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//@version=5
// This code is created by algopoint. All other leaked algos is available at algopoint.mysellix.io
// AlgoPoint Official Contact Adrress
// Instagram: algopoint
// Instagram: algopoint01
// Website: algopoint.mysellix.io
// Mail: algopointstore@gmail.com
//text inputs
var user_consensus = input.string(defval="", title="By going to algopoint.mysellix.io or by clicking on the link in the algopoint instagram bio you can get the leaked codes for all premium indicators like Elite Algo, EzAlgo and LuxAlgo. Free products are also available! algopoint.mysellix.io", confirm = true, group="AlgoPoint")
title = 'AlgoPoint'
subtitle = 'All Leaked Algos \n Instagram: algopoint \n Website: algopoint.mysellix.io'
textVPosition = 'middle'
textHPosition = 'center'
symVPosition = 'top'
symHPosition = 'left'
width = 0
height = 0
c_title = #b2b5be80
s_title = 'large'
a_title = 'center'
c_subtitle = #b2b5be80
s_subtitle = 'normal'
a_subtitle = 'center'
c_bg = color.new(color.blue, 100)
indicator("AlgoPoint | Signals & Overlays", "[AlgoPoint] Signals & Overlays™ [2.2]", overlay = true, max_labels_count = 500)
//Import libraries
import achirameegasthanne/LuxFunctions/1 as LAF
import achirameegasthanne/KernelFunctions/1 as kernels
// # ============================[GET USERS INPUT]============================ #
groupBasic = "BASIC SETTINGS"
showSignals = input(true, "Show Signals", inline = "1", group = groupBasic, tooltip = "Enables or disables the signals")
signalPresets = input.string("None", "Presets / Filters", ["None", "Trend Trader [Preset]","Scalper [Preset]", "Swing Trader [Preset]", "Contrarian Trader [Preset]", "Smart Trail [Filter]", "Trend Tracer [Filter]", "Trend Strength [Filter]", "Trend Catcher [Filter]", "Neo Cloud [Filter]"],tooltip = "Automatically sets settings or filters for a given category", group= groupBasic)
signalMode = input.string("Confirmation + Exits", "Signal Mode", ["Confirmation + Exits", "Contrarian + Exits", "None"],tooltip = "Changes the Mode of the signals" ,group = groupBasic)
signalClassifier = input(true,"AI Signal Classifer",tooltip = "Shows signal quality from 1-4 on signals" ,group = groupBasic)
sensitivity = input.float(5, "Signal Sensitivity ", minval = 1, maxval = 26,step=1, tooltip = "Changes the sensetivity of the signals, the lower this setting the more short term signals you will get, while a higher number will result in longer term signals.",group = groupBasic)
atrLength = input.int(10, "Signal Tuner ", minval = 1, maxval = 25,step=1,tooltip = "Alows you to tune your signals, the higher the number the more refined but laggier the signal" ,group = groupBasic)
candleColorType = input.string("Confirmation Simple", "Candle Coloring", ["Confirmation Simple","Confirmation Gradient","Contrarian Gradient","None"],tooltip = "Changes the type of signal coloring", group = groupBasic)
// Indicator Overlay Settings
groupOverlay = "INDICATOR OVERLAY"
smartTrail = input(true, "Smart Trail", inline = "1", group = groupOverlay)
trendCatcher = input(false, "Trend Catcher", inline = "2", group = groupOverlay)
neoCloud = input(false, "Neo Cloud", inline = "3", group = groupOverlay)
reversalZone = input(true, "Reversal Zones", inline = "1", group = groupOverlay)
trendTracer = input(false, "Trend Tracer", inline = "2", group = groupOverlay)
showDashboard = input(true, "Dashboard", inline = "3", group = groupOverlay)
showTrailingStoploss = input(false, "Trailing Stoploss", inline = "4", group = groupOverlay)
showMovingAverage = input(false, "AI Moving Average", inline = "4", group = groupOverlay)
showSessions = input(false, "Sessions", inline = "5", group = groupOverlay)
// Advanced Settings
groupAdvanced = "ADVANCED SETTINGS"
takeProfitBoxes = input.string("Off", "TP/SL Points", options=["Off","On"], inline = "2", tooltip = "Shows Take Profit and Stop Loss areas",group = groupAdvanced)
takeProfitStopLossDistance = input.int(5,"", minval = 1, maxval = 10, inline = "2", group=groupAdvanced)
autopilotMode = input.string("Off", "Autopilot Sensivity",["Off","Short-Term", "Mid-Term", "Long-Term"],tooltip = "Sets automatic settings for signals and improves their quality" ,inline = "3", group = groupAdvanced)
dashboardLocation = input.string("Bottom Right","Dashboard Location", ["Top Right","Bottom Right","Bottom Left"], inline = "4",tooltip = "Changes dashboard positions" ,group = groupAdvanced)
dashboardSize = input.string("Normal","Dashboard Size", ["Tiny","Small","Normal","Large"], inline = "5",tooltip = "Changes the size of the dashboard" ,group = groupAdvanced)
if (signalPresets == "Trend Trader [Preset]")
smartTrail := true
trendCatcher := true
neoCloud := true
trendTracer := true
smartTrail := true
if (signalPresets == "Scalper [Preset]")
sensitivity := 4
smartTrail := true
trendTracer := true
candleColorType := "Confirmation Gradient"
if (signalPresets == "Swing Trader [Preset]")
sensitivity := 18
neoCloud := true
candleColorType := "Confirmation Simple"
if (signalPresets == "Contrarian Trader [Preset]")
reversalZone := true
smartTrail := true
candleColorType := "Contrarian Gradient"
n = bar_index
// # ============================[BUY/SELL SIGNALS]============================ #
//------------------------------------------------------------------------------
//Settings
//-----------------------------------------------------------------------------{
//-----------------------------------------------------------------------------}
// # ============================[SESSIONS]============================ #
show_sesa = true
sesa_txt = 'New York'
sesa_ses = '1300-2200'
sesa_css = #ff5d00
sesa_range = true
sesa_tl = false
sesa_avg = false
sesa_vwap = false
sesa_maxmin = false
//Session B
show_sesb = true
sesb_txt = 'London'
sesb_ses = '0700-1600'
sesb_css = #2157f3
sesb_range = true
sesb_tl = false
sesb_avg = false
sesb_vwap = false
sesb_maxmin = false
//Timezones
tz_incr = 0
use_exchange = false
//Ranges Options
bg_transp = 90
show_outline = true
show_txt = true
//Dashboard
show_ses_div = false
show_day_div = false
//-----------------------------------------------------------------------------}
//Functions
//-----------------------------------------------------------------------------{
//Get session average
get_avg(session)=>
var len = 1
var float csma = na
var float sma = na
if session > session[1]
len := 1
csma := close
if session and session == session[1] and title == 'AlgoPoint' and subtitle == 'All Leaked Algos \n Instagram: algopoint \n Website: algopoint.mysellix.io' and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center'
len += 1
csma += close
sma := csma / len
sma
//Get trendline coordinates
get_linreg(session)=>
var len = 1
var float cwma = na
var float csma = na
var float csma2 = na
var float y1 = na
var float y2 = na
var float stdev = na
var float r2 = na
if session > session[1]
len := 1
cwma := close
csma := close
csma2 := close * close
if session and session == session[1] and title == 'AlgoPoint' and subtitle == 'All Leaked Algos \n Instagram: algopoint \n Website: algopoint.mysellix.io' and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center'
len += 1
csma += close
csma2 += close * close
cwma += close * len
sma = csma / len
wma = cwma / (len * (len + 1) / 2)
cov = (wma - sma) * (len+1)/2
stdev := math.sqrt(csma2 / len - sma * sma)
r2 := cov / (stdev * (math.sqrt(len*len - 1) / (2 * math.sqrt(3))))
y1 := 4 * sma - 3 * wma
y2 := 3 * wma - 2 * sma
[y1 , y2, stdev, r2]
//Session Vwap
get_vwap(session) =>
var float num = na
var float den = na
if session > session[1] and title == 'AlgoPoint' and subtitle == 'All Leaked Algos \n Instagram: algopoint \n Website: algopoint.mysellix.io' and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center'
num := close * volume
den := volume
else if session and session == session[1] and title == 'AlgoPoint' and subtitle == 'All Leaked Algos \n Instagram: algopoint \n Website: algopoint.mysellix.io' and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center'
num += close * volume
den += volume
else
num := na
[num, den]
//Set line
set_line(session, y1, y2, session_css)=>
var line tl = na
if session > session[1] and title == 'AlgoPoint' and subtitle == 'All Leaked Algos \n Instagram: algopoint \n Website: algopoint.mysellix.io' and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center'
tl := line.new(n, close, n, close, color = session_css)
if session and session == session[1] and title == 'AlgoPoint' and subtitle == 'All Leaked Algos \n Instagram: algopoint \n Website: algopoint.mysellix.io' and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center'
line.set_y1(tl, y1)
line.set_xy2(tl, n, y2)
//Set session range
get_range(session, session_name, session_css)=>
var t = 0
var max = high
var min = low
var box bx = na
var label lbl = na
if session > session[1] and showSessions and title == 'AlgoPoint' and subtitle == 'All Leaked Algos \n Instagram: algopoint \n Website: algopoint.mysellix.io' and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center'
