BUZARA// © Buzzara
// =================================
// PLEASE SUPPORT THE TEAM
// =================================
//
// Telegram: t.me
// =================================
//@version=5
VERSION = ' Buzzara2.0'
strategy('ALGOX V6_1_24', shorttitle = '🚀〄 Buzzara2.0 〄🚀'+ VERSION, overlay = true, explicit_plot_zorder = true, pyramiding = 0, default_qty_type = strategy.percent_of_equity, initial_capital = 1000, default_qty_value = 1, calc_on_every_tick = false, process_orders_on_close = true)
G_SCRIPT01 = '■ ' + 'SAIYAN OCC'
//#region ———— <↓↓↓ G_SCRIPT01 ↓↓↓> {
// === INPUTS ===
res = input.timeframe('15', 'TIMEFRAME', group ="NON REPAINT")
useRes = input(true, 'Use Alternate Signals')
intRes = input(10, 'Multiplier for Alernate Signals')
basisType = input.string('ALMA', 'MA Type: ', options= )
basisLen = input.int(50, 'MA Period', minval=1)
offsetSigma = input.int(5, 'Offset for LSMA / Sigma for ALMA', minval=0)
offsetALMA = input.float(2, 'Offset for ALMA', minval=0, step=0.01)
scolor = input(false, 'Show coloured Bars to indicate Trend?')
delayOffset = input.int(0, 'Delay Open/Close MA', minval=0, step=1,
tooltip = 'Forces Non-Repainting')
tradeType = input.string('BOTH', 'What trades should be taken : ',
options = )
//=== /INPUTS ===
h = input(false, 'Signals for Heikin Ashi Candles')
//INDICATOR SETTINGS
swing_length = input.int(10, 'Swing High/Low Length', group = 'Settings', minval = 1, maxval = 50)
history_of_demand_to_keep = input.int(20, 'History To Keep', minval = 5, maxval = 50)
box_width = input.float(2.5, 'Supply/Demand Box Width', group = 'Settings', minval = 1, maxval = 10, step = 0.5)
//INDICATOR VISUAL SETTINGS
show_zigzag = input.bool(false, 'Show Zig Zag', group = 'Visual Settings', inline = '1')
show_price_action_labels = input.bool(false, 'Show Price Action Labels', group = 'Visual Settings', inline = '2')
supply_color = input.color(#00000000, 'Supply', group = 'Visual Settings', inline = '3')
supply_outline_color = input.color(#00000000, 'Outline', group = 'Visual Settings', inline = '3')
demand_color = input.color(#00000000, 'Demand', group = 'Visual Settings', inline = '4')
demand_outline_color = input.color(#00000000, 'Outline', group = 'Visual Settings', inline = '4')
bos_label_color = input.color(#00000000, 'BOS Label', group = 'Visual Settings', inline = '5')
poi_label_color = input.color(#00000000, 'POI Label', group = 'Visual Settings', inline = '7')
poi_border_color = input.color(#00000000, 'POI border', group = 'Visual Settings', inline = '7')
swing_type_color = input.color(#00000000, 'Price Action Label', group = 'Visual Settings', inline = '8')
zigzag_color = input.color(#00000000, 'Zig Zag', group = 'Visual Settings', inline = '9')
//END SETTINGS
// FUNCTION TO ADD NEW AND REMOVE LAST IN ARRAY
f_array_add_pop(array, new_value_to_add) =>
array.unshift(array, new_value_to_add)
array.pop(array)
// FUNCTION SWING H & L LABELS
f_sh_sl_labels(array, swing_type) =>
var string label_text = na
if swing_type == 1
if array.get(array, 0) >= array.get(array, 1)
label_text := 'HH'
else
label_text := 'LH'
label.new(
bar_index - swing_length,
array.get(array,0),
text = label_text,
style = label.style_label_down,
textcolor = swing_type_color,
color = swing_type_color,
size = size.tiny)
else if swing_type == -1
if array.get(array, 0) >= array.get(array, 1)
label_text := 'HL'
else
label_text := 'LL'
label.new(
bar_index - swing_length,
array.get(array,0),
text = label_text,
style = label.style_label_up,
textcolor = swing_type_color,
color = swing_type_color,
size = size.tiny)
// FUNCTION MAKE SURE SUPPLY ISNT OVERLAPPING
f_check_overlapping(new_poi, box_array, atrValue) =>
atr_threshold = atrValue * 2
okay_to_draw = true
for i = 0 to array.size(box_array) - 1
top = box.get_top(array.get(box_array, i))
bottom = box.get_bottom(array.get(box_array, i))
poi = (top + bottom) / 2
upper_boundary = poi + atr_threshold
lower_boundary = poi - atr_threshold
if new_poi >= lower_boundary and new_poi <= upper_boundary
okay_to_draw := false
break
else
okay_to_draw := true
okay_to_draw
// FUNCTION TO DRAW SUPPLY OR DEMAND ZONE
f_supply_demand(value_array, bn_array, box_array, label_array, box_type, atrValue) =>
atr_buffer = atrValue * (box_width / 10)
box_left = array.get(bn_array, 0)
box_right = bar_index
var float box_top = 0.00
var float box_bottom = 0.00
var float poi = 0.00
if box_type == 1
box_top := array.get(value_array, 0)
box_bottom := box_top - atr_buffer
poi := (box_top + box_bottom) / 2
else if box_type == -1
box_bottom := array.get(value_array, 0)
box_top := box_bottom + atr_buffer
poi := (box_top + box_bottom) / 2
okay_to_draw = f_check_overlapping(poi, box_array, atrValue)
// okay_to_draw = true
//delete oldest box, and then create a new box and add it to the array
if box_type == 1 and okay_to_draw
box.delete( array.get(box_array, array.size(box_array) - 1) )
f_array_add_pop(box_array, box.new( left = box_left, top = box_top, right = box_right, bottom = box_bottom, border_color = supply_outline_color,
bgcolor = supply_color, extend = extend.right, text = 'SUPPLY', text_halign = text.align_center, text_valign = text.align_center, text_color = poi_label_color, text_size = size.small, xloc = xloc.bar_index))
box.delete( array.get(label_array, array.size(label_array) - 1) )
f_array_add_pop(label_array, box.new( left = box_left, top = poi, right = box_right, bottom = poi, border_color = poi_border_color,
bgcolor = poi_border_color, extend = extend.right, text = 'POI', text_halign = text.align_left, text_valign = text.align_center, text_color = poi_label_color, text_size = size.small, xloc = xloc.bar_index))
else if box_type == -1 and okay_to_draw
box.delete( array.get(box_array, array.size(box_array) - 1) )
f_array_add_pop(box_array, box.new( left = box_left, top = box_top, right = box_right, bottom = box_bottom, border_color = demand_outline_color,
bgcolor = demand_color, extend = extend.right, text = 'DEMAND', text_halign = text.align_center, text_valign = text.align_center, text_color = poi_label_color, text_size = size.small, xloc = xloc.bar_index))
box.delete( array.get(label_array, array.size(label_array) - 1) )
f_array_add_pop(label_array, box.new( left = box_left, top = poi, right = box_right, bottom = poi, border_color = poi_border_color,
bgcolor = poi_border_color, extend = extend.right, text = 'POI', text_halign = text.align_left, text_valign = text.align_center, text_color = poi_label_color, text_size = size.small, xloc = xloc.bar_index))
// FUNCTION TO CHANGE SUPPLY/DEMAND TO A BOS IF BROKEN
f_sd_to_bos(box_array, bos_array, label_array, zone_type) =>
if zone_type == 1
for i = 0 to array.size(box_array) - 1
level_to_break = box.get_top(array.get(box_array,i))
// if ta.crossover(close, level_to_break)
if close >= level_to_break
copied_box = box.copy(array.get(box_array,i))
f_array_add_pop(bos_array, copied_box)
mid = (box.get_top(array.get(box_array,i)) + box.get_bottom(array.get(box_array,i))) / 2
box.set_top(array.get(bos_array,0), mid)
box.set_bottom(array.get(bos_array,0), mid)
box.set_extend( array.get(bos_array,0), extend.none)
box.set_right( array.get(bos_array,0), bar_index)
box.set_text( array.get(bos_array,0), 'BOS' )
box.set_text_color( array.get(bos_array,0), bos_label_color)
box.set_text_size( array.get(bos_array,0), size.small)
box.set_text_halign( array.get(bos_array,0), text.align_center)
box.set_text_valign( array.get(bos_array,0), text.align_center)
box.delete(array.get(box_array, i))
box.delete(array.get(label_array, i))
if zone_type == -1
for i = 0 to array.size(box_array) - 1
level_to_break = box.get_bottom(array.get(box_array,i))
// if ta.crossunder(close, level_to_break)
if close <= level_to_break
copied_box = box.copy(array.get(box_array,i))
f_array_add_pop(bos_array, copied_box)
mid = (box.get_top(array.get(box_array,i)) + box.get_bottom(array.get(box_array,i))) / 2
box.set_top(array.get(bos_array,0), mid)
box.set_bottom(array.get(bos_array,0), mid)
box.set_extend( array.get(bos_array,0), extend.none)
box.set_right( array.get(bos_array,0), bar_index)
box.set_text( array.get(bos_array,0), 'BOS' )
box.set_text_color( array.get(bos_array,0), bos_label_color)
box.set_text_size( array.get(bos_array,0), size.small)
box.set_text_halign( array.get(bos_array,0), text.align_center)
box.set_text_valign( array.get(bos_array,0), text.align_center)
box.delete(array.get(box_array, i))
box.delete(array.get(label_array, i))
// FUNCTION MANAGE CURRENT BOXES BY CHANGING ENDPOINT
f_extend_box_endpoint(box_array) =>
for i = 0 to array.size(box_array) - 1
box.set_right(array.get(box_array, i), bar_index + 100)
//
stratRes = timeframe.ismonthly ? str.tostring(timeframe.multiplier * intRes, '###M') :
timeframe.isweekly ? str.tostring(timeframe.multiplier * intRes, '###W') :
timeframe.isdaily ? str.tostring(timeframe.multiplier * intRes, '###D') :
timeframe.isintraday ? str.tostring(timeframe.multiplier * intRes, '####') :
'60'
src = h ? request.security(ticker.heikinashi(syminfo.tickerid),
timeframe.period, close, lookahead = barmerge.lookahead_off) : close
// CALCULATE ATR
atrValue = ta.atr(50)
// CALCULATE SWING HIGHS & SWING LOWS
swing_high = ta.pivothigh(high, swing_length, swing_length)
swing_low = ta.pivotlow(low, swing_length, swing_length)
// ARRAYS FOR SWING H/L & BN
var swing_high_values = array.new_float(5,0.00)
var swing_low_values = array.new_float(5,0.00)
var swing_high_bns = array.new_int(5,0)
var swing_low_bns = array.new_int(5,0)
// ARRAYS FOR SUPPLY / DEMAND
var current_supply_box = array.new_box(history_of_demand_to_keep, na)
var current_demand_box = array.new_box(history_of_demand_to_keep, na)
// ARRAYS FOR SUPPLY / DEMAND POI LABELS
var current_supply_poi = array.new_box(history_of_demand_to_keep, na)
var current_demand_poi = array.new_box(history_of_demand_to_keep, na)
// ARRAYS FOR BOS
var supply_bos = array.new_box(5, na)
var demand_bos = array.new_box(5, na)
//END CALCULATIONS
// NEW SWING HIGH
if not na(swing_high)
//MANAGE SWING HIGH VALUES
f_array_add_pop(swing_high_values, swing_high)
f_array_add_pop(swing_high_bns, bar_index )
if show_price_action_labels
f_sh_sl_labels(swing_high_values, 1)
f_supply_demand(swing_high_values, swing_high_bns, current_supply_box, current_supply_poi, 1, atrValue)
// NEW SWING LOW
else if not na(swing_low)
//MANAGE SWING LOW VALUES
f_array_add_pop(swing_low_values, swing_low)
f_array_add_pop(swing_low_bns, bar_index )
if show_price_action_labels
f_sh_sl_labels(swing_low_values, -1)
f_supply_demand(swing_low_values, swing_low_bns, current_demand_box, current_demand_poi, -1, atrValue)
f_sd_to_bos(current_supply_box, supply_bos, current_supply_poi, 1)
f_sd_to_bos(current_demand_box, demand_bos, current_demand_poi, -1)
f_extend_box_endpoint(current_supply_box)
f_extend_box_endpoint(current_demand_box)
channelBal = input.bool(false, "Channel Balance", group = "CHART")
lr_slope(_src, _len) =>
x = 0.0, y = 0.0, x2 = 0.0, xy = 0.0
for i = 0 to _len - 1
val = _src
per = i + 1
x += per
y += val
x2 += per * per
xy += val * per
_slp = (_len * xy - x * y) / (_len * x2 - x * x)
_avg = y / _len
_int = _avg - _slp * x / _len + _slp
lr_dev(_src, _len, _slp, _avg, _int) =>
upDev = 0.0, dnDev = 0.0
val = _int
for j = 0 to _len - 1
price = high - val
if price > upDev
upDev := price
price := val - low
if price > dnDev
dnDev := price
price := _src
val += _slp
//
= ta.kc(close, 80, 10.5)
= ta.kc(close, 80, 9.5)
= ta.kc(close, 80, 8)
= ta.kc(close, 80, 3)
barsL = 10
barsR = 10
pivotHigh = fixnan(ta.pivothigh(barsL, barsR) )
pivotLow = fixnan(ta.pivotlow(barsL, barsR) )
source = close, period = 150
= lr_slope(source, period)
= lr_dev(source, period, s, a, i)
y1 = low - (ta.atr(30) * 2), y1B = low - ta.atr(30)
y2 = high + (ta.atr(30) * 2), y2B = high + ta.atr(30)
x1 = bar_index - period + 1, _y1 = i + s * (period - 1), x2 = bar_index, _y2 = i
//Functions
//Line Style function
get_line_style(style) =>
out = switch style
'???' => line.style_solid
'----' => line.style_dashed
' ' => line.style_dotted
//Function to get order block coordinates
get_coordinates(condition, top, btm, ob_val)=>
var ob_top = array.new_float(0)
var ob_btm = array.new_float(0)
var ob_avg = array.new_float(0)
var ob_left = array.new_int(0)
float ob = na
//Append coordinates to arrays
if condition
avg = math.avg(top, btm)
array.unshift(ob_top, top)
array.unshift(ob_btm, btm)
array.unshift(ob_avg, avg)
ob := ob_val
//Function to remove mitigated order blocks from coordinate arrays
remove_mitigated(ob_top, ob_btm, ob_left, ob_avg, target, bull)=>
mitigated = false
target_array = bull ? ob_btm : ob_top
for element in target_array
idx = array.indexof(target_array, element)
if (bull ? target < element : target > element)
mitigated := true
array.remove(ob_top, idx)
array.remove(ob_btm, idx)
array.remove(ob_avg, idx)
array.remove(ob_left, idx)
mitigated
//Function to set order blocks
set_order_blocks(ob_top, ob_btm, ob_left, ob_avg, ext_last, bg_css, border_css, lvl_css)=>
var ob_box = array.new_box(0)
var ob_lvl = array.new_line(0)
//Global elements
var os = 0
var target_bull = 0.
var target_bear = 0.
// Create non-repainting security function
rp_security(_symbol, _res, _src) =>
request.security(_symbol, _res, _src )
htfHigh = rp_security(syminfo.tickerid, res, high)
htfLow = rp_security(syminfo.tickerid, res, low)
// Main Indicator
// Functions
smoothrng(x, t, m) =>
wper = t * 2 - 1
avrng = ta.ema(math.abs(x - x ), t)
smoothrng = ta.ema(avrng, wper) * m
rngfilt(x, r) =>
rngfilt = x
rngfilt := x > nz(rngfilt ) ? x - r < nz(rngfilt ) ? nz(rngfilt ) : x - r : x + r > nz(rngfilt ) ? nz(rngfilt ) : x + r
percWidth(len, perc) => (ta.highest(len) - ta.lowest(len)) * perc / 100
securityNoRep(sym, res, src) => request.security(sym, res, src, barmerge.gaps_off, barmerge.lookahead_on)
swingPoints(prd) =>
pivHi = ta.pivothigh(prd, prd)
pivLo = ta.pivotlow (prd, prd)
last_pivHi = ta.valuewhen(pivHi, pivHi, 1)
last_pivLo = ta.valuewhen(pivLo, pivLo, 1)
hh = pivHi and pivHi > last_pivHi ? pivHi : na
lh = pivHi and pivHi < last_pivHi ? pivHi : na
hl = pivLo and pivLo > last_pivLo ? pivLo : na
ll = pivLo and pivLo < last_pivLo ? pivLo : na
f_chartTfInMinutes() =>
float _resInMinutes = timeframe.multiplier * (
timeframe.isseconds ? 1 :
timeframe.isminutes ? 1. :
timeframe.isdaily ? 60. * 24 :
timeframe.isweekly ? 60. * 24 * 7 :
timeframe.ismonthly ? 60. * 24 * 30.4375 : na)
f_kc(src, len, sensitivity) =>
basis = ta.sma(src, len)
span = ta.atr(len)
wavetrend(src, chlLen, avgLen) =>
esa = ta.ema(src, chlLen)
d = ta.ema(math.abs(src - esa), chlLen)
ci = (src - esa) / (0.015 * d)
wt1 = ta.ema(ci, avgLen)
wt2 = ta.sma(wt1, 3)
f_top_fractal(_src) => _src < _src and _src < _src and _src > _src and _src > _src
f_bot_fractal(_src) => _src > _src and _src > _src and _src < _src and _src < _src
top_fractal = f_top_fractal(src)
bot_fractal = f_bot_fractal(src)
f_fractalize (_src) => top_fractal ? 1 : bot_fractal ? -1 : 0
f_findDivs(src, topLimit, botLimit) =>
fractalTop = f_fractalize(src) > 0 and src >= topLimit ? src : na
fractalBot = f_fractalize(src) < 0 and src <= botLimit ? src : na
highPrev = ta.valuewhen(fractalTop, src , 0)
highPrice = ta.valuewhen(fractalTop, high , 0)
lowPrev = ta.valuewhen(fractalBot, src , 0)
lowPrice = ta.valuewhen(fractalBot, low , 0)
bearSignal = fractalTop and high > highPrice and src < highPrev
bullSignal = fractalBot and low < lowPrice and src > lowPrev
// Get user input
enableSR = input(false , "SR On/Off", group="SR")
colorSup = input(#00000000 , "Support Color", group="SR")
colorRes = input(#00000000 , "Resistance Color", group="SR")
strengthSR = input.int(2 , "S/R Strength", 1, group="SR")
lineStyle = input.string("Dotted", "Line Style", , group="SR")
lineWidth = input.int(2 , "S/R Line Width", 1, group="SR")
useZones = input(true , "Zones On/Off", group="SR")
useHLZones = input(true , "High Low Zones On/Off", group="SR")
zoneWidth = input.int(2 , "Zone Width %", 0,
tooltip = "it's calculated using % of the distance between highest/lowest in last 300 bars", group="SR")
expandSR = input(true , "Expand SR")
// Get components
rb = 10
prd = 284
ChannelW = 10
label_loc = 55
style = lineStyle == "Solid" ? line.style_solid :
lineStyle == "Dotted" ? line.style_dotted : line.style_dashed
ph = ta.pivothigh(rb, rb)
pl = ta.pivotlow (rb, rb)
sr_levels = array.new_float(21, na)
prdhighest = ta.highest(prd)
prdlowest = ta.lowest(prd)
cwidth = percWidth(prd, ChannelW)
zonePerc = percWidth(300, zoneWidth)
aas = array.new_bool(41, true)
u1 = 0.0, u1 := nz(u1 )
d1 = 0.0, d1 := nz(d1 )
highestph = 0.0, highestph := highestph
lowestpl = 0.0, lowestpl := lowestpl
var sr_levs = array.new_float(21, na)
label hlabel = na, label.delete(hlabel )
label llabel = na, label.delete(llabel )
var sr_lines = array.new_line(21, na)
var sr_linesH = array.new_line(21, na)
var sr_linesL = array.new_line(21, na)
var sr_linesF = array.new_linefill(21, na)
var sr_labels = array.new_label(21, na)
if (not na(ph) or not na(pl))
for x = 0 to array.size(sr_levels) - 1
array.set(sr_levels, x, na)
highestph := prdlowest
lowestpl := prdhighest
countpp = 0
for x = 0 to prd
if na(close )
break
if not na(ph ) or not na(pl )
highestph := math.max(highestph, nz(ph , prdlowest), nz(pl , prdlowest))
lowestpl := math.min(lowestpl, nz(ph , prdhighest), nz(pl , prdhighest))
countpp += 1
if countpp > 40
break
if array.get(aas, countpp)
upl = (not na(ph ) and (ph != 0) ? high : low ) + cwidth
dnl = (not na(ph ) and (ph != 0) ? high : low ) - cwidth
u1 := countpp == 1 ? upl : u1
d1 := countpp == 1 ? dnl : d1
tmp = array.new_bool(41, true)
cnt = 0
tpoint = 0
for xx = 0 to prd
if na(close )
break
if not na(ph ) or not na(pl )
chg = false
cnt += 1
if cnt > 40
break
if array.get(aas, cnt)
if not na(ph )
if high <= upl and high >= dnl
tpoint += 1
chg := true
if not na(pl )
if low <= upl and low >= dnl
tpoint += 1
chg := true
if chg and cnt < 41
array.set(tmp, cnt, false)
if tpoint >= strengthSR
for g = 0 to 40 by 1
if not array.get(tmp, g)
array.set(aas, g, false)
if (not na(ph ) and countpp < 21)
array.set(sr_levels, countpp, high )
if (not na(pl ) and countpp < 21)
array.set(sr_levels, countpp, low )
// Plot
var line highest_ = na, line.delete(highest_)
var line lowest_ = na, line.delete(lowest_)
var line highest_fill1 = na, line.delete(highest_fill1)
var line highest_fill2 = na, line.delete(highest_fill2)
var line lowest_fill1 = na, line.delete(lowest_fill1)
var line lowest_fill2 = na, line.delete(lowest_fill2)
hi_col = close >= highestph ? colorSup : colorRes
lo_col = close >= lowestpl ? colorSup : colorRes
if enableSR
highest_ := line.new(bar_index - 311, highestph, bar_index, highestph, xloc.bar_index, expandSR ? extend.both : extend.right, hi_col, style, lineWidth)
lowest_ := line.new(bar_index - 311, lowestpl , bar_index, lowestpl , xloc.bar_index, expandSR ? extend.both : extend.right, lo_col, style, lineWidth)
if useHLZones
highest_fill1 := line.new(bar_index - 311, highestph + zonePerc, bar_index, highestph + zonePerc, xloc.bar_index, expandSR ? extend.both : extend.right, na)
highest_fill2 := line.new(bar_index - 311, highestph - zonePerc, bar_index, highestph - zonePerc, xloc.bar_index, expandSR ? extend.both : extend.right, na)
lowest_fill1 := line.new(bar_index - 311, lowestpl + zonePerc , bar_index, lowestpl + zonePerc , xloc.bar_index, expandSR ? extend.both : extend.right, na)
lowest_fill2 := line.new(bar_index - 311, lowestpl - zonePerc , bar_index, lowestpl - zonePerc , xloc.bar_index, expandSR ? extend.both : extend.right, na)
linefill.new(highest_fill1, highest_fill2, hi_col)
linefill.new(lowest_fill1 , lowest_fill2 , lo_col)
if (not na(ph) or not na(pl))
for x = 0 to array.size(sr_lines) - 1
array.set(sr_levs, x, array.get(sr_levels, x))
for x = 0 to array.size(sr_lines) - 1
line.delete(array.get(sr_lines, x))
line.delete(array.get(sr_linesH, x))
line.delete(array.get(sr_linesL, x))
linefill.delete(array.get(sr_linesF, x))
if (not na(array.get(sr_levs, x)) and enableSR)
line_col = close >= array.get(sr_levs, x) ? colorSup : colorRes
array.set(sr_lines, x, line.new(bar_index - 355, array.get(sr_levs, x), bar_index, array.get(sr_levs, x), xloc.bar_index, expandSR ? extend.both : extend.right, line_col, style, lineWidth))
if useZones
array.set(sr_linesH, x, line.new(bar_index - 355, array.get(sr_levs, x) + zonePerc, bar_index, array.get(sr_levs, x) + zonePerc, xloc.bar_index, expandSR ? extend.both : extend.right, na))
array.set(sr_linesL, x, line.new(bar_index - 355, array.get(sr_levs, x) - zonePerc, bar_index, array.get(sr_levs, x) - zonePerc, xloc.bar_index, expandSR ? extend.both : extend.right, na))
array.set(sr_linesF, x, linefill.new(array.get(sr_linesH, x), array.get(sr_linesL, x), line_col))
for x = 0 to array.size(sr_labels) - 1
label.delete(array.get(sr_labels, x))
if (not na(array.get(sr_levs, x)) and enableSR)
lab_loc = close >= array.get(sr_levs, x) ? label.style_label_up : label.style_label_down
lab_col = close >= array.get(sr_levs, x) ? colorSup : colorRes
array.set(sr_labels, x, label.new(bar_index + label_loc, array.get(sr_levs, x), str.tostring(math.round_to_mintick(array.get(sr_levs, x))), color=lab_col , textcolor=#000000, style=lab_loc))
hlabel := enableSR ? label.new(bar_index + label_loc + math.round(math.sign(label_loc)) * 20, highestph, "High Level : " + str.tostring(highestph), color=hi_col, textcolor=#000000, style=label.style_label_down) : na
llabel := enableSR ? label.new(bar_index + label_loc + math.round(math.sign(label_loc)) * 20, lowestpl , "Low Level : " + str.tostring(lowestpl) , color=lo_col, textcolor=#000000, style=label.style_label_up ) : na
// Get components
rsi = ta.rsi(close, 28)
//rsiOb = rsi > 78 and rsi > ta.ema(rsi, 10)
//rsiOs = rsi < 27 and rsi < ta.ema(rsi, 10)
rsiOb = rsi > 65 and rsi > ta.ema(rsi, 10)
rsiOs = rsi < 35 and rsi < ta.ema(rsi, 10)
dHigh = securityNoRep(syminfo.tickerid, "D", high )
dLow = securityNoRep(syminfo.tickerid, "D", low )
dClose = securityNoRep(syminfo.tickerid, "D", close )
ema = ta.ema(close, 144)
emaBull = close > ema
equal_tf(res) => str.tonumber(res) == f_chartTfInMinutes() and not timeframe.isseconds
higher_tf(res) => str.tonumber(res) > f_chartTfInMinutes() or timeframe.isseconds
too_small_tf(res) => (timeframe.isweekly and res=="1") or (timeframe.ismonthly and str.tonumber(res) < 10)
securityNoRep1(sym, res, src) =>
bool bull_ = na
bull_ := equal_tf(res) ? src : bull_
bull_ := higher_tf(res) ? request.security(sym, res, src, barmerge.gaps_off, barmerge.lookahead_on) : bull_
bull_array = request.security_lower_tf(syminfo.tickerid, higher_tf(res) ? str.tostring(f_chartTfInMinutes()) + (timeframe.isseconds ? "S" : "") : too_small_tf(res) ? (timeframe.isweekly ? "3" : "10") : res, src)
if array.size(bull_array) > 1 and not equal_tf(res) and not higher_tf(res)
bull_ := array.pop(bull_array)
array.clear(bull_array)
bull_
// === BASE FUNCTIONS ===
// Returns MA input selection variant, default to SMA if blank or typo.
