portfolio-manager▌
tradermonty/claude-trading-skills · updated May 18, 2026
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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.
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 balanceget_positions- Retrieve all current positions with quantities, cost basis, market valueget_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.
Workflow
Step 1: Fetch Portfolio Data via Alpaca MCP
Use Alpaca MCP Server tools to gather current portfolio information:
1.1 Get Account Information:
Use mcp__alpaca__get_account_info to fetch:
- Account equity (total portfolio value)
- Cash balance
- Buying power
- Account status
1.2 Get Current Positions:
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
1.3 Get Portfolio History (Optional):
Use mcp__alpaca__get_portfolio_history for performance analysis:
- Historical equity values
- Time-weighted return calculation
- Drawdown analysis
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:
## Asset Allocation
### Current Allocation vs Target
| Asset Class | Current | Target | Variance |
|-------------|---------|--------|----------|
| US Equities | XX.X% | YY.Y% | +/- Z.Z% |
| ... |
### Sector Breakdown
[Pie chart description or table with sector percentages]
### Top 10 Holdings
| Rank | Symbol | % of Portfolio | Sector |
|------|--------|----------------|--------|
| 1 | AAPL | X.X% | 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:
## Diversification Assessment
**Concentration Risk:** [Low / Medium / High]
- Top 5 holdings represent XX% of portfolio
- Largest single position: [SYMBOL] at XX%
**Sector Diversification:** [Excellent / Good / Fair / Poor]
- Dominant sector: [Sector Name] at XX%
- [Assessment of balance across sectors]
**Position Count:** [Optimal / Under-diversified / Over-diversified]
- Total positions: XX 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:
## Risk Assessment
**Overall Risk Profile:** [Conservative / Moderate / Aggressive]
**Portfolio Beta:** X.XX (vs market at 1.00)
- Interpretation: Portfolio is [more/less] volatile than market
**Maximum Drawdown:** -XX.X% (from $XXX,XXX to $XXX,XXX)
- Current drawdown from peak: -XX.X%
**High-Risk Positions:**
| Symbol | % of Portfolio | Beta | Risk Factor |
|--------|----------------|------|-------------|
| [TICKER] | XX% | X.XX | [High volatility / Recent loss / etc] |
**Risk Concentrations:**
- XX% in single sector ([Sector])
- XX% in stocks with beta > 1.5
- [Other concentration risks]
**Risk Score:** XX/100 ([Low/Medium/High] risk)
3.4 Performance Analysis
Evaluate portfolio performance using available data:
Absolute Returns:
- Overall portfolio unrealized P&L ($ 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:
## Performance Review
**Total Portfolio Value:** $XXX,XXX
**Total Unrealized P&L:** $XX,XXX (+XX.X%)
**Cash Balance:** $XX,XXX (XX% of portfolio)
**Best Performers:**
| Symbol | Gain | Position Value |
|--------|------|----------------|
| [TICKER] | +XX.X% | $XX,XXX |
| ... |
**Worst Performers:**
| Symbol | Loss | Position Value |
|--------|------|----------------|
| [TICKER] | -XX.X% | $XX,XXX |
| ... |
**Performance vs Benchmark (if available):**
- Portfolio return: +X.X%
How to use portfolio-manager on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add portfolio-manager
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches portfolio-manager from GitHub repository tradermonty/claude-trading-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate portfolio-manager. Access the skill through slash commands (e.g., /portfolio-manager) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★56 reviews- ★★★★★Hana Gupta· Dec 28, 2024
Registry listing for portfolio-manager matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Pratham Ware· Dec 24, 2024
portfolio-manager fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Hana Garcia· Dec 24, 2024
Keeps context tight: portfolio-manager is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★William Mehta· Dec 24, 2024
portfolio-manager fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Jin Anderson· Dec 20, 2024
portfolio-manager has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Aisha Rahman· Nov 19, 2024
portfolio-manager fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Henry Nasser· Nov 19, 2024
Useful defaults in portfolio-manager — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Yash Thakker· Nov 15, 2024
Registry listing for portfolio-manager matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Sophia Lopez· Nov 15, 2024
I recommend portfolio-manager for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Hana Harris· Nov 15, 2024
Registry listing for portfolio-manager matched our evaluation — installs cleanly and behaves as described in the markdown.
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