Machine Learning Support & Resistance [FEELS]

indicator("Machine Learning Support & Resistance [FEELS]", "ML S&R", overlay = true, max_boxes_count = 500, max_lines_count = 100, max_labels_count = 500, max_bars_back = 5000)

pivLen   = input.int(10, "Swing size", minval = 2, maxval = 50, tooltip = "Pivot width on each side. Bigger = fewer, more significant swing points feed the model")
histBars = input.int(1000, "History, bars", minval = 200, maxval = 5000, step = 100, tooltip = "Only swing points from this many recent bars feed the model, so levels come from structure that still matters")
maxPiv   = input.int(250, "Swing points used", minval = 20, maxval = 400, tooltip = "How many of the most recent swing highs and lows the model runs on")
autoK    = input.bool(true, "Model picks the level count", tooltip = "The model tries every level count in the range below, scores how cleanly each one separates the swings, and keeps the best. Turn off to fix the count yourself")
kMin     = input.int(3, "Fewest levels", minval = 2, maxval = 12, tooltip = "Lower end of the range the model searches")
kMax     = input.int(9, "Most levels", minval = 3, maxval = 14, tooltip = "Upper end of the range the model searches")
kFix     = input.int(6, "Level count", minval = 2, maxval = 14, tooltip = "Used when the model is not picking the count itself")
minPts   = input.int(3, "Min swings per level", minval = 2, maxval = 20, tooltip = "A group with fewer swings than this is not treated as a level, and its swings are labelled noise")
selPct   = input.int(60, "Selectivity", minval = 0, maxval = 200, step = 10, tooltip = "How concentrated a group must be to count as a level, measured against the average group on the chart. Higher values keep only the levels where swings really pile up and label the rest noise")
maxZones = input.int(8, "Max levels shown", minval = 1, maxval = 20, tooltip = "Only the levels nearest to price are drawn")
maxDist  = input.float(60, "Max distance from price, %", minval = 0, maxval = 500, step = 10, tooltip = "Levels further than this from the current price are hidden, measured proportionally so up and down are treated alike. Set 0 to show every level")
passes   = input.int(15, "Refinement passes", minval = 3, maxval = 40, tooltip = "How many times the model re-centres the groups before settling")

grpW = "Zone width"
minWIn = input.float(0.15, "Minimum width, %", minval = 0.05, maxval = 5, step = 0.05, group = grpW, tooltip = "A very tight group still draws at least this wide so it stays visible")
maxWIn = input.float(1.0, "Maximum width, %", minval = 0.1, maxval = 25, step = 0.1, group = grpW, tooltip = "Safety cap. A group is never drawn wider than this share of its level")
maxWRng = input.float(6.0, "Maximum width, % of range", minval = 1, maxval = 50, step = 1, group = grpW, tooltip = "Second cap, measured against the whole price range the model looked at. This keeps zones thin on intraday charts, where the range is small and a percentage of price would cover the screen")
softEdge = input.bool(true, "Density edges", group = grpW, tooltip = "Draws each level as a dense core fading into softer edges, so a tightly agreed level looks sharp and a loosely agreed one looks blurred")

grpP = "Density profile"
showProf = input.bool(false, "Show profile", group = grpP, tooltip = "Draws how the swing points are distributed across price, which is the raw data the model works on")
profBins = input.int(48, "Resolution", minval = 12, maxval = 100, group = grpP)
profW    = input.int(70, "Width, bars", minval = 10, maxval = 200, group = grpP)

grpT = "Hold rate"
showStats = input.bool(true, "Show hold rate", group = grpT, tooltip = "Counts how often price entered a level and left from the same side instead of closing through it")
showTable = input.bool(true, "Show table", group = grpT)
statBack  = input.int(500, "Lookback, bars", minval = 100, maxval = 1500, group = grpT)
statWin   = input.int(10, "Reaction window, bars", minval = 3, maxval = 100, group = grpT, tooltip = "How long price has to close through a level before that visit counts as broken")

grpS = "Style"
supCol   = input.color(#089981, "Support", group = grpS)
resCol   = input.color(#f23645, "Resistance", group = grpS)
noiseCol = input.color(#787b86, "Noise", group = grpS, tooltip = "Swings the model rejected as too sparse to form a level")
showDots = input.bool(true, "Show swing points", group = grpS, tooltip = "Draws every swing point the model looked at, coloured by the level it belongs to")
showNoise = input.bool(true, "Show rejected swings", group = grpS)
dotSizeS = input.string("Large", "Swing point size", options = ["Tiny", "Small", "Normal", "Large"], group = grpS)
showMid  = input.bool(true, "Show level centre", group = grpS)
extBars  = input.int(8, "Extend right, bars", minval = 0, maxval = 100, group = grpS)
txtSizeS = input.string("Small", "Text size", options = ["Tiny", "Small", "Normal", "Large"], group = grpS)