variant(type, src, len, offSig, offALMA) =>
v1 = ta.sma(src, len) // Simple
v2 = ta.ema(src, len) // Exponential
v3 = 2 * v2 - ta.ema(v2, len) // Double Exponential
v4 = 3 * (v2 - ta.ema(v2, len)) + ta.ema(ta.ema(v2, len), len) // Triple Exponential
v5 = ta.wma(src, len) // Weighted
v6 = ta.vwma(src, len) // Volume Weighted
v7 = 0.0
sma_1 = ta.sma(src, len) // Smoothed
v7 := na(v7 ) ? sma_1 : (v7 * (len - 1) + src) / len
v8 = ta.wma(2 * ta.wma(src, len / 2) - ta.wma(src, len), math.round(math.sqrt(len))) // Hull
v9 = ta.linreg(src, len, offSig) // Least Squares
v10 = ta.alma(src, len, offALMA, offSig) // Arnaud Legoux
v11 = ta.sma(v1, len) // Triangular (extreme smooth)
// SuperSmoother filter
// 2013 John F. Ehlers
a1 = math.exp(-1.414 * 3.14159 / len)
b1 = 2 * a1 * math.cos(1.414 * 3.14159 / len)
c2 = b1
c3 = -a1 * a1
c1 = 1 - c2 - c3
v12 = 0.0
v12 := c1 * (src + nz(src )) / 2 + c2 * nz(v12 ) + c3 * nz(v12 )
type == 'EMA' ? v2 : type == 'DEMA' ? v3 : type == 'TEMA' ? v4 : type == 'WMA' ? v5 : type == 'VWMA' ? v6 : type == 'SMMA' ? v7 : type == 'HullMA' ? v8 : type == 'LSMA' ? v9 : type == 'ALMA' ? v10 : type == 'TMA' ? v11 : type == 'SSMA' ? v12 : v1
// security wrapper for repeat calls
reso(exp, use, res) =>
security_1 = request.security(syminfo.tickerid, res, exp, gaps = barmerge.gaps_off, lookahead = barmerge.lookahead_on)
use ? security_1 : exp
// === /BASE FUNCTIONS ===
// === SERIES SETUP ===
closeSeries = variant(basisType, close , basisLen, offsetSigma, offsetALMA)
openSeries = variant(basisType, open , basisLen, offsetSigma, offsetALMA)
// === /SERIES ===
// Get Alternate resolution Series if selected.
closeSeriesAlt = reso(closeSeries, useRes, stratRes)
openSeriesAlt = reso(openSeries, useRes, stratRes)
//
lxTrigger = false
sxTrigger = false
leTrigger = ta.crossover (closeSeriesAlt, openSeriesAlt)
seTrigger = ta.crossunder(closeSeriesAlt, openSeriesAlt)
G_RISK = '■ ' + 'Risk Management'
//#region ———— <↓↓↓ G_RISK ↓↓↓> {
// ———————————
//Tooltip
T_LVL = '(%) Exit Level'
T_QTY = '(%) Adjust trade exit volume'
T_MSG = 'Paste JSON message for your bot'
//Webhook Message
O_LEMSG = 'Long Entry'
O_LXMSGSL = 'Long SL'
O_LXMSGTP1 = 'Long TP1'
O_LXMSGTP2 = 'Long TP2'
O_LXMSGTP3 = 'Long TP3'
O_LXMSG = 'Long Exit'
O_SEMSG = 'Short Entry'
O_SXMSGSL = 'Short SL'
O_SXMSGA = 'Short TP1'
O_SXMSGB = 'Short TP2'
O_SXMSGC = 'Short TP3'
O_SXMSGX = 'Short Exit'
// ——————————— | | | Line length guide |
i_lxLvlTP1 = input.float (0.2, 'Level TP1' , group = G_RISK,
tooltip = T_LVL)
i_lxQtyTP1 = input.float (80.0, 'Qty TP1' , group = G_RISK,
tooltip = T_QTY)
i_lxLvlTP2 = input.float (0.5, 'Level TP2' , group = G_RISK,
tooltip = T_LVL)
i_lxQtyTP2 = input.float (10.0, 'Qty TP2' , group = G_RISK,
tooltip = T_QTY)
i_lxLvlTP3 = input.float (7.0, 'Level TP3' , group = G_RISK,
tooltip = T_LVL)
i_lxQtyTP3 = input.float (2, 'Qty TP3' , group = G_RISK,
tooltip = T_QTY)
i_lxLvlSL = input.float (0.5, 'Stop Loss' , group = G_RISK,
tooltip = T_LVL)
i_sxLvlTP1 = i_lxLvlTP1
i_sxQtyTP1 = i_lxQtyTP1
i_sxLvlTP2 = i_lxLvlTP2
i_sxQtyTP2 = i_lxQtyTP2
i_sxLvlTP3 = i_lxLvlTP3
i_sxQtyTP3 = i_lxQtyTP3
i_sxLvlSL = i_lxLvlSL
G_MSG = '■ ' + 'Webhook Message'
i_leMsg = input.string (O_LEMSG ,'Long Entry' , group = G_MSG, tooltip = T_MSG)
i_lxMsgSL = input.string (O_LXMSGSL ,'Long SL' , group = G_MSG, tooltip = T_MSG)
i_lxMsgTP1 = input.string (O_LXMSGTP1,'Long TP1' , group = G_MSG, tooltip = T_MSG)
i_lxMsgTP2 = input.string (O_LXMSGTP2,'Long TP2' , group = G_MSG, tooltip = T_MSG)
i_lxMsgTP3 = input.string (O_LXMSGTP3,'Long TP3' , group = G_MSG, tooltip = T_MSG)
i_lxMsg = input.string (O_LXMSG ,'Long Exit' , group = G_MSG, tooltip = T_MSG)
i_seMsg = input.string (O_SEMSG ,'Short Entry' , group = G_MSG, tooltip = T_MSG)
i_sxMsgSL = input.string (O_SXMSGSL ,'Short SL' , group = G_MSG, tooltip = T_MSG)
i_sxMsgTP1 = input.string (O_SXMSGA ,'Short TP1' , group = G_MSG, tooltip = T_MSG)
i_sxMsgTP2 = input.string (O_SXMSGB ,'Short TP2' , group = G_MSG, tooltip = T_MSG)
i_sxMsgTP3 = input.string (O_SXMSGC ,'Short TP3' , group = G_MSG, tooltip = T_MSG)
i_sxMsg = input.string (O_SXMSGX ,'Short Exit' , group = G_MSG, tooltip = T_MSG)
i_src = close
G_DISPLAY = 'Display'
//
i_alertOn = input.bool (true, 'Alert Labels On/Off' , group = G_DISPLAY)
i_barColOn = input.bool (true, 'Bar Color On/Off' , group = G_DISPLAY)
// ———————————
// @function Calculate the Take Profit line, and the crossover or crossunder
f_tp(_condition, _conditionValue, _leTrigger, _seTrigger, _src, _lxLvlTP, _sxLvlTP)=>
var float _tpLine = 0.0
_topLvl = _src + (_src * (_lxLvlTP / 100))
_botLvl = _src - (_src * (_sxLvlTP / 100))
_tpLine := _condition != _conditionValue and _leTrigger ? _topLvl :
_condition != -_conditionValue and _seTrigger ? _botLvl :
nz(_tpLine )
// @function Similar to "ta.crossover" or "ta.crossunder"
f_cross(_scr1, _scr2, _over)=>
_cross = _over ? _scr1 > _scr2 and _scr1 < _scr2 :
_scr1 < _scr2 and _scr1 > _scr2
// ———————————
//
var float condition = 0.0
var float slLine = 0.0
var float entryLine = 0.0
//
entryLine := leTrigger and condition <= 0.0 ? close :
seTrigger and condition >= 0.0 ? close : nz(entryLine )
//
slTopLvl = i_src + (i_src * (i_lxLvlSL / 100))
slBotLvl = i_src - (i_src * (i_sxLvlSL / 100))
slLine := condition <= 0.0 and leTrigger ? slBotLvl :
condition >= 0.0 and seTrigger ? slTopLvl : nz(slLine )
slLong = f_cross(low, slLine, false)
slShort = f_cross(high, slLine, true )
//
= f_tp(condition, 1.2,leTrigger, seTrigger, i_src, i_lxLvlTP3, i_sxLvlTP3)
= f_tp(condition, 1.1,leTrigger, seTrigger, i_src, i_lxLvlTP2, i_sxLvlTP2)
= f_tp(condition, 1.0,leTrigger, seTrigger, i_src, i_lxLvlTP1, i_sxLvlTP1)
tp3Long = f_cross(high, tp3Line, true )
tp3Short = f_cross(low, tp3Line, false)
tp2Long = f_cross(high, tp2Line, true )
tp2Short = f_cross(low, tp2Line, false)
tp1Long = f_cross(high, tp1Line, true )
tp1Short = f_cross(low, tp1Line, false)
switch
leTrigger and condition <= 0.0 => condition := 1.0
seTrigger and condition >= 0.0 => condition := -1.0
tp3Long and condition == 1.2 => condition := 1.3
tp3Short and condition == -1.2 => condition := -1.3
tp2Long and condition == 1.1 => condition := 1.2
tp2Short and condition == -1.1 => condition := -1.2
tp1Long and condition == 1.0 => condition := 1.1
tp1Short and condition == -1.0 => condition := -1.1
slLong and condition >= 1.0 => condition := 0.0
slShort and condition <= -1.0 => condition := 0.0
lxTrigger and condition >= 1.0 => condition := 0.0
sxTrigger and condition <= -1.0 => condition := 0.0
longE = leTrigger and condition <= 0.0 and condition == 1.0
shortE = seTrigger and condition >= 0.0 and condition == -1.0
longX = lxTrigger and condition >= 1.0 and condition == 0.0
shortX = sxTrigger and condition <= -1.0 and condition == 0.0
longSL = slLong and condition >= 1.0 and condition == 0.0
shortSL = slShort and condition <= -1.0 and condition == 0.0
longTP3 = tp3Long and condition == 1.2 and condition == 1.3
shortTP3 = tp3Short and condition == -1.2 and condition == -1.3
longTP2 = tp2Long and condition == 1.1 and condition == 1.2
shortTP2 = tp2Short and condition == -1.1 and condition == -1.2
longTP1 = tp1Long and condition == 1.0 and condition == 1.1
shortTP1 = tp1Short and condition == -1.0 and condition == -1.1
// ——————————— {
//
if strategy.position_size <= 0 and longE and barstate.isconfirmed
strategy.entry(
'Long',
strategy.long,
alert_message = i_leMsg,
comment = 'LE')
if strategy.position_size > 0 and condition == 1.0
strategy.exit(
id = 'LXTP1',
from_entry = 'Long',
qty_percent = i_lxQtyTP1,
limit = tp1Line,
stop = slLine,
comment_profit = 'LXTP1',
comment_loss = 'SL',
alert_profit = i_lxMsgTP1,
alert_loss = i_lxMsgSL)
if strategy.position_size > 0 and condition == 1.1
strategy.exit(
id = 'LXTP2',
from_entry = 'Long',
qty_percent = i_lxQtyTP2,
limit = tp2Line,
stop = slLine,
comment_profit = 'LXTP2',
comment_loss = 'SL',
alert_profit = i_lxMsgTP2,
alert_loss = i_lxMsgSL)
if strategy.position_size > 0 and condition == 1.2
strategy.exit(
id = 'LXTP3',
from_entry = 'Long',
qty_percent = i_lxQtyTP3,
limit = tp3Line,
stop = slLine,
comment_profit = 'LXTP3',
comment_loss = 'SL',
alert_profit = i_lxMsgTP3,
alert_loss = i_lxMsgSL)
if longX
strategy.close(
'Long',
alert_message = i_lxMsg,
comment = 'LX')
//
if strategy.position_size >= 0 and shortE and barstate.isconfirmed
strategy.entry(
'Short',
strategy.short,
alert_message = i_leMsg,
comment = 'SE')
if strategy.position_size < 0 and condition == -1.0
strategy.exit(
id = 'SXTP1',
from_entry = 'Short',
qty_percent = i_sxQtyTP1,
limit = tp1Line,
stop = slLine,
comment_profit = 'SXTP1',
comment_loss = 'SL',
alert_profit = i_sxMsgTP1,
alert_loss = i_sxMsgSL)
if strategy.position_size < 0 and condition == -1.1
strategy.exit(
id = 'SXTP2',
from_entry = 'Short',
qty_percent = i_sxQtyTP2,
limit = tp2Line,
stop = slLine,
comment_profit = 'SXTP2',
comment_loss = 'SL',
alert_profit = i_sxMsgTP2,
alert_loss = i_sxMsgSL)
if strategy.position_size < 0 and condition == -1.2
strategy.exit(
id = 'SXTP3',
from_entry = 'Short',
qty_percent = i_sxQtyTP3,
limit = tp3Line,
stop = slLine,
comment_profit = 'SXTP3',
comment_loss = 'SL',
alert_profit = i_sxMsgTP3,
alert_loss = i_sxMsgSL)
if shortX
strategy.close(
'Short',
alert_message = i_sxMsg,
comment = 'SX')
// ———————————
c_tp = leTrigger or seTrigger ? na :
condition == 0.0 ? na : color.green
c_entry = leTrigger or seTrigger ? na :
condition == 0.0 ? na : color.blue
c_sl = leTrigger or seTrigger ? na :
condition == 0.0 ? na : color.red
p_tp1Line = plot (
condition == 1.0 or
condition == -1.0 ? tp1Line : na,
title = "TP Line 1",
color = c_tp,
linewidth = 1,
style = plot.style_linebr)
p_tp2Line = plot (
condition == 1.0 or
condition == -1.0 or
condition == 1.1 or
condition == -1.1 ? tp2Line : na,
title = "TP Line 2",
color = c_tp,
linewidth = 1,
style = plot.style_linebr)
p_tp3Line = plot (
condition == 1.0 or
condition == -1.0 or
condition == 1.1 or
condition == -1.1 or
condition == 1.2 or
condition == -1.2 ? tp3Line : na,
title = "TP Line 3",
color = c_tp,
linewidth = 1,
style = plot.style_linebr)
p_entryLine = plot (
condition >= 1.0 or
condition <= -1.0 ? entryLine : na,
title = "Entry Line",
color = c_entry,
linewidth = 1,
style = plot.style_linebr)
p_slLine = plot (
condition == 1.0 or
condition == -1.0 or
condition == 1.1 or
condition == -1.1 or
condition == 1.2 or
condition == -1.2 ? slLine : na,
title = "SL Line",
color = c_sl,
linewidth = 1,
style = plot.style_linebr)
fill(
p_tp3Line, p_entryLine,
color = leTrigger or seTrigger ? na :color.new(color.green, 90))
fill(
p_entryLine, p_slLine,
color = leTrigger or seTrigger ? na :color.new(color.red, 90))
//
plotshape(
i_alertOn and longE,
title = 'Long',
text = 'Long',
textcolor = color.white,
color = color.green,
style = shape.labelup,
size = size.tiny,
location = location.belowbar)
plotshape(
i_alertOn and shortE,
title = 'Short',
text = 'Short',
textcolor = color.white,
color = color.red,
style = shape.labeldown,
size = size.tiny,
location = location.abovebar)
plotshape(
i_alertOn and (longX or shortX) ? close : na,
title = 'Close',
text = 'Close',
textcolor = color.white,
color = color.gray,
style = shape.labelup,
size = size.tiny,
location = location.absolute)
l_tp = i_alertOn and (longTP1 or shortTP1) ? close : na
plotshape(
l_tp,
title = "TP1 Cross",
text = "TP1",
textcolor = color.white,
color = color.olive,
style = shape.labelup,
size = size.tiny,
location = location.absolute)
plotshape(
i_alertOn and (longTP2 or shortTP2) ? close : na,
title = "TP2 Cross",
text = "TP2",
textcolor = color.white,
color = color.olive,
style = shape.labelup,
size = size.tiny,
location = location.absolute)
plotshape(
i_alertOn and (longTP3 or shortTP3) ? close : na,
title = "TP3 Cross",
text = "TP3",
textcolor = color.white,
color = color.olive,
style = shape.labelup,
size = size.tiny,
location = location.absolute)
plotshape(
i_alertOn and (longSL or shortSL) ? close : na,
title = "SL Cross",
text = "SL",
textcolor = color.white,
color = color.maroon,
style = shape.labelup,
size = size.tiny,
location = location.absolute)
//
plot(
na,
title = "─── ───",
editable = false,
display = display.data_window)
plot(
condition,
title = "condition",
editable = false,
display = display.data_window)
plot(
strategy.position_size * 100,
title = ".position_size",
editable = false,
display = display.data_window)
//#endregion }
// ——————————— <↑↑↑ G_RISK ↑↑↑>
//#region ———— <↓↓↓ G_SCRIPT02 ↓↓↓> {
// @function Queues a new element in an array and de-queues its first element.
f_qDq(_array, _val) =>
array.push(_array, _val)
_return = array.shift(_array)
_return
var line a_slLine = array.new_line(1)
var line a_entryLine = array.new_line(1)
var line a_tp3Line = array.new_line(1)
var line a_tp2Line = array.new_line(1)
var line a_tp1Line = array.new_line(1)
var label a_slLabel = array.new_label(1)
var label a_tp3label = array.new_label(1)
var label a_tp2label = array.new_label(1)
var label a_tp1label = array.new_label(1)
var label a_entryLabel = array.new_label(1)
newEntry = longE or shortE
entryIndex = 1
entryIndex := newEntry ? bar_index : nz(entryIndex )
lasTrade = bar_index >= entryIndex
l_right = 10
line.delete(
f_qDq(a_slLine,
line.new(
entryIndex,
slLine,
last_bar_index + l_right,
slLine,
style = line.style_solid,
color = c_sl)))
line.delete(
f_qDq(a_entryLine,
line.new(
entryIndex,
entryLine,
last_bar_index + l_right,
entryLine,
style = line.style_solid,
color = color.blue)))
line.delete(
f_qDq(a_tp3Line,
line.new(
entryIndex,
tp3Line,
last_bar_index + l_right,
tp3Line,
style = line.style_solid,
color = c_tp)))
line.delete(
f_qDq(a_tp2Line,
line.new(
entryIndex,
tp2Line,
last_bar_index + l_right,
tp2Line,
style = line.style_solid,
color = c_tp)))
line.delete(
f_qDq(a_tp1Line,
line.new(
entryIndex,
tp1Line,
last_bar_index + l_right,
tp1Line,
style = line.style_solid,
color = c_tp)))
label.delete(
f_qDq(a_slLabel,
label.new(
last_bar_index + l_right,
slLine,
'SL: ' + str.tostring(slLine, '##.###'),
style = label.style_label_left,
textcolor = color.white,
color = c_sl)))
label.delete(
f_qDq(a_entryLabel,
label.new(
last_bar_index + l_right,
entryLine,
'Entry: ' + str.tostring(entryLine, '##.###'),
style = label.style_label_left,
textcolor = color.white,
color = color.blue)))
label.delete(
f_qDq(a_tp3label,
label.new(
last_bar_index + l_right,
tp3Line,
'TP3: ' + str.tostring(tp3Line, '##.###'),
style = label.style_label_left,
textcolor = color.white,
color = c_tp)))
label.delete(
f_qDq(a_tp2label,
label.new(
last_bar_index + l_right,
tp2Line,
'TP2: ' + str.tostring(tp2Line, '##.###'),
style = label.style_label_left,
textcolor = color.white,
color = c_tp)))
label.delete(
f_qDq(a_tp1label,
label.new(
last_bar_index + l_right,
tp1Line,
'TP1: ' + str.tostring(tp1Line, '##.###'),
style = label.style_label_left,
textcolor = color.white,
color = c_tp)))
// ———————————
//
if longE or shortE or longX or shortX
alert(message = 'Any Alert', freq = alert.freq_once_per_bar_close)
if longE
alert(message = 'Long Entry', freq = alert.freq_once_per_bar_close)
if shortE
alert(message = 'Short Entry', freq = alert.freq_once_per_bar_close)
if longX
alert(message = 'Long Exit', freq = alert.freq_once_per_bar_close)
if shortX
alert(message = 'Short Exit', freq = alert.freq_once_per_bar_close)
//#endregion }
// ——————————— <↑↑↑ G_SCRIPT03 ↑↑↑>
Cerca negli script per "entry"
SMA Cross + Adaptive Q MA + AMA Channel
📘 OPERATIONAL MANUAL: Adaptive Trend & SR Breakout SystemThis system combines non-parametric regression, volatility channels, and automated price action structures to identify high-probability entries.
1. Core IndicatorsAdaptive Q (KAMA): The primary trend line.
Green = Bullish;
Red = Bearish.
AMA Channel: An ATR-based envelope ($1.5 \times ATR$) that defines the "Value Area".
SMA 50 Filter: Global trend filter. Trade Long only above; Short only below.
SR Zones: Automatic boxes marking historical Support
(Blue/Green) and Resistance (Red).Shutterstock
2. Entry Rules
🟢 LONG SETUP:Price is above SMA 50.Large Lime Triangle appears (Channel Cross).Adaptive Q line is Green.Best entry: Price bounces off a Support Box.
🔴 SHORT SETUP:Price is below SMA 50.Large Red Triangle appears (Channel Cross).Adaptive Q line is Red.Best entry: Price rejects a Resistance Box.
3. Risk Management
Stop Loss: Set at $1.5 \times ATR$ or behind the nearest SR Box.
Take Profit: Target the next opposite SR Zone or exit if the Adaptive Q changes color.
4. LegendLarge Triangles: High-conviction volatility signals.
Small Triangles: Standard SMA Cross (early warning).
Red/Green Boxes: Supply and Demand zones for structural confirmation.
CVD Normalizzato (0-100)# 📑 MASTER OPERATING MANUAL: Institutional Order Flow Ecosystem (v2.0)
**Integrated Suite:** PVSRA Dashboard PRO + SR High Volume Boxes + Massive Order Spike Detector + CVD-100
---
## 1. SYSTEM HIERARCHY
This trading ecosystem is designed to decode "Smart Money" footprints. It filters retail noise to identify where institutional participants are placing massive orders.
1. **Bias (Dashboard):** Determines the overall market direction (Sentiment).
2. **Context (SR Boxes):** Identifies the price "Battlefields" (Supply & Demand).
3. **Internal Force (CVD-100):** Reveals aggressive buying/selling pressure (Market Delta).
4. **Trigger (PVSRA & Spikes):** Signals the exact moment of execution.
---
## 2. COMPONENT DICTIONARY
### A. CVD-100 (The Internal Engine)
*Reveals the aggressive pressure of buyers/sellers.*
- **Values > 80:** Aggressive buyers are dominant (Extreme Overbought).
- **Values < 20:** Aggressive sellers are dominant (Extreme Oversold).
- **Green Slope:** Aggressive buyers are increasing pressure.
- **Red Slope:** Aggressive sellers are increasing pressure.
### B. PVSRA Candles (Market Climax)
- 🟢 **Bright Green:** Bull Climax (Highest institutional activity).
- 🟣 **Magenta:** Bear Climax (Panic selling or Institutional offloading).
- 🔵 **Blue/Red:** Rising volume (Professional participation).
### C. SR Boxes & Spikes (The Execution Zones)
- **Teal/Red Boxes:** Areas where significant volume was stored.
- **Triangles (▲/▼):** "Massive Order Spike". Confirms statistical anomaly.
- **Diamonds (◆):** Confirms a level (Box) is successfully holding the price.
---
## 3. INTEGRATED STRATEGIES
### **Strategy A: Institutional Trend Follower**
- **Bias:** Dashboard shows "STRONG BUY" + Price is above SMA 200.
- **Setup:** Price breaks above a **Red Box** (Resistance).
- **Confirmation:** **CVD-100** is sloping up (Green) and a **Massive Buy Spike** (▲) appears.
- **Entry:** On the close of the breakout candle.
### **Strategy B: The Climax Reversal (The Sniper)**
- **Bias:** Price reaches a **Teal Box** (Support) after an extended drop.
- **Setup:** **CVD-100** is below 20 (Deep Oversold/Exhaustion).
- **Trigger:** A **Magenta Climax Candle** (PVSRA) appears, followed immediately by a **Green Diamond (◆)**.
