ICT Killzones & FVG// This source code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
// © Mutharasan12
//@version=5
indicator("ICT Killzones & FVG", "ICT Killzones & FVG", overlay = true, max_labels_count = 500, max_lines_count = 500, max_boxes_count = 500)
// ---------------------------------------- Constant Functions --------------------------------------------------
get_line_type(_style) =>
result = switch _style
'Solid' => line.style_solid
'Dotted' => line.style_dotted
'Dashed' => line.style_dashed
result
get_size(x) =>
result = switch x
'Auto' => size.auto
'Tiny' => size.tiny
'Small' => size.small
'Normal' => size.normal
'Large' => size.large
'Huge' => size.huge
get_table_pos(pos) =>
result = switch pos
"Bottom Center" => position.bottom_center
"Bottom Left" => position.bottom_left
"Bottom Right" => position.bottom_right
"Middle Center" => position.middle_center
"Middle Left" => position.middle_left
"Middle Right" => position.middle_right
"Top Center" => position.top_center
"Top Left" => position.top_left
"Top Right" => position.top_right
get_font_style(s) =>
result = switch s
'Monospace' => font.family_monospace
'Default' => font.family_default
// ---------------------------------------- Constant Functions --------------------------------------------------
// ---------------------------------------- Inputs --------------------------------------------------
gmt_tz = input.string('America/New_York', "Timezone", options = , tooltip = "Note GMT is not adjusted to reflect Daylight Saving Time changes", group = 'Time Zone')
DWM_profile = input.bool(true,"Show All Profile",inline="Show",group = 'Profile')
D_profile = input.bool(true,"Show Daily",inline = "Show",group = 'Profile')
hide_lines_DO = input.timeframe("60","Daily:",inline = "Hide",group = 'Hide Above')
W_profile = input.bool(true,"Weekly",inline="Show",group = 'Profile')
hide_lines_WO = input.timeframe("240","Weekly:",inline="Hide",group = 'Hide Above')
M_profile = input.bool(true,"Monthly",inline="Show",group = 'Profile')
hide_lines_MO = input.timeframe("1D","Monthly:",inline="Hide",group = 'Hide Above')
show_kz = input.bool(false, "Show Killzone Boxes", inline = "skz", group = 'Hide KillZone')
var g_SETTINGS = "Settings"
max_days = input.int(60, "Drawing Limit", 1, inline = 'dl', tooltip = "Only this many drawings will be kept on the chart, for each selected drawing type (killzone boxes, pivot lines, open lines, etc.)", group = g_SETTINGS)
tf_limit = input.timeframe("30", "Timeframe Limit", inline = 'dl', tooltip = "Drawings will not appear on timeframes greater than or equal to this", group = g_SETTINGS)
lbl_size = get_size(input.string('Tiny', "Label Size", inline = 'sc', options = , tooltip = "The size of all labels", group = g_SETTINGS))
font_style = get_font_style(input.string('Monospace', "Font Style", inline = 'sc', group = g_SETTINGS, options = ))
txt_color = input.color(#000000, "Text Color", inline = 'sc', tooltip = "The color of all label and table text", group = g_SETTINGS)
use_cutoff = input.bool(true, "Drawing Cutoff Time", inline = "CO", tooltip = "When enabled, all pivots and open price lines will stop extending at this time", group = g_SETTINGS)
cutoff = input.session("1700-1701", "", inline = "CO", group = g_SETTINGS)
var g_KZ = "Killzones"
show_kz_text = input.bool(false, "Display Text", inline = "KZ", group = g_KZ)
box_transparency = input.int(85, "Box Transparency", 0, 100, group = g_KZ)
text_transparency = input.int(50, "Text Transparency", 0, 100, group = g_KZ)
use_asia = input.bool(true, "", inline = "ASIA", group = g_KZ)
as_txt = input.string("ASIA", "", inline = "ASIA", group = g_KZ)
asia = input.session("2000-0000", "", inline = "ASIA", group = g_KZ)
as_color = input.color(#9598a1, "", inline = "ASIA", group = g_KZ)
use_london = input.bool(true, "", inline = "LONDON", group = g_KZ)
lo_txt = input.string("OPEN", "", inline = "LONDON", group = g_KZ)
london = input.session("0200-0500", "", inline = "LONDON", group = g_KZ)
lo_color = input.color(#9598a1, "", inline = "LONDON", group = g_KZ)
use_nyam = input.bool(true, "", inline = "NYAM", group = g_KZ)
na_txt = input.string("NYAM", "", inline = "NYAM", group = g_KZ)
nyam = input.session("0700-1000", "", inline = "NYAM", group = g_KZ)
na_color = input.color(#9598a1, "", inline = "NYAM", group = g_KZ)
use_nylu = input.bool(false, "", inline = "NYLU", group = g_KZ)
nl_txt = input.string("Lunch", "", inline = "NYLU", group = g_KZ)
nylu = input.session("1200-1300", "", inline = "NYLU", group = g_KZ)
nl_color = input.color(#9598a1, "", inline = "NYLU", group = g_KZ)
use_nypm = input.bool(false, "", inline = "NYPM", group = g_KZ)
np_txt = input.string("NYPM", "", inline = "NYPM", group = g_KZ)
nypm = input.session("1300-1600", "", inline = "NYPM", group = g_KZ)
np_color = input.color(#9598a1, "", inline = "NYPM", group = g_KZ)
use_loop = input.bool(true, "", inline = "LOOP", group = g_KZ)
op_txt = input.string("CBDR", "", inline = "LOOP", group = g_KZ)
loop = input.session("1400-2000", "", inline = "LOOP", group = g_KZ)
op_color = input.color(#9598a1, "", inline = "LOOP", group = g_KZ)
use_locl = input.bool(true, "", inline = "LOCL", group = g_KZ)
cl_txt = input.string("CLOSE", "", inline = "LOCL", group = g_KZ)
locl = input.session("1000-1200", "", inline = "LOCL", group = g_KZ)
cl_color = input.color(#9598a1, "", inline = "LOCL", group = g_KZ)
use_sb1 = input.bool(false, "", inline = "SB1", group = g_KZ)
m1_txt = input.string("London SB", "", inline = "SB1", group = g_KZ)
sb1 = input.session("0300-0400", "", inline = "SB1", group = g_KZ)
m1_color = input.color(#9598a1, "", inline = "SB1", group = g_KZ)
use_sb2 = input.bool(false, "", inline = "SB2", group = g_KZ)
m2_txt = input.string("NewYork AM SB", "", inline = "SB2", group = g_KZ)
sb2 = input.session("1000-1100", "", inline = "SB2", group = g_KZ)
m2_color = input.color(#9598a1, "", inline = "SB2", group = g_KZ)
use_sb3 = input.bool(false, "", inline = "SB3", group = g_KZ)
m3_txt = input.string("NewYork PM SB", "", inline = "SB3", group = g_KZ)
sb3 = input.session("1400-1500", "", inline = "SB3", group = g_KZ)
m3_color = input.color(#9598a1, "", inline = "SB3", group = g_KZ)
var g_LABELS = "Killzone Pivots"
use_alerts = input.bool(false, "Alert Broken Pivots", inline = "PV", group = g_LABELS)
