// This Pine Script™ code is subject to the terms of the Mozilla Public License 2.
0
at https://mozilla.org/MPL/2.0/
// © AlgoAlpha
//@version=5
indicator("AI Adaptive Money Flow Index (Clustering) [AlgoAlpha]", "AlgoAlpha - 🤖
AI MFI")
src = input.source(hlc3, "MFI Source", group = "MFI Settings")
len = input.int(14, "MFI length", minval = 1, group = "MFI Settings")
length = input.int(300, "The Training Data Size", minval = 1, group = "K-means
Clustering Settings")
iterations = input.int(5, "How Many Iterations the k-Means Clustering Logic is
Applied per Bar", minval = 1, group = "K-means Clustering Settings")
overbought = input.float(80.0, "Initial Overbought Value", group = "K-means
Clustering Settings")
neutral = input.float(50.0, "Initial Neutral Value", group = "K-means Clustering
Settings")
oversold = input.float(20.0, "Initial Oversold Value", group = "K-means Clustering
Settings")
adj = input.bool(true, "Use Adjusted MFI", "Adjusts the MFI instead so that the
Overbought, Oversold and Neutral levels stay fixed", group = "Appearance")
mlt = input.float(1.0, "Numbers Distance Multiplier", tooltip = "Increase this to
make the classification numbers appear further away from the oscillator plot",
group = "Appearance")
green = input.color(#00ffbb, "Up Color", group = "Appearance") //#00ffbb
red = input.color(#ff1100, "Down Color", group = "Appearance") //#ff1100
osc = ta.mfi(src, len)
a = overbought
b = neutral
c = oversold
var ob = array.new_float(1, overbought)
var ne = array.new_float(1, neutral)
var os = array.new_float(1, oversold)
if nz(osc) > 0
for j = iterations to 1
ob.clear()
ne.clear()
os.clear()
for i = length to 0
if math.abs(osc[i] - b) < math.abs(osc[i] - a) and math.abs(osc[i] - b)
< math.abs(osc[i] - c)
ne.push(osc[i])
if math.abs(osc[i] - a) < math.abs(osc[i] - b) and math.abs(osc[i] - a)
< math.abs(osc[i] - c)
ob.push(osc[i])
if math.abs(osc[i] - c) < math.abs(osc[i] - a) and math.abs(osc[i] - c)
< math.abs(osc[i] - b)
os.push(osc[i])
a := array.avg(ob)
b := array.avg(ne)
c := array.avg(os)
ne_new = array.avg(ne)
ob_new = array.avg(ob)
os_new = array.avg(os)
positionBetweenBands = 100 * ((osc - os_new)/(ob_new - os_new))
val = adj ? positionBetweenBands : osc
st = ta.stdev(val, len)
col = osc > ne_new ? color.new(green, 90) : color.new(red, 90)
col1 = osc > ne_new ? color.new(green, 0) : color.new(red, 0)
main = plot(val, color = col)
mid = plot(adj ? neutral : ne_new, color = col)
plot(adj ? 100 : ob_new, color = color.from_gradient(osc, ne_new, adj ? 100 :
ob_new, color.new(color.gray, 70), color.new(red, 0)), linewidth = 3)
plot(adj ? 0 : os_new, color = color.from_gradient(osc, adj ? 0 : os_new, ne_new,
color.new(green, 0), color.new(color.gray, 70)), linewidth = 3)
plotchar(math.abs(osc - ne_new) < math.abs(osc - ob_new) and math.abs(osc - ne_new)
< math.abs(osc - os_new) ? val + (osc > ne_new ? st*mlt : -st*mlt) : na, "Ranging
Classification", "0", location.absolute, col1)
plotchar(math.abs(osc - ob_new) < math.abs(osc - ne_new) and math.abs(osc - ob_new)
< math.abs(osc - os_new) ? val + (osc > ne_new ? st*mlt : -st*mlt) : na, "Up-Trend
Classification", "+", location.absolute, col1)
plotchar(math.abs(osc - os_new) < math.abs(osc - ob_new) and math.abs(osc - os_new)
< math.abs(osc - ne_new) ? val + (osc > ne_new ? st*mlt : -st*mlt) : na, "Down-
Trend Classification", "-", location.absolute, col1)
plotchar(ta.crossunder(osc, ob_new) ? (adj ? 100 : ob_new) + 10 : na, "Bearish
Reversal", "▼", location.absolute, red, size = size.tiny)
plotchar(ta.crossover(osc, os_new) ? (adj ? 0 : os_new) - 10 : na, "Bullish
Reversal", "▲", location.absolute, green, size = size.tiny)
fill(mid, main, color = col, title="Trend Fill")
fill(mid, main, val, adj ? 50 : ne_new, color.new(chart.bg_color, 1000), osc >
ne_new ? green : red)
//Alerts
alertcondition(ta.crossover(osc, ne_new), "Bullish Trend")
alertcondition(ta.crossunder(osc, ne_new), "Bearish Trend")
alertcondition(ta.crossover(osc, os_new), "Bullish Reversal")
alertcondition(ta.crossunder(osc, ob_new), "Bearish Reversal")
alertcondition(ta.crossover(osc, ob_new), "Overbought")
alertcondition(ta.crossunder(osc, os_new), "Oversold")
alertcondition(math.abs(osc - ne_new) < math.abs(osc - ob_new) and math.abs(osc -
ne_new) < math.abs(osc - os_new), "MFI Classification Ranging")
alertcondition(math.abs(osc - ob_new) < math.abs(osc - ne_new) and math.abs(osc -
ob_new) < math.abs(osc - os_new), "MFI Classification Up-Trend")
alertcondition(math.abs(osc - os_new) < math.abs(osc - ob_new) and math.abs(osc -
os_new) < math.abs(osc - ne_new), "MFI Classification Down-Trend")
