Technical Analysis

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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: qunyou-agent
• Source: qunyou-agent/finance-skills
• License: MIT

Install and usage instructions live in the source repository linked above.