to benchmark sector weights (e.g., S&P 500)

By Market Cap:

• Large-cap vs Mid-cap vs Small-cap distribution
• Concentration in mega-caps
• Market cap diversification score

By Geography:

• US vs International vs Emerging Markets
• Domestic concentration risk assessment

Output Format (illustrative values):

## Asset Allocation

### Current Allocation vs Target
| Asset Class | Current | Target | Variance |
|-------------|---------|--------|----------|
| US Equities | 70.0% | 60.0% | +10.0 pp |

### Sector Breakdown
[Pie chart description or table with sector percentages]

### Top 10 Holdings
| Rank | Symbol | % of Portfolio | Sector |
|------|--------|----------------|--------|
| 1 | AAPL | 8.5% | Technology |

3.2 Diversification Analysis

Read references/diversification-principles.md for diversification theory

Evaluate portfolio diversification quality:

Position Concentration:

• Identify top holdings and their aggregate weight
• Flag if any single position exceeds 10-15% of portfolio
• Calculate Herfindahl-Hirschman Index (HHI) for concentration measurement

Sector Concentration:

• Identify dominant sectors
• Flag if any sector exceeds 30-40% of portfolio
• Compare to benchmark sector diversity

Correlation Analysis:

• Estimate correlation between major positions
• Identify highly correlated holdings (potential redundancy)
• Assess true diversification benefit

Number of Positions:

• Optimal range: 15-30 stocks for individual portfolios