t := time
max := high
min := low
bx := box.new(n, max, n, min
, bgcolor = color.new(session_css, bg_transp)
, border_color = show_outline ? session_css : na
, border_style = line.style_dotted)
if show_txt and showSessions and title == 'AlgoPoint' and subtitle == 'All Leaked Algos \n Instagram: algopoint \n Website: algopoint.mysellix.io' and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center'
lbl := label.new(t, max, session_name
, xloc = xloc.bar_time
, textcolor = session_css
, style = label.style_label_down
, color = color.new(color.white, 100)
, size = size.tiny)
if session and session == session[1] and showSessions and title == 'AlgoPoint' and subtitle == 'All Leaked Algos \n Instagram: algopoint \n Website: algopoint.mysellix.io' and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center'
max := math.max(high, max)
min := math.min(low, min)
box.set_top(bx, max)
box.set_rightbottom(bx, n, min)
if show_txt
label.set_xy(lbl, int(math.avg(t, time)), max)
[session ? na : max, session ? na : min]
//-----------------------------------------------------------------------------}
//Sessions
//-----------------------------------------------------------------------------{
tf = timeframe.period
var tz = use_exchange ? syminfo.timezone :
str.format('UTC{0}{1}', tz_incr >= 0 ? '+' : '-', math.abs(tz_incr))
is_sesa = math.sign(nz(time(tf, sesa_ses, tz)))
is_sesb = math.sign(nz(time(tf, sesb_ses, tz)))
//-----------------------------------------------------------------------------}
//Dashboard
//-----------------------------------------------------------------------------{
var float max_sesa = na
var float min_sesa = na
var float max_sesb = na
var float min_sesb = na
var float max_sesc = na
var float min_sesc = na
var float max_sesd = na
var float min_sesd = na
//Ranges
if show_sesa and sesa_range and title == 'AlgoPoint' and subtitle == 'All Leaked Algos \n Instagram: algopoint \n Website: algopoint.mysellix.io' and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center'
[max, min] = get_range(is_sesa, sesa_txt, sesa_css)
max_sesa := max
min_sesa := min
if show_sesb and sesb_range and title == 'AlgoPoint' and subtitle == 'All Leaked Algos \n Instagram: algopoint \n Website: algopoint.mysellix.io' and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center'
[max, min] = get_range(is_sesb, sesb_txt, sesb_css)
max_sesb := max
min_sesb := min
//Trendlines
//Mean
if show_sesa and sesa_avg and title == 'AlgoPoint' and subtitle == 'All Leaked Algos \n Instagram: algopoint \n Website: algopoint.mysellix.io' and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center'
avg = get_avg(is_sesa)
set_line(is_sesa, avg, avg, sesa_css)
if show_sesb and sesb_avg and title == 'AlgoPoint' and subtitle == 'All Leaked Algos \n Instagram: algopoint \n Website: algopoint.mysellix.io' and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center'
avg = get_avg(is_sesb)
set_line(is_sesb, avg, avg, sesb_css)
//VWAP
//-----------------------------------------------------------------------------}
//Plots
//-----------------------------------------------------------------------------{
//Plot max/min
plot(showSessions and sesa_maxmin and title == 'AlgoPoint' and subtitle == 'All Leaked Algos \n Instagram: algopoint \n Website: algopoint.mysellix.io' and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? max_sesa : na, 'Session A Maximum', sesa_css, 1, plot.style_linebr, editable = false)
plot(showSessions and sesa_maxmin and title == 'AlgoPoint' and subtitle == 'All Leaked Algos \n Instagram: algopoint \n Website: algopoint.mysellix.io' and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? min_sesa : na, 'Session A Minimum', sesa_css, 1, plot.style_linebr, editable = false)
plot(showSessions and sesb_maxmin and title == 'AlgoPoint' and subtitle == 'All Leaked Algos \n Instagram: algopoint \n Website: algopoint.mysellix.io' and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? max_sesb : na, 'Session B Maximum', sesb_css, 1, plot.style_linebr, editable = false)
plot(showSessions and sesb_maxmin and title == 'AlgoPoint' and subtitle == 'All Leaked Algos \n Instagram: algopoint \n Website: algopoint.mysellix.io' and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? min_sesb : na, 'Session B Minimum', sesb_css, 1, plot.style_linebr, editable = false)
//Plot Divider A
plotshape(is_sesa and show_ses_div and show_sesa and showSessions, "·"
, shape.square
, location.bottom
, na
, text = "."
, textcolor = sesa_css
, size = size.tiny
, display = display.all - display.status_line
, editable = false)
plotshape(is_sesa != is_sesa[1] and show_ses_div and show_sesa and showSessions, "NYE"
, shape.labelup
, location.bottom
, na
, text = "❚"
, textcolor = sesa_css
, size = size.tiny
, display = display.all - display.status_line
, editable = false)
//Plot Divider B
plotshape(is_sesb and show_ses_div and show_sesb and showSessions, "·"
, shape.labelup
, location.bottom
, na
, text = "."
, textcolor = sesb_css
, size = size.tiny
, display = display.all - display.status_line
, editable = false)
plotshape(is_sesb != is_sesb[1] and show_ses_div and show_sesb and showSessions, "LDN"
, shape.labelup
, location.bottom
, na
, text = "❚"
, textcolor = sesb_css
, size = size.tiny
, display = display.all - display.status_line
, editable = false)
// # ============================[FUNCTIONS]============================ #
type bar
float o = open
float h = high
float l = low
float c = close
float v = volume
int i = bar_index
bar b = bar.new()
nzV = nz(b.v)
f_calcV() =>
uV = 0.0
dV = 0.0
switch
(b.c - b.l) > (b.h - b.c) => uV := nzV
(b.c - b.l) < (b.h - b.c) => dV := -nzV
b.c > b.o => uV := nzV
b.c < b.o => dV := -nzV
b.c > nz(b.c[1]) => uV := nzV
b.c < nz(b.c[1]) => dV := -nzV
nz(uV[1]) > 0 => uV := uV + nzV
nz(dV[1]) < 0 => dV := dV - nzV
[uV, dV]
// # ============================[CONSTANT VARIABLES]============================ #
sma4 = ta.sma(close, 4)
sma5 = ta.sma(close, 5)
sma9 = ta.sma(close, 9)
ema50 = ta.ema(close, 50)
ema200 = ta.ema(close, 200)
bullishSignalColor = #59e08a
bearishSignalColor = #ff5959
dashboardRedText = #ee787d
dashboardGreenText = #42bda8
dashboardGreenBackground = #284444
dashboardRedBackground = #49343e
// # ============================[CANDLE COLORING]============================ #
macdFastLength = 12
macdSlowLength = 26
macdSignalLength = 9
if (candleColorType != 'Confirmation Simple')
macdFastLength := 10
macdSlowLength := 25
macdSignalLength:=8
[MacdX, signalX, histX] = ta.macd(close, macdFastLength, macdSlowLength, macdSignalLength)
//candle color scheme
greenHigh = #4ce653
greenMidHigh =#4ce653
greenMidLow =#4ce653
greenLow = #56328f
// Yellow
yellowLow = #56328f
// 4 level of red
redHigh = #ff0000
redMidHigh = #ff0000
redMidLow = #ff0000
redLow = #56328f
if (candleColorType == 'Confirmation Gradient')
greenHigh := #01d70c
greenMidHigh := #269444
greenMidLow :=#4f966c
greenLow := #425970
// Yellow
yellowLow := #513a88
// 4 level of red
redHigh := #ff0000
redMidHigh := #c21637
redMidLow := #c33252
redLow := #8e215f
if (candleColorType == 'Contrarian Gradient')
redHigh := #01d70c
redMidHigh := #269444
redMidLow :=#4f966c
redLow := #425970
// Yellow
yellowLow := #513a88
// 4 level of red
greenHigh := #ff0000
greenMidHigh := #c21637
greenMidLow := #c33252
greenLow := #8e215f
// Default color
candleBody = yellowLow
if histX > 0
if histX > histX[1] and histX[1] > 0 and title == 'AlgoPoint' and subtitle == 'All Leaked Algos \n Instagram: algopoint \n Website: algopoint.mysellix.io' and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center'
candleBody := greenLow
if histX < 0
if histX < histX[1] and histX[1] < 0 and title == 'AlgoPoint' and subtitle == 'All Leaked Algos \n Instagram: algopoint \n Website: algopoint.mysellix.io' and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center'
candleBody := redLow
// Bullish trend
if MacdX > 0 and histX > 0
candleBody := greenMidLow
if histX > histX[1] and MacdX[1] > 0 and histX[1] > 0 and title == 'AlgoPoint' and subtitle == 'All Leaked Algos \n Instagram: algopoint \n Website: algopoint.mysellix.io' and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center'
candleBody := greenMidHigh
if histX > histX[2] and MacdX[2] > 0 and histX[2] > 0 and title == 'AlgoPoint' and subtitle == 'All Leaked Algos \n Instagram: algopoint \n Website: algopoint.mysellix.io' and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center'
candleBody := greenHigh
// Bearish trend
if MacdX < 0 and histX < 0
candleBody := redMidLow
if histX < histX[1] and MacdX[1] < 0 and histX[1] < 0 and title == 'AlgoPoint' and subtitle == 'All Leaked Algos \n Instagram: algopoint \n Website: algopoint.mysellix.io' and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center'
candleBody := redMidHigh
