technical-analysis

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• SKILL.md

SKILL.md (first lines)

# 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

```python
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
```

From LisonEvf/finance-skills, MIT.

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