show_pivots = input.bool(false, "Show Pivots", inline = "SV", group = g_LABELS)
show_labels = input.bool(false, "Pivots Labels", inline = "SV", group = g_LABELS)
show_midpoints = input.bool(false, "Pivots Midpoints",inline = "SV", group = g_LABELS)
ext_pivots = input.string("Until Mitigated", "Extend Pivots...", options = , group = g_LABELS)
ext_which = input.string("Most Recent", "...From Which Sessions", options = , group = g_LABELS)
ash_str = input.string("AS.H", "Killzone 01 Labels", inline = "L_AS", group = g_LABELS)
asl_str = input.string("AS.L", "", inline = "L_AS", group = g_LABELS)
loh_str = input.string("LO.H", "Killzone 02 Labels", inline = "L_LO", group = g_LABELS)
lol_str = input.string("LO.L", "", inline = "L_LO", group = g_LABELS)
nah_str = input.string("NYAM.H", "Killzone 03 Labels", inline = "L_NA", group = g_LABELS)
nal_str = input.string("NYAM.L", "", inline = "L_NA", group = g_LABELS)
nlh_str = input.string("NYL.H", "Killzone 04 Labels", inline = "L_NL", group = g_LABELS)
nll_str = input.string("NYL.L", "", inline = "L_NL", group = g_LABELS)
nph_str = input.string("NYPM.H", "Killzone 05 Labels", inline = "L_NP", group = g_LABELS)
npl_str = input.string("NYPM.L", "", inline = "L_NP", group = g_LABELS)
oph_str = input.string("LOOP.H", "Killzone 06 Labels", inline = "L_OP", group = g_LABELS)
opl_str = input.string("LOOP.L", "", inline = "L_OP", group = g_LABELS)
clh_str = input.string("LOCL.H", "Killzone 07 Labels", inline = "L_CL", group = g_LABELS)
cll_str = input.string("LOCL.L", "", inline = "L_CL", group = g_LABELS)
m1h_str = input.string("SB1.H", "Killzone 08 Labels", inline = "L_M1", group = g_LABELS)
m1l_str = input.string("SB1.L", "", inline = "L_M1", group = g_LABELS)
m2h_str = input.string("SB2.H", "Killzone 09 Labels", inline = "L_M2", group = g_LABELS)
m2l_str = input.string("SB2.L", "", inline = "L_M2", group = g_LABELS)
m3h_str = input.string("SB3.H", "Killzone 10 Labels", inline = "L_M3", group = g_LABELS)
m3l_str = input.string("SB3.L", "", inline = "L_M3", group = g_LABELS)
kzp_style = get_line_type(input.string(defval = 'Solid', title = "Pivot Style", options = , inline = "KZP", group = g_LABELS))
kzp_width = input.int(1, "", inline = "KZP", group = g_LABELS)
kzm_style = get_line_type(input.string(defval = 'Dotted', title = "Midpoint Style", options = , inline = "KZM", group = g_LABELS))
kzm_width = input.int(1, "", inline = "KZM", group = g_LABELS)
var g_RNG = "Killzone Range"
show_range = input.bool(false, "Show Killzone Range", tooltip = "Show the most recent ranges of each selected killzone, from high to low", group = g_RNG)
show_range_avg = input.bool(true, "Show Average", tooltip = "Show the average range of each selected killzone", group = g_RNG)
range_avg = input.int(5, "Average Length", 0, tooltip = "This many previous sessions will be used to calculate the average. If there isn't enough data on the current chart, it will use as many sessions as possible", group = g_RNG)
range_pos = get_table_pos(input.string('Top Right', "Table Position", options = , group = g_RNG))
range_size = get_size(input.string('Normal', "Table Size", options = , group = g_RNG))
var g_DWM = "Day - Week - Month"
dow_labels = input.bool(false, "Day of Week Labels", inline = "DOW", group = g_DWM)
dow_yloc = input.string('Bottom', "", options = , inline = "DOW", group = g_DWM)
dow_xloc = input.string('Midnight', "", options = , inline = "DOW", group = g_DWM)
sep_unlimited = input.bool(true, "Unlimited", tooltip = "Unlimited will show as many of the selected lines as possible. Otherwise, the session drawing limit will be used", group = g_DWM)
show_d_open = input.bool(true, "D Open", inline = "DO", group = g_DWM)
dhl = input.bool(true, "High/Low", inline = "DO", tooltip = "", group = g_DWM)
ds = input.bool(true, "Separators", inline = "DO", tooltip = "Mark where a new day begins", group = g_DWM)
d_color = input.color(color.blue, "", inline = "DO", group = g_DWM)
show_w_open = input.bool(true, "W Open", inline = "WO", group = g_DWM)
whl = input.bool(true, "High/Low", inline = "WO", tooltip = "", group = g_DWM)
ws = input.bool(true, "Separators", inline = "WO", tooltip = "Mark where a new week begins", group = g_DWM)
w_color = input.color(#089981, "", inline = "WO", group = g_DWM)
show_m_open = input.bool(true, "M Open", inline = "MO", group = g_DWM)
mhl = input.bool(true, "High/Low", inline = "MO", tooltip = "", group = g_DWM)
ms = input.bool(true, "Separators", inline = "MO", tooltip = "Mark where a new month begins", group = g_DWM)
m_color = input.color(color.red, "", inline = "MO", group = g_DWM)
htf_style = get_line_type(input.string(defval = 'Dotted', title = "Style", options = , inline = "D0", group = g_DWM))
htf_width = input.int(1, "", inline = "D0", group = g_DWM)
var g_OPEN = "Opening Prices"
open_unlimited = input.bool(true, "Unlimited", tooltip = "Unlimited will show as many of the selected lines as possible. Otherwise, the session drawing limit will be used", group = g_OPEN)
hide_hline = input.bool(true,"Show Opening Price",group = g_OPEN)
hide_lines_h = input.timeframe("60","Hide Above Time Frame",group = g_OPEN)
use_h1 = input.bool(true, "", inline = "H1", group = g_OPEN)
h1_text = input.string("00:00", "", inline = "H1", group = g_OPEN)
h1 = input.session("0000-0001", "", inline = "H1", group = g_OPEN)
h1_color = input.color(#000000, "", inline = "H1", group = g_OPEN)
use_h2 = input.bool(true, "", inline = "H2", group = g_OPEN)
h2_text = input.string("08:30", "", inline = "H2", group = g_OPEN)
h2 = input.session("0830-0831", "", inline = "H2", group = g_OPEN)
h2_color = input.color(#000000, "", inline = "H2", group = g_OPEN)
use_h3 = input.bool(false, "", inline = "H3", group = g_OPEN)
h3_text = input.string("10:00", "", inline = "H3", group = g_OPEN)
h3 = input.session("1000-1001", "", inline = "H3", group = g_OPEN)
h3_color = input.color(#000000, "", inline = "H3", group = g_OPEN)
use_h4 = input.bool(false, "", inline = "H4", group = g_OPEN)
h4_text = input.string("14:00", "", inline = "H4", group = g_OPEN)
h4 = input.session("1400-1401", "", inline = "H4", group = g_OPEN)
h4_color = input.color(#000000, "", inline = "H4", group = g_OPEN)
hz_style = get_line_type(input.string(defval = 'Dotted', title = "Style", options = , inline = "H0", group = g_OPEN))
hz_width = input.int(1, "", inline = "H0", group = g_OPEN)
