Discounted Price Probability

// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © HeWhoMustNotBeNamed

//   __    __            __       __  __                  __       __                        __      __    __              __      _______             __    __                                          __ 
//  /  |  /  |          /  |  _  /  |/  |                /  \     /  |                      /  |    /  \  /  |            /  |    /       \           /  \  /  |                                        /  |
//  $$ |  $$ |  ______  $$ | / \ $$ |$$ |____    ______  $$  \   /$$ | __    __   _______  _$$ |_   $$  \ $$ |  ______   _$$ |_   $$$$$$$  |  ______  $$  \ $$ |  ______   _____  ____    ______    ____$$ |
//  $$ |__$$ | /      \ $$ |/$  \$$ |$$      \  /      \ $$$  \ /$$$ |/  |  /  | /       |/ $$   |  $$$  \$$ | /      \ / $$   |  $$ |__$$ | /      \ $$$  \$$ | /      \ /     \/    \  /      \  /    $$ |
//  $$    $$ |/$$$$$$  |$$ /$$$  $$ |$$$$$$$  |/$$$$$$  |$$$$  /$$$$ |$$ |  $$ |/$$$$$$$/ $$$$$$/   $$$$  $$ |/$$$$$$  |$$$$$$/   $$    $$< /$$$$$$  |$$$$  $$ | $$$$$$  |$$$$$$ $$$$  |/$$$$$$  |/$$$$$$$ |
//  $$$$$$$$ |$$    $$ |$$ $$/$$ $$ |$$ |  $$ |$$ |  $$ |$$ $$ $$/$$ |$$ |  $$ |$$      \   $$ | __ $$ $$ $$ |$$ |  $$ |  $$ | __ $$$$$$$  |$$    $$ |$$ $$ $$ | /    $$ |$$ | $$ | $$ |$$    $$ |$$ |  $$ |
//  $$ |  $$ |$$$$$$$$/ $$$$/  $$$$ |$$ |  $$ |$$ \__$$ |$$ |$$$/ $$ |$$ \__$$ | $$$$$$  |  $$ |/  |$$ |$$$$ |$$ \__$$ |  $$ |/  |$$ |__$$ |$$$$$$$$/ $$ |$$$$ |/$$$$$$$ |$$ | $$ | $$ |$$$$$$$$/ $$ \__$$ |
//  $$ |  $$ |$$       |$$$/    $$$ |$$ |  $$ |$$    $$/ $$ | $/  $$ |$$    $$/ /     $$/   $$  $$/ $$ | $$$ |$$    $$/   $$  $$/ $$    $$/ $$       |$$ | $$$ |$$    $$ |$$ | $$ | $$ |$$       |$$    $$ |
//  $$/   $$/  $$$$$$$/ $$/      $$/ $$/   $$/  $$$$$$/  $$/      $$/  $$$$$$/  $$$$$$$/     $$$$/  $$/   $$/  $$$$$$/     $$$$/  $$$$$$$/   $$$$$$$/ $$/   $$/  $$$$$$$/ $$/  $$/  $$/  $$$$$$$/  $$$$$$$/ 
//                                                                                                                                                                                                          
//                                                                                                                                                                                                          
//
//@version=5
indicator("Discounted Price Probability", overlay=true)
import HeWhoMustNotBeNamed/_matrix/1 as ma
import HeWhoMustNotBeNamed/drawingutils/3 as dr

reference = input.string('Inception', title='Reference', options=['Inception', 'Window'], group='Reference')
startTime = input.time(defval=timestamp('01 Jan 2010 00:00 +0000'), title='Start Time', group='Reference')
endTime = input.time(defval=timestamp('01 Jan 2099 00:00 +0000'), title='End Time', group='Reference')
inDateRange = time >= startTime and time <= endTime

considerEarningPrice = input.bool(true, 'Earning Price', group='Main Factors', inline='1')
considerFundamentalsPerShare = input.bool(true, 'Fundamentals/Share', group='Main Factors', inline='1')
considerMargins = input.bool(true, 'Margins', group='Main Factors', inline='1')
considerReturns = input.bool(true, 'Returns', group='Main Factors', inline='2')
considerInvertedDebtRatios = input.bool(true, 'Inverted Debt Ratios', group='Main Factors', inline='2')