if histX < histX[2] and MacdX[2] < 0 and histX[2] < 0 and title == 'AlgoPoint' and subtitle == 'All Leaked Algos \n Instagram: algopoint \n Website: algopoint.mysellix.io' and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center'
candleBody := redHigh
barcolor(candleColorType == 'None' ? na : candleBody, editable = false)
// # ============================[SMART TRAIL]============================ #
[smartTrailLine, fillerLine, smartTrailDirection] = LAF.getSmartTrail(10, 4, 8)
smartTrail1 = plot(smartTrail and title == 'AlgoPoint' and subtitle == 'All Leaked Algos \n Instagram: algopoint \n Website: algopoint.mysellix.io' and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? smartTrailLine : na, "Smart Trail", style = plot.style_line, color = smartTrailDirection== 'long' ? color.new(#2157f9, 0) : smartTrailDirection == 'short' ? color.new(#ff1100, 0) : na, editable = false)
smartTrail2 = plot(smartTrail and title == 'AlgoPoint' and subtitle == 'All Leaked Algos \n Instagram: algopoint \n Website: algopoint.mysellix.io' and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? fillerLine : na, "Fib 2", style = plot.style_line, transp = 100, editable = false)
fill(smartTrail1, smartTrail2, color = smartTrailDirection == 'long' ? color.new(#2157f9, 80) : smartTrailDirection == 'short' ? color.new(#ff1100, 80) : na, editable = false)
// # ============================[TREND CATCHER]============================ #
[trendCatcherLine, trendCatcherColor] = LAF.getTrendCatcher()
newTrendCatcherColor = trendCatcherColor == color.blue ? #02ff65 : #ff1100
plot(trendCatcher and title == 'AlgoPoint' and subtitle == 'All Leaked Algos \n Instagram: algopoint \n Website: algopoint.mysellix.io' and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? trendCatcherLine : na, title='Trend Catcher', linewidth=2, color=newTrendCatcherColor, editable = false)
// # ============================[NEO CLOUD]============================ #
// # ============================[REVERSAL ZONES]============================ #
// # ============================[TREND TRACER]============================ #
[trendTracerLine, trendTracerDirection] = LAF.getTrendTracer()
plot(trendTracer and title == 'AlgoPoint' and subtitle == 'All Leaked Algos \n Instagram: algopoint \n Website: algopoint.mysellix.io' and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? trendTracerLine : na, title='Trend Tracer', linewidth=2, style=plot.style_cross, color = trendTracerDirection, editable = false)
// # ============================[DASHBOARD COMPONENTS|]============================ #
trendStrengthMetric = math.abs(LAF.getTrendStrengthMetric(14, 'RMA', 21, 'EMA'))
trendStrengthMetric := trendStrengthMetric*2.5
trendIndication = trendStrengthMetric > 30 and title == 'AlgoPoint' and subtitle == 'All Leaked Algos \n Instagram: algopoint \n Website: algopoint.mysellix.io' and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? "🔥" : "❄️"
trendStrengthCellColor = newTrendCatcherColor == #02ff65 ? dashboardGreenBackground : dashboardRedBackground
trendStrengthTextColor = trendStrengthCellColor == dashboardGreenBackground ? dashboardGreenText : dashboardRedText
volatilityMetric = LAF.getVolatilityMetric()
volatilityMetric2 = ta.sma(LAF.getVolatilityMetric(), 8)
volatilityText = volatilityMetric < 30 ? 'Stable' : volatilityMetric < 80 ? 'Moderate' : 'Volatile'
volatilityEmoji = volatilityMetric2 > volatilityMetric ? '📉' : '📈'
volatilityCellColor = newTrendCatcherColor == #02ff65 ? dashboardGreenBackground : dashboardRedBackground
VolatilityTextColor = trendStrengthCellColor == dashboardGreenBackground ? dashboardGreenText : dashboardRedText
squeezeMetric = LAF.getSqueezeMetric(45, 20)
squeezeIsHigh = squeezeMetric >= 80 ? true : false
squeezeCellColor = trendTracerDirection == #02ff65 ? #1a3a3e : #482632
squeezeTextColor = trendTracerDirection != #02ff65 ? #ed3544 : #0a907a
// and title == 'AlgoPoint' and subtitle == 'All Leaked Algos \n Instagram: algopoint \n Website: algopoint.mysellix.io' and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center'
//
[uV, dV] = f_calcV()
totalVolume = uV + math.abs(dV)
//volumecolor = totalVolume >= 50 ? bullish : bearish
volumeCellColor = dashboardRedBackground
volumeTextColor = totalVolume >= 50 ? dashboardGreenText : dashboardRedText
if (totalVolume >= 50)
totalVolume := totalVolume*2
volumeCellColor := dashboardGreenBackground
else
totalVolume := totalVolume*-2
volumeSentiment = totalVolume
table_position = dashboardLocation == 'Bottom Left' ? position.bottom_left
: dashboardLocation == 'Top Right' ? position.top_right
: position.bottom_right
table_size = dashboardSize == 'Tiny' ? size.tiny
: dashboardSize == 'Small' ? size.small
: size.normal
tb = table.new(table_position, 7, 7
, bgcolor = #1e222d
, border_color = #373a46
, border_width = 1
, frame_color = #373a46
, frame_width = 1)
if showDashboard
if barstate.islast
tb.cell(0, 2, autopilotMode == 'Off' ? "🔎 Optimal Sensivity" : "✈️ Autopilot Enabled", text_color = color.white, text_size = table_size, text_halign = text.align_left)
tb.cell(0, 3, str.tostring(trendIndication) + "Trend Strength", text_color = color.white, text_size = table_size, text_halign = text.align_left)
tb.cell(0, 4, volatilityEmoji+ " Lux Volatility", text_color = color.white, text_size = table_size, text_halign = text.align_left)
tb.cell(0, 5, "🔃 Squeeze", text_color = color.white, text_size = table_size, text_halign = text.align_left)
tb.cell(0, 6, "💧 Volume Sentiment", text_color = color.white, text_size = table_size, text_halign = text.align_left)
tb.cell(1, 2, autopilotMode, text_color = color.white, text_size = table_size)
tb.cell(1, 3, str.tostring(trendStrengthMetric, format.percent), text_color=trendStrengthTextColor, text_size=table_size, bgcolor = trendStrengthCellColor)
tb.cell(1, 4, volatilityText, text_color = VolatilityTextColor, text_size = table_size, bgcolor = volatilityCellColor)
tb.cell(1, 5, str.tostring(squeezeMetric, format.percent), text_color= squeezeTextColor, text_size=table_size, bgcolor = squeezeCellColor)
tb.cell(1, 6, str.tostring(math.min(volumeSentiment, 100.), format.percent), text_color = volumeTextColor, text_size = table_size, bgcolor = volumeCellColor)
//************************************************************************************************************
// REV ZONES
//************************************************************************************************************
indiSet = false
source = hlc3
type = 'SuperSmoother'
length = 100
innermult = 1.0
outermult = 2.415
ChartSet = false
drawchannel = true
displayzone = true
zonetransp = 60
displayline = true
MTFSet = false
enable_mtf = true
mtf_disp_typ = 'On Hover'
mtf_typ = 'Auto'
mtf_lvl1 = 'D'
mtf_lvl2 = 'W'
//************************************************************************************************************
// Functions Start {
//************************************************************************************************************
var pi = 2 * math.asin(1)
var mult = pi * innermult
var mult2 = pi * outermult
var gradsize = 0.5
var gradtransp = zonetransp
//-----------------------
// Ehler SwissArmyKnife Function
//-----------------------
SAK_smoothing(_type, _src, _length) =>
c0 = 1.0
c1 = 0.0
b0 = 1.0
b1 = 0.0
b2 = 0.0
a1 = 0.0
a2 = 0.0
alpha = 0.0
beta = 0.0
gamma = 0.0
cycle = 2 * pi / _length
if _type == 'Ehlers EMA'
alpha := (math.cos(cycle) + math.sin(cycle) - 1) / math.cos(cycle)
b0 := alpha
a1 := 1 - alpha
a1
if _type == 'Gaussian'
beta := 2.415 * (1 - math.cos(cycle))
alpha := -beta + math.sqrt(beta * beta + 2 * beta)
c0 := alpha * alpha
a1 := 2 * (1 - alpha)
a2 := -(1 - alpha) * (1 - alpha)
a2
if _type == 'Butterworth'
beta := 2.415 * (1 - math.cos(cycle))
alpha := -beta + math.sqrt(beta * beta + 2 * beta)
c0 := alpha * alpha / 4
b1 := 2
b2 := 1
a1 := 2 * (1 - alpha)
a2 := -(1 - alpha) * (1 - alpha)
a2
if _type == 'BandStop'
beta := math.cos(cycle)
gamma := 1 / math.cos(cycle * 2 * 0.1) // delta default to 0.1. Acceptable delta -- 0.05<d<0.5
alpha := gamma - math.sqrt(gamma * gamma - 1)
c0 := (1 + alpha) / 2
b1 := -2 * beta
b2 := 1
a1 := beta * (1 + alpha)
a2 := -alpha
a2
if _type == 'SMA'
c1 := 1 / _length
b0 := 1 / _length
a1 := 1
a1
if _type == 'EMA'
alpha := 2 / (_length + 1)
b0 := alpha
a1 := 1 - alpha
a1
if _type == 'RMA'
alpha := 1 / _length
b0 := alpha
a1 := 1 - alpha
a1
_Input = _src
_Output = 0.0
_Output := c0 * (b0 * _Input + b1 * nz(_Input[1]) + b2 * nz(_Input[2])) + a1 * nz(_Output[1]) + a2 * nz(_Output[2]) - c1 * nz(_Input[_length])
_Output
//-----------------------
// SuperSmoother Function
//-----------------------
supersmoother(_src, _length) =>
s_a1 = math.exp(-math.sqrt(2) * pi / _length)
s_b1 = 2 * s_a1 * math.cos(math.sqrt(2) * pi / _length)
s_c3 = -math.pow(s_a1, 2)
s_c2 = s_b1
s_c1 = 1 - s_c2 - s_c3
ss = 0.0
ss := s_c1 * _src + s_c2 * nz(ss[1], _src[1]) + s_c3 * nz(ss[2], _src[2])
ss
//-----------------------
// Auto TimeFrame Function
//-----------------------
// ————— Converts current chart resolution into a float minutes value.
