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name: technical-analysis description: Computes technical indicators for stock data including MACD, RSI, moving averages, Bollinger Bands, ATR, and volume analysis. Generates trading signals based on indicator conditions. Trigger when the user requests technical indicators, chart patterns, or signal calculations. metadata: hermes: tags: [technical-analysis, indicators, MACD, RSI, signals] category: trading requires_toolsets: [terminal]

Technical Analysis

Computes and interprets technical indicators for financial time series data.

Real Code Reference

• tradinglearn/utils/technical_indicators.py — calculate_macd(), calculate_ema(), calculate_sma()
• tradinglearn/strategies/macd_strategy.py — MACDStrategy.generate_signals() with golden-cross/dead-cross logic
• tradinglearn/pytdx2/macd_golden_cross.py — A-share MACD golden cross scanner

Supported Indicators

• Trend: SMA, EMA, WMA, MACD, Parabolic SAR, ADX
• Momentum: RSI, Stochastic Oscillator, Williams %R, ROC, CCI
• Volatility: Bollinger Bands, ATR, Keltner Channels
• Volume: OBV, Volume Profile, Money Flow Index, VWAP
• Patterns: Doji, Hammer, Engulfing, Morning/Evening Star

Typical Workflow

1. Fetch K-line data via fetch_stock_data(ticker) or QuotationClient.get_KLine_data()
2. Compute indicators with existing functions in utils/technical_indicators.py
3. Generate signals: crossover events, overbought/oversold thresholds, divergence detection
4. Return DataFrame with indicator columns + signal columns

Usage

from utils.technical_indicators import calculate_macd, calculate_ema, calculate_sma

macd_df = calculate_macd(data, fast_period=12, slow_period=26, signal_period=9)
# Returns DataFrame with MACD, Signal, Histogram columns
signal = macd_df['MACD'] > macd_df['Signal']  # Golden cross condition