trading-strategist

Trading Strategies Skill

This skill generates data-driven trading strategies for cryptocurrencies by integrating multiple data sources and analytical tools.

Core Components

1. Binance Market Data: Real-time price, volume, and historical klines from Binance API
2. Technical Analysis (TA): Calculated indicators including SMA, RSI, MACD, Bollinger Bands, Stochastic, and more
3. 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