var g_VERTICAL = "Timestamps"
hide_vline = input.bool(true,"Show Timestamp",group = g_VERTICAL)
hide_lines_v = input.timeframe("60","Hide Above Time Frame",group = g_VERTICAL)
use_v1 = input.bool(true, "", inline = "V1", group = g_VERTICAL)
v1 = input.session("0000-0001", "", inline = "V1", group = g_VERTICAL)
v1_color = input.color(#000000, "", inline = "V1", group = g_VERTICAL)
use_v2 = input.bool(false, "", inline = "V2", group = g_VERTICAL)
v2 = input.session("0800-0801", "", inline = "V2", group = g_VERTICAL)
v2_color = input.color(#000000, "", inline = "V2", group = g_VERTICAL)
use_v3 = input.bool(false, "", inline = "V3", group = g_VERTICAL)
v3 = input.session("1000-1001", "", inline = "V3", group = g_VERTICAL)
v3_color = input.color(#000000, "", inline = "V3", group = g_VERTICAL)
use_v4 = input.bool(false, "", inline = "V4", group = g_VERTICAL)
v4 = input.session("1200-1201", "", inline = "V4", group = g_VERTICAL)
v4_color = input.color(#000000, "", inline = "V4", group = g_VERTICAL)
vl_style = get_line_type(input.string(defval = 'Dotted', title = "Style", options = , inline = "V0", group = g_VERTICAL))
vl_width = input.int(1, "", inline = "V0", group = g_VERTICAL)
// ---------------------------------------- Inputs --------------------------------------------------
// ---------------------------------------- Variables & Constants --------------------------------------------------
type kz
string _title
box _box
line _hi_line
line _md_line
line _lo_line
label _hi_label
label _lo_label
bool _hi_valid
bool _md_valid
bool _lo_valid
float _range_store
float _range_current
type hz
line LN
label LB
bool CO
type dwm_hl
line hi_line
line lo_line
label hi_label
label lo_label
type dwm_info
string tf
float o = na
float h = na
float l = na
float ph = na
float pl = na
var as_kz = kz.new(as_txt, array.new_box(), array.new_line(), array.new_line(), array.new_line(), array.new_label(), array.new_label(), array.new_bool(), array.new_bool(), array.new_bool(), array.new_float())
var lo_kz = kz.new(lo_txt, array.new_box(), array.new_line(), array.new_line(), array.new_line(), array.new_label(), array.new_label(), array.new_bool(), array.new_bool(), array.new_bool(), array.new_float())
var na_kz = kz.new(na_txt, array.new_box(), array.new_line(), array.new_line(), array.new_line(), array.new_label(), array.new_label(), array.new_bool(), array.new_bool(), array.new_bool(), array.new_float())
var nl_kz = kz.new(nl_txt, array.new_box(), array.new_line(), array.new_line(), array.new_line(), array.new_label(), array.new_label(), array.new_bool(), array.new_bool(), array.new_bool(), array.new_float())
var np_kz = kz.new(np_txt, array.new_box(), array.new_line(), array.new_line(), array.new_line(), array.new_label(), array.new_label(), array.new_bool(), array.new_bool(), array.new_bool(), array.new_float())
var op_kz = kz.new(op_txt, array.new_box(), array.new_line(), array.new_line(), array.new_line(), array.new_label(), array.new_label(), array.new_bool(), array.new_bool(), array.new_bool(), array.new_float())
var cl_kz = kz.new(cl_txt, array.new_box(), array.new_line(), array.new_line(), array.new_line(), array.new_label(), array.new_label(), array.new_bool(), array.new_bool(), array.new_bool(), array.new_float())
var m1_kz = kz.new(m1_txt, array.new_box(), array.new_line(), array.new_line(), array.new_line(), array.new_label(), array.new_label(), array.new_bool(), array.new_bool(), array.new_bool(), array.new_float())
var m2_kz = kz.new(m2_txt, array.new_box(), array.new_line(), array.new_line(), array.new_line(), array.new_label(), array.new_label(), array.new_bool(), array.new_bool(), array.new_bool(), array.new_float())
var m3_kz = kz.new(m3_txt, array.new_box(), array.new_line(), array.new_line(), array.new_line(), array.new_label(), array.new_label(), array.new_bool(), array.new_bool(), array.new_bool(), array.new_float())
var hz_1 = hz.new(array.new_line(), array.new_label(), array.new_bool())
var hz_2 = hz.new(array.new_line(), array.new_label(), array.new_bool())
var hz_3 = hz.new(array.new_line(), array.new_label(), array.new_bool())
var hz_4 = hz.new(array.new_line(), array.new_label(), array.new_bool())
var d_hl = dwm_hl.new(array.new_line(), array.new_line(), array.new_label(), array.new_label())
var w_hl = dwm_hl.new(array.new_line(), array.new_line(), array.new_label(), array.new_label())
var m_hl = dwm_hl.new(array.new_line(), array.new_line(), array.new_label(), array.new_label())
var d_info = dwm_info.new("D")
var w_info = dwm_info.new("W")
var m_info = dwm_info.new("M")
t_as = not na(time("", asia, gmt_tz))
t_lo = not na(time("", london, gmt_tz))
t_na = not na(time("", nyam, gmt_tz))
t_nl = not na(time("", nylu, gmt_tz))
t_np = not na(time("", nypm, gmt_tz))
t_op = not na(time("", loop, gmt_tz))
t_cl = not na(time("", locl, gmt_tz))
t_m1 = not na(time("", sb1, gmt_tz))
t_m2 = not na(time("", sb2, gmt_tz))
t_m3 = not na(time("", sb3, gmt_tz))
t_co = not na(time("", cutoff, gmt_tz))
t_h1 = not na(time("", h1, gmt_tz))
t_h2 = not na(time("", h2, gmt_tz))
t_h3 = not na(time("", h3, gmt_tz))
t_h4 = not na(time("", h4, gmt_tz))
t_v1 = not na(time("", v1, gmt_tz))
t_v2 = not na(time("", v2, gmt_tz))
t_v3 = not na(time("", v3, gmt_tz))
t_v4 = not na(time("", v4, gmt_tz))
var d_sep_line = array.new_line()
var w_sep_line = array.new_line()
var m_sep_line = array.new_line()
var d_line = array.new_line()
var w_line = array.new_line()
var m_line = array.new_line()
var d_label = array.new_label()
var w_label = array.new_label()
var m_label = array.new_label()
var v1_line = array.new_line()
var v2_line = array.new_line()
var v3_line = array.new_line()
var v4_line = array.new_line()
var transparent = #ffffff00
var ext_current = ext_which == 'Most Recent'
var ext_past = ext_pivots == 'Past Mitigation'
update_dwm_info(dwm_info n) =>
if timeframe.change(n.tf)
n.ph := n.h
n.pl := n.l
n.o := open
n.h := high
n.l := low
else
n.h := math.max(high, n.h)
n.l := math.min(low, n.l)
if dhl or show_d_open
update_dwm_info(d_info)
if whl or show_w_open
update_dwm_info(w_info)
if mhl or show_m_open
update_dwm_info(m_info)
// ---------------------------------------- Variables & Constants --------------------------------------------------
// ---------------------------------------- Functions --------------------------------------------------