f_resInMinutes() =>
_resInMinutes = timeframe.multiplier * (timeframe.isseconds ? 1. / 60 : timeframe.isminutes ? 1. : timeframe.isdaily ? 60. * 24 : timeframe.isweekly ? 60. * 24 * 7 : timeframe.ismonthly ? 60. * 24 * 30.4375 : na)
_resInMinutes
get_tf(_lvl) =>
y = f_resInMinutes()
z = timeframe.period
if mtf_typ == 'Auto'
if y < 1
z := _lvl == 1 ? '1' : _lvl == 2 ? '5' : z
z
else if y <= 3
z := _lvl == 1 ? '5' : _lvl == 2 ? '15' : z
z
else if y <= 10
z := _lvl == 1 ? '15' : _lvl == 2 ? '60' : z
z
else if y <= 30
z := _lvl == 1 ? '60' : _lvl == 2 ? '240' : z
z
else if y <= 120
z := _lvl == 1 ? '240' : _lvl == 2 ? 'D' : z
z
else if y <= 240
z := _lvl == 1 ? 'D' : _lvl == 2 ? 'W' : z
z
else if y <= 1440
z := _lvl == 1 ? 'W' : _lvl == 2 ? 'M' : z
z
else if y <= 10080
z := _lvl == 1 ? 'M' : z
z
else
z := z
z
else
z := _lvl == 1 ? mtf_lvl1 : _lvl == 2 ? mtf_lvl2 : z
z
z
//-----------------------
// Mean Reversion Channel Function
//-----------------------
get_mrc() =>
v_condition = 0
v_meanline = source
v_meanrange = supersmoother(ta.tr, length)
//-- Get Line value
if type == 'SuperSmoother'
v_meanline := supersmoother(source, length)
v_meanline
if type != 'SuperSmoother'
v_meanline := SAK_smoothing(type, source, length)
v_meanline
v_upband1 = v_meanline + v_meanrange * mult
v_loband1 = v_meanline - v_meanrange * mult
v_upband2 = v_meanline + v_meanrange * mult2
v_loband2 = v_meanline - v_meanrange * mult2
//-- Check Condition
if close > v_meanline
v_upband2_1 = v_upband2 + v_meanrange * gradsize * 4
v_upband2_9 = v_upband2 + v_meanrange * gradsize * -4
if high >= v_upband2_9 and high < v_upband2
v_condition := 1
v_condition
else if high >= v_upband2 and high < v_upband2_1
v_condition := 2
v_condition
else if high >= v_upband2_1
v_condition := 3
v_condition
else if close <= v_meanline + v_meanrange
v_condition := 4
v_condition
else
v_condition := 5
v_condition
if close < v_meanline
v_loband2_1 = v_loband2 - v_meanrange * gradsize * 4
v_loband2_9 = v_loband2 - v_meanrange * gradsize * -4
if low <= v_loband2_9 and low > v_loband2
v_condition := -1
v_condition
else if low <= v_loband2 and low > v_loband2_1
v_condition := -2
v_condition
else if low <= v_loband2_1
v_condition := -3
v_condition
else if close >= v_meanline + v_meanrange
v_condition := -4
v_condition
else
v_condition := -5
v_condition
[v_meanline, v_meanrange, v_upband1, v_loband1, v_upband2, v_loband2, v_condition]
//-----------------------
// MTF Analysis
//-----------------------
get_stat(_cond) =>
ret = 'Price at Mean Line\n'
if _cond == 1
ret := 'Overbought (Weak)\n'
ret
else if _cond == 2
ret := 'Overbought\n'
ret
else if _cond == 3
ret := 'Overbought (Strong)\n'
ret
else if _cond == 4
ret := 'Price Near Mean\n'
ret
else if _cond == 5
ret := 'Price Above Mean\n'
ret
else if _cond == -1
ret := 'Oversold (Weak)\n'
ret
else if _cond == -2
ret := 'Oversold\n'
ret
else if _cond == -3
ret := 'Oversold (Strong)\n'
ret
else if _cond == -4
ret := 'Price Near Mean\n'
ret
else if _cond == -5
ret := 'Price Below Mean\n'
ret
ret
//-----------------------
// Chart Drawing Function
//-----------------------
format_price(x) =>
y = str.tostring(x, '0.00000')
if x > 10
y := str.tostring(x, '0.000')
y
if x > 1000
y := str.tostring(x, '0.00')
y
y
f_PriceLine(_ref, linecol) =>
line.new(x1=bar_index, x2=bar_index - 1, y1=_ref, y2=_ref, extend=extend.left, color=linecol)
f_MTFLabel(_txt, _yloc) =>
label.new(x=time + math.round(ta.change(time) * 20), y=_yloc, xloc=xloc.bar_time, text=mtf_disp_typ == 'Always Display' ? _txt : 'Check MTF', tooltip=mtf_disp_typ == 'Always Display' ? '' : _txt, color=color.black, textcolor=color.white, size=size.normal, style=mtf_disp_typ == 'On Hover' and displayline ? label.style_label_lower_left : label.style_label_left, textalign=text.align_left)
//} Function End
//************************************************************************************************************
// Calculate Channel
//************************************************************************************************************
var tf_0 = timeframe.period
var tf_1 = get_tf(1)
var tf_2 = get_tf(2)
textstylist = table.new(textVPosition + '_' + textHPosition, 1, 3)
[meanline, meanrange, upband1, loband1, upband2, loband2, condition] = get_mrc()
[mtf1_meanline, mtf1_meanrange, mtf1_upband1, mtf1_loband1, mtf1_upband2, mtf1_loband2, mtf1_condition] = request.security(syminfo.tickerid, tf_1, get_mrc())
[mtf2_meanline, mtf2_meanrange, mtf2_upband1, mtf2_loband1, mtf2_upband2, mtf2_loband2, mtf2_condition] = request.security(syminfo.tickerid, tf_2, get_mrc())
//************************************************************************************************************
// Drawing Start {
//************************************************************************************************************
float p_meanline = drawchannel ? meanline : na
float p_upband1 = drawchannel ? upband1 : na
float p_loband1 = drawchannel ? loband1 : na
float p_upband2 = drawchannel ? upband2 : na
float p_loband2 = drawchannel ? loband2 : na
//z = plot(p_meanline, color=color.new(#FFCD00, 0), style=plot.style_line, title=' Mean', linewidth=2)
//x1 = plot(p_upband1, color=color.new(color.green, 50), style=plot.style_circles, title=' R1', linewidth=1)
//x2 = plot(p_loband1, color=color.new(color.green, 50), style=plot.style_circles, title=' S1', linewidth=1)
//y1 = plot(p_upband2, color=color.new(color.red, 50), style=plot.style_line, title=' R2', linewidth=1)
//y2 = plot(p_loband2, color=color.new(color.red, 50), style=plot.style_line, title=' S2', linewidth=1)
//-----------------------
// Draw zone
//-----------------------
//---
var color1 = #FF0000
var color2 = #FF4200
var color3 = #FF5D00
var color4 = #FF7400
var color5 = #FF9700
var color6 = #FFAE00
var color7 = #FFC500
var color8 = #FFCD00
//---
float upband2_1 = drawchannel and displayzone ? upband2 + meanrange * gradsize * 4 : na
float loband2_1 = drawchannel and displayzone ? loband2 - meanrange * gradsize * 4 : na
float upband2_2 = drawchannel and displayzone ? upband2 + meanrange * gradsize * 3 : na
float loband2_2 = drawchannel and displayzone ? loband2 - meanrange * gradsize * 3 : na
float upband2_3 = drawchannel and displayzone ? upband2 + meanrange * gradsize * 2 : na
float loband2_3 = drawchannel and displayzone ? loband2 - meanrange * gradsize * 2 : na
float upband2_4 = drawchannel and displayzone ? upband2 + meanrange * gradsize * 1 : na
float loband2_4 = drawchannel and displayzone ? loband2 - meanrange * gradsize * 1 : na
float upband2_5 = drawchannel and displayzone ? upband2 + meanrange * gradsize * 0 : na
float loband2_5 = drawchannel and displayzone ? loband2 - meanrange * gradsize * 0 : na
float upband2_6 = drawchannel and displayzone ? upband2 + meanrange * gradsize * -1 : na
float loband2_6 = drawchannel and displayzone ? loband2 - meanrange * gradsize * -1 : na
float upband2_7 = drawchannel and displayzone ? upband2 + meanrange * gradsize * -2 : na
float loband2_7 = drawchannel and displayzone ? loband2 - meanrange * gradsize * -2 : na
float upband2_8 = drawchannel and displayzone ? upband2 + meanrange * gradsize * -3 : na
float loband2_8 = drawchannel and displayzone ? loband2 - meanrange * gradsize * -3 : na
float upband2_9 = drawchannel and displayzone ? upband2 + meanrange * gradsize * -4 : na
float loband2_9 = drawchannel and displayzone ? loband2 - meanrange * gradsize * -4 : na
table.cell(textstylist, 0, 0, title, width, height, c_title, a_title, text_size=s_title, bgcolor=c_bg)
up1 = plot(reversalZone ? upband2_1 : na, color = color.black, transp = 100, editable = false)
up2 = plot(reversalZone ?upband2_5:na, color = color.black, transp = 100, editable = false)
up3 = plot(reversalZone ?upband2_9:na, color = color.black, transp = 100, editable = false)
dp1 = plot(reversalZone ?loband2_1:na, color = color.black, transp = 100, editable = false)
dp2 = plot(reversalZone ?loband2_5:na, color = color.black, transp = 100, editable = false)
dp3 = plot(reversalZone ?loband2_9:na, color = color.black, transp = 100, editable = false)
fill(up1, up2, color = #56202d, transp = 20, editable = false)
fill(up2, up3, color = #3f1d29, transp = 60, editable = false)
fill(dp1, dp2, color = #0f3e3f, transp = 20, editable = false)
fill(dp2, dp3, color = #113135, transp = 60, editable = false)
//[upband2_1, upband2_5, upband2_9, loband2_1, loband2_5, loband2_9]
tenkan_len = 365
tenkan_mult = 3
kijun_len = 365
kijun_mult = 7
spanB_len = 365
spanB_mult = 15
offset = 2
//------------------------------------------------------------------------------
avg(src,length,mult)=>
atr = ta.atr(length)*mult
up = hl2 + atr
dn = hl2 - atr
upper = 0.,lower = 0.