considerBvps = input.bool(true, 'Book Value', group='Fundamentals/Share', inline='1')
considerEps = input.bool(true, 'Earning', group='Fundamentals/Share', inline='1')
considerRps = input.bool(true, 'Revenue', group='Fundamentals/Share', inline='1')
considerFcf = input.bool(true, 'FCF', group='Fundamentals/Share', inline='2')
considerEbit = input.bool(true, 'EBIT', group='Fundamentals/Share', inline='2')
considerEbitda = input.bool(true, 'EBITDA', group='Fundamentals/Share', inline='2')

considerNetMargin = input.bool(true, 'Net', group='Margin', inline='1')
considerGrossMargin = input.bool(true, 'Gross', group='Margin', inline='1')
considerOperatingMargin = input.bool(true, 'Operating', group='Margin', inline='1')
considerEBITDAMargin = input.bool(true, 'EBITDA', group='Margin', inline='1')
considerFCFMargin = input.bool(true, 'FCF', group='Margin', inline='1')

considerROA = input.bool(true, 'ROA', group='Returns', inline='1')
considerROE = input.bool(true, 'ROE', group='Returns', inline='1')
considerROIC = input.bool(true, 'ROIC', group='Returns', inline='1')

considerDTA = input.bool(true, 'DTA', group='Inversed Debt Ratios', inline='1')
considerLDTA = input.bool(true, 'LDTA', group='Inversed Debt Ratios', inline='1')
considerDTE = input.bool(true, 'DTE', group='Inversed Debt Ratios', inline='1')
considerDTR = input.bool(true, 'DTR', group='Inversed Debt Ratios', inline='1')
considerDTEB = input.bool(true, 'DTEBITDA', group='Inversed Debt Ratios', inline='1')

displayTimeframe = input.bool(true, 'Display Timeframe', group='Display')


financialParameters = array.from('Current')
ignoredParameters = array.new_string()

fundamentalParameters = array.new_string()
marginParameters = array.new_string()
returnParameters = array.new_string()
debtParameters = array.new_string()

financialFactorColumns = array.from(1)
financialFactors = array.from('Price')
array.push(considerEarningPrice?financialParameters:ignoredParameters, 'Last Earnings')

array.push(financialFactorColumns, array.get(financialFactorColumns, array.size(financialFactorColumns)-1)+array.size(financialParameters))

array.push(considerFundamentalsPerShare and considerBvps?fundamentalParameters:ignoredParameters, 'Book Value')
array.push(considerFundamentalsPerShare and considerEps?fundamentalParameters:ignoredParameters, 'Earnings')
array.push(considerFundamentalsPerShare and considerRps?fundamentalParameters:ignoredParameters, 'Revenue')
array.push(considerFundamentalsPerShare and considerFcf?fundamentalParameters:ignoredParameters, 'FCF')
array.push(considerFundamentalsPerShare and considerEbit?fundamentalParameters:ignoredParameters, 'EBIT')
array.push(considerFundamentalsPerShare and considerEbitda?fundamentalParameters:ignoredParameters, 'EBITDA')

if(array.size(fundamentalParameters) != 0)
    array.concat(financialParameters, fundamentalParameters)
    array.push(financialFactorColumns, array.get(financialFactorColumns, array.size(financialFactorColumns)-1)+array.size(fundamentalParameters))
    array.push(financialFactors, 'Fundamentals/Share')

array.push(considerMargins and considerNetMargin?marginParameters:ignoredParameters, 'Net')
array.push(considerMargins and considerGrossMargin?marginParameters:ignoredParameters, 'Gross')
array.push(considerMargins and considerOperatingMargin?marginParameters:ignoredParameters, 'Operating')
array.push(considerMargins and considerEBITDAMargin?marginParameters:ignoredParameters, 'EBITDA')
array.push(considerMargins and considerFCFMargin?marginParameters:ignoredParameters, 'FCF')

if(array.size(marginParameters) != 0)
    array.concat(financialParameters, marginParameters)
    array.push(financialFactorColumns, array.get(financialFactorColumns, array.size(financialFactorColumns)-1)+array.size(marginParameters))
    array.push(financialFactors, 'Margin')
    
