const lcjsTrader = require('@lightningchart/lcjs-trader')
const { MovingAverageType, TechnicalAnalysisMethods } = lcjsTrader

lcjsTrader.trader(TRADER_LICENSE).then(async (trader) => {
    const tradingChart = trader.tradingChart({ loadFromStorage: false })
    const taMethods = new TechnicalAnalysisMethods(tradingChart)

    // Reading data from a file.
    await fetch(`${document.head.baseURI}examples/assets/0013/Alphabet Inc (GOOGL).csv`).then((res) => res.text()).then((text) => {
        tradingChart.loadCsvString(text, 'Alphabet Inc (GOOGL)')
        const dataset = tradingChart.getData(false)
        const volumes = tradingChart.getVolumeData()

        // Add a custom overlay.
        const co = tradingChart.indicators().addCustomOverlay()
        co.setLineColor('#30EE50')
        co.setName('WWS + Z-Value')
        co.setOffset(13)
        
        // Create data for the custom overlay by combining Welles Wilder's Smoothing with Z-Value.
        const wwsValues = taMethods.calculateWellesWilderSmoothing(dataset[4], 14)
        const zvValues = taMethods.calculateZValue(dataset[4], 14, MovingAverageType.None)
        const coValues = []
        for (let i = 0; i < wwsValues.length; i++) {
            coValues[i] = wwsValues[i] + zvValues[i]
        }
        co.setData(coValues)

        // Add a custom study.
        const cs = tradingChart.indicators().addCustomStudy()
        cs.setLineColor('#EE3050')
        cs.setName('WAD / SD')
        cs.setOffset(13)

        // Create data for the custom study by combining Williams AD with Standard Deviation.
        const wadValues = taMethods.calculateWilliamsAccumulationDistribution(dataset[4], dataset[2], dataset[3], volumes, true)
        const sdValues = taMethods.calculateStandardDeviation(dataset[4], 14, MovingAverageType.None)
        const csValues = []
        for (let i = 0; i < sdValues.length; i++) {
            csValues[i] = wadValues[i + 12] / sdValues[i]
        }
        cs.setData(csValues)
    })

    tradingChart.setCurrency('USD')
})