Built-in Indicators Reference

Complete reference for technical indicators available in PyPScript

PyPScript includes professional-grade technical indicators across multiple categories, each optimized for deterministic computation and consistent results. All indicators support multi-timeframe analysis and cross-pair compatibility.

Indicator Categories

Moving Averages

• Simple Moving Average (SMA): Smooths price data to identify trend direction.
• Exponential Moving Average (EMA): More responsive to recent price changes than SMA.
• Weighted Moving Average (WMA): Linear weighting with most recent prices having highest weight.
• Volume Weighted Average Price (VWAP): Price weighted by volume, shows fair value.
• Hull Moving Average (HMA): Reduces lag while maintaining smoothness.

Oscillators

• MACD: Shows relationship between two moving averages.
• Relative Strength Index (RSI): Measures speed and magnitude of price changes (0-100 scale).
• Stochastic Oscillator: Compares a particular closing price of a security to a range of its prices over a certain period of time.
• Williams %R: Momentum indicator similar to Stochastic but inverted.
• Stochastic RSI: Applies stochastic formula to RSI values.

Volatility

• Average True Range (ATR): Measures market volatility.
• Bollinger Bands (Upper/Lower/Width/%): Volatility bands placed above and below a moving average.
• Keltner Channels: ATR-based volatility channels.
• Donchian Channels: Identifies breakouts and breakdowns based on high/low ranges.

Pattern Recognition

• Fractals: Identifies potential reversal points through swing highs and lows.
• Pivot Points: Standard, Camarilla, and Woodie's pivot calculations for support/resistance.
• Support & Resistance: Dynamic identification of key price levels.
• Candlestick Patterns: Hammer, Doji, Engulfing, Shooting Star, and more.

Market Structure

• Higher Highs / Higher Lows: Identification of uptrend structure.
• Lower Highs / Lower Lows: Identification of downtrend structure.
• Market Structure Assessment: Overall assessment of trend and range states.
• Order Blocks: Identification of institutional interest areas.

Usage Example

import indicators.sma
import indicators.rsi

strategy "multi_indicator" {
    ma_50 = sma.evaluate(close, 50)
    rsi_14 = rsi.evaluate(close, 14)
    
    patterns {
        above(close, ma_50) and below(rsi_14, 30) -> UP(confidence: 0.85)
        default -> HOLD(confidence: 0.95)
    }
}

Performance Considerations

Optimized Computation

All indicators are optimized for:

• Memory usage: Minimal footprint even with large lookback periods.
• Computation speed: Highly efficient internal processing for real-time results.
• Accuracy: Professional-grade precision for all calculations.

Best Practices

• Focus: Use 5-7 indicators maximum per strategy for best results.
• Validation: Test on multiple timeframes and pairs using the PPE engine.
• Efficiency: Reuse indicator results instead of declaring the same indicator multiple times.

---

For detailed parameters and return values for each indicator, visit the specific category pages:

• Moving Averages
• Oscillators
• Volatility