upper := src[1] < upper[1] ? math.min(up,upper[1]) : up
lower := src[1] > lower[1] ? math.max(dn,lower[1]) : dn
os = 0,max = 0.,min = 0.
os := src > upper ? 1 : src < lower ? 0 : os[1]
spt = os == 1 ? lower : upper
max := ta.cross(src,spt) ? math.max(src,max[1]) : os == 1 ? math.max(src,max[1]) : spt
min := ta.cross(src,spt) ? math.min(src,min[1]) : os == 0 ? math.min(src,min[1]) : spt
math.avg(max,min)
//------------------------------------------------------------------------------
tenkan = avg(close,tenkan_len,tenkan_mult)
kijun = avg(close,kijun_len,kijun_mult)
senkouA = math.avg(kijun,tenkan)
senkouB = avg(close,spanB_len,spanB_mult)
//------------------------------------------------------------------------------
tenkan_css = #2156f300
kijun_css = #ff5e0000
cloud_a = color.new(#006989, 47)
cloud_b = color.new(#ff5252, 66)
chikou_css = #7b1fa2
plot(neoCloud ? tenkan : na,'Tenkan-Sen',tenkan_css, editable = false)
plot(neoCloud ? kijun : na,'Kijun-Sen',kijun_css, editable = false)
plot(neoCloud and ta.crossover(tenkan,kijun) ? kijun : na,'Crossover',#2156f300,3,plot.style_circles, editable = false)
plot(neoCloud and ta.crossunder(tenkan,kijun) ? kijun : na,'Crossunder',#ff5e0000,3,plot.style_circles, editable = false)
A = plot(neoCloud ? senkouA: na,'Senkou Span A',na,offset=offset-1, editable = false)
B = plot(neoCloud ? senkouB : na,'Senkou Span B',na,offset=offset-1, editable = false)
fill(A,B,senkouA > senkouB ? cloud_a : cloud_b)
lastNeo = int(senkouA + senkouB)
last5Neo = ta.sma(lastNeo, 2)
plot(close,'Chikou',chikou_css,offset=-offset+1,display=display.none, editable = false)
// Wylicz pozycję kwadratu
ltp1 = bar_index
rtp1 = bar_index + 40
[lowBound, midBound, highBound] = LAF.getTPSLBoxes(6.0)
// Stwórz rzeczywisty kwadrat
//tp1box = box.new(left=ltp1, top=ttp1, right=rtp1, bottom=btp1, border_color=#3666f5, border_width=2, border_style=line.style_solid, bgcolor=color.new(#3666f5, 53), text="TP1 : " + str.tostring(close), text_size=size.large, text_color=color.new(#3666f5, 0), text_wrap=text.wrap_auto)
//var boxes = array.new<box>()
//boxes.push(box.new(left = ltp1, top = close+highBound, right = rtp1, bottom = close + midBound, border_color=#3666f5, border_width=2, border_style=line.style_solid, bgcolor=color.new(#3666f5, 70), text="TP/SL 2 : " + str.tostring(close), text_size=size.large, text_color=color.new(#3666f5, 0), text_wrap=text.wrap_auto))
//boxes.push(box.new(left = ltp1, top = close+midBound, right = rtp1, bottom = close + lowBound, border_color=#3666f5, border_width=2, border_style=line.style_solid, bgcolor=color.new(#3666f5, 40), text="TP/SL 1 : " + str.tostring(close), text_size=size.large, text_color=color.new(#3666f5, 0), text_wrap=text.wrap_auto))
//SL1 = box.new(left = ltp1, top = close-highBound, right = rtp1, bottom = close - midBound, border_color=#3666f5, border_width=2, border_style=line.style_solid, bgcolor=color.new(#3666f5, 70), text="TP/SL 2 : " + str.tostring(close), text_size=size.large, text_color=color.new(#3666f5, 0), text_wrap=text.wrap_auto)
//SL2 = box.new(left = ltp1, top = close-midBound, right = rtp1, bottom = close - lowBound, border_color=#3666f5, border_width=2, border_style=line.style_solid, bgcolor=color.new(#3666f5, 40), text="TP/SL 1 : " + str.tostring(close), text_size=size.large, text_color=color.new(#3666f5, 0), text_wrap=text.wrap_auto)
// Usuń poprzednie ramki
//box.delete(boxes.shift())
//box.delete(SL1[1])
//box.delete(SL2[1])
//box.delete(boxes.shift())
// ==== Overview ====
// ==================
// WaveTrend 3D (WT3D) is a novel implementation of the famous WaveTrend (WT) indicator and has been completely redesigned from the ground up to address some
// of the inherent shortcomings associated with the traditional WT algorithm, including:
// (1) unbounded extremes
// (2) susceptibility to whipsaw
// (3) lack of insight into other timeframes
// Furthermore, WT3D expands upon the original functionality of WT by providing:
// (1) first-class support for multi-timeframe (MTF) analysis
// (2) kernel-based regression for trend reversal confirmation
// (3) various options for signal smoothing and transformation
// (4) a unique mode for visualizing an input series as a symmetrical, three-dimensional waveform useful for pattern identification and cycle-related analysis
// Fundamental Assumptions:
// (1) There exists a probability density function that describes the relative likelihood for a price to visit a given value.
// (2) The probability density function for price is a function of time.
// (3) The probability density function can approximate a Gaussian distribution (shown below).
// ___
// .::~!:.. |
// :ΞΞΞΞ~!ΞΞΞ!. |
// .ΞJΞΞΞΞ~!ΞΞΞ?J^ |
// :J?ΞΞΞΞΞ~!ΞΞΞΞΞJ^ |
// :J?ΞΞΞΞΞΞ~!ΞΞΞΞΞΞ??. |
// :JΞΞΞΞΞΞΞΞ~!ΞΞΞΞΞΞΞ?J^ |
// :JΞΞΞΞΞΞΞΞΞ~!ΞΞΞΞΞΞΞΞ?J^ [ PRICE ]
// .:~ΞΞΞΞΞΞΞΞΞΞ~!ΞΞΞΞΞΞΞΞΞ!!~ |
// :?~^ΞΞΞΞΞΞΞΞΞΞ~!ΞΞΞΞΞΞΞΞΞ!^Ξ! |
// ~:^^^ΞΞΞΞΞΞΞΞΞΞ~!ΞΞΞΞΞΞΞΞΞ!^^!Ξ. |
// .Ξ!^^^^ΞΞΞΞΞΞΞΞΞΞ~!ΞΞΞΞΞΞΞΞΞ!^^^~Ξ~ |
// .~Ξ~^^^^^ΞΞΞΞΞΞΞΞΞΞ~!ΞΞΞΞΞΞΞΞΞ!^^^^^!Ξ: |
// .~Ξ~^^^^^^^ΞΞΞΞΞΞΞΞΞΞ~!ΞΞΞΞΞΞΞΞΞ!^^^^^^~!!^. |
// ....::^^!~~^^^^^^^^^ΞΞΞΞΞΞΞΞΞΞ~!ΞΞΞΞΞΞΞΞΞ!^^^^^^^^^~!^^::...... |
// ..:::^^^^^^^::::::::::::::ΞΞΞΞΞΞΞΞΞΞ~!ΞΞΞΞΞΞΞΞΞ!::::::::::::^^^^^^^^:::.. |
//
// -------------------------------- [ TIME ] -------------------------------|
// How to use this indicator:
// - The basic usage of WT3D is similar to how one would use the traditional WT indicator.
// - Divergences can be spotted by finding "trigger waves", which are small waves that immediately follow a larger wave. These can also be thought of as Lower-Highs and Higher-Lows in the oscillator.
// - Instead of the SMA-cross in the original WT, the primary mechanism for identifying potential pivots are the crossovers of the fast/normal speed oscillators, denoted by the small red/green circles.
// - The larger red/green circles represent points where there could be a potential trigger wave for a Divergence. Settings related to Divergence detection can be configured in the "Divergence" section.
// - For overbought/oversold conditions, the 0.5 and -0.5 levels are convenient since the normal-speed oscillator will only exceed this level ~25% of the time.
// - For less experienced users, focusing on the three oscillators is recommended since they give critical information from multiple timeframes that can help to identify trends and spot potential divergences.
// - For more experienced users, this indicator also has many other valuable features, such as Center of Gravity (CoG) smoothing, Kernel Estimate Crossovers, a mirrored mode for cycle analysis, and more.
// - Note: Additional resources for learning/using the more advanced features of this indicator are a work in progress, but in the meantime, I am happy to answer any questions.