get_box_color(color c) =>
result = color.new(c, box_transparency)
get_text_color(color c) =>
result = color.new(c, text_transparency)
// ---------------------------------------- Functions --------------------------------------------------
// ---------------------------------------- Core Logic --------------------------------------------------
dwm_sep(string tf, bool use, line arr, color col) =>
if use
if timeframe.change(tf)
arr.unshift(line.new(bar_index, high*1.0001, bar_index, low, style = htf_style, width = htf_width, extend = extend.both, color = col))
if not sep_unlimited and arr.size() > max_days
arr.pop().delete()
dwm_open(string tf, bool use, line lns, label lbls, dwm_info n, color col) =>
if use
if timeframe.change(tf)
lns.unshift(line.new(time, n.o, time, n.o, xloc = xloc.bar_time, style = htf_style, width = htf_width, color = col))
lbls.unshift(label.new(time, n.o, tf + " OPEN", xloc = xloc.bar_time, style = label.style_label_left, color = transparent, textcolor = txt_color, size = lbl_size, text_font_family = font_style))
if not sep_unlimited and lns.size() > max_days
lns.pop().delete()
lbls.pop().delete()
else if lns.size() > 0
lns.get(0).set_x2(time)
lbls.get(0).set_x(time)
dwm_hl(string tf, bool use, dwm_hl hl, dwm_info n, color col) =>
if use
if timeframe.change(tf)
hl.hi_line.unshift(line.new(time, n.ph, time, n.ph, xloc = xloc.bar_time, style = htf_style, width = htf_width, color = col))
hl.lo_line.unshift(line.new(time, n.pl, time, n.pl, xloc = xloc.bar_time, style = htf_style, width = htf_width, color = col))
hl.hi_label.unshift(label.new(time, n.ph, "P"+tf+"H", xloc = xloc.bar_time, style = label.style_label_left, color = transparent, textcolor = txt_color, size = lbl_size, text_font_family = font_style))
hl.lo_label.unshift(label.new(time, n.pl, "P"+tf+"L", xloc = xloc.bar_time, style = label.style_label_left, color = transparent, textcolor = txt_color, size = lbl_size, text_font_family = font_style))
if not sep_unlimited and hl.hi_line.size() > max_days
hl.hi_line.pop().delete()
hl.lo_line.pop().delete()
hl.hi_label.pop().delete()
hl.lo_label.pop().delete()
else if hl.hi_line.size() > 0
hl.hi_line.get(0).set_x2(time)
hl.lo_line.get(0).set_x2(time)
hl.hi_label.get(0).set_x(time)
hl.lo_label.get(0).set_x(time)
dwm() =>
//if timeframe.in_seconds() <= hide_lines_sessions *60
if DWM_profile
if D_profile and timeframe.in_seconds("") <= timeframe.in_seconds(hide_lines_DO)
// Draw Daily separators, open lines, and high/low lines
dwm_sep("D", ds, d_sep_line, d_color)
dwm_open("D", show_d_open, d_line, d_label, d_info, d_color)
dwm_hl("D", dhl, d_hl, d_info, d_color)
// Draw Weekly and Monthly only if Daily profile allows it
if W_profile and timeframe.in_seconds("") <= timeframe.in_seconds(hide_lines_WO)
dwm_sep("W", ws, w_sep_line, w_color)
dwm_open("W", show_w_open, w_line, w_label, w_info, w_color)
dwm_hl("W", whl, w_hl, w_info, w_color)
if M_profile and timeframe.in_seconds("") <= timeframe.in_seconds(hide_lines_MO)
dwm_sep("M", ms, m_sep_line, m_color)
dwm_open("M", show_m_open, m_line, m_label, m_info, m_color)
dwm_hl("M", mhl, m_hl, m_info, m_color)
else if W_profile and timeframe.in_seconds("") <= timeframe.in_seconds(hide_lines_WO)
// Draw Weekly separators, open lines, and high/low lines
dwm_sep("W", ws, w_sep_line, w_color)
dwm_open("W", show_w_open, w_line, w_label, w_info, w_color)
dwm_hl("W", whl, w_hl, w_info, w_color)
// Draw Monthly only if Weekly profile allows it
if M_profile and timeframe.in_seconds("") <= timeframe.in_seconds(hide_lines_MO)
dwm_sep("M", ms, m_sep_line, m_color)
dwm_open("M", show_m_open, m_line, m_label, m_info, m_color)
dwm_hl("M", mhl, m_hl, m_info, m_color)
else if M_profile and timeframe.in_seconds("") <= timeframe.in_seconds(hide_lines_MO)
// Draw Monthly separators, open lines, and high/low lines
dwm_sep("M", ms, m_sep_line, m_color)
dwm_open("M", show_m_open, m_line, m_label, m_info, m_color)
dwm_hl("M", mhl, m_hl, m_info, m_color)
vline(bool use, bool t, line arr, color col) =>
if use
if t and not t
arr.unshift(line.new(bar_index, high*1.0001, bar_index, low, style = vl_style, width = vl_width, extend = extend.both, color = col))
vlines() =>
if hide_vline
if timeframe.in_seconds("") <= timeframe.in_seconds(hide_lines_v)
vline(use_v1, t_v1, v1_line, v1_color)
vline(use_v2, t_v2, v2_line, v2_color)
vline(use_v3, t_v3, v3_line, v3_color)
vline(use_v4, t_v4, v4_line, v4_color)
hz_line(bool use, bool t, hz hz, string txt, color col) =>
if use
if t and not t
hz.LN.unshift(line.new(bar_index, open, bar_index, open, style = hz_style, width = hz_width, color = col))
hz.LB.unshift(label.new(bar_index, open, txt, style = label.style_label_left, color = transparent, textcolor = txt_color, size = lbl_size, text_font_family = font_style))
array.unshift(hz.CO, false)
if not open_unlimited and hz.LN.size() > max_days
hz.LN.pop().delete()
hz.LB.pop().delete()
hz.CO.pop()
if not t and hz.CO.size() > 0
if not hz.CO.get(0)
hz.LN.get(0).set_x2(bar_index)
hz.LB.get(0).set_x(bar_index)
if (use_cutoff ? t_co : false)
hz.CO.set(0, true)
hz_lines() =>
if hide_hline
if timeframe.in_seconds("") <= timeframe.in_seconds(hide_lines_h)
hz_line(use_h1, t_h1, hz_1, h1_text, h1_color)
hz_line(use_h2, t_h2, hz_2, h2_text, h2_color)
hz_line(use_h3, t_h3, hz_3, h3_text, h3_color)
hz_line(use_h4, t_h4, hz_4, h4_text, h4_color)
del_kz(kz k) =>
if k._box.size() > max_days
k._box.pop().delete()
if k._hi_line.size() > max_days
k._hi_line.pop().delete()
k._lo_line.pop().delete()
k._hi_valid.pop()
k._lo_valid.pop()
if show_midpoints
k._md_line.pop().delete()
k._md_valid.pop()
if k._hi_label.size() > max_days
k._hi_label.pop().delete()
k._lo_label.pop().delete()
adjust_in_kz(kz kz, bool t) =>
if t
kz._box.get(0).set_right(time)
kz._box.get(0).set_top(math.max(kz._box.get(0).get_top(), high))
kz._box.get(0).set_bottom(math.min(kz._box.get(0).get_bottom(), low))
kz._range_current := kz._box.get(0).get_top() - kz._box.get(0).get_bottom()
if show_pivots and kz._hi_line.size() > 0
if high > kz._hi_line.get(0).get_y1()
kz._hi_line.get(0).set_xy1(time, high)
kz._hi_line.get(0).set_xy2(time, high)
if low < kz._lo_line.get(0).get_y1()
kz._lo_line.get(0).set_xy1(time, low)
kz._lo_line.get(0).set_xy2(time, low)
if show_midpoints
kz._md_line.get(0).set_xy1(time, math.avg(kz._hi_line.get(0).get_y2(), kz._lo_line.get(0).get_y2()))
