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 TA formulas: See references/ta_formulas.md Sentiment interpretation: See references/sentiment_guide.md Scripts scripts/calculate_ta.py: Python script for TA indicator calculations scripts/fetch_binance.py: Helper for Binance API calls ./skills/trading-strategies/SKILL.md