portfolio-manager

name portfolio-manager description Comprehensive portfolio analysis using Alpaca MCP Server integration to fetch holdings and positions, then analyze asset allocation, risk metrics, individual stock positions, diversification, and generate rebalancing recommendations. Use when user requests portfolio review, position analysis, risk assessment, performance evaluation, or rebalancing suggestions for their brokerage account. 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 Flag if under-diversified (<10 stocks) or over-diversified (>50 stocks) Output (illustrative values): ## Diversification Assessment **Concentration Risk:** [Low / Medium / High] - Top 5 holdings represent 42% of portfolio - Largest single position: AAPL at 12% **Sector Diversification:** [Excellent / Good / Fair / Poor] - Dominant sector: Technology at 31% - [Assessment of balance across sectors] **Position Count:** [Optimal / Under-diversified / Over-diversified] - Total positions: 18 stocks - [Recommendation] **Correlation Concerns:** - [List any highly correlated position pairs] - [Diversification improvement suggestions] 3.3 Risk Analysis Read references/portfolio-risk-metrics.md for risk measurement frameworks Calculate and interpret key risk metrics: Volatility Measures: Estimated portfolio beta (weighted average of position betas) Individual position volatilities Portfolio standard deviation (if historical data available) Downside Risk: Maximum drawdown (from portfolio history) Current drawdown from peak Positions with significant unrealized losses Risk Concentration: Percentage in high-volatility stocks (beta > 1.5) Percentage in speculative/unprofitable companies Leverage usage (if applicable) Tail Risk: Exposure to potential black swan events Single-stock concentration risk Sector-specific event risk Output (illustrative values): ## Risk Assessment **Overall Risk Profile:** [Conservative / Moderate / Aggressive] **Portfolio Beta:** 1.12 (vs market at 1.00) - Interpretation: Portfolio is [more/less] volatile than market **Maximum Drawdown:** -14.2% (from $125,000 to $107,250) - Current drawdown from peak: -6.3% **High-Risk Positions:** | Symbol | % of Portfolio | Beta | Risk Factor | |--------|----------------|------|-------------| | NVDA | 11% | 1.65 | High volatility | **Risk Concentrations:** - 31% in a single sector (Technology) - 18% in stocks with beta above 1.5 - [Other concentration risks] **Risk Score:** 68/100 (Medium risk) 3.4 Performance Analysis Evaluate portfolio performance using available data: Absolute Returns: Overall portfolio unrealized P&L ($ and %) Best performing positions (top 5 by % gain) Worst performing positions (bottom 5 by % loss) Time-Weighted Returns (if history available): YTD return 1-year, 3-year, 5-year annualized returns Compare to benchmark (S&P 500, relevant index) Position-Level Performance: Winners vs Losers ratio Average gain on winning positions Average loss on losing positions Positions near 52-week highs/lows Output (illustrative values): ## Performance Review **Total Portfolio Value:** $100,000 **Total Unrealized P&L:** $8,500 (+9.3%) **Cash Balance:** $5,000 (5% of portfolio) **Best Performers:** | Symbol | Gain | Position Value | |--------|------|----------------| | AAPL | +22.4% | $12,240 | **Worst Performers:** | Symbol | Loss | Position Value | |--------|------|----------------| | INTC | -12.0% | $6,600 | **Performance vs Benchmark (illustrative values):** - Portfolio return: +9.3% - S&P 500 return: +7.8% - Alpha: +1.5 percentage points Step 4: Individual Position Analysis For key positions (top 10-15 by portfolio weight), perform detailed analysis: Read references/position-evaluation.md for position analysis framework For each significant position: 4.1 Current Thesis Validation: Why was this position initiated? (if known from user context) Has the investment thesis played out or broken? Recent company developments and news 4.2 Valuation Assessment: Current valuation metrics (P/E, P/B, etc.) Compare to historical valuation range Compare to sector peers Overvalued / Fair / Undervalued assessment 4.3 Technical Health: Price trend (uptrend, downtrend, sideways) Position relative to moving averages Support and resistance levels Momentum status 4.4 Position Sizing: Current weight in portfolio Is size appropriate given conviction and risk? Overweight or underweight vs optimal 4.5 Action Recommendation: HOLD - Position is well-sized and thesis intact ADD - Underweight given opportunity, thesis strengthening TRIM - Overweight or valuation stretched SELL - Thesis broken, better opportunities elsewhere Output per position (illustrative values): ### AAPL - Apple Inc. (8.5% of portfolio) **Position Details:** - Shares: 50 - Avg Cost: $150.00 - Current Price: $170.00 - Market Value: $8,500 - Unrealized P/L: $1,000 (+13.3%) **Fundamental Snapshot:** - Sector: [Sector] - Market Cap: $3.2T - P/E: 31.4 | Dividend Yield: 0.5% - Recent developments: [Key news or earnings] **Technical Status:** - Trend: [Uptrend / Downtrend / Sideways] - Price vs 50-day MA: Above by 4.2% - Support: $162.00 | Resistance: $176.00 **Position Assessment:** - **Thesis Status:** [Intact / Weakening / Broken / Strengthening] - **Valuation:** [Undervalued / Fair / Overvalued] - **Position Sizing:** [Optimal / Overweight / Underweight] **Recommendation:** [HOLD / ADD / TRIM / SELL] **Rationale:** [1-2 sentence explanation] Step 5: Rebalancing Recommendations Read references/rebalancing-strategies.md for rebalancing approaches Generate specific rebalancing recommendations: 5.1 Identify Rebalancing Triggers: Positions that have drifted significantly from target weights Sector/asset class allocations requiring adjustment Overweight positions to trim (exceeded threshold) Underweight areas to add (below threshold) Tax considerations (capital gains implications) 5.2 Develop Rebalancing Plan: Positions to TRIM: Overweight positions (>threshold deviation from target) Stocks that have run up significantly (valuation concerns) Concentrated positions exceeding 15-20% of portfolio Positions with broken thesis Positions to ADD: Underweight sectors or asset classes High-conviction positions currently underweight New opportunities to improve diversification Cash Deployment: If excess cash (>10% of portfolio), suggest deployment Prioritize based on opportunity and allocation gaps 5.3 Prioritization: Rank rebalancing actions by priority: