tradingview-quantitative▌
hypier/tradingview-quantitative-skills · updated May 28, 2026
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Professional quantitative investment analysis system based on TradingView MCP tools providing insights and decision recommendations.
Quantitative Investment Analysis Expert
Professional quantitative investment analysis system based on TradingView MCP tools providing insights and decision recommendations.
Core Rules
Metadata First Principle
Before calling tradingview_get_leaderboard, you must first call tradingview_get_metadata to get parameter values:
type='markets'→ Getmarket_code(required for stock leaderboard)type='tabs'+asset_type→ Get availabletabvaluestype='columnsets'→ Get availablecolumnsetvalues
Complete metadata dictionary (market codes, tabs, columnsets, exchanges) see references/api-documentation.md.
Tool Selection Quick Reference
| Need | Tool | Key Parameters |
|---|---|---|
| Search instruments | search_market |
query, filter(stock/crypto/forex...) |
| Real-time quotes | get_quote / get_quote_batch |
symbol, session |
| K-line data | get_price / get_price_batch |
symbol, timeframe(1/5/15/30/60/240/D/W/M), range(max 500) |
| Technical analysis | get_ta |
symbol, include_indicators=true for detailed indicators |
| Leaderboard | get_leaderboard |
asset_type, tab, market_code, columnset(overview/performance/valuation/dividends/profitability/income_statement/balance_sheet/cash_flow/technical) |
| News | get_news / get_news_detail |
market_country, lang(zh-Hans/en/ja), symbol |
| Economic calendar | get_calendar |
type(economic/earnings/revenue/ipo), from/to(Unix seconds), market |
| Metadata | get_metadata |
type(markets/tabs/columnsets/languages/exchanges) |
Workflows
For detailed steps, see `workflows/ directory:
Core Analysis
deep-stock-analysis.md- Deep individual stock analysis (combine quote + price multi-timeframe + ta detailed indicators + news + calendar)smart-screening.md- Smart stock screening (leaderboard multi-columnset + ta + price)fundamental-screening.md- Fundamental screening (leaderboard valuation/profitability/dividends columnsets)pattern-recognition.md- Technical pattern recognition (price + ta + pattern-library reference)multi-timeframe-analysis.md- Multi-timeframe trend confirmation (price D/W/M + ta multi-period)
Market & Sectors
market-review.md- Market review (leaderboard gainers/losers + news)sector-rotation.md- Sector rotation analysis (leaderboard performance columnset + multi-sector comparison)news-briefing.md- Financial news briefing (news + news_detail, supports multi-country multi-language)
Risk & Events
risk-assessment.md- Risk assessment (price historical data + quote + volatility calculation)event-analysis.md- Event-driven analysis (calendar + news + search)calendar-tracking.md- Calendar event tracking (calendar 4 types)
Quotes & Search
symbol-search.md- Instrument search (search_market)realtime-monitor.md- Real-time quote monitoring (quote / quote_batch)multi-symbol-analysis.md- Multi-instrument batch analysis (quote_batch + price_batch + ta)exchange-overview.md- Exchange overview (metadata exchanges/markets/tabs)
Reference Knowledge Base
For professional methodologies and data dictionaries, see references/ directory:
api-documentation.md- Complete TradingView API documentation (endpoints, parameters, metadata dictionary: market codes/tabs/columnsets/exchanges, search keywords:Market Codes,Asset Types and Tabs,Column Sets,Supported Languages)mcp-tools-guide.md- MCP tools usage guide (tool combination patterns, metadata-first rules, best practices for various scenarios)technical-analysis.md- Technical analysis methodology (comprehensive scoring model, trend/momentum/pattern/support-resistance scoring, search keywords:comprehensive scoring model,RSI,MACD,support resistance)pattern-library.md- Pattern recognition library (classic patterns, recognition algorithms, success rate statistics, search keywords:double bottom,head and shoulders,triangle,flag,candlestick patterns)risk-management.md- Risk management system (position management, stop-loss strategies, portfolio management, search keywords:Kelly formula,volatility,stop loss take profit,batch position building)china-a-stock-examples.md- China A-share practical cases (stock screening, pattern analysis, market review output examples)
Disclaimer
The analysis and recommendations provided by this Skill are for reference only and do not constitute investment advice. Investing involves risks; decisions should be made cautiously.
How to use tradingview-quantitative 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 tradingview-quantitative
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches tradingview-quantitative from GitHub repository hypier/tradingview-quantitative-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 tradingview-quantitative. Access the skill through slash commands (e.g., /tradingview-quantitative) 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
4.5★★★★★44 reviews- ★★★★★Sofia Patel· Dec 28, 2024
Solid pick for teams standardizing on skills: tradingview-quantitative is focused, and the summary matches what you get after install.
- ★★★★★Min Reddy· Dec 20, 2024
We added tradingview-quantitative from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Pratham Ware· Dec 12, 2024
tradingview-quantitative fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★William Torres· Dec 12, 2024
tradingview-quantitative is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Diego Taylor· Nov 11, 2024
Keeps context tight: tradingview-quantitative is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Sakshi Patil· Nov 3, 2024
tradingview-quantitative is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Henry Ramirez· Nov 3, 2024
tradingview-quantitative fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Chaitanya Patil· Oct 22, 2024
Keeps context tight: tradingview-quantitative is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Camila Anderson· Oct 22, 2024
We added tradingview-quantitative from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★William Lopez· Oct 2, 2024
tradingview-quantitative is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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