meridianalgo-js/dist/volatility.d.ts

Advanced algorithmic trading library for Node.js & TypeScript with 100+ technical indicators, pattern recognition, and risk management tools.

/**
 * Volatility Technical Indicators
 *
 * This module provides volatility-based technical analysis indicators including
 * Keltner Channels, Donchian Channels, Standard Deviation, and other volatility metrics.
 *
 * @fileoverview Volatility indicators for technical analysis
 * @author MeridianAlgo
 * @version 1.0.0
 */
/**
 * Keltner Channels
 *
 * Keltner Channels are volatility-based envelopes set above and below an exponential
 * moving average. The channels are typically set 2 standard deviations away from
 * the EMA, and the indicator is used to identify overbought and oversold conditions.
 *
 * @param high - Array of high prices
 * @param low - Array of low prices
 * @param close - Array of closing prices
 * @param period - Number of periods for EMA calculation (default: 20)
 * @param multiplier - Standard deviation multiplier (default: 2)
 * @param atrPeriod - ATR period for calculation (default: 10)
 * @returns Object containing upper, middle, and lower channel values
 *
 * @example
 * ```typescript
 * const high = [100, 101, 102, 103, 104];
 * const low = [99, 100, 101, 102, 103];
 * const close = [99.5, 100.5, 101.5, 102.5, 103.5];
 * const keltner = VolatilityIndicators.keltnerChannels(high, low, close);
 * ```
 */
export declare function keltnerChannels(high: number[], low: number[], close: number[], period?: number, multiplier?: number, atrPeriod?: number): {
    upper: number[];
    middle: number[];
    lower: number[];
};
/**
 * Standard Deviation
 *
 * Calculates the standard deviation of price data over a specified period.
 * This is useful for measuring price volatility and creating volatility-based indicators.
 *
 * @param prices - Array of price data
 * @param period - Number of periods for calculation (default: 20)
 * @returns Array of standard deviation values
 */
export declare function standardDeviation(prices: number[], period?: number): number[];
/**
 * Variance
 *
 * Calculates the variance of price data over a specified period.
 * Variance is the square of standard deviation and measures price dispersion.
 *
 * @param prices - Array of price data
 * @param period - Number of periods for calculation (default: 20)
 * @returns Array of variance values
 */
export declare function variance(prices: number[], period?: number): number[];
/**
 * Average Deviation
 *
 * Calculates the average deviation (mean absolute deviation) of price data
 * over a specified period. This provides a measure of price volatility.
 *
 * @param prices - Array of price data
 * @param period - Number of periods for calculation (default: 20)
 * @returns Array of average deviation values
 */
export declare function averageDeviation(prices: number[], period?: number): number[];
/**
 * Historical Volatility
 *
 * Calculates the historical volatility of price data using the standard deviation
 * of logarithmic returns over a specified period.
 *
 * @param prices - Array of price data
 * @param period - Number of periods for calculation (default: 20)
 * @param annualized - Whether to annualize the volatility (default: true)
 * @returns Array of historical volatility values
 */
export declare function historicalVolatility(prices: number[], period?: number, annualized?: boolean): number[];
/**
 * Parkinson Volatility
 *
 * Parkinson volatility uses high and low prices to estimate volatility.
 * It's more efficient than close-to-close volatility as it uses more information.
 *
 * @param high - Array of high prices
 * @param low - Array of low prices
 * @param period - Number of periods for calculation (default: 20)
 * @param annualized - Whether to annualize the volatility (default: true)
 * @returns Array of Parkinson volatility values
 */
export declare function parkinsonVolatility(high: number[], low: number[], period?: number, annualized?: boolean): number[];
/**
 * Garman-Klass Volatility
 *
 * Garman-Klass volatility uses open, high, low, and close prices to estimate volatility.
 * It's more efficient than Parkinson volatility as it uses all OHLC data.
 *
 * @param open - Array of opening prices
 * @param high - Array of high prices
 * @param low - Array of low prices
 * @param close - Array of closing prices
 * @param period - Number of periods for calculation (default: 20)
 * @param annualized - Whether to annualize the volatility (default: true)
 * @returns Array of Garman-Klass volatility values
 */
export declare function garmanKlassVolatility(open: number[], high: number[], low: number[], close: number[], period?: number, annualized?: boolean): number[];
/**
 * Rogers-Satchell Volatility
 *
 * Rogers-Satchell volatility is another OHLC-based volatility estimator
 * that doesn't require the assumption of zero drift.
 *
 * @param open - Array of opening prices
 * @param high - Array of high prices
 * @param low - Array of low prices
 * @param close - Array of closing prices
 * @param period - Number of periods for calculation (default: 20)
 * @param annualized - Whether to annualize the volatility (default: true)
 * @returns Array of Rogers-Satchell volatility values
 */
export declare function rogersSatchellVolatility(open: number[], high: number[], low: number[], close: number[], period?: number, annualized?: boolean): number[];
/**
 * Yang-Zhang Volatility
 *
 * Yang-Zhang volatility is the most efficient volatility estimator that uses
 * OHLC data and doesn't require the assumption of zero drift.
 *
 * @param open - Array of opening prices
 * @param high - Array of high prices
 * @param low - Array of low prices
 * @param close - Array of closing prices
 * @param period - Number of periods for calculation (default: 20)
 * @param annualized - Whether to annualize the volatility (default: true)
 * @returns Array of Yang-Zhang volatility values
 */
export declare function yangZhangVolatility(open: number[], high: number[], low: number[], close: number[], period?: number, annualized?: boolean): number[];
/**
 * Volatility Ratio
 *
 * Calculates the ratio of current volatility to historical volatility.
 * Values above 1 indicate higher than normal volatility.
 *
 * @param prices - Array of price data
 * @param shortPeriod - Short period for current volatility (default: 10)
 * @param longPeriod - Long period for historical volatility (default: 20)
 * @returns Array of volatility ratio values
 */
export declare function volatilityRatio(prices: number[], shortPeriod?: number, longPeriod?: number): number[];
/**
 * Collection of volatility-based technical indicators
 */
export declare const VolatilityIndicators: {
    keltnerChannels: typeof keltnerChannels;
    standardDeviation: typeof standardDeviation;
    variance: typeof variance;
    averageDeviation: typeof averageDeviation;
    historicalVolatility: typeof historicalVolatility;
    parkinsonVolatility: typeof parkinsonVolatility;
    garmanKlassVolatility: typeof garmanKlassVolatility;
    rogersSatchellVolatility: typeof rogersSatchellVolatility;
    yangZhangVolatility: typeof yangZhangVolatility;
    volatilityRatio: typeof volatilityRatio;
};
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