array.push(considerReturns and considerROA?returnParameters:ignoredParameters, 'ROA')
array.push(considerReturns and considerROE?returnParameters:ignoredParameters, 'ROE')
array.push(considerReturns and considerROIC?returnParameters:ignoredParameters, 'ROIC')

if(array.size(returnParameters) != 0)
    array.concat(financialParameters, returnParameters)
    array.push(financialFactorColumns, array.get(financialFactorColumns, array.size(financialFactorColumns)-1)+array.size(returnParameters))
    array.push(financialFactors, 'Returns')
    
array.push(considerInvertedDebtRatios and considerDTA?debtParameters:ignoredParameters, 'Assets')
array.push(considerInvertedDebtRatios and considerLDTA?debtParameters:ignoredParameters, 'Assets(Long Term)')
array.push(considerInvertedDebtRatios and considerDTE?debtParameters:ignoredParameters, 'Equity')
array.push(considerInvertedDebtRatios and considerDTR?debtParameters:ignoredParameters, 'Revenue')
array.push(considerInvertedDebtRatios and considerDTEB?debtParameters:ignoredParameters, 'EBITDA')

if(array.size(debtParameters) != 0)
    array.concat(financialParameters, debtParameters)
    array.push(financialFactorColumns, array.get(financialFactorColumns, array.size(financialFactorColumns)-1)+array.size(debtParameters))
    array.push(financialFactors, 'Inverted Debt Ratios')
    
f_getFinancials(financial_id, period) =>
    request.financial(syminfo.tickerid, financial_id, period)
f_getOptimalFinancialBasic(financial_id) =>
    f_getFinancials(financial_id, syminfo.currency == 'USD' ? 'FQ' : 'FY')

tso = request.financial(syminfo.tickerid, 'TOTAL_SHARES_OUTSTANDING', 'FQ')
bvps = request.financial(syminfo.tickerid, 'BOOK_VALUE_PER_SHARE', 'FQ')
eps = request.financial(syminfo.tickerid, 'EARNINGS_PER_SHARE_BASIC', 'TTM')
rps = request.financial(syminfo.tickerid, 'TOTAL_REVENUE', 'TTM') / tso

ebitda = request.financial(syminfo.tickerid, 'EBITDA', 'TTM') / tso
ebit = f_getOptimalFinancialBasic('EBIT') / tso
fcf = f_getOptimalFinancialBasic('FREE_CASH_FLOW') / tso
//currentassets = f_getOptimalFinancialBasic("NCAVPS_RATIO")

nmargin = f_getOptimalFinancialBasic('NET_MARGIN')
gmargin = f_getOptimalFinancialBasic('GROSS_MARGIN')
omargin = f_getOptimalFinancialBasic('OPERATING_MARGIN')
emargin = f_getOptimalFinancialBasic('EBITDA_MARGIN')
fmargin = f_getOptimalFinancialBasic('FREE_CASH_FLOW_MARGIN')

roa = f_getOptimalFinancialBasic('RETURN_ON_ASSETS')
roe = f_getOptimalFinancialBasic('RETURN_ON_EQUITY')
roic = f_getOptimalFinancialBasic('RETURN_ON_INVESTED_CAPITAL')

ldta = 1 / f_getOptimalFinancialBasic('LONG_TERM_DEBT_TO_ASSETS')
dta = 1 / f_getOptimalFinancialBasic('DEBT_TO_ASSET')
dte = 1 / f_getOptimalFinancialBasic('DEBT_TO_EQUITY')
dtr = 1 / f_getOptimalFinancialBasic('DEBT_TO_REVENUE')
dteb = 1 / f_getOptimalFinancialBasic('DEBT_TO_EBITDA')

NUMBER_OF_ITEMS = 20

earnings = request.earnings(syminfo.tickerid, earnings.actual, barmerge.gaps_on, barmerge.lookahead_off, true)

getPercentile(value, defaultToZero = true)=>
    var arr = array.new_float()
    array.unshift(arr, value)
    array.size(arr)==1? 100.0 : array.percentrank(arr, 0)

getPercentileDrawdown(value, defaultToZero = true)=>
    var arr = array.new_float()
    sIndex = array.binary_search_rightmost(arr, value)
    array.insert(arr, sIndex, value)
    index = defaultToZero? array.indexof(arr, value) : array.lastindexof(arr, value)
    percentile = (index+1)*100/array.size(arr)
    percentile
    