// ================
// ==== Inputs ====
// ================
// Signal Settings
src = close
useMirror = false
useEma = false
emaLength = 3
useCog = false
cogLength = 6
oscillatorLookback =20
quadraticMeanLength = 50
src := useEma ? ta.ema(src, emaLength) : src
src := useCog ? ta.cog(src, cogLength) : src
speedToEmphasize = 'Normal'
emphasisWidth = 2
useKernelMA = false
useKernelEmphasis = false
// Oscillator Settings
offset := 0
showOsc = true
showOsc := showOsc
float f_length = 0.75
float f_smoothing = 0.45
float n_length = 1.0
float n_smoothing = 1.0
float s_length = 1.75
float s_smoothing = 2.5
// Divergence Detection
divThreshold = 30
sizePercent = 40
// Overbought/Oversold Zones (Reversal Zones)
showObOs = false
invertObOsColors = false
// Transparencies and Gradients
areaBackgroundTrans = 128.
areaForegroundTrans = 64.
lineBackgroundTrans = 2.6
lineForegroundTrans = 2.
customTransparency = 30
maxStepsForGradient = 8
// The defaults are colors that Google uses for its Data Science libraries (e.g. TensorFlow). They are considered to be colorblind-safe.
var color fastBullishColor = color.black
var color normalBullishColor = color.black
var color slowBullishColor = color.black
var color fastBearishColor = color.black
var color normalBearishColor = color.black
var color slowBearishColor =color.black
var color c_bullish = color.black
var color c_bearish = color.black
lineBackgroundTrans := lineBackgroundTrans * customTransparency
areaBackgroundTrans := areaBackgroundTrans * customTransparency
lineForegroundTrans := lineForegroundTrans * customTransparency
areaForegroundTrans := areaForegroundTrans * customTransparency
areaFastTrans = areaBackgroundTrans
lineFastTrans = lineBackgroundTrans
areaNormalTrans = areaBackgroundTrans
lineNormalTrans = lineBackgroundTrans
areaSlowTrans = areaForegroundTrans
lineSlowTrans = lineForegroundTrans
switch speedToEmphasize
"Slow" =>
areaFastTrans := areaBackgroundTrans
lineFastTrans := lineBackgroundTrans
areaNormalTrans := areaBackgroundTrans
lineNormalTrans := lineBackgroundTrans
areaSlowTrans := areaForegroundTrans
lineSlowTrans := lineForegroundTrans
"Normal" =>
areaFastTrans := areaBackgroundTrans
lineFastTrans := lineBackgroundTrans
areaNormalTrans := areaForegroundTrans
lineNormalTrans := lineForegroundTrans
areaSlowTrans := areaBackgroundTrans
lineSlowTrans := lineBackgroundTrans
"Fast" =>
areaFastTrans := areaForegroundTrans
lineFastTrans := lineForegroundTrans
areaNormalTrans := areaBackgroundTrans
lineNormalTrans := lineBackgroundTrans
areaSlowTrans := areaBackgroundTrans
lineSlowTrans := lineBackgroundTrans
"None" =>
areaFastTrans := areaBackgroundTrans
lineFastTrans := lineBackgroundTrans
areaNormalTrans := areaBackgroundTrans
lineNormalTrans := lineBackgroundTrans
areaSlowTrans := areaBackgroundTrans
lineSlowTrans := lineBackgroundTrans
// =================================
// ==== Color Helper Functions =====
// =================================
getPlotColor(signal, bullColor, bearColor) =>
signal >= 0.0 ? bullColor : bearColor
getAreaColor(signal, useMomentum, bullColor, bearColor) =>
if useMomentum
ta.rising(signal, 1) ? bullColor : bearColor
else
signal >= 0.0 ? bullColor : bearColor
getColorGradientFromSteps(_source, _center, _steps, weakColor, strongColor) =>
var float _qtyAdvDec = 0.
var float _maxSteps = math.max(1, _steps)
bool _xUp = ta.crossover(_source, _center)
bool _xDn = ta.crossunder(_source, _center)
float _chg = ta.change(_source)
bool _up = _chg > 0
bool _dn = _chg < 0
bool _srcBull = _source > _center
bool _srcBear = _source < _center
_qtyAdvDec := _srcBull ? _xUp ? 1 : _up ? math.min(_maxSteps, _qtyAdvDec + 1) : _dn ? math.max(1, _qtyAdvDec - 1) : _qtyAdvDec : _srcBear ? _xDn ? 1 : _dn ? math.min(_maxSteps, _qtyAdvDec + 1) : _up ? math.max(1, _qtyAdvDec - 1) : _qtyAdvDec : _qtyAdvDec
color colorGradient = color.from_gradient(_qtyAdvDec, 1, _maxSteps, weakColor, strongColor)
colorGradient
getColorGradientFromSource(series, _min, _max, weakColor, strongColor) =>
var float baseLineSeries = _min + (_max - _min) / 2
color colorGradient = series >= baseLineSeries ? color.from_gradient(value=series, bottom_value=baseLineSeries, top_value=_max, bottom_color=weakColor, top_color=strongColor) : color.from_gradient(series, _min, baseLineSeries, strongColor, weakColor)
colorGradient
// ================================
// ==== Main Helper Functions =====
// ================================
normalizeDeriv(_src, _quadraticMeanLength) =>
float derivative = _src - _src[2]
quadraticMean = math.sqrt(nz(math.sum(math.pow(derivative, 2), _quadraticMeanLength) / _quadraticMeanLength))
derivative/quadraticMean
tanh(series float _src) =>
-1 + 2/(1 + math.exp(-2*_src))
dualPoleFilter(float _src, float _lookback) =>
float _omega = -99 * math.pi / (70 * _lookback)
float _alpha = math.exp(_omega)
float _beta = -math.pow(_alpha, 2)
float _gamma = math.cos(_omega) * 2 * _alpha
float _delta = 1 - _gamma - _beta
float _slidingAvg = 0.5 * (_src + nz(_src[1], _src))
float _filter = na
_filter := (_delta*_slidingAvg) + _gamma*nz(_filter[1]) + _beta*nz(_filter[2])
_filter
getOscillator(float src, float smoothingFrequency, int quadraticMeanLength) =>
nDeriv = normalizeDeriv(src, quadraticMeanLength)
hyperbolicTangent = tanh(nDeriv)
result = dualPoleFilter(hyperbolicTangent, smoothingFrequency)
// =================================
// ==== Oscillator Calculations ====
// =================================
// Fast Oscillator + Mirror
offsetFast = offset
f_lookback = f_smoothing * oscillatorLookback
signalFast = getOscillator(src, f_lookback, quadraticMeanLength)
seriesFast = f_length*signalFast+offsetFast
seriesFastMirror = useMirror ? -seriesFast + 2*offsetFast : na
// Normal Oscillator + Mirror
offsetNormal = 0
n_lookback = n_smoothing * oscillatorLookback
signalNormal = getOscillator(src, n_lookback, quadraticMeanLength)
seriesNormal = n_length*signalNormal+offsetNormal
seriesNormalMirror = useMirror ? -seriesNormal + 2*offsetNormal : na
// Slow Oscillator + Mirror
offsetSlow = -offset
s_lookback = s_smoothing * oscillatorLookback
signalSlow = getOscillator(src, s_lookback, quadraticMeanLength)
seriesSlow = s_length*signalSlow+offsetSlow
seriesSlowMirror = useMirror ? -seriesSlow + 2*offsetSlow : na
// =====================================
// ==== Color Gradient Calculations ====
// =====================================
// Fast Color Gradients (Areas and Lines)
fastBaseColor = getPlotColor(signalFast, fastBullishColor, fastBearishColor)
fastBaseColorInverse = getPlotColor(signalFast, fastBearishColor, fastBullishColor)
fastAreaGradientFromSource = getColorGradientFromSource(seriesFast, -1.+offsetFast, 1+offsetFast, color.new(fastBaseColor, areaFastTrans), fastBaseColor)
fastAreaGradientFromSteps = getColorGradientFromSteps(seriesFast, offsetFast, maxStepsForGradient, color.new(fastBaseColor, areaFastTrans), fastBaseColor)
fastLineGradientFromSource = getColorGradientFromSource(seriesFast, -1+offsetFast, 1+offsetFast, color.new(fastBaseColor, lineFastTrans), fastBaseColor)
fastLineGradientFromSteps = getColorGradientFromSteps(seriesFast, offsetFast, maxStepsForGradient, color.new(fastBaseColor, lineFastTrans), fastBaseColor)
fastAreaGradientFromSourceInverse = getColorGradientFromSource(seriesFast, -1.+offsetFast, 1+offsetFast, color.new(fastBaseColorInverse, areaFastTrans), fastBaseColorInverse)
fastAreaGradientFromStepsInverse = getColorGradientFromSteps(seriesFast, offsetFast, maxStepsForGradient, color.new(fastBaseColorInverse, areaFastTrans), fastBaseColorInverse)
// Normal Color Gradients (Areas and Lines)
normalBaseColor = getPlotColor(signalNormal, normalBullishColor, normalBearishColor)
normalBaseColorInverse = getPlotColor(signalNormal, normalBearishColor, normalBullishColor)
normalAreaGradientFromSource = getColorGradientFromSource(seriesNormal, -1.+offsetNormal, 1.+offsetNormal, color.new(normalBaseColor, areaNormalTrans), normalBaseColor)
normalAreaGradientFromSteps = getColorGradientFromSteps(seriesNormal, offsetNormal, maxStepsForGradient, color.new(normalBaseColor, areaNormalTrans), normalBaseColor)
normalLineGradientFromSource = getColorGradientFromSource(seriesNormal, -1+offsetNormal, 1+offsetNormal, color.new(normalBaseColor, lineNormalTrans), normalBaseColor)
normalLineGradientFromSteps = getColorGradientFromSteps(seriesNormal, offsetNormal, maxStepsForGradient, color.new(normalBaseColor, lineNormalTrans), normalBaseColor)
normalAreaGradientFromSourceInverse = getColorGradientFromSource(seriesNormal, -1.+offsetNormal, 1.+offsetNormal, color.new(normalBaseColorInverse, areaNormalTrans), normalBaseColorInverse)
normalAreaGradientFromStepsInverse = getColorGradientFromSteps(seriesNormal, offsetNormal, maxStepsForGradient, color.new(normalBaseColorInverse, areaNormalTrans), normalBaseColorInverse)
// Slow Color Gradients (Areas and Lines)
slowBaseColor = getPlotColor(signalSlow, slowBullishColor, slowBearishColor)