kz._md_line.get(0).set_xy2(time, math.avg(kz._hi_line.get(0).get_y2(), kz._lo_line.get(0).get_y2()))
if show_labels and kz._hi_label.size() > 0
if high > kz._hi_label.get(0).get_y()
kz._hi_label.get(0).set_xy(time, high)
if low < kz._lo_label.get(0).get_y()
kz._lo_label.get(0).set_xy(time, low)
adjust_out_kz(kz kz, bool t) =>
if not t and kz._box.size() > 0
if t
array.unshift(kz._range_store, kz._range_current)
if kz._range_store.size() > range_avg
kz._range_store.pop()
if kz._box.size() > 0 and show_pivots
for i = 0 to kz._box.size() - 1
if not ext_current or (ext_current and i == 0)
if ext_past ? true : (kz._hi_valid.get(i) == true)
kz._hi_line.get(i).set_x2(time)
if high > kz._hi_line.get(i).get_y1() and kz._hi_valid.get(i) == true
if use_alerts and i == 0
alert("Broke "+kz._title+" High", alert.freq_once_per_bar)
kz._hi_valid.set(i, false)
else if (use_cutoff ? t_co : false)
kz._hi_valid.set(i, false)
if ext_past ? true : (kz._lo_valid.get(i) == true)
kz._lo_line.get(i).set_x2(time)
if low < kz._lo_line.get(i).get_y1() and kz._lo_valid.get(i) == true
if use_alerts and i == 0
alert("Broke "+kz._title+" Low", alert.freq_once_per_bar)
kz._lo_valid.set(i, false)
else if (use_cutoff ? t_co : false)
kz._lo_valid.set(i, false)
if show_midpoints
kz._md_line.get(i).set_x2(time)
else
break
manage_kz(kz kz, bool use, bool t, color c, string box_txt, string hi_txt, string lo_txt) =>
if timeframe.in_seconds("") <= timeframe.in_seconds(tf_limit) and use
if t and not t
_c = get_box_color(c)
_t = get_text_color(c)
kz._box.unshift(box.new(time, high, time, low, xloc = xloc.bar_time, border_color = show_kz ? _c : na, bgcolor = show_kz ? _c : na, text = (show_kz and show_kz_text) ? box_txt : na, text_color = _t))
if show_pivots
kz._hi_line.unshift(line.new(time, high, time, high, xloc = xloc.bar_time, style = kzp_style, color = c, width = kzp_width))
kz._lo_line.unshift(line.new(time, low, time, low, xloc = xloc.bar_time, style = kzp_style, color = c, width = kzp_width))
if show_midpoints
kz._md_line.unshift(line.new(time, math.avg(high, low), time, math.avg(high, low), xloc = xloc.bar_time, style = kzm_style, color = c, width = kzm_width))
array.unshift(kz._md_valid, true)
array.unshift(kz._hi_valid, true)
array.unshift(kz._lo_valid, true)
if show_labels
kz._hi_label.unshift(label.new(time, high, hi_txt, xloc = xloc.bar_time, color = transparent, textcolor = txt_color, style = label.style_label_down, size = lbl_size))
kz._lo_label.unshift(label.new(time, low, lo_txt, xloc = xloc.bar_time, color = transparent, textcolor = txt_color, style = label.style_label_up, size = lbl_size))
del_kz(kz)
adjust_in_kz(kz, t)
adjust_out_kz(kz, t)
manage_kz(as_kz, use_asia, t_as, as_color, as_txt, ash_str, asl_str)
manage_kz(lo_kz, use_london, t_lo, lo_color, lo_txt, loh_str, lol_str)
manage_kz(na_kz, use_nyam, t_na, na_color, na_txt, nah_str, nal_str)
manage_kz(nl_kz, use_nylu, t_nl, nl_color, nl_txt, nlh_str, nll_str)
manage_kz(np_kz, use_nypm, t_np, np_color, np_txt, nph_str, npl_str)
manage_kz(op_kz, use_loop, t_op, op_color, op_txt, oph_str, opl_str)
manage_kz(cl_kz, use_locl, t_cl, cl_color, cl_txt, clh_str, cll_str)
manage_kz(m1_kz, use_sb1, t_m1, m1_color, m1_txt, m1h_str, m1l_str)
manage_kz(m2_kz, use_sb2, t_m2, m2_color, m2_txt, m2h_str, m2l_str)
manage_kz(m3_kz, use_sb3, t_m3, m3_color, m3_txt, m3h_str, m3l_str)
dwm()
vlines()
hz_lines()
new_dow_time = dow_xloc == 'Midday' ? time - timeframe.in_seconds("D") / 2 * 1000 : time
new_day = dayofweek(new_dow_time, gmt_tz) != dayofweek(new_dow_time, gmt_tz)
var dow_top = dow_yloc == 'Top'
var sunday = "SUNDAY"
var monday = "MONDAY"
var tuesday = "TUESDAY"
var wednesday = "WEDNESDAY"
var thursday = "THURSDAY"
var friday = "FRIDAY"
plotchar(dow_labels and timeframe.isintraday and dayofweek(new_dow_time, gmt_tz) == 1 and new_day, location = dow_top ? location.top : location.bottom, char = "", textcolor = txt_color, text = sunday)
plotchar(dow_labels and timeframe.isintraday and dayofweek(new_dow_time, gmt_tz) == 2 and new_day, location = dow_top ? location.top : location.bottom, char = "", textcolor = txt_color, text = monday)
plotchar(dow_labels and timeframe.isintraday and dayofweek(new_dow_time, gmt_tz) == 3 and new_day, location = dow_top ? location.top : location.bottom, char = "", textcolor = txt_color, text = tuesday)
plotchar(dow_labels and timeframe.isintraday and dayofweek(new_dow_time, gmt_tz) == 4 and new_day, location = dow_top ? location.top : location.bottom, char = "", textcolor = txt_color, text = wednesday)
plotchar(dow_labels and timeframe.isintraday and dayofweek(new_dow_time, gmt_tz) == 5 and new_day, location = dow_top ? location.top : location.bottom, char = "", textcolor = txt_color, text = thursday)
plotchar(dow_labels and timeframe.isintraday and dayofweek(new_dow_time, gmt_tz) == 6 and new_day, location = dow_top ? location.top : location.bottom, char = "", textcolor = txt_color, text = friday)
get_min_days_stored() =>
store = array.new_int()
if as_kz._range_store.size() > 0
store.push(as_kz._range_store.size())
if lo_kz._range_store.size() > 0
store.push(lo_kz._range_store.size())
if na_kz._range_store.size() > 0
store.push(na_kz._range_store.size())
if nl_kz._range_store.size() > 0
store.push(nl_kz._range_store.size())
if np_kz._range_store.size() > 0
store.push(np_kz._range_store.size())
if op_kz._range_store.size() > 0
store.push(op_kz._range_store.size())
if cl_kz._range_store.size() > 0
store.push(cl_kz._range_store.size())
if m1_kz._range_store.size() > 0
store.push(m1_kz._range_store.size())
if m2_kz._range_store.size() > 0
store.push(m2_kz._range_store.size())
if m3_kz._range_store.size() > 0
store.push(m3_kz._range_store.size())
result = store.min()
set_table(table tbl, kz kz, int row, string txt, bool use, bool t, color col) =>
if use
table.cell(tbl, 0, row, txt, text_size = range_size, bgcolor = get_box_color(col), text_color = txt_color)
table.cell(tbl, 1, row, str.tostring(kz._range_current), text_size = range_size, bgcolor = t ? get_box_color(col) : na, text_color = txt_color)
if show_range_avg
table.cell(tbl, 2, row, str.tostring(kz._range_store.avg()), text_size = range_size, text_color = txt_color)
if show_range and barstate.islast
var tbl = table.new(range_pos, 10, 10, chart.bg_color, chart.fg_color, 2, chart.fg_color, 1)