f_calculate_drawdown_stats(value) =>
    var float ath = na
    var float drawdownPercentile = 0
    var drawdown = 0.0
    var percentile = 0.0
    if(not na(value))
        percentile := getPercentile(value)
        ath := math.max(value, nz(ath, value))
        drawdown := math.round(math.abs(ath - value)*100 / math.max(math.abs(ath), math.abs(value)))
        drawdownPercentile := getPercentileDrawdown(drawdown)
    
    percentileColor = color.from_gradient(percentile, 0, 100, color.red, color.green)
    drawdownColor = color.from_gradient(drawdownPercentile, 0, 100, color.green, color.red)
    
    [array.from(value, ath, percentile, drawdown, drawdownPercentile),
         array.from(percentileColor, percentileColor, percentileColor, drawdownColor, drawdownColor)]

comparitive_drawdown(value, priceDrawdown, pricePercentile, statsMatrix, colorMatrix, condition=true) =>
    if(condition)
        [statsArray, colorArray] = f_calculate_drawdown_stats(value)
        drawdownDiff = priceDrawdown - array.get(statsArray, 3)
        percentileDiff = array.get(statsArray, 2)-pricePercentile
        diffDrawdownPercentile = getPercentile(drawdownDiff)
        diffPricePercentile = getPercentile(percentileDiff)
        array.push(statsArray, diffPricePercentile)
        array.push(statsArray, diffDrawdownPercentile)
        array.push(colorArray, color.from_gradient(diffPricePercentile, 0, 100, color.red, color.green))
        array.push(colorArray, color.from_gradient(diffDrawdownPercentile, 0, 100, color.red, color.green))
        ma.push(statsMatrix, statsArray, NUMBER_OF_ITEMS)
        ma.push(colorMatrix, colorArray, NUMBER_OF_ITEMS)

if reference == 'Inception' or inDateRange
    if displayTimeframe
        dr.runTimer()
    [priceDrawdownStats, priceDrawdownColors] = f_calculate_drawdown_stats(high)
    drawdown = array.get(priceDrawdownStats, 3)
    percentile = array.get(priceDrawdownStats, 2)
    var percentileArray = array.new_float(20, 0)
    var diffArray = array.new_float(20, 0)
    var drawdownArray = array.new_float(20, 0)
    var matrix<float> statMatrix = matrix.new<float>()
    var matrix<color> colorMatrix = matrix.new<color>()
    
    statParameters = array.from("Value", "ATH", "Percentile", "Drawdown", "Drawdown\nPercentile", "Differential Percentile\n(Absolute)", "Differential Percentile\n(Drawdown)")
    if(not na(earnings))
        ma.clear(statMatrix)
        ma.clear(colorMatrix)
        comparitive_drawdown(close, drawdown, percentile, statMatrix, colorMatrix, considerEarningPrice)
        comparitive_drawdown(bvps, drawdown, percentile, statMatrix, colorMatrix, considerFundamentalsPerShare and considerBvps)
        comparitive_drawdown(eps, drawdown, percentile, statMatrix, colorMatrix, considerFundamentalsPerShare and considerEps)
        comparitive_drawdown(rps, drawdown, percentile, statMatrix, colorMatrix, considerFundamentalsPerShare and considerRps)
        comparitive_drawdown(fcf, drawdown, percentile, statMatrix, colorMatrix, considerFundamentalsPerShare and considerFcf)
        comparitive_drawdown(ebit, drawdown, percentile, statMatrix, colorMatrix, considerFundamentalsPerShare and considerEbit)
        comparitive_drawdown(ebitda, drawdown, percentile, statMatrix, colorMatrix, considerFundamentalsPerShare and considerEbitda)
        comparitive_drawdown(nmargin, drawdown, percentile, statMatrix, colorMatrix, considerMargins and considerNetMargin)
        comparitive_drawdown(gmargin, drawdown, percentile, statMatrix, colorMatrix, considerMargins and considerGrossMargin)
        comparitive_drawdown(omargin, drawdown, percentile, statMatrix, colorMatrix, considerMargins and considerOperatingMargin)
        comparitive_drawdown(emargin, drawdown, percentile, statMatrix, colorMatrix, considerMargins and considerEBITDAMargin)
        comparitive_drawdown(fmargin, drawdown, percentile, statMatrix, colorMatrix, considerMargins and considerFCFMargin)
        comparitive_drawdown(roa, drawdown, percentile, statMatrix, colorMatrix, considerReturns and considerROA)
        comparitive_drawdown(roe, drawdown, percentile, statMatrix, colorMatrix, considerReturns and considerROE)
        comparitive_drawdown(roic, drawdown, percentile, statMatrix, colorMatrix, considerReturns and considerROIC)
        comparitive_drawdown(dta, drawdown, percentile, statMatrix, colorMatrix, considerInvertedDebtRatios and considerDTA)
        comparitive_drawdown(ldta, drawdown, percentile, statMatrix, colorMatrix, considerInvertedDebtRatios and considerLDTA)
        comparitive_drawdown(dte, drawdown, percentile, statMatrix, colorMatrix, considerInvertedDebtRatios and considerDTE)
        comparitive_drawdown(dtr, drawdown, percentile, statMatrix, colorMatrix, considerInvertedDebtRatios and considerDTR)
        comparitive_drawdown(dteb, drawdown, percentile, statMatrix, colorMatrix, considerInvertedDebtRatios and considerDTEB)