txtSize = txtSizeS == "Tiny" ? size.tiny : txtSizeS == "Normal" ? size.normal : txtSizeS == "Large" ? size.large : size.small
dotSize = dotSizeS == "Large" ? size.large : dotSizeS == "Normal" ? size.normal : dotSizeS == "Small" ? size.small : size.tiny

var pvP = array.new<float>()
var pvB = array.new<int>()
var boxes = array.new<box>()
var tags  = array.new<label>()
var dots  = array.new<label>()
var mids  = array.new<line>()

ph = ta.pivothigh(pivLen, pivLen)
pl = ta.pivotlow(pivLen, pivLen)

f_kmeans(array<float> data, int k, int iters) =>
    n = array.size(data)
    cent = array.new<float>(k, 0.0)
    if n > 0
        srt = array.copy(data)
        array.sort(srt)
        for i = 0 to k - 1
            idx = math.max(0, math.min(n - 1, int((i + 0.5) * n / k)))
            array.set(cent, i, array.get(srt, idx))
        for it = 1 to iters
            sums = array.new<float>(k, 0.0)
            cnts = array.new<int>(k, 0)
            for j = 0 to n - 1
                v = array.get(data, j)
                best = 0
                bd = math.abs(v - array.get(cent, 0))
                for c = 1 to k - 1
                    d = math.abs(v - array.get(cent, c))
                    if d < bd
                        bd := d
                        best := c
                array.set(sums, best, array.get(sums, best) + v)
                array.set(cnts, best, array.get(cnts, best) + 1)
            for c = 0 to k - 1
                if array.get(cnts, c) > 0
                    array.set(cent, c, array.get(sums, c) / array.get(cnts, c))
    cent

f_nearest(array<float> cent, float v) =>
    best = 0
    bd = math.abs(v - array.get(cent, 0))
    for c = 1 to array.size(cent) - 1
        d = math.abs(v - array.get(cent, c))
        if d < bd
            bd := d
            best := c
    best

f_silhouette(array<float> data, array<float> cent) =>
    n = array.size(data)
    k = array.size(cent)
    tot = 0.0
    cnt = 0
    for j = 0 to n - 1
        v = array.get(data, j)
        d1 = 1e9
        d2 = 1e9
        for c = 0 to k - 1
            d = math.abs(v - array.get(cent, c))
            if d < d1
                d2 := d1
                d1 := d
            else if d < d2
                d2 := d
        if d2 > 0
            tot += (d2 - d1) / d2
            cnt += 1
    cnt > 0 ? tot / cnt : 0.0

f_holdRate(float bot, float top) =>
    visits = 0
    held = 0
    lim = math.min(statBack, bar_index - 1)
    if lim > statWin + 2
        for o = lim to statWin + 1
            if o <= 4980
                inNow = low[o] <= top and high[o] >= bot
                inPrev = low[o + 1] <= top and high[o + 1] >= bot
                if inNow and not inPrev
                    fromBelow = close[o + 1] < bot
                    fromAbove = close[o + 1] > top
                    if fromBelow or fromAbove
                        visits += 1
                        broke = false
                        for f = 1 to statWin
                            c = close[o - f]
                            if not broke and (fromBelow ? c > top : c < bot)
                                broke := true
                        if not broke
                            held += 1
    [visits, held]

f_clear() =>
    if array.size(boxes) > 0
        for i = 0 to array.size(boxes) - 1
            box.delete(array.get(boxes, i))
        array.clear(boxes)
    if array.size(tags) > 0
        for i = 0 to array.size(tags) - 1
            label.delete(array.get(tags, i))
        array.clear(tags)
    if array.size(dots) > 0
        for i = 0 to array.size(dots) - 1
            label.delete(array.get(dots, i))
        array.clear(dots)
    if array.size(mids) > 0
        for i = 0 to array.size(mids) - 1
            line.delete(array.get(mids, i))
        array.clear(mids)

if barstate.isconfirmed
    if not na(ph) and ph > 0
        array.push(pvP, ph)
        array.push(pvB, bar_index - pivLen)
    if not na(pl) and pl > 0
        array.push(pvP, pl)
        array.push(pvB, bar_index - pivLen)
    while array.size(pvP) > maxPiv
        array.shift(pvP)
        array.shift(pvB)
    while array.size(pvB) > 0 and bar_index - array.get(pvB, 0) > histBars
        array.shift(pvP)
        array.shift(pvB)

var table stat = table.new(position.top_right, 3, 24, border_width = 1)
touched = false
brokeZ  =

Показано первые 50% кода (символов: 12965)