- **Entry:** Long when price breaks the high of the Climax candle.
---
## 4. THE ULTIMATE CONFLUENCE CHECKLIST
| Priority | Confirmation | Indicator Tool |
| :--- | :--- | :--- |
| **1. Bias** | Is the Dashboard "STRONG" in the trade direction? | PVSRA Dashboard |
| **2. Level** | Is the price at/inside a Teal or Red Box? | SR Boxes |
| **3. Volume** | Is the candle Climax or Rising color? | PVSRA Candles |
| **4. Delta** | Is CVD-100 aligned with your direction? | CVD-100 |
| **5. Trigger**| Has a Triangle (Spike) or Diamond (Hold) appeared? | Spike Detector |
---
## 5. TECHNICAL CONFIGURATION
| Setting | Value | Goal |
| :--- | :--- | :--- |
| **PVSRA Climax Factor** | 2.7 | Filter for institutional impact only. |
| **Spike Multiplier** | 4.0 | Isolate statistical extreme volume. |
| **CVD Normalization** | 50 (Stoch) | Standardize delta for clear overbought/sold. |
| **SMA Bias** | 200 | Institutional trend filter. |
---
## 6. PRO TIPS & RISK NOTES
- ⚠️ **Divergence:** If Price makes a new high but **CVD-100** makes a lower high, the trend is exhausted. Prepare for a reversal.
- ⚠️ **News Filter:** High-impact news causes "Spikes" but invalidates "Boxes". Wait 15 mins for the market to stabilize.
- ⚠️ **Absorption:** A Climax candle with a tiny body inside a Box is "Absorption". Institutions are soaking up orders. Wait for the box breakout.
---
*Created for: Professional Trading Operations*
MDZ Strategy v4.2 - Multi-factor trend strategyWhat This Strategy Does
MDZ (Momentum Divergence Zones) v4.2 is a trend-following strategy that enters long positions when multiple momentum and trend indicators align. It's designed for swing trading on higher timeframes (2H-4H) and uses ATR-based position management.
The strategy waits for strong trend confirmation before entry, requiring agreement across five different filters. This reduces trade frequency but aims to improve signal quality.
Entry Logic
A long entry triggers when ALL of the following conditions are true:
1. EMA Stack (Trend Structure)
Price > EMA 20 > EMA 50 > EMA 200
This "stacked" alignment indicates a strong established uptrend
2. RSI Filter (Momentum Window)
RSI between 45-75 (default)
Confirms momentum without entering overbought territory
3. ADX Filter (Trend Strength)
ADX > 20 (default)
Ensures the trend has sufficient strength, not a ranging market
4. MACD Confirmation
MACD line above signal line
Histogram increasing (momentum accelerating)
5. Directional Movement
+DI > -DI
Confirms bullish directional pressure
Exit Logic
Positions are managed with ATR-based levels:
ParameterDefaultDescriptionStop Loss2.5 × ATRBelow entry priceTake Profit6.0 × ATRAbove entry priceTrailing Stop2.0 × ATROptional, activates after entry
The default configuration produces a 1:2.4 risk-reward ratio.
Presets
The strategy includes optimized presets based on historical testing:
PresetTimeframeNotes1H Standard1 HourMore frequent signals2H Low DD2 HourConservative settings3H Optimized3 HourBalanced approach4H Swing4 HourWider stops for swing tradesCustomAnyFull manual control
Select "Custom" to adjust all parameters manually.
Inputs Explained
EMAs
Fast EMA (20): Short-term trend
Slow EMA (50): Medium-term trend
Trend EMA (200): Long-term trend filter
RSI
Length: Lookback period (default 14)
Min/Max: Entry window to avoid extremes
ADX
Min ADX: Minimum trend strength threshold
Risk
Stop Loss ATR: Multiplier for stop distance
Take Profit ATR: Multiplier for target distance
Trail ATR: Trailing stop distance (if enabled)
Session (Optional)
Filter entries by time of day
Recommended OFF for 3H+ timeframes
What's Displayed
Info Panel (Top Right)
Current preset
Trend status (Strong/Wait)
ADX, RSI, MACD readings
Position status
Risk-reward ratio
Stats Panel (Top Left)
Net P&L %
Total trades
Win rate
Profit factor
Maximum drawdown
Chart
EMA lines (20 blue, 50 orange, 200 purple)
Green background during strong uptrend
Triangle markers on entry signals
Important Notes
⚠️ This is a long-only strategy. It does not take short positions.
⚠️ Historical results do not guarantee future performance. Backtests show what would have happened in the past under specific conditions. Markets change, and any strategy can experience drawdowns or extended losing periods.
⚠️ Risk management is your responsibility. The default settings risk 100% of equity per trade for backtesting purposes. In live trading, appropriate position sizing based on your risk tolerance is essential.
⚠️ Slippage and commissions matter. The backtest includes 0.02% commission and 1 tick slippage, but actual execution costs vary by broker and market conditions.
Best Practices
Test on your specific market — Results vary significantly across different instruments
Use appropriate position sizing — Never risk more than you can afford to lose
Combine with your own analysis — No indicator replaces understanding market context
Paper trade first — Validate the strategy matches your trading style before risking capital
Alerts
Two alerts are available:
MDZ Long Entry: Fires when all entry conditions are met
Uptrend Started: Fires when EMA stack first aligns bullish
Methodology
This strategy is based on the principle that trend continuation has better odds than reversal when multiple timeframe momentum indicators agree. By requiring five independent confirmations, it filters out weak setups at the cost of fewer total signals.
The ATR-based exits adapt to current volatility rather than using fixed pip/point targets, which helps the strategy adjust to different market conditions.
Questions? Leave a comment below.
Golder/Silter SetupsGolden/Silver Strategy
Overview
The Tony Rago Golden/Silver Strategy is a high-precision mean-reversion system specifically engineered for the Nasdaq (NQ/MNQ). It leverages the psychological 100-point price blocks to identify institutional exhaustion and reversal points.
Unlike standard "grid" bots, this strategy uses a sophisticated "Arm & Fire" logic: it requires a specific price "touch" to arm the setup, followed by a retracement to a "Golden" entry level to execute.
Key Logic: The 100-Point Grid
The strategy divides price action into 100-point blocks (e.g., 19500 to 19600).
Golden Setup (Long): Triggered when price touches the 50 level (mid-point). The order is placed at the 26 level on the retracement.
Silver Setup (Short): Triggered when price touches the 00 or 100 levels (block boundaries). The order is placed at the 77 or 26 levels on the retracement.
Professional Risk Management
This edition features a Dual-Contract Management system designed for Prop Firm consistency:
Contract 1 (The Scalp): Aims for a quick 24-point target (TP1) to secure realized gains and cover costs.
Contract 2 (The Runner): Stays in the trade for an extended 51-point target (TP2).
Automated Break-Even (BE): The moment TP1 is hit, the Stop Loss for the Runner is automatically moved to the entry price (plus a small offset). This ensures a "risk-free" environment for the remainder of the trade.
Independent Stop Losses: The Scalp and the Runner use different SL distances to account for Nasdaq volatility, preventing a single "noise" wick from wiping out the entire position.
Intelligent Filters
ADX Range Filter: The strategy monitors market trend strength. It only allows trades when the ADX is below a user-defined threshold (default 25), ensuring you only play mean-reversion during ranging or "choppy" markets.
MA Visual Semaphor: The 50 EMA changes color dynamically based on ADX (Lime/Green for Range, Orange/Red for Trend), giving you an instant visual "Go/No-Go" signal.
Time-Session Filtering: Optimized for three custom sessions (NY Open, Mid-Day Reversal, and Late Night). Outside these sessions, the strategy can "Arm" setups in memory but will not "Fire" orders.
How to Use
Timeframe: Optimized for 1-Minute or 2-Minute charts for precision entry.
Asset: Nasdaq 100 (NQ, MNQ) or similar high-volatility indices.
Setup: * Enable Session Filters to avoid news volatility.
Adjust TP/SL in Points (1 Point = 4 Ticks) to suit your specific risk appetite.
Watch for the "Armados" labels—these indicate the system is ready and waiting for the Golden/Silver entry.
Visual Interface
Dynamic Boxes: Real-time visual representation of your TP1, TP2, and SL levels.
Activation Labels: Clear indications of when a Long or Short setup has been "Armed" in memory.
Status Dashboard: A clean top-right table showing current ADX values, Session status, and Risk settings.
Disclaimer
Trading involves significant risk. This strategy is a tool for decision support and backtesting. Past performance does not guarantee future results. Always test on a demo account before risking live capital.
W/D/4HR OTE Aligner (V6) - Alerts This indicator is a multi-timeframe (MTF) alignment and Optimal Trade Entry (OTE) alert tool designed for discretionary manual trading on the 15-minute timeframe.
Here is a description of its core functionality:
W/D/4HR OTE Aligner (V6) - Alerts
This custom TradingView indicator assists manual traders by identifying high-probability trading setups that meet specific structural and momentum criteria across multiple timeframes. It does not place trades automatically but generates a "Trade Signal" used for setting up reliable alerts.
Key Features:
Multi-Timeframe Bias Confirmation: The indicator uses a 50-period Exponential Moving Average (EMA) to confirm that the Weekly, Daily, and 4-Hour timeframes are all aligned in the same direction (all above for bullish, all below for bearish). This provides a strong directional bias.
OTE Zone Identification: It dynamically calculates recent swing highs and lows on the 4-hour chart (using reliable pivot detection) and highlights the Optimal Trade Entry (OTE) zone, typically centered around the 0.618 Fibonacci Retracement level.
15-Minute Entry Signal: Once price enters the OTE zone within the aligned trend direction, the indicator looks for a confirmation entry signal on the 15-minute chart, specifically a 9-period EMA crossing the 20-period EMA.
Manual Alert System: A transparent "Trade Signal" plot provides the trigger source for a manual TradingView alert, notifying the user exactly when all criteria are met for a potential long or short trade entry.
This indicator is a tool for finding precise entry points within dominant, confirmed trends.
for clarity i built this using Google AI to help with being away from the charts it reflects how i wish to progress on my journey so any tips or feed back with me much appreciated
ICT OTE - Clean v6 (Indicator)ICT OTE — Indicator (Pine v6)
Comprehensive Guide & Rule-Based Trade Plan
This guide explains how to read and trade the Pine v6 indicator version of ICT’s Optimal Trade Entry (OTE). It covers chart elements, the 62–79% OTE zone (with 70.5% mid), confirmation logic, kill-zone gating, rule-based entries/stops/targets, and practical workflows.
1. Overview
The Optimal Trade Entry (OTE) is a core ICT concept that locates high-probability entries inside a Fibonacci retracement window between 62% and 79%, with 70.5% often used as a precise mid level. The indicator highlights that zone for the latest impulse (swing low to swing high for bullish, swing high to swing low for bearish), prints signal labels when price retraces into the box and a confirmation candle forms, and plots visual stop-loss and 1R/2R targets from your chosen entry line (62/70.5/79).
2. What the Indicator Draws
• OTE Box (62–79%): A green box for bullish OTE (drawn between 62% and 79% of the latest bullish impulse) and a red box for bearish OTE (drawn between 62% and 79% retracement of the latest bearish impulse).
• 70.5% Mid Line: A horizontal line through the OTE box at 70.5% (optional).
• Entry Line: Your selected entry reference: Top 62%, Mid 70.5%, or Bottom 79%.
• Signal Labels: ‘OTE Long’ appears when price touches the bullish OTE box and a confirmation candle prints; ‘OTE Short’ for the bearish side.
• Stop-Loss Guide: For longs: swing low of the dealing range; for shorts: swing high of the dealing range (visual plot).
• 1R/2R Target Lines: Two projected lines from the chosen entry to visualize 1R and 2R objectives (purely visual).
• Kill-Zone Shading (optional): Grey shading during London, NY-AM, and NY-PM windows, if gating is enabled.
3. Inputs & Settings
• Pivot Left/Right: Swing detection for impulses using pivot highs/lows (default 3/3).
• Draw OTE Box: Toggle drawing the 62–79% zone.
• Plot 70.5% Mid Line: Toggle the mid-line inside OTE.
• Entry Line: Choose the visual entry anchor (62% / 70.5% / 79%).
• Confirmation: Require a strong candle (default: close > open + continuation vs prior bar for longs; inverse for shorts).
• Stop/Targets: Toggle plotting the swing SL and 1R/2R targets.
• Kill-Zone Gating: If enabled, signals only fire in London (02:00–05:00 NY), NY-AM (08:00–11:00 NY), and NY-PM (13:00–15:00 NY).
• OTE Box Opacity: Adjust visual opacity of the box (default 85).
4. Rule-Based Entry Model
Use these steps exactly; do not skip.
1. Window: Optional — ensure you are inside an ICT kill-zone (London / NY-AM / NY-PM) if gating is enabled.
2. Impulse: Confirm the latest dealing range (pivot-based). Bullish = last swing low precedes last swing high; bearish = last swing high precedes last swing low.
3. OTE Box: Ensure price retraces into the 62–79% zone for the active impulse.
4. Confirmation: Wait for the required confirmation candle (strong or basic, per setting).
5. Entry Anchor: Use your selected line (62/70.5/79) as the reference for planning the fill.
6. Stops: Place SL at the swing extreme of the dealing range (longs: swing low; shorts: swing high).
7. Targets: Map 1R and 2R visual levels from the entry. Optionally prefer opposite liquidity or prior swing if closer (manual).
5. How to Read the Chart Step-by-Step
• Check background shading: are you inside a kill-zone (if enabled)?
• Identify the latest swing high/low markers (dealing range).
• Locate the OTE box for that impulse; confirm price retraced into the box.
• Inspect the confirmation candle: strong body and continuation (for longs: close > open AND close > prior high; for shorts: mirror).
• Use the entry line (62/70.5/79) as the reference; map SL and 1R/2R lines.
• If a signal label prints (‘OTE Long’ or ‘OTE Short’), you have alignment: timing, retracement, and confirmation.
6. Examples
Example Long (NY-AM): Price makes an impulse up (swing low then swing high). During NY-AM kill-zone, price retraces into the green OTE box. A strong bullish candle forms. Entry reference: 70.5% mid. SL at the swing low. Visual TP1/TP2 at 1R/2R.
Example Short (London): Price makes a bearish impulse (swing high then swing low). During London kill-zone, price retraces into the red OTE box. A strong bearish candle forms. Entry reference: 62%. SL at the swing high. Visual TP1/TP2 at 1R/2R.
7. Risk Management
• Fixed percent per trade: e.g., 1% risk relative to account equity (visual targets help sizing).
• One signal per kill-zone window: avoid overtrading inside the same hour.
• Respect invalidations: if a full body closes through the entire box or structure fails, stand down.
• Favor time/volatility windows for execution; avoid thin hours unless testing.
8. Common Pitfalls
• Forcing entries outside OTE: wait for a proper 62–79% retrace.
• Ignoring impulse definition: use confirmed pivots to avoid measuring from noise.
• Skipping confirmation: entries without a qualifying candle are lower quality.
• Inconsistent stops: SL belongs at the impulse extreme; avoid random micro-level stops.
• No timing discipline: kill-zone gating exists to filter low-probability periods.
9. Indicator Parameters — Quick Reference
• Pivot Left/Right: controls swing detection sensitivity (higher = stricter).
• Entry line: choose between 62%, 70.5%, and 79% for your signal anchor.
• Confirmation strength: toggle strong vs basic candle validation.
• Kill-zone gating: optional session-based signal filtering.
• Opacity/colors: cosmetic; adjust to your chart style.
10. Workflow Checklist
• Is the impulse correctly identified (pivot low/high order)?
• Is price inside the OTE box (62–79%)?
• Did the confirmation candle print?
• Is signal gating satisfied (if enabled)?
• Is SL mapped to the impulse extreme?
• Are 1R/2R levels clear from the entry?
• Any scheduled macro events in the window? (avoid surprise volatility).
11. References (ICT OTE & Kill-Zones)
• ICT Fibonacci / OTE levels: 62–79% with 70.5% mid — multiple tutorials and guides.
• ICT Kill-Zones: commonly used London / New York windows for timing entries.
King Trade 4 and 3 hour buy-sell strategy V2This strategy is a Trend-Following Breakout System specifically designed for high-volatility environments like the 4-hour and 3 hour (3h) (4H) timeframe. It focuses on identifying "Smart Money" movements by combining price action with significant volume surges.
Here is the breakdown of the strategy logic:
1. The Core Signal (Breakout)
The strategy identifies a level using the previous candle's High and Low.
Long Entry: Occurs when the current price crosses above the previous candle's high.
Short Entry: Occurs when the current price crosses below the previous candle's low.
2. The Smart Money Filter (Volume)
A price breakout without volume is often a "Bull Trap" or "Bear Trap." To minimize fake signals, this strategy calculates a Volume Moving Average (SMA 20).
It only enters a trade if the breakout candle's volume is at least 1.5x (or your chosen multiplier) higher than the average. This ensures that the move is backed by institutional or high-intensity trading.
3. The Trend Filter (EMA 200)
To stay on the right side of the market, the strategy uses the 200-period Exponential Moving Average (EMA) as a "Trend Guard":
Only Longs are allowed if the price is above the EMA 200 (Uptrend).
Only Shorts are allowed if the price is below the EMA 200 (Downtrend). This prevents you from buying into a crashing market or selling during a strong bull run.
Smart Signals [Vdubus]Smart Signals
Concept & Philosophy
Smart Signals is a "Regime-Filtered" oscillator designed to solve the biggest problem with standard indicators: Counter-trend noise.
Most oscillators (like Stochastic or RSI) are "dumb" to market context—they will signal "Sell" continuously during a strong uptrend simply because the price is high. Smart Signals fixes this by first determining the Market Regime (Bullish or Bearish) and then strictly filtering out any signal that contradicts that trend.
It creates a "Tiered" trading system that separates standard trend-following entries from high-probability "Sniper" entries (Hidden Divergence), all presented in a clean, color-blind-friendly visual interface.
Core Functions
1. The "Sheriff" (Trend Filter)
At the heart of the indicator is a heavy, modified Hull Moving Average (HMA 200) that acts as the trend baseline.
Bullish Regime: When the baseline is sloping UP, the indicator enters "Buy Only" mode. All Sell signals are mathematically deleted.
Bearish Regime: When the baseline is sloping DOWN, the indicator enters "Sell Only" mode. All Buy signals are mathematically deleted.
The Math: It uses a custom difference-weighted formula (wmaHalf = Length / 1) to create a stable, chop-resistant trend anchor.
2. Dual-Signal Engine
The indicator scans for two distinct types of entries simultaneously:
♦ Standard Signals (Blue/Red Diamonds):
Logic: A classic Stochastic pullback (Cross 20/80) aligned with the trend.
Use Case: These are frequent "Bread and Butter" trend entries. They are excellent for scaling into a position or adding to a winner as the trend continues.
Location: Plotted at the top (Sell) and bottom (Buy) edges of the panel.
+ Sniper Signals (Gold Crosses):
Logic: Hidden Divergence. The script detects when Price holds structure (Higher Low) while Momentum resets (Lower Low). This is a "Slingshot" setup.
Use Case: These are rare, high-conviction entries. They often mark the end of a complex correction and the resumption of the main trend.
Location: Plotted on the Zero Line to indicate structural strength.
3. Smart Momentum Histogram
The histogram visualizes the "Energy" of the move (MACD 21, 34, 7), but with a twist. It is color-coded to the signal priority:
Gold Bars: A Sniper (Divergence) setup is active.
Solid Blue/Red Bars: A Standard Signal is active.
Faded Blue/Red Bars: The trend is active, but momentum is resetting (waiting mode).
Gray Bars: Counter-trend noise (Ignore).
How to Trade It
Check the "Road": Look at the general color of the histogram columns.
Blue Columns: Look for Longs.
Red Columns: Look for Shorts.
The "Sniper" Entry: Wait for a Gold Cross (+) on the zero line. This is your primary signal to enter a trade with normal risk.
The "Pyramid" Entry: If the trend continues and you see Blue/Red Diamonds (♦) appear at the edges, these are safe places to add to your position.
The Exit: Since this is a trend-following tool, exit when the histogram color flips (e.g., from Blue to Red/Gray), or use your own support/resistance targets.
Alerts Configuration
The indicator comes with a full suite of alerts for automation:
Gold Buy / Gold Sell: Notifies you only for the high-probability Hidden Divergence setups.
Standard Buy / Standard Sell: Notifies you for every trend pullback.
ANY BUY / ANY SELL: A combo alert that triggers on either signal type (useful for simplifying your alert limits).
Accessibility
Color Blind Friendly: The default palette uses High-Contrast Blue (#2962FF) and Soft Red (#FF5252) instead of standard Green/Red, ensuring visibility for all users.
Zero Clutter: No text labels or confusing lines. Just clear, distinct shapes (Diamonds and Crosses) at fixed locations.
Institutional Alpha Vector | D_QUANTInstitutional Alpha Vector | D_QUANT
Overview
The Institutional Alpha Vector (IAV) is an enterprise-grade composite trend ecosystem designed to identify high-probability institutional order flow. Unlike standard indicators that rely on a single data point, the IAV synthesizes Price Action, Momentum Physics, Volatility Regimes, and Volume Flow into a single, unified Alpha Vector.
This tool is not merely a signal generator, it is a market regime filter. It visualizes the consensus of four distinct quantitative models through a sophisticated "Electric Conduit" rendering engine, allowing traders to visualize the strength, direction, and volatility of the current trend instantly.
The Quantitative Engine
The IAV constructs its signal by calculating a weighted consensus across four distinct modules. These modules work in synergy to filter out noise and highlight sustained institutional moves.
MODULE 1 :: PRICE_FILTER : A highly responsive Hull Moving Average that serves as the "Spine" of the trend, reducing lag while maintaining smoothness.
MODULE 2 :: VOLATILITY_REGIME : A dynamic envelope that adjusts to market noise. This creates the "Conduit" width—expanding during high-volatility expansion and contracting during consolidation.
MODULE 3 :: MOMENTUM_PHYSICS : A directional movement filter that ensures signals are only generated when trend strength exceeds a specific threshold (default: 20).
MODULE 4 :: INSTITUTIONAL_FLOW : A volume-weighted money flow engine that confirms if price movement is supported by actual volume (Smart Money participation).
The Alpha Vector
The core of this system is the Alpha Vector calculation. The indicator normalizes the outputs of all active modules into a composite score between -1.0 (Strong Bearish Consensus) and +1.0 (Strong Bullish Consensus).
Bullish Entry: When the Alpha Vector crosses above the Long Threshold (Default: 0.1).
Bearish Entry: When the Alpha Vector crosses below the Short Threshold (Default: -0.1).
Neutral/Cash: When the consensus is weak or conflicting, the ribbon turns Grey/Flat, advising the trader to remain on the sidelines.
// ==========================================
// 3. SIGNAL AGGREGATION
// ==========================================
calc_composite_matrix() =>
_hma = calc_hma_series(hma_src, hma_len)
_hma_sig_v = 0
if ta.crossover(close, _hma)
_hma_sig_v := 1
else if ta.crossunder(close, _hma)
_hma_sig_v := -1
_adx_sig_v = calc_adx_state(adx_len, adx_thresh)
_cmf_sig_v = calc_cmf_state(cmf_len)
_rma_sig_v = calc_rma_filter_state(hma_src, lookback, atr_len)
var int s_hma = 0
var int s_adx = 0
var int s_cmf = 0
var int s_rma = 0
if _hma_sig_v != 0
s_hma := _hma_sig_v
if _adx_sig_v != 0
s_adx := _adx_sig_v
if _cmf_sig_v != 0
s_cmf := _cmf_sig_v
if _rma_sig_v != 0
s_rma := _rma_sig_v
= request.security(syminfo.ticker, "D", calc_composite_matrix(), lookahead=barmerge.lookahead_on)
float alpha_score_num = 0.0
int alpha_score_den = 0
if use_hma
alpha_score_num += d_hma
alpha_score_den += 1
if use_adx
alpha_score_num += d_adx
alpha_score_den += 1
if use_cmf
alpha_score_num += d_cmf
alpha_score_den += 1
if use_rma
alpha_score_num += d_rma
alpha_score_den += 1
alpha_vector = alpha_score_den > 0 ? alpha_score_num / alpha_score_den : 0.0
Visual Intelligence: The "Electric Conduit"
The visualization logic uses a Differential Gradient Model to represent market energy:
The Spine (Core): The solid, bright center line represents the immediate trend direction.
The Conduit (Fill): The gradient fill represents the Volatility Regime.
Thick/Wide Ribbon: High Volatility (Expansion Phase).
Thin/Tight Ribbon: Low Volatility (Contraction/Squeeze Phase).
Bar Coloring: Native bar coloring is integrated to instantly align price action with the Alpha Vector, removing the need for mental processing.
Settings
The indicator is fully customizable via a "Compute-Style" configuration menu:
MODULES: Toggle specific engines (HMA, ADX, CMF, RMA) on or off to adapt the Alpha Vector to your specific asset class (Crypto, Forex, or Indices).
THRESHOLDS: Adjust sensitivity for Long/Short entries.
VISUALS: Customize the "Core" and "Edge" colors to match your charting theme.
“Alpha is not about predicting the future. It is about aligning with the mathematical consensus of the present.” — D_QUANT
Advanced Footprint Analysis1. ABSORPTION = BEST ENTRY SIGNALS
When BTC hits support and shows bullish absorption:
You know big money is buying
Price won't fall further (supply absorbed)
Risk/reward is optimal (tight stop below absorption)
Win rate on these setups is 70-80%
2. EXHAUSTION = REVERSAL TIMING
Catches exact moment selling/buying pressure is exhausted
No more guessing "is the dip over?"