    percentileDAvg = matrix.columns(statMatrix) > 6 ? array.avg(matrix.col(statMatrix, 6)) : na
    percentilePAvg = matrix.columns(statMatrix) > 5 ? array.avg(matrix.col(statMatrix, 5)) : na

    if(barstate.islastconfirmedhistory)

        stats = table.new(position.middle_center, matrix.columns(statMatrix)+2, matrix.rows(statMatrix)+3, border_width=2)
        table.clear(stats, 0, 0, matrix.columns(statMatrix)+1, matrix.rows(statMatrix)+2)
        
        
        table.cell(stats, 0, 0, "P " +str.tostring(percentilePAvg, format.percent), bgcolor=color.from_gradient(percentilePAvg, 0, 100, color.red, color.green),
                                 text_color=color.white, text_size=size.huge, tooltip='Probability based on absolute percentile')
        table.cell(stats, 0, 1, "D " +str.tostring(percentileDAvg, format.percent), bgcolor=color.from_gradient(percentileDAvg, 0, 100, color.red, color.green),
                                 text_color=color.white, text_size=size.huge, tooltip='Probability based on drawdown percentile')
        for i=0 to array.size(financialFactors)-1
            table.cell(stats, 0, array.get(financialFactorColumns, i)+1, array.get(financialFactors, i), bgcolor=color.maroon, text_color=color.white, text_size=size.small)
            table.merge_cells(stats, 0, array.get(financialFactorColumns, i)+1, 0, array.get(financialFactorColumns, i+1))

        table.merge_cells(stats, 0, 0, 1, 0)
        table.merge_cells(stats, 0, 1, 1, 1)
        
        for i=0 to array.size(financialParameters)-1
            table.cell(stats, 1, i+2, array.get(financialParameters, i), bgcolor=color.teal, text_color=color.white, text_size=size.small)
        
        for j=0 to array.size(priceDrawdownStats)-1
            table.cell(stats, j+2, 2, str.tostring(array.get(priceDrawdownStats,j), j<2? '#.00' : format.percent), bgcolor=color.blue, text_color=color.white, text_size=size.small)
        table.cell(stats, array.size(priceDrawdownStats)+2, 2,  '', bgcolor=color.blue, text_color=color.white, text_size=size.small)
        table.cell(stats, array.size(priceDrawdownStats)+3, 2, '', bgcolor=color.blue, text_color=color.white, text_size=size.small)
        for j=0 to array.size(statParameters)-1
            table.cell(stats, j+2, 1, array.get(statParameters, j), bgcolor=color.maroon, text_color=color.white, text_size=size.small)
        for i=0 to matrix.rows(statMatrix)==0? na : matrix.rows(statMatrix)-1
            for j=0 to matrix.columns(statMatrix)-1
                table.cell(stats, j+2,i+2+1, str.tostring(matrix.get(statMatrix,i,j), j<2? '#.00' : format.percent), bgcolor=matrix.get(colorMatrix,i,j), text_color=color.white, text_size=size.small)