table.cell(tbl, 0, 0, "Killzone", text_size = range_size, text_color = txt_color)
table.cell(tbl, 1, 0, "Range", text_size = range_size, text_color = txt_color)
if show_range_avg
table.cell(tbl, 2, 0, "Avg ("+str.tostring(get_min_days_stored())+")", text_size = range_size, text_color = txt_color)
set_table(tbl, as_kz, 1, as_txt, use_asia, t_as, as_color)
set_table(tbl, lo_kz, 2, lo_txt, use_london, t_lo, lo_color)
set_table(tbl, na_kz, 3, na_txt, use_nyam, t_na, na_color)
set_table(tbl, nl_kz, 4, nl_txt, use_nylu, t_nl, nl_color)
set_table(tbl, np_kz, 5, np_txt, use_nypm, t_np, np_color)
set_table(tbl, op_kz, 6, op_txt, use_loop, t_op, op_color)
set_table(tbl, cl_kz, 7, cl_txt, use_locl, t_cl, cl_color)
set_table(tbl, m1_kz, 8, m1_txt, use_sb1, t_m1, m1_color)
set_table(tbl, m2_kz, 9, m2_txt, use_sb2, t_m2, m2_color)
set_table(tbl, m3_kz, 10, m3_txt, use_sb3, t_m3, m3_color)
// ---------------------------------------- Core Logic --------------------------------------------------
//-----------------------------------------------------------------------------------------------------------------------------------------------//
// This source code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
// ©Mutharasan2
// Prev Published: 608
// Curr Published: 907
//@version=5
//2nd indicator("ICT HTF Candles (fadi)", overlay=true, max_boxes_count = 500, max_lines_count = 500, max_bars_back = 5000)
type Candle
float o
float c
float h
float l
int o_idx
int c_idx
int h_idx
int l_idx
box body
line wick_up
line wick_down
type Trace
line o
line c
line h
line l
label o_l
label c_l
label h_l
label l_l
type Imbalance
box b
int idx
type CandleSettings
bool show
string htf
int max_display
type Settings
int max_sets
color bull_body
color bull_border
color bull_wick
color bear_body
color bear_border
color bear_wick
int offset
int buffer
int htf_buffer
int width
bool trace_show
color trace_o_color
string trace_o_style
int trace_o_size
color trace_c_color
string trace_c_style
int trace_c_size
color trace_h_color
string trace_h_style
int trace_h_size
color trace_l_color
string trace_l_style
int trace_l_size
string trace_anchor
bool label_show
color label_color
string label_size
bool fvg_show
color fvg_color
bool vi_show
color vi_color
bool htf_label_show
color htf_label_color
string htf_label_size
bool htf_timer_show
color htf_timer_color
string htf_timer_size
string level
string liquidity_open_style
string liquidity_claimed_style
color liquidity_open_color
color liquidity_claimed_color
int liquidity_open_size
int liquidity_claimed_size
int max_lines
int extend
type CandleSet
Candle candles
Imbalance imbalances
CandleSettings settings
label tfName
label tfTimer
type Helper
string name = "Helper"
Settings settings = Settings.new()
var CandleSettings SettingsHTF1 = CandleSettings.new()
var CandleSettings SettingsHTF2 = CandleSettings.new()
var CandleSettings SettingsHTF3 = CandleSettings.new()
var CandleSettings SettingsHTF4 = CandleSettings.new()
var CandleSettings SettingsHTF5 = CandleSettings.new()
var CandleSettings SettingsHTF6 = CandleSettings.new()
var Candle candles_1 = array.new(0)
var Candle candles_2 = array.new(0)
var Candle candles_3 = array.new(0)
var Candle candles_4 = array.new(0)
var Candle candles_5 = array.new(0)
var Candle candles_6 = array.new(0)
var Imbalance imbalances_1 = array.new()
var Imbalance imbalances_2 = array.new()
var Imbalance imbalances_3 = array.new()
var Imbalance imbalances_4 = array.new()
var Imbalance imbalances_5 = array.new()
var Imbalance imbalances_6 = array.new()
var CandleSet htf1 = CandleSet.new()
htf1.settings := SettingsHTF1
htf1.candles := candles_1
htf1.imbalances := imbalances_1
var CandleSet htf2 = CandleSet.new()
htf2.settings := SettingsHTF2
htf2.candles := candles_2
htf2.imbalances := imbalances_2
var CandleSet htf3 = CandleSet.new()
htf3.settings := SettingsHTF3
htf3.candles := candles_3
htf3.imbalances := imbalances_3
var CandleSet htf4 = CandleSet.new()
htf4.settings := SettingsHTF4
htf4.candles := candles_4
htf4.imbalances := imbalances_4
var CandleSet htf5 = CandleSet.new()
htf5.settings := SettingsHTF5
htf5.candles := candles_5
htf5.imbalances := imbalances_5
var CandleSet htf6 = CandleSet.new()
htf6.settings := SettingsHTF6
htf6.candles := candles_6
htf6.imbalances := imbalances_6
//+------------------------------------------------------------------------------------------------------------+//
//+--- Settings ---+//
//+------------------------------------------------------------------------------------------------------------+//
htf1.settings.show := input.bool(false, "HTF 1 ", inline="htf1")
htf_1 = input.timeframe("60", "", inline="htf1")
htf1.settings.htf := htf_1
htf1.settings.max_display := input.int(1, "", inline="htf1")
htf2.settings.show := input.bool(false, "HTF 2 ", inline="htf2")
htf_2 = input.timeframe("240", "", inline="htf2")
htf2.settings.htf := htf_2
htf2.settings.max_display := input.int(1, "", inline="htf2")
htf3.settings.show := input.bool(true, "HTF 3 ", inline="htf3")
htf_3 = input.timeframe("1D", "", inline="htf3")
htf3.settings.htf := htf_3
htf3.settings.max_display := input.int(1, "", inline="htf3")
htf4.settings.show := input.bool(true, "HTF 4 ", inline="htf4")
htf_4 = input.timeframe("1W", "", inline="htf4")
htf4.settings.htf := htf_4
htf4.settings.max_display := input.int(1, "", inline="htf4")
htf5.settings.show := input.bool(true, "HTF 5 ", inline="htf5")
htf_5 = input.timeframe("1M", "", inline="htf5")
htf5.settings.htf := htf_5
htf5.settings.max_display := input.int(1, "", inline="htf5")
htf6.settings.show := input.bool(false, "HTF 6 ", inline="htf6")
htf_6 = input.timeframe("3M", "", inline="htf6")
htf6.settings.htf := htf_6
htf6.settings.max_display := input.int(1, "", inline="htf6")
settings.max_sets := input.int(6, "Limit to next HTFs only", minval=1, maxval=6)
settings.bull_body := input.color(color.rgb(76, 175, 80), "Body ", inline="body")
settings.bear_body := input.color(color.rgb(0, 0, 0), "", inline="body")
settings.bull_border := input.color(color.rgb(93, 96, 107), "Borders", inline="borders")
settings.bear_border := input.color(color.rgb(0, 0, 0), "", inline="borders")
settings.bull_wick := input.color(color.rgb(93, 96, 107), "Wick ", inline="wick")
settings.bear_wick := input.color(color.rgb(93, 96, 107), "", inline="wick")