Volume confirms the reversal
3. IMBALANCES = CONTINUATION TRADES
Stacked imbalances show trend strength
Enter pullbacks in strong trends
Avoid counter-trend trades when imbalance is strong
4. DELTA DIVERGENCE = EARLY WARNING
Cumulative delta rising but price flat = accumulation (buy setup)
Cumulative delta falling but price rising = distribution (sell setup)
This divergence appears BEFORE price moves
5. FILTERS OUT NOISE
Crypto has tons of fake volume and wash trading
By requiring volume to be significantly above average (2x, 3x), you ignore the noise
Only trade when institutions are active
6. WORKS ON ALL CRYPTO PAIRS
BTC, ETH, SOL - same patterns
Especially powerful on perpetual futures (more volume data)
PRACTICAL 5M CRYPTO ALGO STRATEGY:
LONG ENTRY:
Wait for bullish absorption OR bullish exhaustion
Confirm with positive stacked imbalances (3 bars)
Enter when price breaks above absorption high
Stop below absorption low
Target: 2-3x risk or next resistance
SHORT ENTRY:
Wait for bearish absorption OR bearish exhaustion
Confirm with negative stacked imbalances
Enter when price breaks below absorption low
Stop above absorption high
Target: 2-3x risk or next support
FILTER:
Only trade in direction of cumulative delta trend
Avoid when volume is below average (no institutional activity)
Commodity Channel Index CCI + EMA strategy
================================================================================
COMMODITY CHANNEL INDEX CCI + EMA STRATEGY - STRATEGY GUIDE 📊
================================================================================
💡 COLLABORATION & SUPPORT
---------------------------
If you want to collaborate, have an idea for a strategy, or need help writing
or customizing code, send an email to burdytrader@gmail.com or send me a
message. Suggestions, ideas, and comments are always welcome! 🤝
I also develop automated trading codes for other trading platforms including:
- CTrader (C#)
- MetaTrader 4 (MQL4)
- MetaTrader 5 (MQL5)
If you need a strategy converted or developed for any of these platforms, feel
free to contact me!
================================================================================
⚠️ IMPORTANT: INSTRUMENT SELECTION 📈
-------------------------------------
This strategy performs BEST with currency pairs (forex). The CCI indicator
works particularly well in the forex market due to the nature of currency
movements and the effectiveness of the CCI in identifying overbought and
oversold conditions in trending markets.
Why Currency Pairs? 🎯
- CCI is highly effective in identifying reversals in forex markets
- Currency pairs show clear overbought/oversold patterns
- EMA filter (50/200) aligns well with major forex trends
- High liquidity ensures reliable signal execution
Performance Highlights:
In specific currency pairs, when properly configured, this strategy can achieve:
- Profit Factor: Over 2.0
- Win Rate: Up to 70%
- Particularly effective pairs: USDCAD, EURUSD, GBPJPY
While the strategy can work with other instruments (stocks, indices, commodities),
currency pairs provide the most consistent and reliable results. For optimal
performance, focus on major forex pairs with good liquidity and clear trending
characteristics.
================================================================================
WHAT DOES THIS STRATEGY DO? 🎯
---------------------------
This strategy combines the Commodity Channel Index (CCI) with Exponential
Moving Averages (EMA) to identify high-probability trading opportunities.
The strategy uses CCI crossovers with a smoothing moving average and filters
signals using EMA trend confirmation. The strategy automatically enters trades
when CCI crosses the smoothing MA in specific zones, indicating potential trend
reversals or continuations.
HOW IT WORKS? ⚙️
---------------
1. CCI CALCULATION 📈
The strategy calculates the Commodity Channel Index using:
- CCI = (Price - SMA(Price, length)) / (0.015 × Deviation(Price, length))
- Default length: 20 periods
- Source: HLC3 (typical price)
The CCI shows:
- Values above +100 = Overbought conditions
- Values below -100 = Oversold conditions
- Values around 0 = Neutral conditions
2. SMOOTHING MOVING AVERAGE 📊
A moving average is applied to the CCI to smooth out fluctuations:
- Types available: SMA, EMA, SMMA (RMA), WMA, VWMA
- Default: SMA with length 14
- Can be disabled (set to "None")
This smoothed line acts as a reference for crossover signals.
3. EMA TREND FILTER 🎯
Two EMAs are calculated on the CCI:
- EMA 50 (fast EMA)
- EMA 200 (slow EMA)
When the EMA filter is enabled:
- LONG signals only occur when EMA50 > EMA200 (uptrend confirmation)
- SHORT signals only occur when EMA50 < EMA200 (downtrend confirmation)
This filter can be enabled/disabled via the "Use EMA Filter" option.
4. ENTRY CONDITIONS 🎲
LONG ENTRY (Buy Signal):
- CCI crosses ABOVE the Smoothing MA (crossover)
- CCI is between Lower Level (-100) and Middle Level (0)
- EMA Filter: EMA50 > EMA200 (if filter enabled)
- No existing positions (or close opposite positions first)
SHORT ENTRY (Sell Signal):
- CCI crosses BELOW the Smoothing MA (crossunder)
- CCI is between Middle Level (0) and Upper Level (+100)
- EMA Filter: EMA50 < EMA200 (if filter enabled)
- No existing positions (or close opposite positions first)
5. POSITION MANAGEMENT 💰
The strategy uses a simple position management approach:
- Only ONE position at a time (no pyramiding)
- If a signal occurs in the opposite direction, closes existing position first
- Then opens new position in the new direction
- This prevents overexposure and simplifies risk management
6. TAKE PROFIT & STOP LOSS SETTINGS 🎯
The strategy uses percentage-based TP/SL:
- Take Profit: 1.0% (default, configurable)
- Stop Loss: 0.5% (default, configurable)
- Risk/Reward Ratio: 2:1 (TP is double the SL)
TP/SL are calculated once when the position opens and remain fixed.
AVAILABLE PARAMETERS ⚙️
-----------------------
CCI SETTINGS:
1. CCI Length (Default: 20)
- Period for CCI calculation
- Lower values = More sensitive to recent price action
- Higher values = More smoothed, less sensitive
2. CCI Source (Default: HLC3)
- Price source for CCI calculation
- Options: close, open, high, low, hlc3, hlcc4, ohlc4
3. CCI Lower Level (Default: -100)
- Lower boundary for LONG entry zone
- Typically -100 for oversold conditions
4. CCI Middle Level (Default: 0)
- Neutral level separating LONG and SHORT zones
5. CCI Upper Level (Default: +100)
- Upper boundary for SHORT entry zone
- Typically +100 for overbought conditions
SMOOTHING MA:
6. Type (Default: SMA)
- Moving average type: None, SMA, EMA, SMMA (RMA), WMA, VWMA
- Set to "None" to disable smoothing
7. Length (Default: 14)
- Period for smoothing MA
- Range: 7-28, step 7
EMA FILTER:
8. EMA 1 Length (Default: 50)
- Fast EMA period applied to CCI
9. EMA 2 Length (Default: 200)
- Slow EMA period applied to CCI
10. Use EMA Filter (Default: true)
- Enable/disable EMA trend filter
- When enabled: LONG only if EMA50 > EMA200, SHORT only if EMA50 < EMA200
RISK MANAGEMENT:
11. Take Profit (%) (Default: 1.0%)
- Profit target as percentage of entry price
- For LONG: Entry × (1 + TP%)
- For SHORT: Entry × (1 - TP%)
12. Stop Loss (%) (Default: 0.5%)
- Stop loss as percentage of entry price
- For LONG: Entry × (1 - SL%)
- For SHORT: Entry × (1 + SL%)
VISUALIZATION 📊
---------------
The strategy displays in a separate panel below the price chart:
1. CCI LINE
- Blue line showing the CCI value
- Oscillates around zero
2. SMOOTHING MA LINE
- Yellow line showing the smoothed CCI
- Reference line for crossover signals
3. CCI LEVEL LINES
- Red dashed line: Upper Level (+100)
- Green dashed line: Lower Level (-100)
- Yellow dashed line: Middle Level (0)
4. ENTRY SIGNALS
- Green cross: LONG entry signal (when CCI crosses above MA)
- Red cross: SHORT entry signal (when CCI crosses below MA)
RECOMMENDED SETTINGS 🎯
-----------------------
To get started, you can use these settings:
CCI SETTINGS:
- CCI Length: 20 (default)
- CCI Source: HLC3 (default)
- CCI Lower Level: -100 (default)
- CCI Middle Level: 0 (default)
- CCI Upper Level: +100 (default)
SMOOTHING MA:
- Type: SMA (default) or EMA for faster response
- Length: 14 (default)
EMA FILTER:
- EMA 1 Length: 50 (default)
- EMA 2 Length: 200 (default)
- Use EMA Filter: true (recommended for better signal quality)
RISK MANAGEMENT:
- Take Profit (%): 1.0% (adjust based on your risk/reward preference)
- Stop Loss (%): 0.5% (adjust based on your risk tolerance)
For more aggressive trading:
- Reduce CCI Length to 14-16
- Reduce Smoothing MA Length to 7
- Disable EMA Filter
For more conservative trading:
- Increase CCI Length to 24-30
- Increase Smoothing MA Length to 21-28
- Keep EMA Filter enabled
RECOMMENDED CURRENCY PAIRS 💱
------------------------------
This strategy is optimized for currency pairs and performs exceptionally well
on the following pairs when properly configured:
TOP PERFORMING PAIRS:
- USDCAD: Can achieve Profit Factor > 2.0 and Win Rate up to 70%
- EURUSD: Excellent performance with consistent signals
- GBPJPY: Strong results with proper EMA filter configuration
These pairs have shown the best historical performance due to:
- Clear trending characteristics
- Good response to CCI overbought/oversold levels
- Strong alignment with EMA 50/200 trend filter
- High liquidity ensuring reliable execution
When trading these pairs, use the default settings or slightly adjusted
parameters based on the pair's volatility. Always backtest on historical
data before using real money to find the optimal configuration for each
specific pair.
PRACTICAL EXAMPLE 📝
--------------------
Scenario: LONG Entry on EUR/USD
1. Market conditions:
- Price: 1.1000
- CCI: -80 (in oversold zone)
- Smoothing MA: -90
- CCI crosses above Smoothing MA (crossover occurs)
- EMA50: -50, EMA200: -70 (EMA50 > EMA200, uptrend confirmed)
2. Strategy checks conditions:
✓ Smoothing MA enabled: Yes
✓ Crossover: Yes (CCI crosses above MA)
✓ CCI in range: Yes (-100 <= -80 <= 0)
✓ EMA Filter: Yes (EMA50 > EMA200)
✓ No existing position: Yes
3. Strategy opens position:
- Direction: LONG (Buy)
- Entry: 1.1000 (current close)
- Take Profit: 1.1110 (1.0% above entry)
- Stop Loss: 1.0945 (0.5% below entry)
- Risk/Reward: 2:1
4. Outcome scenarios:
- If price rises to 1.1110 → Take Profit hit (profit)
- If price falls to 1.0945 → Stop Loss hit (loss limited)
IMPORTANT NOTE ⚠️
-----------------
This strategy is a technical analysis tool based on CCI and EMA indicators.
Like all trading strategies, it does NOT guarantee profits. Trading involves
significant risks and you can lose money, including your entire investment.
Past performance does not guarantee future results.
Always:
- Use appropriate risk management
- Never risk more than you can afford to lose
- Test the strategy on historical data (backtesting) before using real money
- Start with small position sizes or paper trading
- Understand that no strategy works 100% of the time
- Consider market conditions, news events, and other factors
- Keep a trading journal to learn and improve
The author and contributors are NOT responsible for any losses incurred from
using this strategy. Trading decisions are your own responsibility. Profits
are NOT guaranteed, and losses are possible.
LICENSE 📄
----------
This code is open source and available for modification. You are free to use,
modify, and distribute this strategy. If you republish or share a modified
version, please kindly mention the original author.
================================================================================
HMA 9/50 Crossover + RSI 50 Filter1. The Core Indicators
HMA 9 (Fast): Acts as the primary trigger line. Its unique calculation minimizes lag compared to standard moving averages, allowing for faster entries.
HMA 50 (Slow): Defines the medium-term trend direction and acts as the "anchor" for crossover signals.
RSI 14: Serves as a "momentum gate." Instead of traditional overbought/oversold levels, we use the 50 midline to confirm that the directional strength supports the crossover.
2. Entry Conditions
Long Entry: Triggered when the HMA 9 crosses above the HMA 50 AND the RSI is greater than 50.
Short Entry: Triggered when the HMA 9 crosses below the HMA 50 AND the RSI is less than 50.
3. Execution & Reversal
This strategy is currently configured as an Always-in-the-Market system.
A "Long" position is automatically closed when a "Short" signal is triggered.
To prevent "pyramiding" (buying multiple positions in one direction), the script checks the current position_size before opening new entries.
How to Use
Timeframe: Optimized for 3-minute (3m) candles but can be tuned for 1m to 15m scalping.
Settings: Use the Inputs panel to adjust HMA lengths based on the volatility of your specific asset (e.g., shorter for stable stocks, longer for volatile crypto).
Visuals:
Aqua Line: HMA 9
Orange Line: HMA 50
Green Background: Bullish RSI Momentum (> 50)
Red Background: Bearish RSI Momentum (< 50)
Risk Disclosure
Whipsaws: This strategy is likely to underperform in sideways markets.
Backtesting: Past performance does not guarantee future results. Always test this strategy in the Strategy Tester with appropriate commission and slippage settings before live use.
PA SystemPA System
短简介 Short Description（放在最上面）
中文：
PA System 是一套以 AL Brooks 价格行为为核心的策略（Strategy），将 结构（HH/HL/LH/LL）→ 回调（H1/L1）→ 二次入场（H2/L2 微平台突破） 串成完整可回测流程，并可选叠加 BoS/CHoCH 结构突破过滤 与 Liquidity Sweep（扫流动性）确认。内置风险管理：定风险仓位、部分止盈、保本、移动止损、时间止损、冷却期。
English:
PA System is an AL Brooks–inspired Price Action strategy that chains Market Structure (HH/HL/LH/LL) → Pullback (H1/L1) → Second Entry (H2/L2 via Micro Range Breakout) into a complete backtestable workflow, with optional BoS/CHoCH structure-break filtering and Liquidity Sweep confirmation. Built-in risk management includes risk-based sizing, partial exits, breakeven, trailing stops, time stop, and cooldown.
⸻
1) 核心理念 Core Idea
中文：
这不是“指标堆叠”，而是一条清晰的价格行为决策链：
结构确认 → 回调出现 → 小平台突破（二次入场）→ 风控出场。
策略把 Brooks 常见的“二次入场”思路程序化，同时用可选的结构突破与扫流动性模块提升信号质量、减少震荡误入。
English:
This is not an “indicator soup.” It’s a clear price-action decision chain:
Confirmed structure → Pullback → Micro-range breakout (second entry) → Risk-managed exits.
The system programmatically implements the Brooks-style “second entry” concept, and optionally adds structure-break and liquidity-sweep context to reduce chop and improve trade quality.
⸻
2) 主要模块 Main Modules
A. 结构识别 Market Structure (HH/HL/LH/LL)
中文：
使用 pivot 摆动点确认结构，标记 HH/HL/LH/LL，并可显示最近一组摆动水平线，方便对照结构位置。
English:
Uses confirmed pivot swings to label HH/HL/LH/LL and optionally plots the most recent swing levels for clean structure context.
B. 状态机 Market Regime (State Machine + “Always In”)
中文：
基于趋势K强度、EMA关系与波动范围，识别市场环境（Breakout/Channel/Range）以及 Always-In 方向，用于过滤不合适的交易环境。
English:
A lightweight regime engine detects Breakout/Channel/Range and an “Always In” directional bias using momentum and EMA/range context to avoid low-quality conditions.
C. 二次入场 Second Entry Engine (H1→H2 / L1→L2)
中文：
• H1/L1：回调到结构附近并出现反转迹象
• H2/L2：在 H1/L1 后等待最小 bars，然后触发 Micro Range Breakout（小平台突破）并要求信号K收盘强度达标
这一段是策略的“主发动机”。
English:
• H1/L1: Pullback into structure with reversal intent
• H2/L2: After a minimum wait, triggers on Micro Range Breakout plus a configurable close-strength filter
This is the main “entry engine.”
D. 可选过滤器 Optional Filters (Quality Boost)
BoS/CHoCH（结构突破过滤）
中文： 可识别 BoS / CHoCH，并可要求“入场前最近 N bars 必须有同向 break”。
English: Detects BoS/CHoCH and can require a recent same-direction break within N bars.
Liquidity Sweeps（扫流动性确认）
中文： 画出 pivot 高/低的流动性水平线，检测“刺破后收回”的 sweep，并可要求入场前出现同向 sweep。
English: Tracks pivot-based liquidity levels, confirms sweeps (pierce-and-reclaim), and can require a recent sweep before entry.
E. FVG 可视化 FVG Visualization
中文： 提供 FVG 区域盒子与管理模式（仅保留未回补 / 仅保留最近N），主要用于区域理解与复盘，不作为强制入场条件（可自行扩展）。
English: Displays FVG boxes with retention modes (unfilled-only or last-N). Primarily for context/analysis; not required for entries (you can extend it as a filter/target).
⸻
3) 风险管理 Risk Management (Built-In)
中文：
• 定风险仓位：按账户权益百分比计算仓位
• SL/TP：基于结构 + ATR 缓冲，且限制最大止损 ATR 倍
• 部分止盈：到达指定 R 后减仓
• 保本：到达指定 R 后推到 BE
• 移动止损：到达指定 R 后开始跟随
• 时间止损：持仓太久不动则退出
• 冷却期：出场后等待 N bars 再允许新单
English:
• Risk-based sizing: position size from equity risk %
• SL/TP: structure + ATR buffer with max ATR risk cap
• Partial exits at an R threshold
• Breakeven at an R threshold
• Trailing stop activation at an R threshold
• Time stop to reduce chop damage
• Cooldown after exit to avoid rapid re-entries
⸻
4) 推荐使用方式 Recommended Usage
中文：
• 推荐从 5m / 15m / 1H 开始测试
• 想更稳：开启 EMA Filter + Break Filter + Sweep Filter，并提高 Close Strength
• 想更多信号：关闭 Break/Sweep 过滤或降低 Swing Length / Close Strength
• 回测时务必设置合理的手续费与滑点，尤其是期货/指数
English:
• Start testing on 5m / 15m / 1H
• For higher quality: enable EMA Filter + Break Filter + Sweep Filter and increase Close Strength
• For more signals: disable Break/Sweep filters or reduce Swing Length / Close Strength
• Use realistic commissions/slippage in backtests (especially for futures/indices)
⸻
5) 重要说明 Notes
中文：
结构 pivot 需要右侧确认 bars，因此结构点存在天然滞后（确认后不会再变）。策略逻辑尽量避免不必要的对象堆叠，并对数组/对象做了稳定管理，适合长期运行与复盘。
English:
Pivot-based structure requires right-side confirmation (inherent lag; once confirmed it won’t change). The script is designed for stability and resource-safe object management, suitable for long sessions and review.
⸻
免责声明 Disclaimer（建议原样保留）
中文：
本脚本仅用于教育与研究目的，不构成任何投资建议。策略回测结果受市场条件、手续费、滑点、交易时段、数据质量等影响显著。使用者需自行验证并承担全部风险。过往表现不代表未来结果。
English:
This script is for educational and research purposes only and does not constitute financial advice. Backtest results are highly sensitive to market conditions, fees, slippage, session settings, and data quality. Use at your own risk. Past performance is not indicative of future results.
Swing Trade System# Swing Trade Strategy - Complete Guide
## Overview
This is a comprehensive swing trading indicator for TradingView that identifies high-probability trend continuation setups using multi-timeframe analysis, pullback patterns, and momentum confirmation. The strategy combines technical indicators with risk management tools to help traders capture swing moves with defined risk-reward parameters.
## What It Does
The indicator identifies two types of signals:
1. **Base Signals** (small markers) - Initial setup detection with basic criteria met
2. **High Confidence (HC) Signals** (large markers) - Fully confirmed setups with all filters passed, including optional higher timeframe confirmation
Once a HC signal triggers, the indicator automatically plots:
- Dynamic stop loss levels (trailing, break-even, or static)
- Partial take profit (TP1) at 1R
- Final take profit (TP2) at your chosen risk-reward multiple
- Real-time R-multiple tracking
- Confluence dashboard showing all conditions
## How It Works
### Core Signal Logic
The strategy identifies pullback-to-trend entries using this sequence:
**For LONG signals:**
1. **Trend Filter**: Fast EMA (20) above Slow EMA (50) = uptrend confirmed
2. **Pullback**: Previous candle closed between the two EMAs (pulled back but didn't break structure)
3. **RSI Swing Zone**: RSI between 40-60 (not overbought/oversold, just resting)
4. **Reclaim**: Current candle crosses back above Fast EMA (momentum returning)
5. **Volume Spike** (optional): Current volume > 1.5x the 20-period average
6. **HTF Confirmation** (optional): Daily timeframe shows: price > 50 EMA, RSI > 50, and rising momentum
**For SHORT signals:**
The same logic applies in reverse (downtrend, pullback above fast EMA, reclaim below, etc.)
### Risk Management Features
**Stop Loss Placement:**
- Initial stop: Swing low/high over the last 10 bars
- Can upgrade to ATR trailing stop (2x ATR below/above price)
- Can move to break-even after reaching 1R profit
**Take Profit Levels:**
- TP1: 1R (optional partial exit point)
- TP2: 2R default (adjustable to your preference)
**Position Monitoring:**
- Live R-multiple display shows current profit/loss in risk units
- Dynamic stop updates visually on chart
- Color-coded confidence score (0-100%) based on confluence of factors
## Best Way to Use These Signals
### 1. **Wait for High Confidence Signals Only**
- Don't trade every base signal (small markers)
- Only take trades when you see the large "HC L" or "HC S" markers
- These have passed all your filters including higher timeframe alignment
### 2. **Ideal Entry Timing**
**On the Signal Candle:**
- Enter at market close when HC signal fires
- This ensures all conditions were met by candle close
- Your stop and targets are calculated from this close price
**On the Next Candle (more conservative):**
- Wait for the candle after the signal
- Enter if price continues in the signal direction
- Helps avoid false breakouts but may miss some moves
### 3. **Position Sizing**
Use the automatic risk calculation:
- Your risk = Entry price - Stop loss
- Position size = (Account Risk %) ÷ (Entry - Stop)
- Example: Risk $100 on account, Entry $50, Stop $48 = $100 ÷ $2 = 50 shares
### 4. **Trade Management**
**Scaling Out:**
- Exit 50% position at TP1 (1R) to lock profits
- Move stop to break-even on remaining position
- Let rest run to TP2 (2R) or trail with ATR stop
**Manual Override:**
- If price action deteriorates (breaks below both EMAs, RSI divergence), consider early exit
- The dynamic stop is a guide, not gospel—trust price action
## Breakout vs. Retest Strategy
### Understanding Breakout Types
**1. First Touch Breakout (Aggressive)**
- HC signal fires on first touch of fast EMA after pullback
- Higher win rate if volume is strong
- Best in strongly trending markets
- Risk: Could be a false breakout if momentum weak
**2. Retest Entry (Conservative)**
- Wait for price to pull back *again* after initial HC signal
- Enter when price retests the fast EMA a second time
- Look for: lower volume on retest, RSI still in swing zone, fast EMA still above slow EMA
- Lower risk but may miss some fast moves
### Which Breakouts to Take
**Take the FIRST breakout (signal candle) when:**
- ✅ Higher timeframe is strongly aligned (HTF confirmation on)
- ✅ Volume spike is present (>1.5x average)
- ✅ Confidence score ≥70%
- ✅ Trend is fresh (EMAs recently crossed, not extended)
- ✅ Price closed strongly above/below fast EMA (not barely crossed)
- ✅ No major resistance/support nearby
**Wait for a RETEST when:**
- ⚠️ No volume confirmation on first signal
- ⚠️ Confidence score 40-69% (moderate)
- ⚠️ Price barely crossed the fast EMA (weak momentum)
- ⚠️ Trend is extended (price far from slow EMA)
- ⚠️ Major resistance/support level just ahead
- ⚠️ Late in the trading day/week (could see pullback)
### How to Trade Retests
**Setup:**
1. HC signal fires but you decide to wait
2. Price pulls back toward fast EMA over next 1-3 candles
3. Watch for second bounce at the fast EMA
**Confirmation for Retest Entry:**
- Price holds above fast EMA (for longs) without closing below it
- Volume decreases on the pullback (profit-taking, not reversal)
- RSI stays above 50 for longs (or below 50 for shorts)
- Bullish candlestick pattern forms (hammer, engulfing, etc.)
- Slow EMA is still providing support/resistance
**Retest Entry Trigger:**
- Enter when price crosses back in signal direction with momentum
- Or enter with a limit order at the fast EMA
- Use same stop loss as original signal (swing low/high)
- Targets remain the same (measured from your new entry)
## Dashboard Reference
The top confluence table shows real-time status:
- **Trend**: Current trend direction based on EMAs
- **HTF**: Higher timeframe alignment (if enabled)
- **RSI Zone**: Whether RSI is in the 40-60 swing zone
- **Volume**: Volume spike present or not
- **Signal**: Current signal status (HC LONG/SHORT or None)
- **R Risk**: Current profit/loss in R-multiples
- **Stop**: Current stop loss price
- **TP1/TP2**: Status of take profit levels
- **Conf %**: Overall confidence score (70%+ = high probability)
## Alert Setup
The indicator includes 8 alert types:
1. **HC LONG/SHORT ENTRY** - Main trade signals
2. **LONG/SHORT TP1 Reached** - Partial profit alerts
3. **LONG/SHORT Final TP Reached** - Full target hit
4. **LONG/SHORT Stop Hit** - Exit alerts
Set up alerts in TradingView:
- Click "Create Alert" on the indicator
- Choose the specific alert condition
- Set to "Once Per Bar Close" to avoid false alerts
- Configure notification method (app, email, webhook, etc.)
