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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/price and /api/v3/ticker/24hr)
- Retrieve historical klines (
/api/v3/klines with 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
Scripts
scripts/calculate_ta.py: Python script for TA indicator calculationsscripts/fetch_binance.py: Helper for Binance API calls ./skills/trading-strategies/SKILL.md
Weekly Installs
468
Repository
kukapay/crypto-skills
GitHub Stars
14
First Seen
Jan 25, 2026
Security Audits
Gen Agent Trust HubFailSocketPassSnykWarn
Installed on
gemini-cli395
opencode393
codex389
github-copilot374
cursor369
kimi-cli361