settings.offset := input.int(10, "padding from current candles", minval = 1)
settings.buffer := input.int(1, "space between candles", minval = 1, maxval = 4)
settings.htf_buffer := input.int(10, "space between Higher Timeframes", minval = 1, maxval = 10)
settings.width := input.int(4, "Candle Width", minval = 1, maxval = 10)*2
settings.htf_label_show := input.bool(false, "HTF Label ", inline="HTFlabel")
settings.htf_label_color := input.color(color.rgb(0, 0, 0), "", inline='HTFlabel')
settings.htf_label_size := input.string(size.tiny, "", , inline="HTFlabel")
settings.htf_timer_show := input.bool(false, "Remaining time ", inline="timer")
settings.htf_timer_color := input.color(color.rgb(0, 0, 0), "", inline='timer')
settings.htf_timer_size := input.string(size.tiny, "", , inline="timer")
settings.fvg_show := input.bool(true, "Fair Value Gap ", group="Imbalance", inline="fvg")
settings.fvg_color := input.color(color.new(color.gray, 80), "", inline='fvg', group="Imbalance")
settings.vi_show := input.bool(true, "Volume Imbalance", group="Imbalance", inline="vi")
settings.vi_color := input.color(color.new(color.red, 50), "", inline='vi', group="Imbalance")
settings.trace_show := input.bool(false, "Trace lines", group="trace")
settings.trace_o_color := input.color(color.rgb(0, 0, 0), "Open ", inline='1', group="trace")
settings.trace_o_style := input.string('····', '', options = , inline='1', group="trace")
settings.trace_o_size := input.int(1, '', options = , inline='1', group="trace")
settings.trace_c_color := input.color(color.rgb(0, 0, 0), "Close ", inline='2', group="trace")
settings.trace_c_style := input.string('····', '', options = , inline='2', group="trace")
settings.trace_c_size := input.int(1, '', options = , inline='2', group="trace")
settings.trace_h_color := input.color(color.rgb(0, 0, 0), "High ", inline='3', group="trace")
settings.trace_h_style := input.string('····', '', options = , inline='3', group="trace")
settings.trace_h_size := input.int(1, '', options = , inline='3', group="trace")
settings.trace_l_color := input.color(color.rgb(0, 0, 0), "Low ", inline='4', group="trace")
settings.trace_l_style := input.string('····', '', options = , inline='4', group="trace")
settings.trace_l_size := input.int(1, '', options = , inline='4', group="trace")
settings.trace_anchor := input.string("First Timeframe", "Anchor to", options= , group="trace")
settings.label_show := input.bool(false, "Price Label ", inline="label")
settings.label_color := input.color(color.rgb(0, 0, 0), "", inline='label')
settings.label_size := input.string(size.small, "", , inline="label")
//+------------------------------------------------------------------------------------------------------------+//
//+--- Variables ---+//
//+------------------------------------------------------------------------------------------------------------+//
Helper helper = Helper.new()
var Trace trace = Trace.new()
color color_transparent = #ffffff00
//+------------------------------------------------------------------------------------------------------------+//
//+--- Internal Functions ---+//
//+------------------------------------------------------------------------------------------------------------+//
method LineStyle(Helper helper, string style) =>
helper.name := style
out = switch style
'----' => line.style_dashed
'····' => line.style_dotted
=> line.style_solid
method ValidTimeframe(Helper helper, string HTF) =>
helper.name := HTF
if timeframe.in_seconds(HTF) >= timeframe.in_seconds("D") and timeframe.in_seconds(HTF) > timeframe.in_seconds()
true
else
n1 = timeframe.in_seconds()
n2 = timeframe.in_seconds(HTF)
n3 = n1 % n2
(n1 < n2 and math.round(n2/n1) == n2/n1)
method RemainingTime(Helper helper, string HTF) =>
helper.name := HTF
if barstate.isrealtime
timeRemaining = (time_close(HTF) - timenow)/1000
days = math.floor(timeRemaining / 86400)
hours = math.floor((timeRemaining - (days*86400)) / 3600)
minutes = math.floor((timeRemaining - (days*86400) - (hours*3600))/ 60)
seconds = math.floor(timeRemaining - (days*86400) - (hours*3600) - (minutes*60))
r = str.tostring(seconds, "00")
if minutes > 0 or hours > 0 or days > 0
r := str.tostring(minutes, "00") + ":" + r
if hours > 0 or days > 0
r := str.tostring(hours, "00") + ":" + r
if days > 0
r := str.tostring(days) + "D " + r
r
else
"n/a"
method HTFName(Helper helper, string HTF) =>
helper.name := "HTFName"
formatted = HTF
seconds = timeframe.in_seconds(HTF)
if seconds < 60
formatted := str.tostring(seconds) + "s"
else if (seconds / 60) < 60
formatted := str.tostring((seconds/60)) + "m"
else if (seconds/60/60) < 24
formatted := str.tostring((seconds/60/60)) + "H"
formatted
method HTFEnabled(Helper helper) =>
helper.name := "HTFEnabled"
int enabled =0
enabled += htf1.settings.show ? 1 : 0
enabled += htf2.settings.show ? 1 : 0
enabled += htf3.settings.show ? 1 : 0
enabled += htf4.settings.show ? 1 : 0
enabled += htf5.settings.show ? 1 : 0
enabled += htf6.settings.show ? 1 : 0
int last = math.min(enabled, settings.max_sets)
last
method CandleSetHigh(Helper helper, Candle candles, float h) =>
helper.name := "CandlesSetHigh"
float _h = h
if array.size(candles) > 0
for i = 0 to array.size(candles)-1
Candle c = array.get(candles, i)
if c.h > _h
_h := c.h
_h
method CandlesHigh(Helper helper, Candle candles) =>
helper.name := "CandlesHigh"
h = 0.0
int cnt = 0
int last = helper.HTFEnabled()
if htf1.settings.show and helper.ValidTimeframe(htf1.settings.htf)
h := helper.CandleSetHigh(htf1.candles, h)
cnt += 1
if htf2.settings.show and helper.ValidTimeframe(htf2.settings.htf) and cnt < last
h := helper.CandleSetHigh(htf2.candles, h)
cnt +=1
if htf3.settings.show and helper.ValidTimeframe(htf3.settings.htf) and cnt < last
h := helper.CandleSetHigh(htf3.candles, h)
cnt += 1
if htf4.settings.show and helper.ValidTimeframe(htf4.settings.htf) and cnt < last
h := helper.CandleSetHigh(htf4.candles, h)
cnt += 1
if htf5.settings.show and helper.ValidTimeframe(htf5.settings.htf) and cnt < last
h := helper.CandleSetHigh(htf5.candles, h)
cnt += 1
if htf6.settings.show and helper.ValidTimeframe(htf6.settings.htf) and cnt < last
h := helper.CandleSetHigh(htf6.candles, h)
h
if array.size(candles) > 0
for i = 0 to array.size(candles)-1
Candle c = array.get(candles, i)
if c.h > h
h := c.h
h
method Reorder(CandleSet candleSet, int offset) =>