## Recommended Settings
**For Stock Swing Trading (4H-Daily):**
- Fast EMA: 20 | Slow EMA: 50
- Swing Lookback: 10
- RSI Zone: 40-60
- HTF: Daily (if trading 4H charts)
- Risk-Reward: 2R minimum
**For Crypto (faster moves):**
- Fast EMA: 12 | Slow EMA: 26
- Swing Lookback: 7
- RSI Zone: 35-65
- Volume Spike: ON
- Risk-Reward: 1.5-2R
**For Conservative Trading:**
- Enable HTF Confirmation
- Enable Volume Spike requirement
- Use Break-even stop (move after 1R)
- Only trade when Confidence ≥70%
- Wait for retests on marginal setups
## Risk Warning
This indicator is a tool, not a guarantee. Always:
- Use proper position sizing (risk 1-2% per trade)
- Respect the stop losses
- Consider market context (news, earnings, major levels)
- Backtest on your instruments before live trading
- Never override risk management for FOMO
The best signals combine technical confluence with good market conditions and disciplined execution.
AI Reversal Signals Custom [wjdtks255]📊 Indicator Overview: AI Reversal Signals Custom
This indicator is a comprehensive trend-following and reversal detection tool. It combines the long-term trend bias of a 200 EMA with highly sensitive RSI-based reversal signals and momentum visualization. It is designed to capture market bottoms and tops by identifying exhaustion points in price action.
Key Features
200 EMA (Trend Filter): A gold line representing the long-term institutional trend. It helps traders distinguish between "buying the dip" and "catching a falling knife."
Reversal Buy/Sell Labels: Real-time signals that appear when the market recovers from extreme overbought or oversold conditions.
Dynamic Background Clouds: Visual indicators of trend strength changes, highlighting potential entry zones.
Momentum Histogram: Internal calculations mimic the "Bottom Bars" seen in professional suites to track the velocity of price movement.
📈 Trading Strategy (How to Trade)
1. High-Probability Long Setup (Buy)
Trend Confirmation: Price should ideally be trading above the 200 EMA for the highest success rate.
Signal: Wait for the "BUY" label to appear below the candle.
Momentum: Confirm with the Light Green background or histogram shift indicating recovery.
Entry: Enter on the close of the signal candle.
2. High-Probability Short Setup (Sell)
Trend Confirmation: Price should ideally be trading below the 200 EMA.
Signal: Wait for the "SELL" label to appear above the candle.
Momentum: Confirm with the Red background or histogram fading from green to red.
Entry: Enter on the close of the signal candle.
3. Risk Management
Stop Loss: Place your Stop Loss slightly below the recent swing low for Buy orders, or above the recent swing high for Sell orders.
Take Profit: Exit when the price reaches a major support/resistance level or when an opposing signal appears.
💡 Professional Tip
For the best results, use this indicator on the 15-minute or 1-hour timeframes. The most powerful "Ultimate Reversal" signals occur when there is a Bullish Divergence (Price making lower lows while the RSI makes higher lows) followed by a confirmed "BUY" label.
Unmitigated MTF High Low Pro - Cave Diving Bookmap Heatmap Plot
Unmitigated MTF High Low Pro - Cave Diving Bookmap Heatmap Plot
---
## 📖 Table of Contents
1. (#what-this-indicator-does)
2. (#core-concepts)
3. (#visual-components)
4. (#the-cave-diving-framework)
5. (#how-to-use-it-for-trading)
6. (#settings--customization)
7. (#best-practices)
8. (#common-scenarios)
---
## What This Indicator Does
The **Unmitigated MTF High Low v2.0** tracks unmitigated (untouch) high and low levels across multiple timeframes, helping you identify key support and resistance zones that the market hasn't revisited yet. Think of it as a sophisticated memory system for price action - it remembers where price has been, and more importantly, where it *hasn't been back to*.
### Why "Unmitigated" Matters
In futures trading, especially on instruments like NQ and ES, the market has a tendency to revisit levels where liquidity was left behind. An "unmitigated" level is one that hasn't been touched since it was formed. These levels often act as magnets for price, and understanding their age and proximity gives you a significant edge in:
- **Entry timing** - Waiting for price to approach tested levels
- **Exit planning** - Taking profits before ancient resistance/support
- **Risk management** - Avoiding entries when approaching multiple old levels
- **Liquidity mapping** - Visualizing where orders likely cluster
---
## Core Concepts
### 1. **Sessions & Age**
The indicator uses **New York trading sessions** (6:00 PM to 5:59 PM NY time) as the primary time measurement. This aligns with how futures markets naturally segment their activity.
**Age Categories:**
- 🟢 **New (0-1 sessions)** - Fresh levels, recently formed
- 🟡 **Medium (2-3 sessions)** - Tested by time, gaining significance
- 🔴 **Old (4-6 sessions)** - Highly significant, survived multiple days
- 🟣 **Ancient (7+ sessions)** - Extreme significance, major support/resistance
The longer a level remains unmitigated, the more significant it becomes. Think of it like compound interest - time adds weight to these zones.
### 2. **Multi-Timeframe Tracking**
You can set the indicator to track high/low levels from any timeframe (default is 15 minutes). This means you're watching for unmitigated 15-minute highs and lows while trading on, say, a 1-minute or 5-minute chart.
**Why this matters:**
- Higher timeframe levels have more weight
- You can see multiple timeframe structure simultaneously
- Helps you avoid fighting larger timeframe momentum
### 3. **Mitigation**
A level becomes "mitigated" (deactivated) when price touches it:
- **High levels** are mitigated when price reaches or exceeds them
- **Low levels** are mitigated when price reaches or goes below them
Once mitigated, the level disappears from view. The indicator only shows you the untouch levels that still matter.
---
## Visual Components
### 📊 The Dashboard Table
Located in the corner of your chart (configurable), the table shows:
```
┌─────────┬───────────┬────────┬─────┬───────┐
│ Level │ Price │ Points │ Age │ % │
├─────────┼───────────┼────────┼─────┼───────┤
│ ↑↑↑↑↑ │ 21,450.25 │ +45.50 │ 8 │ +0.21%│ ← 5th High (Ancient)
│ ↑↑↑↑ │ 21,430.00 │ +25.25 │ 5 │ +0.12%│ ← 4th High (Old)
│ ↑↑↑ │ 21,420.50 │ +15.75 │ 3 │ +0.07%│ ← 3rd High (Medium)
│ ↑↑ │ 21,412.00 │ +7.25 │ 1 │ +0.03%│ ← 2nd High (New)
│ ↑ ⚠️ │ 21,408.25 │ +3.50 │ 0 │ +0.02%│ ← 1st High (Proximity Alert!)
├─────────┼───────────┼────────┼─────┼───────┤
│ 15 mins │ 🟢 │ Δ 8.75 │ 2U │ │ ← Status Row
├─────────┼───────────┼────────┼─────┼───────┤
│ ↓ ⚠️ │ 21,399.50 │ -5.25 │ 0 │ -0.02%│ ← 1st Low (Proximity Alert!)
│ ↓↓ │ 21,395.00 │ -9.75 │ 2 │ -0.05%│ ← 2nd Low (Medium)
│ ↓↓↓ │ 21,385.25 │ -19.50 │ 4 │ -0.09%│ ← 3rd Low (Old)
│ ↓↓↓↓ │ 21,370.00 │ -34.75 │ 6 │ -0.16%│ ← 4th Low (Old)
│ ↓↓↓↓↓ │ 21,350.75 │ -54.00 │ 9 │ -0.25%│ ← 5th Low (Ancient)
├─────────┼───────────┼────────┼─────┼───────┤
│ 📊 15↑ / 12↓ │ ← Statistics (optional)
└─────────┴───────────┴────────┴─────┴───────┘
```
**Reading the Table:**
- **Level Column**: Number of arrows indicates position (1-5), color shows age
- **Price**: The actual price level
- **Points**: Distance from current price (+ for highs, - for lows)
- **Age**: Number of full sessions since creation
- **%**: Percentage distance from current price
- **⚠️**: Proximity alert - price is within threshold distance
- **Status Row**: Shows timeframe, direction (🟢 bullish/🔴 bearish), tunnel width (Δ), and Strat pattern
### 📈 Visual Elements on Chart
**1. Level Lines**
- Horizontal lines showing each unmitigated level
- **Color-coded by age**: Bright colors = new, darker = older, deep purple/teal = ancient
- **Line style**: Customizable (solid, dashed, dotted)
- Automatically turn **yellow** when price gets close (proximity alert)
**2. Price Labels**
- Show the exact price and age: "21,450.25 (8d)"
- Fixed at small size for clean readability
- Positioned with configurable offset from current bar
**3. Bands (Optional)**
- Shaded zones between pairs of unmitigated levels
- Default: Between 1st and 2nd levels (the "tunnel")
- Can switch to 1st-3rd, 2nd-3rd, or disable entirely
- **Upper band** (pink/maroon) - Between unmitigated highs
- **Lower band** (blue/teal) - Between unmitigated lows
- These represent the "no man's land" or consolidation zones
---
## The Cave Diving Framework
This indicator is designed around the **Cave Diving Trading Framework** - a psychological and technical approach that maps cave diving safety protocols to futures trading risk management.
### 🤿 The Core Metaphor
**Cave diving has clear danger zones based on depth and overhead environment. Your trading should too.**
#### Shallow Water (New Levels, 0-1 Sessions)
- **Light**: Bright colors (bright red highs, bright green lows)
- **Psychology**: Fresh territory, recently tested
- **Trading**: Be aware but not overly concerned
- **Cave Diving Parallel**: You can see the surface, easy exit
#### Penetration Depth (Medium Levels, 2-3 Sessions)
- **Light**: Medium intensity colors
- **Psychology**: Building significance, market memory forming
- **Trading**: Start respecting these levels for entries/exits
- **Cave Diving Parallel**: Deeper in, need to track your line back
#### Deep Dive Zone (Old Levels, 4-6 Sessions)
- **Light**: Dark colors (deep maroon, dark blue)
- **Psychology**: Highly tested support/resistance
- **Trading**: Major decision points, plan accordingly
- **Cave Diving Parallel**: Significant overhead, careful navigation required
#### Overhead Environment (Ancient Levels, 7+ Sessions)
- **Light**: Very dark, purple/deep teal
- **Psychology**: Extreme caution required, major liquidity zones
- **Trading**: These are your "turn back" signals - don't fight ancient levels
- **Cave Diving Parallel**: Maximum danger, no room for error
### 🎯 The Proximity Alert System
Just like a cave diver's depth gauge that warns at critical thresholds, the proximity alerts (⚠️) tell you when you're entering a danger zone. When price gets within your configured threshold (default 5 points), the indicator:
- Highlights the level in **yellow** on the chart
- Shows **⚠️** in the table
- Signals: "You're entering a high-significance zone - adjust your position accordingly"
This prevents the trading equivalent of going deeper into a cave without checking your air supply.
---
## How to Use It for Trading
### 🎯 Entry Strategies
**1. The "Bounce Setup" (Mean Reversion)**
- Wait for price to approach an old or ancient unmitigated level
- Look for confluence: multiple levels nearby, bands narrowing
- Enter when price shows rejection (reversal candle patterns)
- **Example**: Price drops to a 6-session-old low, shows bullish engulfing → Long entry
**2. The "Break and Retest" (Trend Following)**
- Wait for price to break through an unmitigated level (mitigates it)
- Enter on the retest of the newly broken level
- **Example**: Price breaks above 4-session-old high → Wait for pullback to that level → Long entry
**3. The "Tunnel Trade" (Range Trading)**
- When bands are active, trade the range between 1st-2nd levels
- Short near upper band resistance, long near lower band support
- Exit at opposite side or when bands break
### 🚨 Risk Management Rules
**The Ancient Level Rule**
> Never fight ancient levels (7+ sessions). If you're long and approaching an ancient high, take profits. If you're short and approaching an ancient low, take profits.
These levels have survived a full trading week without being touched - there's likely significant liquidity and institutional interest there.
**The Proximity Exit Rule**
> When you see ⚠️ proximity alerts on multiple levels above/below your position, tighten stops or scale out.
This is your "overhead environment" warning. You're in dangerous territory.
**The New Level Filter**
> Be cautious taking positions based solely on new levels (0-1 sessions). Wait for them to age or combine with other confluence.
Fresh levels haven't been tested by time. They're like unconfirmed support/resistance.
### 📊 Reading Market Structure
**Bullish Structure (🟢 in status row)**
- Unmitigated lows are aging and holding
- Price respecting the lower band
- Old lows below acting as strong support
- **Bias**: Look for long entries at lower levels
**Bearish Structure (🔴 in status row)**
- Unmitigated highs are aging and holding
- Price respecting the upper band
- Old highs above acting as strong resistance
- **Bias**: Look for short entries at higher levels
**The Tunnel Compression**
- When the Δ (delta) in the status row is small, levels are tight
- This often precedes a breakout
- **Trading**: Wait for breakout direction, then trade the break
### 🔄 Strat Integration
The indicator shows Strat patterns in the status row:
- **1** - Inside bar (consolidation)
- **2U** - Broke high only (bullish)
- **2D** - Broke low only (bearish)
- **3** - Broke both (wide range, volatility)
Use these with the unmitigated levels:
- **2U near old high** → Potential resistance, watch for rejection
- **2D near old low** → Potential support, watch for bounce
- **3 pattern** → High volatility, respect wider stops
---
## Settings & Customization
### 📅 Session & Timeframe Settings
**HL Interval** (Default: 15 minutes)
- The timeframe for high/low calculation
- **Lower (1m, 5m)**: More levels, more noise, good for scalping
- **Higher (30m, 1H, 4H)**: Fewer levels, stronger significance, good for swing trading
- **Recommendation for NQ/ES**: 15m or 30m for day trading, 1H for swing trading
**Session Age Threshold** (Default: 2)
- How many sessions before a level is considered "old"
- Lower = more levels classified as old
- Higher = stricter definition of significance
### 📊 Level Display Options
**Show Level Lines**
- Toggle: Display horizontal lines for each level
- **Turn off** if you prefer a cleaner chart and only want the table
**Show Level Labels**
- Toggle: Display price labels on the chart
- **Turn off** for minimal visual clutter
**Label Offset**
- Distance (in bars) from current price bar to place labels
- Increase if labels overlap with price action
**Level Line Width & Style**
- Customize visual appearance
- **Thin solid**: Minimal distraction
- **Thick dashed**: High visibility
### 🎨 Age-Based Color Coding
Customize colors for each age category (high and low separately):
- **New (0-1 sessions)**: Default bright red/green
- **Medium (2-3 sessions)**: Default medium intensity
- **Old (4+ sessions)**: Default dark red/blue
- **Ancient (7+ sessions)**: Default deep purple/teal
**Color Strategy Tips:**
- Keep ancient levels in highly contrasting colors
- Use opacity (transparency) if you want subtler lines
- Match your chart's color scheme for aesthetic coherence
### 🎯 Band Settings
**Band Mode**
- **1st-2nd** (Default): The primary "tunnel" between most recent levels
- **1st-3rd**: Wider band, more room for price action
- **2nd-3rd**: Band between less immediate levels
- **Disabled**: No bands, lines only
**Band Colors & Borders**
- Customize fill color and border separately
- **Tip**: Keep bands very transparent (90-95% transparency) to avoid obscuring price action
### ⚠️ Proximity Alert Settings
**Enable Proximity Alerts**
- Toggle: Turn on/off the warning system
- When enabled, levels within threshold distance show ⚠️ and turn yellow
**Alert Threshold** (Default: 5.0 points)
- Distance in points to trigger the alert
- **For NQ**: 5-10 points is reasonable
- **For ES**: 2-5 points is reasonable
- **For MES/MNQ**: Scale down proportionally
**Alert Highlight Color**
- The color lines/labels turn when proximity is triggered
- Default: Yellow (high visibility)
### 📋 Table Settings
**Show Table**
- Toggle: Display the dashboard table
**Table Location**
- Top Left, Top Right, Bottom Left, Bottom Right
- Choose based on your chart layout and other indicators
**Text Size**
- Tiny, Small, Normal, Large
- **Recommendation**: Normal for 1080p monitors, Small for 4K
**Show % Distance**
- Toggle: Add percentage distance column to table
- Useful for comparing relative distances across different price ranges
**Show Statistics Row**
- Toggle: Show total count of unmitigated highs/lows
- Format: "📊 15↑ / 12↓" (15 unmitigated highs, 12 unmitigated lows)
- Useful for gauging overall market structure
### ⚡ Performance Settings
**Enable Level Cleanup**
- Automatically remove very old levels to maintain performance
- **Keep on** unless you want unlimited history
**Max Lookback Levels** (Default: 10,000)
- Maximum number of levels to track
- 10,000 ≈ 6+ months of 15-minute bars
- **Increase** if you want more history
- **Decrease** if experiencing performance issues
**Max Boxes Per Band** (Default: 245)
- TradingView limit is 500 total boxes
- With 2 bands, 245 each = 490 total (safe maximum)
---
## Best Practices
### 🎯 Position Management
**1. Scaling In Near Old Levels**
```
Price approaching 5-session-old low:
- First position: 30% size at proximity alert (⚠️)
- Second position: 40% size at exact level
- Third position: 30% size if it shows strong rejection
```
**2. Scaling Out Near Ancient Levels**
```
Holding long position, approaching 8-session-old high:
- Exit 50% at proximity alert (⚠️)
- Exit 30% at exact level
- Trail stop on remaining 20%
```
### 🧠 Trading Psychology Integration
Drawing from principles in *The Mountain Is You*, this indicator helps you:
**1. Recognize Self-Sabotage Patterns**
- **The Premature Entry**: Entering before price reaches your planned level
- **Solution**: Set alerts at unmitigated levels, wait for proximity warnings
- **The Profit-Taking Problem**: Exiting too early from fear
- **Solution**: Identify the next unmitigated level and commit to holding until proximity alert
- **The Loss Holding**: Refusing to exit losing trades
- **Solution**: When price breaks through and mitigates your entry level, it's telling you the structure changed
**2. Building Better Habits**
The color-coded age system trains your brain to:
- Respect levels that have proven themselves over time
- Distinguish between noise (new levels) and structure (old levels)
- Make decisions based on objective data, not fear or greed
**3. Emotional Regulation**
The proximity alerts serve as:
- **Circuit breakers** - Forcing you to re-evaluate before dangerous zones
- **Permission to act** - Giving you objective signals to exit without second-guessing
- **Validation** - Confirming when you're in alignment with market structure
### 📝 Pre-Market Routine
**Daily Setup Checklist:**
1. ✅ Identify the 3 nearest unmitigated highs above current price
2. ✅ Identify the 3 nearest unmitigated lows below current price
3. ✅ Note which are ancient (7+) - these are your "no-go" zones
4. ✅ Check the tunnel width (Δ in status row) - tight or wide?
5. ✅ Set alerts at the 1st high and 1st low for proximity warnings
6. ✅ Plan: "If we go up, I exit at ___. If we go down, I enter at ___."
### 🔄 Timeframe Confluence
**Multi-Timeframe Strategy:**
Run the indicator on **three instances**:
- **15-minute** (short-term structure)
- **1-hour** (intermediate structure)
- **4-hour** (major structure)
**Strong Setup**: When all three timeframes show unmitigated levels converging at the same price zone.
**Example:**
- 15m: Old low at 21,400
- 1H: Ancient low at 21,398
- 4H: Ancient low at 21,395
- **Result**: 21,395-21,400 is a monster support zone
### ⚠️ What This Indicator Doesn't Do
**Not a Crystal Ball**
- It doesn't predict where price will go
- It shows you where price *hasn't been* and how long it's been avoided
- The trading decisions are still yours
**Not an Entry Signal Generator**
- It provides context and structure
- You need to combine it with your entry methodology (price action, indicators, order flow, etc.)
**Not Foolproof**
- Ancient levels get broken
- Proximity alerts can trigger early in strong trends
- The market doesn't "owe" you a reversal at any level
---
## Common Scenarios
### Scenario 1: "Level Cluster Ahead"
**Situation**: You're long at 21,400. The table shows:
- 1st High: 21,425 (2 sessions old)
- 2nd High: 21,428 (3 sessions old)
- 3rd High: 21,435 (6 sessions old)
**Interpretation**: There's a resistance cluster just 25-35 points away. The 6-session-old level is particularly significant.
**Action**:
- Set first profit target at 21,420 (before the cluster)
- Set second target at 21,426 (between 1st and 2nd)
- Trail remaining position, but be ready to exit on rejection at 21,435
**Cave Diving Analogy**: You're approaching an overhead section with limited clearance. Lighten your load (reduce position) before entering.
---
### Scenario 2: "Ancient Level Approaches"
**Situation**: The market is grinding higher. You see ⚠️ appear next to a 9-session-old high at 21,500.
**Interpretation**: This level has survived over a week without being touched. Massive potential liquidity zone.
**Action**:
- If long, this is your absolute exit zone. Take profits before or at level.
- If looking to short, wait for clear rejection (price taps and reverses)
- Don't try to buy the breakout until it clearly breaks and retests
**Cave Diving Analogy**: Your dive computer is beeping - you've reached your planned turn-back depth. No matter how interesting it looks ahead, honor your plan.
---
### Scenario 3: "Mitigated Levels Create New Structure"
**Situation**: Price breaks and mitigates the 1st High. The previous 2nd High becomes the new 1st High.
**Interpretation**: The structure just shifted. What was the 2nd level is now most relevant.
**Action**:
- Watch how price reacts to the newly-mitigated level
- If it holds below (acts as resistance), bearish
- If it reclaims and holds above (acts as support), bullish
- The NEW 1st High is your next target/resistance
**Cave Diving Analogy**: You've passed through a restriction - the cave layout ahead is different now. Update your mental map.
---
### Scenario 4: "Tight Tunnel, Upcoming Breakout"
**Situation**: The Δ in the status row shows 3.25 points (very tight). Bands are converging.
**Interpretation**: Price is consolidating between very close unmitigated levels. Breakout likely.
**Action**:
- Don't try to predict direction
- Set alerts above 1st High and below 1st Low
- When break occurs, trade the retest
- Expect volatility - use wider stops
**Cave Diving Analogy**: You're in a narrow passage. Movement will be sudden and directional once it starts.
---
### Scenario 5: "Imbalanced Structure"
**Situation**: The statistics row shows "📊 22↑ / 7↓"
**Interpretation**: There are many more unmitigated highs than lows. This suggests:
- Price has been declining (hitting lows, leaving highs behind)
- Potential bullish reversal zone (lots of overhead supply mitigated)
- Or continued bearish structure (resistance everywhere above)
**Action**:
- Look at the age of those 22 highs
- If mostly new (0-2 sessions): Just a recent downmove, not significant yet
- If many old/ancient: Strong overhead resistance, be cautious on longs
- Compare to price action: Is price respecting the remaining lows?
**Cave Diving Analogy**: You've swam deeper than your starting point - most of your markers are above you now. Are you planning the ascent or going deeper?
---
## Final Thoughts: The Philosophy
This indicator is built on a simple but powerful principle: **The market has memory, and that memory has weight.**
Every unmitigated level represents:
- Liquidity left behind
- Orders waiting to be filled
- Institutional interest potentially parked
- Psychological significance for participants
The longer a level remains unmitigated, the more "charged" it becomes. When price finally revisits it, something significant usually happens - either a strong reversal or a definitive break.
Your job as a trader isn't to predict which outcome will occur. Your job is to:
1. **Recognize** when you're approaching these charged zones
2. **Respect** them by adjusting position size and risk
3. **React** appropriately based on how price behaves at them
4. **Remember** that ancient levels (like ancient wisdom) deserve extra reverence
The Cave Diving Framework embedded in this indicator serves as a constant reminder: Trading, like cave diving, requires rigorous respect for environmental hazards, meticulous planning, and the discipline to turn back when your limits are reached.
**Every proximity alert is the market asking you**: *"Do you really want to go deeper?"*
Sometimes the answer is yes - when your setup, confluence, and risk management all align.
Often, the answer should be no - and that's the trader avoiding the accident that would have happened to the gambler.
---
### 🎯 Quick Reference Card
**Color System:**
- 🟢 Bright colors = New (0-1 sessions) = Shallow water
- 🟡 Medium colors = Medium (2-3 sessions) = Penetration depth
- 🔴 Dark colors = Old (4-6 sessions) = Deep dive zone
- 🟣 Deep dark colors = Ancient (7+ sessions) = Overhead environment
**Symbols:**
- ↑ ↑↑ ↑↑↑ ↑↑↑↑ ↑↑↑↑↑ = High levels (1st through 5th)
- ↓ ↓↓ ↓↓↓ ↓↓↓↓ ↓↓↓↓↓ = Low levels (1st through 5th)
- ⚠️ = Proximity alert (danger zone)
- 🟢 = Bullish structure
- 🔴 = Bearish structure
- Δ = Tunnel width (distance between 1st high and 1st low)
**Critical Rules:**
1. Never fight ancient levels (7+ sessions)
2. Respect proximity alerts (⚠️)
3. Scale out near old/ancient resistance
4. Wait for confluence when entering
5. Let mitigated levels prove their new role
---
**Remember**: The indicator gives you structure. The trading edge comes from your discipline in respecting that structure.
Trade safe, trade smart, and always know your exit before your entry. 🎯
---
*"You don't become your best self by denying your patterns. You become your best self by recognizing them, understanding them, and choosing differently." - Adapted from The Mountain Is You*
In trading: You don't become profitable by ignoring market structure. You become profitable by recognizing it, understanding it, and choosing your entries accordingly.
Futures Risk-Based Position CalculatorFutures Risk‑Based Position Calculator — Description
This TradingView indicator automatically calculates and displays Entry, Stop Loss (SL), and Take Profit (TP) levels for futures trades based on a fixed dollar‑risk amount.
What it does
Uses your account balance, dollar risk, number of contracts, point value, and tick size to compute how far the stop should be from the entry.
Determines the take‑profit level using a chosen risk‑to‑reward ratio.