slowBaseColorInverse = getPlotColor(signalSlow, slowBearishColor, slowBullishColor)
slowAreaGradientFromSource = getColorGradientFromSource(seriesSlow, -1.75+offsetSlow, 1.75+offsetSlow, color.new(slowBaseColor, areaSlowTrans), slowBaseColor)
slowAreaGradientFromSteps = getColorGradientFromSteps(seriesSlow, offsetSlow, maxStepsForGradient, color.new(slowBaseColor, areaSlowTrans), slowBaseColor)
slowLineGradientFromSource = getColorGradientFromSource(seriesSlow, -1.75+offsetSlow, 1.75+offsetSlow, color.new(slowBaseColor, lineSlowTrans), slowBaseColor)
slowLineGradientFromSteps = getColorGradientFromSteps(seriesSlow, offsetSlow, maxStepsForGradient, color.new(slowBaseColor, lineSlowTrans), slowBaseColor)
slowAreaGradientFromSourceInverse = getColorGradientFromSource(seriesSlow, -1.75+offsetSlow, 1.75+offsetSlow, color.new(slowBaseColorInverse, areaSlowTrans), slowBaseColorInverse)
slowAreaGradientFromStepsInverse = getColorGradientFromSteps(seriesSlow, offsetSlow, maxStepsForGradient, color.new(slowBaseColorInverse, areaSlowTrans), slowBaseColorInverse)
// =========================================
// ==== Plot Parameters and Logic Gates ====
// =========================================
// Speed Booleans
isSlow = speedToEmphasize == "Slow"
isNormal = speedToEmphasize == "Normal"
isFast = speedToEmphasize == "Fast"
// Series Colors
seriesSlowColor = showOsc or isSlow ? color.new(slowLineGradientFromSource, lineSlowTrans) : na
seriesNormalColor = showOsc or isNormal ? color.new(normalLineGradientFromSource, lineNormalTrans) : na
seriesFastColor = showOsc or isFast ? color.new(fastLineGradientFromSource, lineFastTrans) : na
seriesSlowMirrorColor = useMirror ? seriesSlowColor : na
seriesNormalMirrorColor = useMirror ? seriesNormalColor : na
seriesFastMirrorColor = useMirror ? seriesFastColor : na
// Series Line Widths
seriesSlowWidth = isSlow ? emphasisWidth : 1
seriesNormalWidth = isNormal ? emphasisWidth : 1
seriesFastWidth = isFast ? emphasisWidth : 1
seriesSlowMirrorWidth = useMirror ? seriesSlowWidth : na
seriesNormalMirrorWidth = useMirror ? seriesNormalWidth : na
seriesFastMirrorWidth = useMirror ? seriesFastWidth : na
// Speed Related Switches
seriesEmphasis = switch
isFast => seriesFast
isNormal => seriesNormal
isSlow => seriesSlow
=> na
//
colorLineEmphasis = switch
isFast => fastLineGradientFromSource
isNormal => normalLineGradientFromSource
isSlow => slowLineGradientFromSource
=> na
colorAreaEmphasis = switch
isFast => fastAreaGradientFromSource
isNormal => normalAreaGradientFromSource
isSlow => slowAreaGradientFromSource
=> na
// Crossover Signals
bearishCross = ta.crossunder(seriesFast, seriesNormal) and seriesNormal > 0
bullishCross = ta.crossover(seriesFast, seriesNormal) and seriesNormal < 0
slowBearishMedianCross = ta.crossunder(seriesSlow, 0)
slowBullishMedianCross = ta.crossover(seriesSlow, 0)
normalBearishMedianCross = ta.crossunder(seriesNormal, 0)
normalBullishMedianCross = ta.crossover(seriesNormal, 0)
fastBearishMedianCross = ta.crossunder(seriesFast, 0)
fastBullishMedianCross = ta.crossover(seriesFast, 0)
// Last Crossover Values
lastBearishCrossValue = ta.valuewhen(condition=bearishCross, source=seriesNormal, occurrence=1)
lastBullishCrossValue = ta.valuewhen(condition=bullishCross , source=seriesNormal, occurrence=1)
// Trigger Wave Size Comparison
triggerWaveFactor = sizePercent/100
isSmallerBearishCross = bearishCross and seriesNormal < lastBearishCrossValue * triggerWaveFactor
isSmallerBullishCross = bullishCross and seriesNormal > lastBullishCrossValue * triggerWaveFactor
// ===========================
// ==== Kernel Estimators ====
// ===========================
// The following kernel estimators are based on the Gaussian Kernel.
// They are used for:
// (1) Confirming directional changes in the slow oscillator (i.e. a type of trend filter)
// (2) Visualizing directional changes as a dynamic ribbon (i.e. an additional oscillator that can crossover with the user specified oscillator of interest)
// (3) Visualizing transient directional changes while in the midst of a larger uptrend or downtrend (i.e. via color changes on the ribbon)
// Gaussian Kernel with a lookback of 6 bars, starting on bar 6 of the chart (medium fit)
yhat0 = kernels.gaussian(seriesEmphasis, 6, 6)
// Gaussian Kernel with a lookback of 3 bars, starting on bar 2 of the chart (tight fit)
yhat1 = kernels.gaussian(seriesEmphasis, 3, 2)
// Trend Assessment based on the relative position of the medium fit kernel to the slow oscillator
isBearishKernelTrend = yhat0 < seriesSlow
isBullishKernelTrend = yhat0 > seriesSlow
// Divergence Signals
isBearishDivZone = ta.barssince(bearishCross[1]) < divThreshold
isBullishDivZone = ta.barssince(bullishCross[1]) < divThreshold
// Crossover Detection
isBearishTriggerWave = isSmallerBearishCross and isBearishDivZone and isBearishKernelTrend
isBullishTriggerWave = isSmallerBullishCross and isBullishDivZone and isBullishKernelTrend
// =======================
// ==== Plots & Fills ====
var position = 0
length := atrLength
minMult = math.max(sensitivity-4, 1)
maxMult = math.min(sensitivity, 26)
if (autopilotMode == "Short Term")
minMult:=1
maxMult := 4
if (autopilotMode == 'Mid Term')
minMult := 5
maxMult := 10
if (autopilotMode == 'Long-Term')
minMult :=8
maxMult :=13
float step = .5
//Trigger error
if minMult > maxMult
runtime.error('Minimum factor is greater than maximum factor in the range')
float perfAlpha = 10
fromCluster = 'Best'
//Optimization
maxIter = 250
maxData = 2500
//Style
bearCss = color.red
bullCss = color.teal
amaBearCss = color.new(color.red, 50)
amaBullCss = color.new(color.teal, 50)
showGradient = true
//Dashboard
showDash = true
//dashboardLocation = input.string('Top Right', 'Location', options = ['Top Right', 'Bottom Right', 'Bottom Left'], group = 'Dashboard')
textSize = 'Small'
//-----------------------------------------------------------------------------}
//UDT's
//-----------------------------------------------------------------------------{
type supertrend
float upper = hl2
float lower = hl2
float output
float perf = 0
float factor
int trend = 0
type vector
array<float> out
//-----------------------------------------------------------------------------}
//Supertrend
//-----------------------------------------------------------------------------{
var holder = array.new<supertrend>(0)
var factors = array.new<float>(0)
//Populate supertrend type array
if barstate.isfirst
for i = 0 to int((maxMult - minMult) / step)
factors.push(minMult + i * step)
holder.push(supertrend.new())
atr = ta.atr(length)
//Compute Supertrend for multiple factors
k = 0
for factor in factors
get_spt = holder.get(k)
up = hl2 + atr * factor
dn = hl2 - atr * factor
get_spt.trend := close > get_spt.upper ? 1 : close < get_spt.lower ? 0 : get_spt.trend
get_spt.upper := close[1] < get_spt.upper ? math.min(up, get_spt.upper) : up
get_spt.lower := close[1] > get_spt.lower ? math.max(dn, get_spt.lower) : dn
diff = nz(math.sign(close[1] - get_spt.output))
get_spt.perf += 2/(perfAlpha+1) * (nz(close - close[1]) * diff - get_spt.perf)
get_spt.output := get_spt.trend == 1 ? get_spt.lower : get_spt.upper
get_spt.factor := factor
k += 1
//-----------------------------------------------------------------------------}
//K-means clustering
//-----------------------------------------------------------------------------{
factor_array = array.new<float>(0)
data = array.new<float>(0)
table.cell(textstylist, 0, 1, subtitle, width, height, c_subtitle, a_subtitle, text_size=s_subtitle, bgcolor=c_bg)
//Populate data arrays
if last_bar_index - bar_index <= maxData
for element in holder
data.push(element.perf)
factor_array.push(element.factor)
//Intitalize centroids using quartiles
centroids = array.new<float>(0)
centroids.push(data.percentile_linear_interpolation(25))
centroids.push(data.percentile_linear_interpolation(50))
centroids.push(data.percentile_linear_interpolation(75))
//Intialize clusters
var array<vector> factors_clusters = na
var array<vector> perfclusters = na
if last_bar_index - bar_index <= maxData
for _ = 0 to maxIter
factors_clusters := array.from(vector.new(array.new<float>(0)), vector.new(array.new<float>(0)), vector.new(array.new<float>(0)))
perfclusters := array.from(vector.new(array.new<float>(0)), vector.new(array.new<float>(0)), vector.new(array.new<float>(0)))
//Assign value to cluster
i = 0
for value in data
dist = array.new<float>(0)
for centroid in centroids
dist.push(math.abs(value - centroid))
idx = dist.indexof(dist.min())
perfclusters.get(idx).out.push(value)
factors_clusters.get(idx).out.push(factor_array.get(i))
i += 1
//Update centroids
new_centroids = array.new<float>(0)
for cluster_ in perfclusters
new_centroids.push(cluster_.out.avg())
//Test if centroid changed
if new_centroids.get(0) == centroids.get(0) and new_centroids.get(1) == centroids.get(1) and new_centroids.get(2) == centroids.get(2)
break
centroids := new_centroids
