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</content>
<parameter name="filePath">./skills/trading-strategies/SKILL.md