Draws three lines on the chart:
Entry line
Stop loss line
Take profit line
Places labels next to the SL and TP lines showing prices and point distances.
Key features
Supports long or short calculation mode.
Configurable line styling:
Width, style (solid/dashed/dotted), color, opacity.
Separate styling for entry, SL, and TP.
Configurable label behavior:
Optional background.
Text color choices.
Adjustable vertical offset to avoid overlapping the lines.
Lines extend left/right by user‑defined bar amounts.
Values are always rounded to the market's tick size.
How levels are calculated
Entry = current close rounded to tick size.
Stop distance (points) = dollarRisk / (contracts × pointValue).
SL = entry − distance (long) or entry + distance (short).
TP = entry + distance × RR (long) or entry − distance × RR (short).
Visual behavior
Lines and labels update only on the last bar to avoid clutter.
Labels show:
SL: price, point distance, and contract count.
TP: price and point distance.
Liquidity Sentiment Profile | LUPENIndicator Guide: Liquidity Sentiment Profile (LSP).
What is the LSP?
The Liquidity Sentiment Profile (LSP) is a "Next-Generation" oscillator designed to look beyond simple price action. While standard indicators (like RSI or MACD) primarily focus on where a candle closes, the LSP analyzes the micro-structure of the entire candle—specifically the relationship between the candle's Body, its Wicks (Shadows), and the Volume.
The Core Philosophy:
Wicks tell the truth: A long lower wick indicates that sellers pushed the price down, but buyers aggressively absorbed that liquidity and pushed it back up.
That is hidden bullish strength.
Volume validates intent: A price move with low volume is noise. A price move (or wick rejection) with high volume is a commitment by institutional players.
The LSP calculates a "Sentiment Score" between -100 and +100 based on these factors.
How to Read the Visuals
The Colors (Intensity)
color: Light Green - Bullish Acceleration. Buyers are in control, and momentum is increasing. This is the ideal time to be in a Long trade.
color: Dark Green - Bullish Deceleration. Buyers are still in control (price is likely rising), but the momentum is fading. This is a warning sign to tighten stop-losses or take profits.
color: Light Red - Bearish Acceleration. Sellers are dominating, and panic is increasing. This is the ideal time to be Short.
color: Dark Red - Bearish Deceleration. Sellers are still in control, but the downward pressure is exhausted. Be careful with new short positions.
The Lines & Fills
The Main Line: The actual LSP sentiment value.
The Yellow Signal Line: A smoothed average of the sentiment.
The Core Fill: The colored area between the Main Line and the Signal Line. When this area "glows", the trend is strong. When it dims (Dark), the trend is weak. Bearish Deceleration. Sellers are still in control, but the downward pressure is exhausted. Be careful with new short positions.
The Lines & Fills
The Main Line: The actual LSP sentiment value.
The Yellow Signal Line: A smoothed average of the sentiment.
The Core Fill: The colored area between the Main Line and the Signal Line. When this area "glows" (Neon), the trend is strong. When it dims (Dark), the trend is weak.
How to Use It (Trading Strategies)
Strategy A: The "Power Cross" (Trend Entry)
Use this for entering trends when the market wakes up.
Long Entry: Wait for the LSP line to cross ABOVE the Yellow Signal Line.
Confirmation: The fill color must turn Neon Green.
Short Entry: Wait for the LSP line to cross BELOW the Yellow Signal Line.
Confirmation: The fill color must turn Neon Red.
Strategy B: The "Absorption" Play (Reversals)
This is where the LSP shines. It detects when liquidity is being absorbed before price turns.
Bullish Absorption: The Price makes a Lower Low, but the LSP makes a Higher Low. This happens because the LSP detects the Volume on the Lower Wicks (buyers absorbing selling pressure). This is a high-probability reversal signal.
Bearish Absorption: The Price makes a Higher High, but the LSP makes a Lower High. The volume on the Upper Wicks suggests sellers are absorbing the buy orders.
Strategy C: The "Dimming" Exit (Risk Management)
Don't wait for the price to crash to exit a trade.
If you are in a Long trade (Neon Green) and the color instantly shifts to Dark Green, it means the "fuel" is running out. Consider taking partial profits or moving your Stop Loss to break even.
Standard oscillators (like RSI) often give false signals during strong trends (showing "Overbought" while price keeps going up). The LSP avoids this because it weights Volume and Wicks. If price goes up and volume increases, the LSP stays Neon Green, telling you the move is genuine, not just overextended.
[SM-021] Gaussian Trend System [Optimized]This script is a comprehensive trend-following strategy centered around a Gaussian Channel. It is designed to capture significant market movements while filtering out noise during consolidation phases. This version (v2) introduces code optimizations using Pine Script v6 Arrays and a new Intraday Time Control feature.
1. Core Methodology & Math
The foundation of this strategy is the Gaussian Filter, originally conceptualized by @DonovanWall.
Gaussian Poles: Unlike standard moving averages (SMA/EMA), this filter uses "poles" (referencing signal processing logic) to reduce lag while maintaining smoothness.
Array Optimization: In this specific iteration, the f_pole function has been refactored to utilize Pine Script Arrays. This improves calculation efficiency and rendering speed compared to recursive variable calls, especially when calculating deep historical data.
Channel Logic: The strategy calculates a "Filtered True Range" to create High and Low bands around the main Gaussian line.
Long Entry: Price closes above the High Band.
Short Entry: Price closes below the Low Band.
2. Signal Filtering (Confluence)
To reduce false signals common in trend-following systems, the strategy employs a "confluence" approach using three additional layers:
Baseline Filter: A 200-period (customizable) EMA or SMA acts as a regime filter. Longs are only taken above the baseline; Shorts only below.
ADX Filter (Volatility): The Average Directional Index (ADX) is used to measure trend strength. If the ADX is below a user-defined threshold (default: 20), the market is considered "choppy," and new entries are blocked.
Momentum Check: A Stochastic RSI check ensures that momentum aligns with the breakout direction.
3. NEW: Intraday Session Filter
Per user requests, a time-based filter has been added to restrict trading activity to specific market sessions (e.g., the New York Open).
How it works: Users can toggle a checkbox to enable/disable the filter.
Configuration: You can define a specific time range (Default: 09:30 - 16:00) and a specific Timezone (Default: New York).
Logic: The strategy longCondition and shortCondition now check if the current bar's timestamp falls within this window. If outside the window, no new entries are generated, though existing trades are managed normally.
4. Risk Management
The strategy relies on volatility-based exits rather than fixed percentage stops:
ATR Stop Loss: A multiple of the Average True Range (ATR) is calculated at the moment of entry to set a dynamic Stop Loss.
ATR Take Profit: An optional Reward-to-Risk (RR) ratio can be set to place a Take Profit target relative to the Stop Loss distance.
Band Exit: If the trend reverses and price crosses the opposite band, the trade is closed immediately to prevent large drawdowns.
Credits & Attribution
Original Gaussian Logic: Developed by @DonovanWalll. This script utilizes his mathematical formula for the pole filters.
Strategy Wrapper & Array Refactor: Developed by @sebamarghella.
Community Request: The Intraday Session Filter was added to assist traders focusing on specific liquidity windows.
Disclaimer: This strategy is for educational purposes. Past performance is not indicative of future results. Please use the settings menu to adjust the Session Time and Risk parameters to fit your specific asset class.
Momentum by Trading BiZonesSqueeze Momentum Indicator with EMA
Overview
The Squeeze Momentum Indicator with EMA is a powerful technical analysis tool that combines the original Squeeze Momentum concept with an Exponential Moving Average (EMA) overlay. This enhanced version helps traders identify market momentum, volatility contractions (squeezes), and potential trend reversals with greater precision.
Core Concept
The indicator operates on the principle of volatility contraction and expansion:
Squeeze Phase: When Bollinger Bands move inside the Keltner Channel, indicating low volatility and potential energy buildup
Expansion Phase: When momentum breaks out of the squeeze, signaling potential directional moves
Key Components
1. Squeeze Momentum Calculation
Formula: Momentum = Linear Regression(Close - Average Price)
Where Average Price = (Highest High + Lowest Low + SMA(Close)) / 3
Visualization: Histogram bars showing positive (green) and negative (red) momentum
Zero Line: Represents equilibrium point between buyers and sellers
2. EMA Overlay
Purpose: Smooths momentum values to identify underlying trends
Customization:
Adjustable period (default: 20)
Toggle on/off display
Customizable color and line thickness
Cross Signals: Buy/sell signals when momentum crosses above/below EMA
3. Volatility Bands
Bollinger Bands (20-period, 2 standard deviations)
Keltner Channels (20-period, 1.5 ATR multiplier)
Squeeze Detection: Visual background shading when BB are inside KC
Trading Signals
Buy Signals (Green Upward Triangle)
Momentum histogram crosses ABOVE EMA line
Occurs during or after squeeze release
Confirmed by expanding histogram bars
Sell Signals (Red Downward Triangle)
Momentum histogram crosses BELOW EMA line
Often precedes market downturns
Watch for increasing negative momentum
Squeeze Warnings (Gray Background)
Market in low volatility state
Prepare for potential breakout
Direction indicated by momentum bias
Indicator Settings
Main Parameters
Length: Period for calculations (default: 20)
Show EMA: Toggle EMA visibility
EMA Period: Smoothing period for EMA
Visual Settings
Histogram color-coding based on momentum direction
EMA line color and thickness
Signal marker size and visibility
Squeeze zone background display
Practical Applications
Trend Identification
Uptrend: Consistently positive momentum with EMA support
Downtrend: Consistently negative momentum with EMA resistance
Range-bound: Oscillating around zero line
Entry/Exit Points
Conservative Entry: Wait for squeeze release + EMA crossover
Aggressive Entry: Anticipate breakout during squeeze
Exit: Opposite crossover or momentum divergence
Risk Management
Use squeeze zones as warning periods
EMA crossovers as confirmation signals
Combine with support/resistance levels
Advanced Interpretation
Momentum Strength
Strong Bullish: Tall green bars above EMA
Weak Bullish: Short green bars near EMA
Strong Bearish: Tall red bars below EMA
Weak Bearish: Short red bars near EMA
Divergence Detection
Price makes higher high, momentum makes lower high → Bearish divergence
Price makes lower low, momentum makes higher low → Bullish divergence
Squeeze Characteristics
Long squeezes: More potential energy
Frequent squeezes: Choppy market conditions
No squeezes: High volatility, trending markets
Recommended Timeframes
Scalping: 1-15 minute charts
Day Trading: 15-minute to 4-hour charts
Swing Trading: 4-hour to daily charts
Position Trading: Daily to weekly charts
Best Practices
Confirmation
Use with volume indicators
Check higher timeframe direction
Wait for candle close confirmation
Filtering Signals
Ignore signals during extreme volatility
Require minimum bar size for crossovers
Consider market context (news, sessions)
Combination Suggestions
With RSI: Confirm overbought/oversold conditions
With Volume Profile: Identify high-volume nodes
With Support/Resistance: Key level reactions
With Trend Lines: Breakout confirmations
Limitations
Lagging indicator (based on past data)
Works best in trending markets
May give false signals in ranging markets
Requires proper risk management
Conclusion
The Squeeze Momentum Indicator with EMA provides a comprehensive view of market dynamics by combining volatility analysis, momentum measurement, and trend smoothing. Its visual clarity and customizable parameters make it suitable for traders of all experience levels seeking to identify high-probability trading opportunities during volatility contractions and expansions.
Adaptive Genesis Engine [AGE]ADAPTIVE GENESIS ENGINE (AGE)
Pure Signal Evolution Through Genetic Algorithms
Where Darwin Meets Technical Analysis
🧬 WHAT YOU'RE GETTING - THE PURE INDICATOR
This is a technical analysis indicator - it generates signals, visualizes probability, and shows you the evolutionary process in real-time. This is NOT a strategy with automatic execution - it's a sophisticated signal generation system that you control .
What This Indicator Does:
Generates Long/Short entry signals with probability scores (35-88% range)
Evolves a population of up to 12 competing strategies using genetic algorithms
Validates strategies through walk-forward optimization (train/test cycles)
Visualizes signal quality through premium gradient clouds and confidence halos
Displays comprehensive metrics via enhanced dashboard
Provides alerts for entries and exits
Works on any timeframe, any instrument, any broker
What This Indicator Does NOT Do:
Execute trades automatically
Manage positions or calculate position sizes
Place orders on your behalf
Make trading decisions for you
This is pure signal intelligence. AGE tells you when and how confident it is. You decide whether and how much to trade.
🔬 THE SCIENCE: GENETIC ALGORITHMS MEET TECHNICAL ANALYSIS
What Makes This Different - The Evolutionary Foundation
Most indicators are static - they use the same parameters forever, regardless of market conditions. AGE is alive . It maintains a population of competing strategies that evolve, adapt, and improve through natural selection principles:
Birth: New strategies spawn through crossover breeding (combining DNA from fit parents) plus random mutation for exploration
Life: Each strategy trades virtually via shadow portfolios, accumulating wins/losses, tracking drawdown, and building performance history
Selection: Strategies are ranked by comprehensive fitness scoring (win rate, expectancy, drawdown control, signal efficiency)
Death: Weak strategies are culled periodically, with elite performers (top 2 by default) protected from removal
Evolution: The gene pool continuously improves as successful traits propagate and unsuccessful ones die out
This is not curve-fitting. Each new strategy must prove itself on out-of-sample data through walk-forward validation before being trusted for live signals.
🧪 THE DNA: WHAT EVOLVES
Every strategy carries a 10-gene chromosome controlling how it interprets market data:
Signal Sensitivity Genes
Entropy Sensitivity (0.5-2.0): Weight given to market order/disorder calculations. Low values = conservative, require strong directional clarity. High values = aggressive, act on weaker order signals.
Momentum Sensitivity (0.5-2.0): Weight given to RSI/ROC/MACD composite. Controls responsiveness to momentum shifts vs. mean-reversion setups.
Structure Sensitivity (0.5-2.0): Weight given to support/resistance positioning. Determines how much price location within swing range matters.
Probability Adjustment Genes
Probability Boost (-0.10 to +0.10): Inherent bias toward aggressive (+) or conservative (-) entries. Acts as personality trait - some strategies naturally optimistic, others pessimistic.
Trend Strength Requirement (0.3-0.8): Minimum trend conviction needed before signaling. Higher values = only trades strong trends, lower values = acts in weak/sideways markets.
Volume Filter (0.5-1.5): Strictness of volume confirmation. Higher values = requires strong volume, lower values = volume less important.
Risk Management Genes
ATR Multiplier (1.5-4.0): Base volatility scaling for all price levels. Controls whether strategy uses tight or wide stops/targets relative to ATR.
Stop Multiplier (1.0-2.5): Stop loss tightness. Lower values = aggressive profit protection, higher values = more breathing room.
Target Multiplier (1.5-4.0): Profit target ambition. Lower values = quick scalping exits, higher values = swing trading holds.
Adaptation Gene
Regime Adaptation (0.0-1.0): How much strategy adjusts behavior based on detected market regime (trending/volatile/choppy). Higher values = more reactive to regime changes.
The Magic: AGE doesn't just try random combinations. Through tournament selection and fitness-weighted crossover, successful gene combinations spread through the population while unsuccessful ones fade away. Over 50-100 bars, you'll see the population converge toward genes that work for YOUR instrument and timeframe.
📊 THE SIGNAL ENGINE: THREE-LAYER SYNTHESIS
Before any strategy generates a signal, AGE calculates probability through multi-indicator confluence:
Layer 1 - Market Entropy (Information Theory)
Measures whether price movements exhibit directional order or random walk characteristics:
The Math:
Shannon Entropy = -Σ(p × log(p))
Market Order = 1 - (Entropy / 0.693)
What It Means:
High entropy = choppy, random market → low confidence signals
Low entropy = directional market → high confidence signals
Direction determined by up-move vs down-move dominance over lookback period (default: 20 bars)
Signal Output: -1.0 to +1.0 (bearish order to bullish order)
Layer 2 - Momentum Synthesis
Combines three momentum indicators into single composite score:
Components:
RSI (40% weight): Normalized to -1/+1 scale using (RSI-50)/50
Rate of Change (30% weight): Percentage change over lookback (default: 14 bars), clamped to ±1
MACD Histogram (30% weight): Fast(12) - Slow(26), normalized by ATR
Why This Matters: RSI catches mean-reversion opportunities, ROC catches raw momentum, MACD catches momentum divergence. Weighting favors RSI for reliability while keeping other perspectives.
Signal Output: -1.0 to +1.0 (strong bearish to strong bullish)
Layer 3 - Structure Analysis
Evaluates price position within swing range (default: 50-bar lookback):
Position Classification:
Bottom 20% of range = Support Zone → bullish bounce potential
Top 20% of range = Resistance Zone → bearish rejection potential
Middle 60% = Neutral Zone → breakout/breakdown monitoring
Signal Logic:
At support + bullish candle = +0.7 (strong buy setup)
At resistance + bearish candle = -0.7 (strong sell setup)
Breaking above range highs = +0.5 (breakout confirmation)
Breaking below range lows = -0.5 (breakdown confirmation)
Consolidation within range = ±0.3 (weak directional bias)
Signal Output: -1.0 to +1.0 (bearish structure to bullish structure)
Confluence Voting System
Each layer casts a vote (Long/Short/Neutral). The system requires minimum 2-of-3 agreement (configurable 1-3) before generating a signal:
Examples:
Entropy: Bullish, Momentum: Bullish, Structure: Neutral → Signal generated (2 long votes)
Entropy: Bearish, Momentum: Neutral, Structure: Neutral → No signal (only 1 short vote)
All three bullish → Signal generated with +5% probability bonus
This is the key to quality. Single indicators give too many false signals. Triple confirmation dramatically improves accuracy.
📈 PROBABILITY CALCULATION: HOW CONFIDENCE IS MEASURED
Base Probability:
Raw_Prob = 50% + (Average_Signal_Strength × 25%)
Then AGE applies strategic adjustments:
Trend Alignment:
Signal with trend: +4%
Signal against strong trend: -8%
Weak/no trend: no adjustment
Regime Adaptation:
Trending market (efficiency >50%, moderate vol): +3%
Volatile market (vol ratio >1.5x): -5%
Choppy market (low efficiency): -2%
Volume Confirmation:
Volume > 70% of 20-bar SMA: no change
Volume below threshold: -3%
Volatility State (DVS Ratio):
High vol (>1.8x baseline): -4% (reduce confidence in chaos)
Low vol (<0.7x baseline): -2% (markets can whipsaw in compression)
Moderate elevated vol (1.0-1.3x): +2% (trending conditions emerging)
Confluence Bonus:
All 3 indicators agree: +5%
2 of 3 agree: +2%
Strategy Gene Adjustment:
Probability Boost gene: -10% to +10%
Regime Adaptation gene: scales regime adjustments by 0-100%
Final Probability: Clamped between 35% (minimum) and 88% (maximum)
Why These Ranges?
Below 35% = too uncertain, better not to signal
Above 88% = unrealistic, creates overconfidence
Sweet spot: 65-80% for quality entries
🔄 THE SHADOW PORTFOLIO SYSTEM: HOW STRATEGIES COMPETE
Each active strategy maintains a virtual trading account that executes in parallel with real-time data:
Shadow Trading Mechanics
Entry Logic:
Calculate signal direction, probability, and confluence using strategy's unique DNA
Check if signal meets quality gate:
Probability ≥ configured minimum threshold (default: 65%)
Confluence ≥ configured minimum (default: 2 of 3)
Direction is not zero (must be long or short, not neutral)
Verify signal persistence:
Base requirement: 2 bars (configurable 1-5)
Adapts based on probability: high-prob signals (75%+) enter 1 bar faster, low-prob signals need 1 bar more
Adjusts for regime: trending markets reduce persistence by 1, volatile markets add 1
Apply additional filters:
Trend strength must exceed strategy's requirement gene
Regime filter: if volatile market detected, probability must be 72%+ to override
Volume confirmation required (volume > 70% of average)
If all conditions met for required persistence bars, enter shadow position at current close price
Position Management:
Entry Price: Recorded at close of entry bar
Stop Loss: ATR-based distance = ATR × ATR_Mult (gene) × Stop_Mult (gene) × DVS_Ratio
Take Profit: ATR-based distance = ATR × ATR_Mult (gene) × Target_Mult (gene) × DVS_Ratio
Position: +1 (long) or -1 (short), only one at a time per strategy
Exit Logic:
Check if price hit stop (on low) or target (on high) on current bar
Record trade outcome in R-multiples (profit/loss normalized by ATR)
Update performance metrics:
Total trades counter incremented
Wins counter (if profit > 0)
Cumulative P&L updated
Peak equity tracked (for drawdown calculation)
Maximum drawdown from peak recorded
Enter cooldown period (default: 8 bars, configurable 3-20) before next entry allowed
Reset signal age counter to zero
Walk-Forward Tracking:
During position lifecycle, trades are categorized:
Training Phase (first 250 bars): Trade counted toward training metrics
Testing Phase (next 75 bars): Trade counted toward testing metrics (out-of-sample)
Live Phase (after WFO period): Trade counted toward overall metrics
Why Shadow Portfolios?
No lookahead bias (uses only data available at the bar)
Realistic execution simulation (entry on close, stop/target checks on high/low)
Independent performance tracking for true fitness comparison
Allows safe experimentation without risking capital
Each strategy learns from its own experience
🏆 FITNESS SCORING: HOW STRATEGIES ARE RANKED
Fitness is not just win rate. AGE uses a comprehensive multi-factor scoring system:
Core Metrics (Minimum 3 trades required)
Win Rate (30% of fitness):
WinRate = Wins / TotalTrades
Normalized directly (0.0-1.0 scale)
Total P&L (30% of fitness):
Normalized_PnL = (PnL + 300) / 600
Clamped 0.0-1.0. Assumes P&L range of -300R to +300R for normalization scale.
Expectancy (25% of fitness):
Expectancy = Total_PnL / Total_Trades
Normalized_Expectancy = (Expectancy + 30) / 60
Clamped 0.0-1.0. Rewards consistency of profit per trade.
Drawdown Control (15% of fitness):
Normalized_DD = 1 - (Max_Drawdown / 15)
Clamped 0.0-1.0. Penalizes strategies that suffer large equity retracements from peak.
Sample Size Adjustment
Quality Factor:
<50 trades: 1.0 (full weight, small sample)
50-100 trades: 0.95 (slight penalty for medium sample)
100 trades: 0.85 (larger penalty for large sample)
Why penalize more trades? Prevents strategies from gaming the system by taking hundreds of tiny trades to inflate statistics. Favors quality over quantity.
Bonus Adjustments
Walk-Forward Validation Bonus:
if (WFO_Validated):
Fitness += (WFO_Efficiency - 0.5) × 0.1
Strategies proven on out-of-sample data receive up to +10% fitness boost based on test/train efficiency ratio.
Signal Efficiency Bonus (if diagnostics enabled):
if (Signals_Evaluated > 10):
Pass_Rate = Signals_Passed / Signals_Evaluated
Fitness += (Pass_Rate - 0.1) × 0.05
Rewards strategies that generate high-quality signals passing the quality gate, not just profitable trades.
Final Fitness: Clamped at 0.0 minimum (prevents negative fitness values)
Result: Elite strategies typically achieve 0.50-0.75 fitness. Anything above 0.60 is excellent. Below 0.30 is prime candidate for culling.
🔬 WALK-FORWARD OPTIMIZATION: ANTI-OVERFITTING PROTECTION
This is what separates AGE from curve-fitted garbage indicators.
The Three-Phase Process
Every new strategy undergoes a rigorous validation lifecycle:
Phase 1 - Training Window (First 250 bars, configurable 100-500):
Strategy trades normally via shadow portfolio
All trades count toward training performance metrics
System learns which gene combinations produce profitable patterns
Tracks independently: Training_Trades, Training_Wins, Training_PnL
Phase 2 - Testing Window (Next 75 bars, configurable 30-200):
Strategy continues trading without any parameter changes
Trades now count toward testing performance metrics (separate tracking)
This is out-of-sample data - strategy has never seen these bars during "optimization"
Tracks independently: Testing_Trades, Testing_Wins, Testing_PnL
Phase 3 - Validation Check:
Minimum_Trades = 5 (configurable 3-15)
IF (Train_Trades >= Minimum AND Test_Trades >= Minimum):
WR_Efficiency = Test_WinRate / Train_WinRate
Expectancy_Efficiency = Test_Expectancy / Train_Expectancy
WFO_Efficiency = (WR_Efficiency + Expectancy_Efficiency) / 2
IF (WFO_Efficiency >= 0.55): // configurable 0.3-0.9
Strategy.Validated = TRUE
Strategy receives fitness bonus
ELSE:
Strategy receives 30% fitness penalty
ELSE:
Validation deferred (insufficient trades in one or both periods)
What Validation Means
Validated Strategy (Green "✓ VAL" in dashboard):
Performed at least 55% as well on unseen data compared to training data
Gets fitness bonus: +(efficiency - 0.5) × 0.1
Receives priority during tournament selection for breeding
More likely to be chosen as active trading strategy
Unvalidated Strategy (Orange "○ TRAIN" in dashboard):
Failed to maintain performance on test data (likely curve-fitted to training period)
Receives 30% fitness penalty (0.7x multiplier)
Makes strategy prime candidate for culling
Can still trade but with lower selection probability
Insufficient Data (continues collecting):
Hasn't completed both training and testing periods yet
OR hasn't achieved minimum trade count in both periods
Validation check deferred until requirements met
Why 55% Efficiency Threshold?
If a strategy earned 10R during training but only 5.5R during testing, it still proved an edge exists beyond random luck. Requiring 100% efficiency would be unrealistic - market conditions change between periods. But requiring >50% ensures the strategy didn't completely degrade on fresh data.
The Protection: Strategies that work great on historical data but fail on new data are automatically identified and penalized. This prevents the population from being polluted by overfitted strategies that would fail in live trading.
