@socktrader/indicators

SockTrader

Indicators

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What is "SockTrader"?

More info the SockTrader github page

Quickstart

1. Install library: npm install @socktrader/indicators
2. Use library activeReturn([0.003], [0.04], 12)

Indicators

Performance metrics

    const x = [0.003, 0.026, 0.015, -0.009, 0.014, 0.024, 0.015, 0.066, -0.014, 0.039]
    const z = [0.04, -0.022, 0.043, 0.028, -0.078, -0.011, 0.033, -0.049, 0.09, 0.087]
    
    activeReturn(x, z, 12)
    // 0.041978811047168385
    
    annualReturn(x, 12)
    // 0.2338146820656939
    
    compoundAnnualGrowthRate(x, x.length / 12)
    // 0.22938756017127182
    
    internalRateOfReturn([250000, 25000, -10000, -285000])
    // 0.024712563094781776
    
    mdietz(1200, 1000, [10, 50, 35, 20], [15, 38, 46, 79], 90)
    // 0.0804331826306382
    
    percpos(x)
    // 0.8
    
    rateOfReturn(x)
    // 0.18779277315203946
    
    timeWeightedReturn([250000, 255000, 257000, 288000, 293000, 285000], [0, 0, 25000, 0, -10000, 0])
    // 0.07564769566198049
    

Risk metrics

    const x = [0.003, 0.026, 0.015, -0.009, 0.014, 0.024, 0.015, 0.066, -0.014, 0.039]
    const y = [-0.005, 0.081, 0.04, -0.037, -0.061, 0.058, -0.049, -0.021, 0.062, 0.058]
    const z = [0.04, -0.022, 0.043, 0.028, -0.078, -0.011, 0.033, -0.049, 0.09, 0.087]
    
    adjustedSharpeRatio(x, 0.02 / 12)
    // 0.7481337144481773
    
    annualAdjustedSharpeRatio(x, 0.02, 12, Mode.geometric)
    // 3.3767236091658313
    
    annualRisk(x, 12)
    // 0.08047276972160623
    
    avgDrawdown(x)
    // 0.01150000000000001
    
    burkeRatio(x, 0, 12)
    // 14.048562698619559
    
    calmarRatio(x, 0, 12)
    // 16.70104871897814
    
    continuousDrawdown(x)
    // [0.009000000000000008, 0.014000000000000012]
    
    downsidePotential(x, 0.1 / 100)
    // 0.0024999999999999996
    
    downsideRisk(x, 0.1 / 100)
    // 0.005700877125495689
    
    drawdown(x) 
    // {
    //    'dd': [0, 0, 0, 0.00900000000000004, 0, 0, 0, 0, 0.013999999999999995, 0],
    //    'ddrecov': [0, 0, 0, 4, 0, 0, 0, 0, 9, 0],
    //    'maxdd': 0.013999999999999995,
    //    'maxddrecov': [8, 9]
    // }
    
    histcondvar(x)
    // 0.014
    
    histvar(x)
    // 0.013999999999999999
    
    informationRatio(x, y)
    // 0.09369148584852913
    
    jensenAlpha(x, y)
    // 0.01760907323602524
    
    m2sortino(x, y, 0, 0, 12)
    // 0.504144074388577
    
    martinRatio(x, 0, 12)
    // 44.42545597931942
    
    modigliani(x, y)
    // 0.040694064858387835
    
    monteCarloValueAtRisk(x, 0.95, 1, 0, 1, 10000)
    // random: 0 < x < 1
    
    omegaRatio(x)
    // 8.782608695652174
    
    painIndex(x)
    // 0.0023000000000000034
    
    painRatio(x, 0, 12)
    // 101.04495520047377
    
    parametricConditionalValueAtRisk(mean(x), std(x))
    // 0.030017825479120894
    
    parametricValueAtRisk([0, 0, 0], [1, 2, 3])
    // [1.6448536127562643, 3.2897072255125286, 4.934560838268792]
    
    sharpeRatio(x, 0.02 / 12)
    // 0.6987943426529188
    
    sortino(x, 0.02 / 12)
    // 3.0843795993743215
    
    sterlingRatio(x, 0, 12)
    // 16.70104871897812
    
    trackingError(x, z)
    // 0.06843618276256436
    
    treynorRatio(x, z, 0.01 / 12)
    // -0.09568702060685172
    
    ulcerIndex(x)
    // 0.005263078946776312
    
    upsidePotential(x, 0.1 / 100)
    // 0.0194