trading-strategist▌
kukapay/crypto-skills · updated Jun 3, 2026
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Data-driven cryptocurrency trading strategies combining Binance market data, technical indicators, and sentiment analysis.
- ›Integrates real-time and historical price/volume data from Binance with calculated TA indicators (SMA, RSI, MACD, Bollinger Bands, Stochastic)
- ›Aggregates market sentiment from crypto RSS feeds to inform buy/sell/hold signals and entry/exit recommendations
- ›Generates risk management guidance including stop-loss levels, position sizing (1-5% of capital), and volatil
Trading Strategies Skill
This skill generates data-driven trading strategies for cryptocurrencies by integrating multiple data sources and analytical tools.
Core Components
- Binance Market Data: Real-time price, volume, and historical klines from Binance API
- Technical Analysis (TA): Calculated indicators including SMA, RSI, MACD, Bollinger Bands, Stochastic, and more
- Market Sentiment: Aggregated sentiment scores from popular crypto RSS feeds
Workflow
Step 1: Data Collection
- Fetch current ticker data from Binance API (
/api/v3/ticker/priceand/api/v3/ticker/24hr) - Retrieve historical klines (
/api/v3/klineswith 30-100 days of data) - Aggregate sentiment using the market-sentiment skill
Step 2: TA Calculation
Use the scripts/calculate_ta.py script to compute indicators from historical data.
Step 3: Strategy Generation
Combine TA signals, price action, and sentiment score to recommend:
- Buy/Sell/Hold signals
- Entry/exit points
- Risk management (stop-loss, position sizing)
- Timeframes (swing, day trading)
Usage Examples
Basic Strategy Request
For ETH, generate a trading strategy based on current market data.
→ Fetch ETH data, calculate TA, get sentiment, output strategy.
Advanced Analysis
Analyze BTC with 50-day history, include sentiment, recommend swing trade.
→ Use longer history, focus on swing signals.
Risk Management
- Always include stop-loss recommendations
- Suggest position sizes (1-5% of capital)
- Warn about volatility and leverage risks
- Note: Not financial advice
References
- TA formulas: See references/ta_formulas.md
- Sentiment interpretation: See references/sentiment_guide.md
Scripts
scripts/calculate_ta.py: Python script for TA indicator calculationsscripts/fetch_binance.py: Helper for Binance API calls ./skills/trading-strategies/SKILL.md
How to use trading-strategist 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 trading-strategist
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches trading-strategist from GitHub repository kukapay/crypto-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 trading-strategist. Access the skill through slash commands (e.g., /trading-strategist) 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
4.7★★★★★32 reviews- ★★★★★Shikha Mishra· Dec 24, 2024
We added trading-strategist from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Ganesh Mohane· Dec 20, 2024
trading-strategist is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Kabir Gill· Dec 4, 2024
trading-strategist reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Evelyn Patel· Nov 23, 2024
Registry listing for trading-strategist matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Aanya Okafor· Nov 15, 2024
Solid pick for teams standardizing on skills: trading-strategist is focused, and the summary matches what you get after install.
- ★★★★★Sakshi Patil· Nov 11, 2024
Keeps context tight: trading-strategist is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Kaira Iyer· Oct 14, 2024
trading-strategist fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Aditi Shah· Oct 6, 2024
We added trading-strategist from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Chaitanya Patil· Oct 2, 2024
trading-strategist has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Ira Bansal· Sep 21, 2024
trading-strategist has been reliable in day-to-day use. Documentation quality is above average for community skills.
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