🌊 DYNAMIC VOLATILITY SCALING (DVS): ADAPTIVE STOP/TARGET PLACEMENT
AGE doesn't use fixed stop distances. It adapts to current volatility conditions in real-time.
Four Volatility Measurement Methods
1. ATR Ratio (Simple Method):
Current_Vol = ATR(14) / Close
Baseline_Vol = SMA(Current_Vol, 100)
Ratio = Current_Vol / Baseline_Vol
Basic comparison of current ATR to 100-bar moving average baseline.
2. Parkinson (High-Low Range Based):
For each bar: HL = log(High / Low)
Parkinson_Vol = sqrt(Σ(HL²) / (4 × Period × log(2)))
More stable than close-to-close volatility. Captures intraday range expansion without overnight gap noise.
3. Garman-Klass (OHLC Based):
HL_Term = 0.5 × ²
CO_Term = (2×log(2) - 1) × ²
GK_Vol = sqrt(Σ(HL_Term - CO_Term) / Period)
Most sophisticated estimator. Incorporates all four price points (open, high, low, close) plus gap information.
4. Ensemble Method (Default - Median of All Three):
Ratio_1 = ATR_Current / ATR_Baseline
Ratio_2 = Parkinson_Current / Parkinson_Baseline
Ratio_3 = GK_Current / GK_Baseline
DVS_Ratio = Median(Ratio_1, Ratio_2, Ratio_3)
Why Ensemble?
Takes median to avoid outliers and false spikes
If ATR jumps but range-based methods stay calm, median prevents overreaction
If one method fails, other two compensate
Most robust approach across different market conditions
Sensitivity Scaling
Scaled_Ratio = (Raw_Ratio) ^ Sensitivity
Sensitivity 0.3: Cube root - heavily dampens volatility impact
Sensitivity 0.5: Square root - moderate dampening
Sensitivity 0.7 (Default): Balanced response to volatility changes
Sensitivity 1.0: Linear - full 1:1 volatility impact
Sensitivity 1.5: Exponential - amplified response to volatility spikes
Safety Clamps: Final DVS Ratio always clamped between 0.5x and 2.5x baseline to prevent extreme position sizing or stop placement errors.
How DVS Affects Shadow Trading
Every strategy's stop and target distances are multiplied by the current DVS ratio:
Stop Loss Distance:
Stop_Distance = ATR × ATR_Mult (gene) × Stop_Mult (gene) × DVS_Ratio
Take Profit Distance:
Target_Distance = ATR × ATR_Mult (gene) × Target_Mult (gene) × DVS_Ratio
Example Scenario:
ATR = 10 points
Strategy's ATR_Mult gene = 2.5
Strategy's Stop_Mult gene = 1.5
Strategy's Target_Mult gene = 2.5
DVS_Ratio = 1.4 (40% above baseline volatility - market heating up)
Stop = 10 × 2.5 × 1.5 × 1.4 = 52.5 points (vs. 37.5 in normal vol)
Target = 10 × 2.5 × 2.5 × 1.4 = 87.5 points (vs. 62.5 in normal vol)
Result:
During volatility spikes: Stops automatically widen to avoid noise-based exits, targets extend for bigger moves
During calm periods: Stops tighten for better risk/reward, targets compress for realistic profit-taking
Strategies adapt risk management to match current market behavior
🧬 THE EVOLUTIONARY CYCLE: SPAWN, COMPETE, CULL
Initialization (Bar 1)
AGE begins with 4 seed strategies (if evolution enabled):
Seed Strategy #0 (Balanced):
All sensitivities at 1.0 (neutral)
Zero probability boost
Moderate trend requirement (0.4)
Standard ATR/stop/target multiples (2.5/1.5/2.5)
Mid-level regime adaptation (0.5)
Seed Strategy #1 (Momentum-Focused):
Lower entropy sensitivity (0.7), higher momentum (1.5)
Slight probability boost (+0.03)
Higher trend requirement (0.5)
Tighter stops (1.3), wider targets (3.0)
Seed Strategy #2 (Entropy-Driven):
Higher entropy sensitivity (1.5), lower momentum (0.8)
Slight probability penalty (-0.02)
More trend tolerant (0.6)
Wider stops (1.8), standard targets (2.5)
Seed Strategy #3 (Structure-Based):
Balanced entropy/momentum (0.8/0.9), high structure (1.4)
Slight probability boost (+0.02)
Lower trend requirement (0.35)
Moderate risk parameters (1.6/2.8)
All seeds start with WFO validation bypassed if WFO is disabled, or must validate if enabled.
Spawning New Strategies
Timing (Adaptive):
Historical phase: Every 30 bars (configurable 10-100)
Live phase: Every 200 bars (configurable 100-500)
Automatically switches to live timing when barstate.isrealtime triggers
Conditions:
Current population < max population limit (default: 8, configurable 4-12)
At least 2 active strategies exist (need parents)
Available slot in population array
Selection Process:
Run tournament selection 3 times with different seeds
Each tournament: randomly sample active strategies, pick highest fitness
Best from 3 tournaments becomes Parent 1
Repeat independently for Parent 2
Ensures fit parents but maintains diversity
Crossover Breeding:
For each of 10 genes:
Parent1_Fitness = fitness
Parent2_Fitness = fitness
Weight1 = Parent1_Fitness / (Parent1_Fitness + Parent2_Fitness)
Gene1 = parent1's value
Gene2 = parent2's value
Child_Gene = Weight1 × Gene1 + (1 - Weight1) × Gene2
Fitness-weighted crossover ensures fitter parent contributes more genetic material.
Mutation:
For each gene in child:
IF (random < mutation_rate):
Gene_Range = GENE_MAX - GENE_MIN
Noise = (random - 0.5) × 2 × mutation_strength × Gene_Range
Mutated_Gene = Clamp(Child_Gene + Noise, GENE_MIN, GENE_MAX)
Historical mutation rate: 20% (aggressive exploration)
Live mutation rate: 8% (conservative stability)
Mutation strength: 12% of gene range (configurable 5-25%)
Initialization of New Strategy:
Unique ID assigned (total_spawned counter)
Parent ID recorded
Generation = max(parent generations) + 1
Birth bar recorded (for age tracking)
All performance metrics zeroed
Shadow portfolio reset
WFO validation flag set to false (must prove itself)
Result: New strategy with hybrid DNA enters population, begins trading in next bar.
Competition (Every Bar)
All active strategies:
Calculate their signal based on unique DNA
Check quality gate with their thresholds
Manage shadow positions (entries/exits)
Update performance metrics
Recalculate fitness score
Track WFO validation progress
Strategies compete indirectly through fitness ranking - no direct interaction.
Culling Weak Strategies
Timing (Adaptive):
Historical phase: Every 60 bars (configurable 20-200, should be 2x spawn interval)
Live phase: Every 400 bars (configurable 200-1000, should be 2x spawn interval)
Minimum Adaptation Score (MAS):
Initial MAS = 0.10
MAS decays: MAS × 0.995 every cull cycle
Minimum MAS = 0.03 (floor)
MAS represents the "survival threshold" - strategies below this fitness level are vulnerable.
Culling Conditions (ALL must be true):
Population > minimum population (default: 3, configurable 2-4)
At least one strategy has fitness < MAS
Strategy's age > culling interval (prevents premature culling of new strategies)
Strategy is not in top N elite (default: 2, configurable 1-3)
Culling Process:
Find worst strategy:
For each active strategy:
IF (age > cull_interval):
Fitness = base_fitness
IF (not WFO_validated AND WFO_enabled):
Fitness × 0.7 // 30% penalty for unvalidated
IF (Fitness < MAS AND Fitness < worst_fitness_found):
worst_strategy = this_strategy
worst_fitness = Fitness
IF (worst_strategy found):
Count elite strategies with fitness > worst_fitness
IF (elite_count >= elite_preservation_count):
Deactivate worst_strategy (set active flag = false)
Increment total_culled counter
Elite Protection:
Even if a strategy's fitness falls below MAS, it survives if fewer than N strategies are better. This prevents culling when population is generally weak.
Result: Weak strategies removed from population, freeing slots for new spawns. Gene pool improves over time.
Selection for Display (Every Bar)
AGE chooses one strategy to display signals:
Best fitness = -1
Selected = none
For each active strategy:
Fitness = base_fitness
IF (WFO_validated):
Fitness × 1.3 // 30% bonus for validated strategies
IF (Fitness > best_fitness):
best_fitness = Fitness
selected_strategy = this_strategy
Display selected strategy's signals on chart
Result: Only the highest-fitness (optionally validated-boosted) strategy's signals appear as chart markers. Other strategies trade invisibly in shadow portfolios.
🎨 PREMIUM VISUALIZATION SYSTEM
AGE includes sophisticated visual feedback that standard indicators lack:
1. Gradient Probability Cloud (Optional, Default: ON)
Multi-layer gradient showing signal buildup 2-3 bars before entry:
Activation Conditions:
Signal persistence > 0 (same directional signal held for multiple bars)
Signal probability ≥ minimum threshold (65% by default)
Signal hasn't yet executed (still in "forming" state)
Visual Construction:
7 gradient layers by default (configurable 3-15)
Each layer is a line-fill pair (top line, bottom line, filled between)
Layer spacing: 0.3 to 1.0 × ATR above/below price
Outer layers = faint, inner layers = bright
Color transitions from base to intense based on layer position
Transparency scales with probability (high prob = more opaque)
Color Selection:
Long signals: Gradient from theme.gradient_bull_mid to theme.gradient_bull_strong
Short signals: Gradient from theme.gradient_bear_mid to theme.gradient_bear_strong
Base transparency: 92%, reduces by up to 8% for high-probability setups
Dynamic Behavior:
Cloud grows/shrinks as signal persistence increases/decreases
Redraws every bar while signal is forming
Disappears when signal executes or invalidates
Performance Note: Computationally expensive due to linefill objects. Disable or reduce layers if chart performance degrades.
2. Population Fitness Ribbon (Optional, Default: ON)
Histogram showing fitness distribution across active strategies:
Activation: Only draws on last bar (barstate.islast) to avoid historical clutter
Visual Construction:
10 histogram layers by default (configurable 5-20)
Plots 50 bars back from current bar
Positioned below price at: lowest_low(100) - 1.5×ATR (doesn't interfere with price action)
Each layer represents a fitness threshold (evenly spaced min to max fitness)
Layer Logic:
For layer_num from 0 to ribbon_layers:
Fitness_threshold = min_fitness + (max_fitness - min_fitness) × (layer / layers)
Count strategies with fitness ≥ threshold
Height = ATR × 0.15 × (count / total_active)
Y_position = base_level + ATR × 0.2 × layer
Color = Gradient from weak to strong based on layer position
Line_width = Scaled by height (taller = thicker)
Visual Feedback:
Tall, bright ribbon = healthy population, many fit strategies at high fitness levels
Short, dim ribbon = weak population, few strategies achieving good fitness
Ribbon compression (layers close together) = population converging to similar fitness
Ribbon spread = diverse fitness range, active selection pressure
Use Case: Quick visual health check without opening dashboard. Ribbon growing upward over time = population improving.
3. Confidence Halo (Optional, Default: ON)
Circular polyline around entry signals showing probability strength:
Activation: Draws when new position opens (shadow_position changes from 0 to ±1)
Visual Construction:
20-segment polyline forming approximate circle
Center: Low - 0.5×ATR (long) or High + 0.5×ATR (short)
Radius: 0.3×ATR (low confidence) to 1.0×ATR (elite confidence)
Scales with: (probability - min_probability) / (1.0 - min_probability)
Color Coding:
Elite (85%+): Cyan (theme.conf_elite), large radius, minimal transparency (40%)
Strong (75-85%): Strong green (theme.conf_strong), medium radius, moderate transparency (50%)
Good (65-75%): Good green (theme.conf_good), smaller radius, more transparent (60%)
Moderate (<65%): Moderate green (theme.conf_moderate), tiny radius, very transparent (70%)
Technical Detail:
Uses chart.point array with index-based positioning
5-bar horizontal spread for circular appearance (±5 bars from entry)
Curved=false (Pine Script polyline limitation)
Fill color matches line color but more transparent (88% vs line's transparency)
Purpose: Instant visual probability assessment. No need to check dashboard - halo size/brightness tells the story.
4. Evolution Event Markers (Optional, Default: ON)
Visual indicators of genetic algorithm activity:
Spawn Markers (Diamond, Cyan):
Plots when total_spawned increases on current bar
Location: bottom of chart (location.bottom)
Color: theme.spawn_marker (cyan/bright blue)
Size: tiny
Indicates new strategy just entered population
Cull Markers (X-Cross, Red):
Plots when total_culled increases on current bar
Location: bottom of chart (location.bottom)
Color: theme.cull_marker (red/pink)
Size: tiny
Indicates weak strategy just removed from population
What It Tells You:
Frequent spawning early = population building, active exploration
Frequent culling early = high selection pressure, weak strategies dying fast
Balanced spawn/cull = healthy evolutionary churn
No markers for long periods = stable population (evolution plateaued or optimal genes found)
5. Entry/Exit Markers
Clear visual signals for selected strategy's trades:
Long Entry (Triangle Up, Green):
Plots when selected strategy opens long position (position changes 0 → +1)
Location: below bar (location.belowbar)
Color: theme.long_primary (green/cyan depending on theme)
Transparency: Scales with probability:
Elite (85%+): 0% (fully opaque)
Strong (75-85%): 10%
Good (65-75%): 20%
Acceptable (55-65%): 35%
Size: small
Short Entry (Triangle Down, Red):
Plots when selected strategy opens short position (position changes 0 → -1)
Location: above bar (location.abovebar)
Color: theme.short_primary (red/pink depending on theme)
Transparency: Same scaling as long entries
Size: small
Exit (X-Cross, Orange):
Plots when selected strategy closes position (position changes ±1 → 0)
Location: absolute (at actual exit price if stop/target lines enabled)
Color: theme.exit_color (orange/yellow depending on theme)
Transparency: 0% (fully opaque)
Size: tiny
Result: Clean, probability-scaled markers that don't clutter chart but convey essential information.
6. Stop Loss & Take Profit Lines (Optional, Default: ON)
Visual representation of shadow portfolio risk levels:
Stop Loss Line:
Plots when selected strategy has active position
Level: shadow_stop value from selected strategy
Color: theme.short_primary with 60% transparency (red/pink, subtle)
Width: 2
Style: plot.style_linebr (breaks when no position)
Take Profit Line:
Plots when selected strategy has active position
Level: shadow_target value from selected strategy
Color: theme.long_primary with 60% transparency (green, subtle)
Width: 2
Style: plot.style_linebr (breaks when no position)
Purpose:
Shows where shadow portfolio would exit for stop/target
Helps visualize strategy's risk/reward ratio
Useful for manual traders to set similar levels
Disable for cleaner chart (recommended for presentations)
7. Dynamic Trend EMA
Gradient-colored trend line that visualizes trend strength:
Calculation:
EMA(close, trend_length) - default 50 period (configurable 20-100)
Slope calculated over 10 bars: (current_ema - ema ) / ema × 100
Color Logic:
Trend_direction:
Slope > 0.1% = Bullish (1)
Slope < -0.1% = Bearish (-1)
Otherwise = Neutral (0)
Trend_strength = abs(slope)
Color = Gradient between:
- Neutral color (gray/purple)
- Strong bullish (bright green) if direction = 1
- Strong bearish (bright red) if direction = -1
Gradient factor = trend_strength (0 to 1+ scale)
Visual Behavior:
Faint gray/purple = weak/no trend (choppy conditions)
Light green/red = emerging trend (low strength)
Bright green/red = strong trend (high conviction)
Color intensity = trend strength magnitude
Transparency: 50% (subtle, doesn't overpower price action)
Purpose: Subconscious awareness of trend state without checking dashboard or indicators.
8. Regime Background Tinting (Subtle)
Ultra-low opacity background color indicating detected market regime:
Regime Detection:
Efficiency = directional_movement / total_range (over trend_length bars)
Vol_ratio = current_volatility / average_volatility
IF (efficiency > 0.5 AND vol_ratio < 1.3):
Regime = Trending (1)
ELSE IF (vol_ratio > 1.5):
Regime = Volatile (2)
ELSE:
Regime = Choppy (0)
Background Colors:
Trending: theme.regime_trending (dark green, 92-93% transparency)
Volatile: theme.regime_volatile (dark red, 93% transparency)
Choppy: No tint (normal background)
Purpose:
Subliminal regime awareness
Helps explain why signals are/aren't generating
Trending = ideal conditions for AGE
Volatile = fewer signals, higher thresholds applied
Choppy = mixed signals, lower confidence
Important: Extremely subtle by design. Not meant to be obvious, just subconscious context.
📊 ENHANCED DASHBOARD
Comprehensive real-time metrics in single organized panel (top-right position):
Dashboard Structure (5 columns × 14 rows)
Header Row:
Column 0: "🧬 AGE PRO" + phase indicator (🔴 LIVE or ⏪ HIST)
Column 1: "POPULATION"
Column 2: "PERFORMANCE"
Column 3: "CURRENT SIGNAL"
Column 4: "ACTIVE STRATEGY"
Column 0: Market State
Regime (📈 TREND / 🌊 CHAOS / ➖ CHOP)
DVS Ratio (current volatility scaling factor, format: #.##)
Trend Direction (▲ BULL / ▼ BEAR / ➖ FLAT with color coding)
Trend Strength (0-100 scale, format: #.##)
Column 1: Population Metrics
Active strategies (count / max_population)
Validated strategies (WFO passed / active total)
Current generation number
Total spawned (all-time strategy births)
Total culled (all-time strategy deaths)
Column 2: Aggregate Performance
Total trades across all active strategies
Aggregate win rate (%) - color-coded:
Green (>55%)
Orange (45-55%)
Red (<45%)
Total P&L in R-multiples - color-coded by positive/negative
Best fitness score in population (format: #.###)
MAS - Minimum Adaptation Score (cull threshold, format: #.###)
Column 3: Current Signal Status
Status indicator:
"▲ LONG" (green) if selected strategy in long position
"▼ SHORT" (red) if selected strategy in short position
"⏳ FORMING" (orange) if signal persisting but not yet executed
"○ WAITING" (gray) if no active signal
Confidence percentage (0-100%, format: #.#%)
Quality assessment:
"🔥 ELITE" (cyan) for 85%+ probability
"✓ STRONG" (bright green) for 75-85%
"○ GOOD" (green) for 65-75%
"- LOW" (dim) for <65%
Confluence score (X/3 format)
Signal age:
"X bars" if signal forming
"IN TRADE" if position active
"---" if no signal
Column 4: Selected Strategy Details
Strategy ID number (#X format)
Validation status:
"✓ VAL" (green) if WFO validated
"○ TRAIN" (orange) if still in training/testing phase
Generation number (GX format)
Personal fitness score (format: #.### with color coding)
Trade count
P&L and win rate (format: #.#R (##%) with color coding)
Color Scheme:
Panel background: theme.panel_bg (dark, low opacity)
Panel headers: theme.panel_header (slightly lighter)
Primary text: theme.text_primary (bright, high contrast)
Secondary text: theme.text_secondary (dim, lower contrast)
Positive metrics: theme.metric_positive (green)
Warning metrics: theme.metric_warning (orange)
Negative metrics: theme.metric_negative (red)
Special markers: theme.validated_marker, theme.spawn_marker
Update Frequency: Only on barstate.islast (current bar) to minimize CPU usage
Purpose:
Quick overview of entire system state
No need to check multiple indicators
Trading decisions informed by population health, regime state, and signal quality
Transparency into what AGE is thinking
🔍 DIAGNOSTICS PANEL (Optional, Default: OFF)
Detailed signal quality tracking for optimization and debugging:
Panel Structure (3 columns × 8 rows)
Position: Bottom-right corner (doesn't interfere with main dashboard)
Header Row:
Column 0: "🔍 DIAGNOSTICS"
Column 1: "COUNT"
Column 2: "%"
Metrics Tracked (for selected strategy only):
Total Evaluated:
Every signal that passed initial calculation (direction ≠ 0)
Represents total opportunities considered
✓ Passed:
Signals that passed quality gate and executed
Green color coding
Percentage of evaluated signals
Rejection Breakdown:
⨯ Probability:
Rejected because probability < minimum threshold
Most common rejection reason typically
⨯ Confluence:
Rejected because confluence < minimum required (e.g., only 1 of 3 indicators agreed)
⨯ Trend:
Rejected because signal opposed strong trend
Indicates counter-trend protection working
⨯ Regime:
Rejected because volatile regime detected and probability wasn't high enough to override
Shows regime filter in action
⨯ Volume:
Rejected because volume < 70% of 20-bar average
Indicates volume confirmation requirement
Color Coding:
Passed count: Green (success metric)
Rejection counts: Red (failure metrics)
Percentages: Gray (neutral, informational)
Performance Cost: Slight CPU overhead for tracking counters. Disable when not actively optimizing settings.
How to Use Diagnostics
Scenario 1: Too Few Signals
Evaluated: 200
Passed: 10 (5%)
⨯ Probability: 120 (60%)
⨯ Confluence: 40 (20%)
⨯ Others: 30 (15%)
Diagnosis: Probability threshold too high for this strategy's DNA.
Solution: Lower min probability from 65% to 60%, or allow strategy more time to evolve better DNA.
Scenario 2: Too Many False Signals
Evaluated: 200
Passed: 80 (40%)
Strategy win rate: 45%
Diagnosis: Quality gate too loose, letting low-quality signals through.
Solution: Raise min probability to 70%, or increase min confluence to 3 (all indicators must agree).
Scenario 3: Regime-Specific Issues
⨯ Regime: 90 (45% of rejections)
Diagnosis: Frequent volatile regime detection blocking otherwise good signals.
Solution: Either accept fewer trades during chaos (recommended), or disable regime filter if you want signals regardless of market state.
