quantitivecalc/dist/TechnicalIndicators/calculateBollingerBands.js

A TypeScript library providing advanced quantitative finance functions for risk analysis, performance metrics, and technical indicators. (Currently in development)

"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
exports.calculateBollingerBands = calculateBollingerBands;
const calculateMovingAverage_1 = require("./calculateMovingAverage");
/**
 * Calculates Bollinger Bands for a given dataset.
 *
 * Bollinger Bands consist of three lines: the middle band (simple moving average),
 * the upper band (SMA + N standard deviations), and the lower band (SMA - N standard deviations).
 *
 * @param data - Array of data objects to calculate Bollinger Bands for.
 * @param sourceColumn - The key in each data object containing the source value (e.g., closing price).
 * @param upperColumn - The key to store the calculated upper band value. Defaults to 'bb_upper'.
 * @param middleColumn - The key to store the calculated middle band (SMA) value. Defaults to 'bb_middle'.
 * @param lowerColumn - The key to store the calculated lower band value. Defaults to 'bb_lower'.
 * @param windowSize - The number of periods to use for the moving average and standard deviation. Defaults to 20.
 * @param numStdDev - The number of standard deviations to use for the upper and lower bands. Defaults to 2.
 * @returns A new array of data objects with Bollinger Bands columns added.
 */
function calculateBollingerBands(data, sourceColumn, upperColumn = 'bb_upper', middleColumn = 'bb_middle', lowerColumn = 'bb_lower', windowSize = 20, numStdDev = 2) {
    if (!data || data.length === 0) {
        return [];
    }
    // Calculate the middle band (Simple Moving Average)
    const withMA = (0, calculateMovingAverage_1.calculateMovingAverage)(data, sourceColumn, middleColumn, windowSize, 'simple');
    return withMA.map((row, i) => {
        if (i < windowSize - 1 || row[middleColumn] === null) {
            return {
                ...row,
                [upperColumn]: null,
                [lowerColumn]: null,
            };
        }
        // Calculate standard deviation for the window
        let sumSquaredDeviations = 0;
        const mean = row[middleColumn];
        // Collect valid values for the window
        const windowValues = [];
        for (let j = i - windowSize + 1; j <= i; j++) {
            const value = withMA[j][sourceColumn];
            if (typeof value === 'number' && !isNaN(value)) {
                windowValues.push(value);
            }
        }
        if (windowValues.length === 0) {
            return {
                ...row,
                [upperColumn]: null,
                [lowerColumn]: null,
            };
        }
        // Calculate standard deviation using the collected values
        for (const value of windowValues) {
            sumSquaredDeviations += Math.pow(value - mean, 2);
        }
        // Use sample standard deviation (n-1) for consistency with typical Bollinger Bands calculation
        const variance = sumSquaredDeviations / (windowValues.length - 1);
        const stdDev = Math.sqrt(variance);
        return {
            ...row,
            [upperColumn]: mean + numStdDev * stdDev,
            [lowerColumn]: mean - numStdDev * stdDev,
        };
    });
}