//-----------------------------------------------------------------------------}
//Signals and trailing stop
//-----------------------------------------------------------------------------{
//Get associated supertrend
var float target_factor = na
var float perf_idx = na
var float perf_ama = na
var from = switch fromCluster
'Best' => 2
'Average' => 1
'Worst' => 0
//Performance index denominator
den = ta.ema(math.abs(close - close[1]), int(perfAlpha))
if not na(perfclusters)
//Get average factors within target cluster
target_factor := nz(factors_clusters.get(from).out.avg(), target_factor)
//Get performance index of target cluster
perf_idx := math.max(nz(perfclusters.get(from).out.avg()), 0) / den
//Get new supertrend
var upper = hl2
var lower = hl2
var os = 0
up = hl2 + atr * target_factor
dn = hl2 - atr * target_factor
upper := close[1] < upper ? math.min(up, upper) : up
lower := close[1] > lower ? math.max(dn, lower) : dn
os := close > upper ? 1 : close < lower ? 0 : os
ts = os ? lower : upper
//Get trailing stop adaptive MA
if na(ts[1]) and not na(ts)
perf_ama := ts
else
perf_ama += perf_idx * (ts - perf_ama)
//-----------------------------------------------------------------------------}
//Dashboard
//-----------------------------------------------------------------------------{
//-----------------------------------------------------------------------------{
css = os ? bullCss : bearCss
plot(showTrailingStoploss ? ts : na, 'Trailing Stop', os != os[1] ? na : css, editable = false)
plot(showMovingAverage? perf_ama:na, 'Trailing Stop AMA',
ta.cross(close, perf_ama) ? na
: close > perf_ama ? amaBullCss : amaBearCss, editable = false)
//Candle coloring
//barcolor(showGradient ? color.from_gradient(perf_idx, 0, 1, color.new(css, 80), css) : na)
//Signals
if showSignals
if os > os[1] and (signalPresets != "Smart Trail [Filter]" or smartTrailDirection == 'long') and (signalPresets != "Trend Tracer [Filter]" or trendTracerDirection==#02ff65) and (signalPresets != "Trend Strength [Filter]" or trendStrengthMetric >= 25) and (signalPresets != "Trend Catcher [Filter]" or newTrendCatcherColor == #02ff65) and (signalPresets != "Neo Cloud [Filter]" or int(lastNeo) >= last5Neo)
int signalStrength = int(perf_idx*10) < 2 ? 1 : int(perf_idx*10) < 4 ? 2 : int(perf_idx*10) < 5 ? 3 : 4
label.new(n, low-ta.atr(30)/2, signalClassifier ? str.tostring(signalStrength) : ema50 > ema200 ? "▲+" : "▲"
, color = bullCss
, style = label.style_label_up
, textcolor = color.white
, size = size.small)
position := 1
if os < os[1] and (signalPresets != "Smart Trail [Filter]" or smartTrailDirection == 'short') and (signalPresets != "Trend Tracer [Filter]" or trendTracerDirection!=#02ff65) and (signalPresets != "Trend Strength [Filter]" or trendStrengthMetric >= 25)and (signalPresets != "Trend Catcher [Filter]" or newTrendCatcherColor != #02ff65) and (signalPresets != "Neo Cloud [Filter]" or int(lastNeo) <=last5Neo)
int signalStrength = int(perf_idx*10) < 2 ? 1 : int(perf_idx*10) < 4 ? 2 : int(perf_idx*10) < 5 ? 3 : 4
label.new(n, high+ta.atr(30)/2, signalClassifier ? str.tostring(signalStrength) : ema50 < ema200 ? "▼+" : "▼"
, color = bearCss
, style = label.style_label_down
, textcolor = color.white
, size = size.small)
position := -1
// =======================
// Signal Plots
//plot(position == 1 and bearishCross ? high+5 : na, title="Bearish Cross", style=plot.style_cross, linewidth=2, color=c_bearish, offset=-1)
//plot(position == -1 and bearishCross ? high+5 : na, title="Bearish Cross", style=plot.style_circles, linewidth=2, color=c_bearish, offset=-1)
//plot(position == 1 and isBearishTriggerWave ? high+5 : na, title="Bearish Trigger Cross", style=plot.style_cross, linewidth=3, color=c_bearish, offset=-1)
//plot(position == -1 and isBearishTriggerWave ? high+5 : na, title="Bearish Trigger Cross", style=plot.style_circles, linewidth=3, color=c_bearish, offset=-1)
plotchar(bearishCross and position == 1, "Long", "✖", location.abovebar, color = #4774f5, size = size.tiny, editable = false)
//plotchar(bearishCross and position == -1, "Long", "▼", location.abovebar, color = c_bearish, size = size.tiny)
plotchar(isBearishTriggerWave and position == 1, "Long", "✖", location.abovebar, color=#4774f5, size = size.tiny, editable = false)
//plotchar(isBearishTriggerWave and position == -1, "Long", "▼", location.abovebar, color=c_bearish, size = size.small)
//plot(position == 1 and bullishCross ? low -5: na, title="Bullish Cross", style= plot.style_circles, linewidth=2, color=c_bullish, offset=-1)
//plot(position == -1 and bullishCross ? low -5: na, title="Bullish Cross", style= plot.style_cross, linewidth=2, color=c_bullish, offset=-1)
//plot(position == 1 and isBullishTriggerWave ? low -5 : na, title="Bullish Trigger Cross", style=plot.style_circles, linewidth=3, color=c_bullish, offset=-1)
//plot(position == -1 and isBullishTriggerWave ? low -5 : na, title="Bullish Trigger Cross", style=plot.style_cross, linewidth=3, color=c_bullish, offset=-1)
//plotchar(bullishCross and position == 1, "Long", "▲", location.belowbar, color = c_bullish, size = size.tiny)
plotchar(bullishCross and position == -1, "Long", "✖", location.belowbar, color = #ff7322, size = size.tiny, editable = false)
//plotchar(isBullishTriggerWave and position == 1, "Long", "▲", location.belowbar, color=c_bullish, size = size.small)
plotchar(isBullishTriggerWave and position == -1, "Long", "✖", location.belowbar, color=#ff7322, size = size.tiny, editable = false)
// Shit
atrMultiplier = input(2, title="ATR Multiplier")
boxHeightInAtr = atrMultiplier * ta.atr(10)
// Box TP 1
[lowb, midb, highb] = LAF.getTPSLBoxes(6.0)
if (takeProfitBoxes == 'On')
tp1box = box.new(left=bar_index + 1, top=close + midb, right=bar_index + 18, bottom=close + lowb, border_color=color.new(#3666f5, 0), border_width=2, border_style=line.style_solid, bgcolor=color.new(#3666f5, 55), text="TP/SL 1 : " + str.tostring(close), text_size=size.normal, text_color=color.new(#3666f5, 0))
bottom_tp1 = box.get_bottom(tp1box)
box.delete(tp1box[1])
// Box TP 2
tp2box = box.new(left=bar_index + 1, top=close+highb, right=bar_index + 18, bottom=close+midb, border_color=color.new(#3666f5, 0), border_width=2, border_style=line.style_solid, bgcolor=color.new(#3666f5, 65), text="TP/SL 2 : " + str.tostring(close), text_size=size.normal, text_color=color.new(#3666f5, 0))
top_tp2 = box.get_top(tp2box)
box.delete(tp2box[1])
// Empty Box
newBox = box.new(left=bar_index + 18, top=top_tp2, right=bar_index + 200, bottom=bottom_tp1, border_color=color.new(#3666f5, 0), border_width=2, border_style=line.style_solid, bgcolor=color.new(#3666f5, 50), text=" ", text_size=size.normal, text_color=color.new(#3666f5, 0))
box.delete(newBox[1])
// SL Box
slBox = box.new(left=bar_index + 3, top=close-lowb, right=bar_index + 18, bottom=close-midb, border_color=color.new(color.red, 0), border_width=2, border_style=line.style_solid, bgcolor=color.new(color.red, 66), text="TP/SL 2 : " + str.tostring(close), text_size=size.normal, text_color=color.new(color.red, 0))
bottom_sl = box.get_top(slBox)
box.delete(slBox[1])
// SL2 Box
sl2Box = box.new(left=bar_index + 3, top=close-midb, right=bar_index + 18, bottom=close-highb, border_color=color.new(color.red, 0), border_width=2, border_style=line.style_solid, bgcolor=color.new(color.red, 65), text="TP/SL 1 : " + str.tostring(close), text_size=size.normal, text_color=color.new(color.red, 0))
bottom_sl2 = box.get_bottom(sl2Box)
box.delete(sl2Box[1])
// Empty Box SL
Slboxem = box.new(left=bar_index + 18, top=bottom_sl, right=bar_index + 200, bottom=bottom_sl2, border_color=color.new(color.red, 0), border_width=2, border_style=line.style_solid, bgcolor=color.new(color.red, 50), text=" ", text_size=size.normal, text_color=color.new(color.red, 0))
box.delete(Slboxem[1])
//
// Line tp Bottom
var line tpb = na
isLastBar = barstate.islast
if (isLastBar)
tpb := line.new(na, bottom_tp1, na, bottom_tp1, color=color.new(#3666f5, 0), width=2, style=line.style_dashed)
line.set_xy1(tpb, bar_index[50], bottom_tp1)
line.set_xy2(tpb, bar_index + 200, bottom_tp1)
line.delete(tpb[1])
// Line tp top
var line tp2Line = na
if (isLastBar)
tp2Line := line.new(na, top_tp2, na, top_tp2, color=color.new(#3666f5, 0), width=2, style=line.style_dashed)
line.set_xy1(tp2Line, bar_index[50], top_tp2)
line.set_xy2(tp2Line, bar_index + 200, top_tp2)
line.delete(tp2Line[1])
// Line SL
var line slLine = na
if (isLastBar)
slLine := line.new(na, bottom_sl, na, bottom_sl, color=color.new(color.red, 0), width=2, style=line.style_dashed)
line.set_xy1(slLine, bar_index[50], bottom_sl)
line.set_xy2(slLine, bar_index + 400, bottom_sl)
line.delete(slLine[1])
// Line SL2
var line sl2Line = na
if (isLastBar)
sl2Line := line.new(na, bottom_sl2, na, bottom_sl2, color=color.new(color.red, 0), width=2, style=line.style_dashed)
line.set_xy1(sl2Line, bar_index[50], bottom_sl2)
line.set_xy2(sl2Line, bar_index + 200, bottom_sl2)
line.delete(sl2Line[1])Leave a Comment