Optimization Workflow:
Enable diagnostics
Run 200+ bars
Analyze rejection patterns
Adjust settings based on data
Re-run and compare pass rate
Disable diagnostics when satisfied
⚙️ CONFIGURATION GUIDE
🧬 Evolution Engine Settings
Enable AGE Evolution (Default: ON):
ON: Full genetic algorithm (recommended for best results)
OFF: Uses only 4 seed strategies, no spawning/culling (static population for comparison testing)
Max Population (4-12, Default: 8):
Higher = more diversity, more exploration, slower performance
Lower = faster computation, less exploration, risk of premature convergence
Sweet spot: 6-8 for most use cases
4 = minimum for meaningful evolution
12 = maximum before diminishing returns
Min Population (2-4, Default: 3):
Safety floor - system never culls below this count
Prevents population extinction during harsh selection
Should be at least half of max population
Elite Preservation (1-3, Default: 2):
Top N performers completely immune to culling
Ensures best genes always survive
1 = minimal protection, aggressive selection
2 = balanced (recommended)
3 = conservative, slower gene pool turnover
Historical: Spawn Interval (10-100, Default: 30):
Bars between spawning new strategies during historical data
Lower = faster evolution, more exploration
Higher = slower evolution, more evaluation time per strategy
30 bars = ~1-2 hours on 15min chart
Historical: Cull Interval (20-200, Default: 60):
Bars between culling weak strategies during historical data
Should be 2x spawn interval for balanced churn
Lower = aggressive selection pressure
Higher = patient evaluation
Live: Spawn Interval (100-500, Default: 200):
Bars between spawning during live trading
Much slower than historical for stability
Prevents population chaos during live trading
200 bars = ~1.5 trading days on 15min chart
Live: Cull Interval (200-1000, Default: 400):
Bars between culling during live trading
Should be 2x live spawn interval
Conservative removal during live trading
Historical: Mutation Rate (0.05-0.40, Default: 0.20):
Probability each gene mutates during breeding (20% = 2 out of 10 genes on average)
Higher = more exploration, slower convergence
Lower = more exploitation, faster convergence but risk of local optima
20% balances exploration vs exploitation
Live: Mutation Rate (0.02-0.20, Default: 0.08):
Mutation rate during live trading
Much lower for stability (don't want population to suddenly degrade)
8% = mostly inherits parent genes with small tweaks
Mutation Strength (0.05-0.25, Default: 0.12):
How much genes change when mutated (% of gene's total range)
0.05 = tiny nudges (fine-tuning)
0.12 = moderate jumps (recommended)
0.25 = large leaps (aggressive exploration)
Example: If gene range is 0.5-2.0, 12% strength = ±0.18 possible change
📈 Signal Quality Settings
Min Signal Probability (0.55-0.80, Default: 0.65):
Quality gate threshold - signals below this never generate
0.55-0.60 = More signals, accept lower confidence (higher risk)
0.65 = Institutional-grade balance (recommended)
0.70-0.75 = Fewer but higher-quality signals (conservative)
0.80+ = Very selective, very few signals (ultra-conservative)
Min Confluence Score (1-3, Default: 2):
Required indicator agreement before signal generates
1 = Any single indicator can trigger (not recommended - too many false signals)
2 = Requires 2 of 3 indicators agree (RECOMMENDED for balance)
3 = All 3 must agree (very selective, few signals, high quality)
Base Persistence Bars (1-5, Default: 2):
Base bars signal must persist before entry
System adapts automatically:
High probability signals (75%+) enter 1 bar faster
Low probability signals (<68%) need 1 bar more
Trending regime: -1 bar (faster entries)
Volatile regime: +1 bar (more confirmation)
1 = Immediate entry after quality gate (responsive but prone to whipsaw)
2 = Balanced confirmation (recommended)
3-5 = Patient confirmation (slower but more reliable)
Cooldown After Trade (3-20, Default: 8):
Bars to wait after exit before next entry allowed
Prevents overtrading and revenge trading
3 = Minimal cooldown (active trading)
8 = Balanced (recommended)
15-20 = Conservative (position trading)
Entropy Length (10-50, Default: 20):
Lookback period for market order/disorder calculation
Lower = more responsive to regime changes (noisy)
Higher = more stable regime detection (laggy)
20 = works across most timeframes
Momentum Length (5-30, Default: 14):
Period for RSI/ROC calculations
14 = standard (RSI default)
Lower = more signals, less reliable
Higher = fewer signals, more reliable
Structure Length (20-100, Default: 50):
Lookback for support/resistance swing range
20 = short-term swings (day trading)
50 = medium-term structure (recommended)
100 = major structure (position trading)
Trend EMA Length (20-100, Default: 50):
EMA period for trend detection and direction bias
20 = short-term trend (responsive)
50 = medium-term trend (recommended)
100 = long-term trend (position trading)
ATR Period (5-30, Default: 14):
Period for volatility measurement
14 = standard ATR
Lower = more responsive to vol changes
Higher = smoother vol calculation
📊 Volatility Scaling (DVS) Settings
Enable DVS (Default: ON):
Dynamic volatility scaling for adaptive stop/target placement
Highly recommended to leave ON
OFF only for testing fixed-distance stops
DVS Method (Default: Ensemble):
ATR Ratio: Simple, fast, single-method (good for beginners)
Parkinson: High-low range based (good for intraday)
Garman-Klass: OHLC based (sophisticated, considers gaps)
Ensemble: Median of all three (RECOMMENDED - most robust)
DVS Memory (20-200, Default: 100):
Lookback for baseline volatility comparison
20 = very responsive to vol changes (can overreact)
100 = balanced adaptation (recommended)
200 = slow, stable baseline (minimizes false vol signals)
DVS Sensitivity (0.3-1.5, Default: 0.7):
How much volatility affects scaling (power-law exponent)
0.3 = Conservative, heavily dampens vol impact (cube root)
0.5 = Moderate dampening (square root)
0.7 = Balanced response (recommended)
1.0 = Linear, full 1:1 vol response
1.5 = Aggressive, amplified response (exponential)
🔬 Walk-Forward Optimization Settings
Enable WFO (Default: ON):
Out-of-sample validation to prevent overfitting
Highly recommended to leave ON
OFF only for testing or if you want unvalidated strategies
Training Window (100-500, Default: 250):
Bars for in-sample optimization
100 = fast validation, less data (risky)
250 = balanced (recommended) - about 1-2 months on daily, 1-2 weeks on 15min
500 = patient validation, more data (conservative)
Testing Window (30-200, Default: 75):
Bars for out-of-sample validation
Should be ~30% of training window
30 = minimal test (fast validation)
75 = balanced (recommended)
200 = extensive test (very conservative)
Min Trades for Validation (3-15, Default: 5):
Required trades in BOTH training AND testing periods
3 = minimal sample (risky, fast validation)
5 = balanced (recommended)
10+ = conservative (slow validation, high confidence)
WFO Efficiency Threshold (0.3-0.9, Default: 0.55):
Minimum test/train performance ratio required
0.30 = Very loose (test must be 30% as good as training)
0.55 = Balanced (recommended) - test must be 55% as good
0.70+ = Strict (test must closely match training)
Higher = fewer validated strategies, lower risk of overfitting
🎨 Premium Visuals Settings
Visual Theme:
Neon Genesis: Cyberpunk aesthetic (cyan/magenta/purple)
Carbon Fiber: Industrial look (blue/red/gray)
Quantum Blue: Quantum computing (blue/purple/pink)
Aurora: Northern lights (teal/orange/purple)
⚡ Gradient Probability Cloud (Default: ON):
Multi-layer gradient showing signal buildup
Turn OFF if chart lags or for cleaner look
Cloud Gradient Layers (3-15, Default: 7):
More layers = smoother gradient, more CPU intensive
Fewer layers = faster, blockier appearance
🎗️ Population Fitness Ribbon (Default: ON):
Histogram showing fitness distribution
Turn OFF for cleaner chart
Ribbon Layers (5-20, Default: 10):
More layers = finer fitness detail
Fewer layers = simpler histogram
⭕ Signal Confidence Halo (Default: ON):
Circular indicator around entry signals
Size/brightness scales with probability
Minimal performance cost
🔬 Evolution Event Markers (Default: ON):
Diamond (spawn) and X (cull) markers
Shows genetic algorithm activity
Minimal performance cost
🎯 Stop/Target Lines (Default: ON):
Shows shadow portfolio stop/target levels
Turn OFF for cleaner chart (recommended for screenshots/presentations)
📊 Enhanced Dashboard (Default: ON):
Comprehensive metrics panel
Should stay ON unless you want zero overlays
🔍 Diagnostics Panel (Default: OFF):
Detailed signal rejection tracking
Turn ON when optimizing settings
Turn OFF during normal use (slight performance cost)
📈 USAGE WORKFLOW - HOW TO USE THIS INDICATOR
Phase 1: Initial Setup & Learning
Add AGE to your chart
Recommended timeframes: 15min, 30min, 1H (best signal-to-noise ratio)
Works on: 5min (day trading), 4H (swing trading), Daily (position trading)
Load 1000+ bars for sufficient evolution history
Let the population evolve (100+ bars minimum)
First 50 bars: Random exploration, poor results expected
Bars 50-150: Population converging, fitness improving
Bars 150+: Stable performance, validated strategies emerging
Watch the dashboard metrics
Population should grow toward max capacity
Generation number should advance regularly
Validated strategies counter should increase
Best fitness should trend upward toward 0.50-0.70 range
Observe evolution markers
Diamond markers (cyan) = new strategies spawning
X markers (red) = weak strategies being culled
Frequent early activity = healthy evolution
Activity slowing = population stabilizing
Be patient. Evolution takes time. Don't judge performance before 150+ bars.
Phase 2: Signal Observation
Watch signals form
Gradient cloud builds up 2-3 bars before entry
Cloud brightness = probability strength
Cloud thickness = signal persistence
Check signal quality
Look at confidence halo size when entry marker appears
Large bright halo = elite setup (85%+)
Medium halo = strong setup (75-85%)
Small halo = good setup (65-75%)
Verify market conditions
Check trend EMA color (green = uptrend, red = downtrend, gray = choppy)
Check background tint (green = trending, red = volatile, clear = choppy)
Trending background + aligned signal = ideal conditions
Review dashboard signal status
Current Signal column shows:
Status (Long/Short/Forming/Waiting)
Confidence % (actual probability value)
Quality assessment (Elite/Strong/Good)
Confluence score (2/3 or 3/3 preferred)
Only signals meeting ALL quality gates appear on chart. If you're not seeing signals, population is either still learning or market conditions aren't suitable.
Phase 3: Manual Trading Execution
When Long Signal Fires:
Verify confidence level (dashboard or halo size)
Confirm trend alignment (EMA sloping up, green color)
Check regime (preferably trending or choppy, avoid volatile)
Enter long manually on your broker platform
Set stop loss at displayed stop line level (if lines enabled), or use your own risk management
Set take profit at displayed target line level, or trail manually
Monitor position - exit if X marker appears (signal reversal)
When Short Signal Fires:
Same verification process
Confirm downtrend (EMA sloping down, red color)
Enter short manually
Use displayed stop/target levels or your own
AGE tells you WHEN and HOW CONFIDENT. You decide WHETHER and HOW MUCH.
Phase 4: Set Up Alerts (Never Miss a Signal)
Right-click on indicator name in legend
Select "Add Alert"
Choose condition:
"AGE Long" = Long entry signal fired
"AGE Short" = Short entry signal fired
"AGE Exit" = Position reversal/exit signal
Set notification method:
Sound alert (popup on chart)
Email notification
Webhook to phone/trading platform
Mobile app push notification
Name the alert (e.g., "AGE BTCUSD 15min Long")
Save alert
Recommended: Set alerts for both long and short, enable mobile push notifications. You'll get alerted in real-time even if not watching charts.
Phase 5: Monitor Population Health
Weekly Review:
Check dashboard Population column:
Active count should be near max (6-8 of 8)
Validated count should be >50% of active
Generation should be advancing (1-2 per week typical)
Check dashboard Performance column:
Aggregate win rate should be >50% (target: 55-65%)
Total P&L should be positive (may fluctuate)
Best fitness should be >0.50 (target: 0.55-0.70)
MAS should be declining slowly (normal adaptation)
Check Active Strategy column:
Selected strategy should be validated (✓ VAL)
Personal fitness should match best fitness
Trade count should be accumulating
Win rate should be >50%
Warning Signs:
Zero validated strategies after 300+ bars = settings too strict or market unsuitable
Best fitness stuck <0.30 = population struggling, consider parameter adjustment
No spawning/culling for 200+ bars = evolution stalled (may be optimal or need reset)
Aggregate win rate <45% sustained = system not working on this instrument/timeframe
Health Check Pass:
50%+ strategies validated
Best fitness >0.50
Aggregate win rate >52%
Regular spawn/cull activity
Selected strategy validated
Phase 6: Optimization (If Needed)
Enable Diagnostics Panel (bottom-right) for data-driven tuning:
Problem: Too Few Signals
Evaluated: 200
Passed: 8 (4%)
⨯ Probability: 140 (70%)
Solutions:
Lower min probability: 65% → 60% or 55%
Reduce min confluence: 2 → 1
Lower base persistence: 2 → 1
Increase mutation rate temporarily to explore new genes
Check if regime filter is blocking signals (⨯ Regime high?)
Problem: Too Many False Signals
Evaluated: 200
Passed: 90 (45%)
Win rate: 42%
Solutions:
Raise min probability: 65% → 70% or 75%
Increase min confluence: 2 → 3
Raise base persistence: 2 → 3
Enable WFO if disabled (validates strategies before use)
Check if volume filter is being ignored (⨯ Volume low?)
Problem: Counter-Trend Losses
⨯ Trend: 5 (only 5% rejected)
Losses often occur against trend
Solutions:
System should already filter trend opposition
May need stronger trend requirement
Consider only taking signals aligned with higher timeframe trend
Use longer trend EMA (50 → 100)
Problem: Volatile Market Whipsaws
⨯ Regime: 100 (50% rejected by volatile regime)
Still getting stopped out frequently
Solutions:
System is correctly blocking volatile signals
Losses happening because vol filter isn't strict enough
Consider not trading during volatile periods (respect the regime)
Or disable regime filter and accept higher risk
Optimization Workflow:
Enable diagnostics
Run 200+ bars with current settings
Analyze rejection patterns and win rate
Make ONE change at a time (scientific method)
Re-run 200+ bars and compare results
Keep change if improvement, revert if worse
Disable diagnostics when satisfied
Never change multiple parameters at once - you won't know what worked.
Phase 7: Multi-Instrument Deployment
AGE learns independently on each chart:
Recommended Strategy:
Deploy AGE on 3-5 different instruments
Different asset classes ideal (e.g., ES futures, EURUSD, BTCUSD, SPY, Gold)
Each learns optimal strategies for that instrument's personality
Take signals from all 5 charts
Natural diversification reduces overall risk
Why This Works:
When one market is choppy, others may be trending
Different instruments respond to different news/catalysts
Portfolio-level win rate more stable than single-instrument
Evolution explores different parameter spaces on each chart
Setup:
Same settings across all charts (or customize if preferred)
Set alerts for all
Take every validated signal across all instruments
Position size based on total account (don't overleverage any single signal)
⚠️ REALISTIC EXPECTATIONS - CRITICAL READING
What AGE Can Do
✅ Generate probability-weighted signals using genetic algorithms
✅ Evolve strategies in real-time through natural selection
✅ Validate strategies on out-of-sample data (walk-forward optimization)
✅ Adapt to changing market conditions automatically over time
✅ Provide comprehensive metrics on population health and signal quality
✅ Work on any instrument, any timeframe, any broker
✅ Improve over time as weak strategies are culled and fit strategies breed
What AGE Cannot Do
❌ Win every trade (typical win rate: 55-65% at best)
❌ Predict the future with certainty (markets are probabilistic, not deterministic)
❌ Work perfectly from bar 1 (needs 100-150 bars to learn and stabilize)
❌ Guarantee profits under all market conditions
❌ Replace your trading discipline and risk management
❌ Execute trades automatically (this is an indicator, not a strategy)
❌ Prevent all losses (drawdowns are normal and expected)
❌ Adapt instantly to regime changes (re-learning takes 50-100 bars)
Performance Realities
Typical Performance After Evolution Stabilizes (150+ bars):
Win Rate: 55-65% (excellent for trend-following systems)
Profit Factor: 1.5-2.5 (realistic for validated strategies)
Signal Frequency: 5-15 signals per 100 bars (quality over quantity)
Drawdown Periods: 20-40% of time in equity retracement (normal trading reality)
Max Consecutive Losses: 5-8 losses possible even with 60% win rate (probability says this is normal)
Evolution Timeline:
Bars 0-50: Random exploration, learning phase - poor results expected, don't judge yet
Bars 50-150: Population converging, fitness climbing - results improving
Bars 150-300: Stable performance, most strategies validated - consistent results
Bars 300+: Mature population, optimal genes dominant - best results
Market Condition Dependency:
Trending Markets: AGE excels - clear directional moves, high-probability setups
Choppy Markets: AGE struggles - fewer signals generated, lower win rate
Volatile Markets: AGE cautious - higher rejection rate, wider stops, fewer trades
Market Regime Changes:
When market shifts from trending to choppy overnight
Validated strategies can become temporarily invalidated
AGE will adapt through evolution, but not instantly
Expect 50-100 bar re-learning period after major regime shifts
Fitness may temporarily drop then recover
This is NOT a holy grail. It's a sophisticated signal generator that learns and adapts using genetic algorithms. Your success depends on:
Patience during learning periods (don't abandon after 3 losses)
Proper position sizing (risk 0.5-2% per trade, not 10%)
Following signals consistently (cherry-picking defeats statistical edge)
Not abandoning system prematurely (give it 200+ bars minimum)
Understanding probability (60% win rate means 40% of trades WILL lose)
Respecting market conditions (trending = trade more, choppy = trade less)
Managing emotions (AGE is emotionless, you need to be too)
Expected Drawdowns:
Single-strategy max DD: 10-20% of equity (normal)
Portfolio across multiple instruments: 5-15% (diversification helps)
Losing streaks: 3-5 consecutive losses expected periodically
No indicator eliminates risk. AGE manages risk through:
Quality gates (rejecting low-probability signals)
Confluence requirements (multi-indicator confirmation)
Persistence requirements (no knee-jerk reactions)
Regime awareness (reduced trading in chaos)
Walk-forward validation (preventing overfitting)
But it cannot prevent all losses. That's inherent to trading.
🔧 TECHNICAL SPECIFICATIONS
Platform: TradingView Pine Script v5
Indicator Type: Overlay indicator (plots on price chart)
Execution Type: Signals only - no automatic order placement
Computational Load:
Moderate to High (genetic algorithms + shadow portfolios)
8 strategies × shadow portfolio simulation = significant computation
Premium visuals add additional load (gradient cloud, fitness ribbon)
TradingView Resource Limits (Built-in Caps):
Max Bars Back: 500 (sufficient for WFO and evolution)
Max Labels: 100 (plenty for entry/exit markers)
Max Lines: 150 (adequate for stop/target lines)
Max Boxes: 50 (not heavily used)
Max Polylines: 100 (confidence halos)
Recommended Chart Settings:
Timeframe: 15min to 1H (optimal signal/noise balance)
5min: Works but noisier, more signals
4H/Daily: Works but fewer signals
Bars Loaded: 1000+ (ensures sufficient evolution history)
Replay Mode: Excellent for testing without risk
Performance Optimization Tips:
Disable gradient cloud if chart lags (most CPU intensive visual)
Disable fitness ribbon if still laggy
Reduce cloud layers from 7 to 3
Reduce ribbon layers from 10 to 5
Turn off diagnostics panel unless actively tuning
Close other heavy indicators to free resources
Browser/Platform Compatibility:
Works on all modern browsers (Chrome, Firefox, Safari, Edge)
Mobile app supported (full functionality on phone/tablet)
Desktop app supported (best performance)
Web version supported (may be slower on older computers)
Data Requirements:
Real-time or delayed data both work
No special data feeds required
Works with TradingView's standard data
Historical + live data seamlessly integrated
🎓 THEORETICAL FOUNDATIONS
AGE synthesizes advanced concepts from multiple disciplines:
Evolutionary Computation
Genetic Algorithms (Holland, 1975): Population-based optimization through natural selection metaphor
Tournament Selection: Fitness-based parent selection with diversity preservation
Crossover Operators: Fitness-weighted gene recombination from two parents
Mutation Operators: Random gene perturbation for exploration of new parameter space
Elitism: Preservation of top N performers to prevent loss of best solutions
Adaptive Parameters: Different mutation rates for historical vs. live phases
Technical Analysis
Support/Resistance: Price structure within swing ranges
Trend Following: EMA-based directional bias
Momentum Analysis: RSI, ROC, MACD composite indicators
Volatility Analysis: ATR-based risk scaling
Volume Confirmation: Trade activity validation
Information Theory
Shannon Entropy (1948): Quantification of market order vs. disorder
Signal-to-Noise Ratio: Directional information vs. random walk
Information Content: How much "information" a price move contains
Statistics & Probability
Walk-Forward Analysis: Rolling in-sample/out-of-sample optimization
Out-of-Sample Validation: Testing on unseen data to prevent overfitting
Monte Carlo Principles: Shadow portfolio simulation with realistic execution
Expectancy Theory: Win rate × avg win - loss rate × avg loss
Probability Distributions: Signal confidence quantification
Risk Management
ATR-Based Stops: Volatility-normalized risk per trade
Volatility Regime Detection: Market state classification (trending/choppy/volatile)
Drawdown Control: Peak-to-trough equity measurement
R-Multiple Normalization: Performance measurement in risk units
Machine Learning Concepts
Online Learning: Continuous adaptation as new data arrives
Fitness Functions: Multi-objective optimization (win rate + expectancy + drawdown)
Exploration vs. Exploitation: Balance between trying new strategies and using proven ones
Overfitting Prevention: Walk-forward validation as regularization
Novel Contribution:
AGE is the first TradingView indicator to apply genetic algorithms to real-time indicator parameter optimization while maintaining strict anti-overfitting controls through walk-forward validation.
Most "adaptive" indicators simply recalibrate lookback periods or thresholds. AGE evolves entirely new strategies through competitive selection - it's not parameter tuning, it's Darwinian evolution of trading logic itself.
The combination of:
Genetic algorithm population management
Shadow portfolio simulation for realistic fitness evaluation
Walk-forward validation to prevent overfitting
Multi-indicator confluence for signal quality
Dynamic volatility scaling for adaptive risk
...creates a system that genuinely learns and improves over time while avoiding the curse of curve-fitting that plagues most optimization approaches.
🏗️ DEVELOPMENT NOTES
This project represents months of intensive development, facing significant technical challenges:
Challenge 1: Making Genetics Actually Work
Early versions spawned garbage strategies that polluted the gene pool:
Random gene combinations produced nonsensical parameter sets
Weak strategies survived too long, dragging down population
No clear convergence toward optimal solutions
Solution:
Comprehensive fitness scoring (4 factors: win rate, P&L, expectancy, drawdown)
Elite preservation (top 2 always protected)
Walk-forward validation (unproven strategies penalized 30%)
Tournament selection (fitness-weighted breeding)
Adaptive culling (MAS decay creates increasing selection pressure)
Challenge 2: Balancing Evolution Speed vs. Stability
Too fast = population chaos, no convergence. Too slow = can't adapt to regime changes.
Solution:
Dual-phase timing: Fast evolution during historical (30/60 bar intervals), slow during live (200/400 bar intervals)
Adaptive mutation rates: 20% historical, 8% live
Spawn/cull ratio: Always 2:1 to prevent population collapse
Challenge 3: Shadow Portfolio Accuracy
Needed realistic trade simulation without lookahead bias:
Can't peek at future bars for exits
Must track multiple portfolios simultaneously
Stop/target checks must use bar's high/low correctly
Solution:
Entry on close (realistic)
Exit checks on current bar's high/low (realistic)
Independent position tracking per strategy
Cooldown periods to prevent unrealistic rapid re-entry
ATR-normalized P&L (R-multiples) for fair comparison across volatility regimes
Challenge 4: Pine Script Compilation Limits
Hit TradingView's execution limits multiple times:
Too many array operations
Too many variables
Too complex conditional logic
Solution:
Optimized data structures (single DNA array instead of 8 separate arrays)
Minimal visual overlays (only essential plots)
Efficient fitness calculations (vectorized where possible)
Strategic use of barstate.islast to minimize dashboard updates
Challenge 5: Walk-Forward Implementation
Standard WFO is difficult in Pine Script:
Can't easily "roll forward" through historical data
Can't re-optimize strategies mid-stream
Must work in real-time streaming environment
Solution:
Age-based phase detection (first 250 bars = training, next 75 = testing)
Separate metric tracking for train vs. test
Efficiency calculation at fixed interval (after test period completes)
Validation flag persists for strategy lifetime
Challenge 6: Signal Quality Control
Early versions generated too many signals with poor win rates:
Single indicators produced excessive noise
No trend alignment
No regime awareness
Instant entries on single-bar spikes
Solution:
Three-layer confluence system (entropy + momentum + structure)
Minimum 2-of-3 agreement requirement
Trend alignment checks (penalty for counter-trend)
Regime-based probability adjustments
Persistence requirements (signals must hold multiple bars)
Volume confirmation
Quality gate (probability + confluence thresholds)
The Result
A system that:
Truly evolves (not just parameter sweeps)
Truly validates (out-of-sample testing)
Truly adapts (ongoing competition and breeding)
Stays within TradingView's platform constraints
Provides institutional-quality signals
Maintains transparency (full metrics dashboard)
Development time: 3+ months of iterative refinement
Lines of code: ~1500 (highly optimized)
Test instruments: ES, NQ, EURUSD, BTCUSD, SPY, AAPL
Test timeframes: 5min, 15min, 1H, Daily
🎯 FINAL WORDS
The Adaptive Genesis Engine is not just another indicator - it's a living system that learns, adapts, and improves through the same principles that drive biological evolution. Every bar it observes adds to its experience. Every strategy it spawns explores new parameter combinations. Every strategy it culls removes weakness from the gene pool.
This is evolution in action on your charts.
You're not getting a static formula locked in time. You're getting a system that thinks , that competes , that survives through natural selection. The strongest strategies rise to the top. The weakest die. The gene pool improves generation after generation.
AGE doesn't claim to predict the future - it adapts to whatever the future brings. When markets shift from trending to choppy, from calm to volatile, from bullish to bearish - AGE evolves new strategies suited to the new regime.
Use it on any instrument. Any timeframe. Any market condition. AGE will adapt.
This indicator gives you the pure signal intelligence. How you choose to act on it - position sizing, risk management, execution discipline - that's your responsibility. AGE tells you when and how confident . You decide whether and how much .
Trust the process. Respect the evolution. Let Darwin work.
"In markets, as in nature, it is not the strongest strategies that survive, nor the most intelligent - but those most responsive to change."
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
— Happy Holiday's
DCA Ladder CalculatorThis script is a DCA (Dollar-Cost Averaging) Ladder Calculator with Risk & Leverage Management baked in.
It’s designed for both LONG and SHORT positions, and helps you:
🎯 Strategically scale into positions across multiple entry points
🔐 Control risk exposure via defined capital allocation
⚖️ Utilize leverage responsibly — for efficiency, not destruction
🧮 Visualize risk, stop loss level, and entry distribution
🔁 Adapt to trend reversals or key zones, especially when combined with reversal indicators or higher timeframe signals
🧠 How It Works
This tool takes a capital allocation approach to building a ladder of positions:
1. You define:
- Portfolio value
- Risk per trade (as %)
- Leverage
- Number of DCA levels
- Entry multiplier (e.g. 1x, 2x, 4x...)
2. The script then:
- Calculates total margin to risk = Portfolio × Risk %
- Calculates total leveraged position size = Margin × Leverage
- Distributes entries according to exponential weights (1x, 2x, 4x...), totaling 7 for 3 levels
- Calculates per-entry:
- Entry price (based on price zone spacing)
- Multiplier
- Exact margin per entry
- Leverage per entry (margin × leverage)
- Computes:
- Average entry price (margin-weighted)
- Approximate stop loss level based on recent ATR and price structure
- % drawdown to SL
- Total margin and position size
3. Displays all this in a clean on-chart table.
📈 How to Use It
1. Apply the indicator to a chart (default: 1D — ideal for clean zones).
2. Configure your:
- Portfolio Value (total trading capital)
- Risk per Trade (%) (your acceptable loss)
- Leverage (exchange or strategy-based)
- DCA Levels (e.g. 3 = anchor + 2 entries)
- Multiplier (typically 2.0 for doubling)
3. Choose LONG or SHORT mode depending on direction.
4. The table will show:
- Entry price ladder
- Margin used per entry
- Total position size
- Approx. stop loss (where your full risk is defined)
Use in conjunction with price action, S/R zones, trendline breaks, volume divergence, or reversal indicators.
✅ Best Practices for Using This Tool
- Leverage is a tool, not a weapon. Use it to scale smartly — not recklessly.
- Use fewer, higher-conviction entries. Don’t blindly ladder; combine with price structure and signals.
- Stick to your risk percent. Never risk more than you can afford to lose. Let this calculator enforce discipline.
- Combine with other confirmation tools, like RSI divergence, momentum shifts, OB zones, etc.
- Avoid martingale-style over-exposure. This is not a gambling tool — it’s for capital efficiency.
🛡️ What This Tool Does NOT Do
- This is not a trade signal indicator.
- It does not place trades or auto-manage positions.
- It does not replace personal responsibility or strategy — it's a tool to help apply structure.
⚠️ Disclaimer
This script is for educational and informational purposes only.
It does not constitute financial advice, nor is it a recommendation to buy or sell any financial instrument.
Always consult a licensed financial advisor before making investment decisions.
Use of leverage involves high risk and can lead to substantial losses.
The author and publisher assume no liability for any trading losses resulting from use of this script.