size = candleSet.candles.size()
if size > 0
for i = size-1 to 0
Candle candle = candleSet.candles.get(i)
t_buffer = offset + ((settings.width+settings.buffer)*(size-i-1))
box.set_left(candle.body, bar_index + t_buffer)
box.set_right(candle.body, bar_index + settings.width + t_buffer)
line.set_x1(candle.wick_up, bar_index+((settings.width)/2) + t_buffer)
line.set_x2(candle.wick_up, bar_index+((settings.width)/2) + t_buffer)
line.set_x1(candle.wick_down, bar_index+((settings.width)/2) + t_buffer)
line.set_x2(candle.wick_down, bar_index+((settings.width)/2) + t_buffer)
candleSet
top = helper.CandlesHigh(candleSet.candles)
left = bar_index + offset + ((settings.width+settings.buffer)*(size-1))/2
if settings.htf_label_show
var label l = candleSet.tfName
string lbl = helper.HTFName(candleSet.settings.htf)
if settings.htf_timer_show
lbl += "\n"
if not na(l)
label.set_xy(l, left, top)
else
l := label.new(left, top, lbl, color=color_transparent, textcolor = settings.htf_label_color, style=label.style_label_down, size = settings.htf_label_size)
if settings.htf_timer_show
var label t = candleSet.tfTimer
string tmr = "(" + helper.RemainingTime(candleSet.settings.htf) + ")"
if not na(t)
label.set_xy(t, left, top)
else
t := label.new(left, top, tmr, color=color_transparent, textcolor = settings.htf_timer_color, style=label.style_label_down, size = settings.htf_timer_size)
candleSet
method FindImbalance(CandleSet candleSet) =>
if barstate.isrealtime or barstate.islast
if candleSet.imbalances.size() > 0
for i = candleSet.imbalances.size()-1 to 0
Imbalance del = candleSet.imbalances.get(i)
box.delete(del.b)
candleSet.imbalances.pop()
if candleSet.candles.size() > 3 and settings.fvg_show
for i = 1 to candleSet.candles.size() -3
candle1 = candleSet.candles.get(i)
candle2 = candleSet.candles.get(i+2)
candle3 = candleSet.candles.get(i+1)
if (candle1.l > candle2.h and math.min(candle1.o, candle1.c) > math.max(candle2.o, candle2.c))
Imbalance imb = Imbalance.new()
imb.b := box.new(box.get_left(candle2.body), candle2.h, box.get_right(candle1.body), candle1.l, bgcolor=settings.fvg_color, border_color = color_transparent, xloc=xloc.bar_index)
candleSet.imbalances.push(imb)
if (candle1.h < candle2.l and math.max(candle1.o, candle1.c) < math.min(candle2.o, candle2.c))
Imbalance imb = Imbalance.new()
imb.b := box.new(box.get_right(candle1.body), candle1.h, box.get_left(candle2.body), candle2.l, bgcolor=settings.fvg_color, border_color = color_transparent)
candleSet.imbalances.push(imb)
box temp = box.copy(candle3.body)
box.delete(candle3.body)
candle3.body := temp
if candleSet.candles.size() > 2 and settings.vi_show
for i = 1 to candleSet.candles.size() -2
candle1 = candleSet.candles.get(i)
candle2 = candleSet.candles.get(i+1)
if (candle1.l < candle2.h and math.min(candle1.o, candle1.c) > math.max(candle2.o, candle2.c))
Imbalance imb = Imbalance.new()
imb.b := box.new(box.get_left(candle2.body), math.min(candle1.o, candle1.c), box.get_right(candle1.body), math.max(candle2.o, candle2.c), bgcolor=settings.vi_color, border_color = color_transparent)
candleSet.imbalances.push(imb)
if (candle1.h > candle2.l and math.max(candle1.o, candle1.c) < math.min(candle2.o, candle2.c))
Imbalance imb = Imbalance.new()
imb.b := box.new(box.get_right(candle1.body), math.min(candle2.o, candle2.c), box.get_left(candle2.body), math.max(candle1.o, candle1.c), bgcolor=settings.vi_color, border_color = color_transparent)
candleSet.imbalances.push(imb)
candleSet
method Monitor(CandleSet candleSet) =>
HTFBarTime = time(candleSet.settings.htf)
isNewHTFCandle = ta.change(HTFBarTime)
if isNewHTFCandle
Candle candle = Candle.new()
candle.o := open
candle.c := close
candle.h := high
candle.l := low
candle.o_idx := bar_index
candle.c_idx := bar_index
candle.h_idx := bar_index
candle.l_idx := bar_index
bull = candle.c > candle.o
candle.body := box.new(bar_index, math.max(candle.o, candle.c), bar_index+2, math.min(candle.o, candle.c), bull ? settings.bull_border : settings.bear_border, 1, bgcolor = bull ? settings.bull_body : settings.bear_body)
candle.wick_up := line.new(bar_index+1, candle.h, bar_index, math.max(candle.o, candle.c), color=bull ? settings.bull_wick : settings.bear_wick)
candle.wick_down := line.new(bar_index+1, math.min(candle.o, candle.c), bar_index, candle.l, color=bull ? settings.bull_wick : settings.bear_wick)
candleSet.candles.unshift(candle)
if candleSet.candles.size() > candleSet.settings.max_display
Candle delCandle = array.pop(candleSet.candles)
box.delete(delCandle.body)
line.delete(delCandle.wick_up)
line.delete(delCandle.wick_down)
candleSet
method Update(CandleSet candleSet, int offset, bool showTrace) =>
if candleSet.candles.size() > 0
Candle candle = candleSet.candles.first()
candle.h_idx := high > candle.h ? bar_index : candle.h_idx
candle.h := high > candle.h ? high : candle.h
candle.l_idx := low < candle.l ? bar_index : candle.l_idx
candle.l := low < candle.l ? low : candle.l
candle.c := close
candle.c_idx := bar_index
bull = candle.c > candle.o
box.set_top(candle.body, candle.o)
box.set_bottom(candle.body, candle.c)
box.set_bgcolor(candle.body, bull ? settings.bull_body : settings.bear_body)
box.set_border_color(candle.body, bull ? settings.bull_border : settings.bear_border)
line.set_color(candle.wick_up, bull ? settings.bull_wick : settings.bear_wick)
line.set_color(candle.wick_down, bull ? settings.bull_wick : settings.bear_wick)
line.set_y1(candle.wick_up, candle.h)
line.set_y2(candle.wick_up, math.max(candle.o, candle.c))
line.set_y1(candle.wick_down, candle.l)
line.set_y2(candle.wick_down, math.min(candle.o, candle.c))
if barstate.isrealtime or barstate.islast
candleSet.Reorder(offset)
if settings.trace_show and showTrace
if bar_index - candle.o_idx < 5000
if na(trace.o)
trace.o := line.new(candle.o_idx, candle.o, box.get_left(candle.body), candle.o, xloc= xloc.bar_index, color=settings.trace_o_color, style= helper.LineStyle(settings.trace_o_style), width=settings.trace_o_size)
else
line.set_xy1(trace.o, candle.o_idx, candle.o)
line.set_xy2(trace.o, box.get_left(candle.body), candle.o)
if settings.label_show
if na(trace.o_l)
trace.o_l := label.new(box.get_right(candle.body), candle.o, str.tostring(candle.o), textalign = text.align_center, style=label.style_label_left, size=settings.label_size, color=color_transparent, textcolor=settings.label_color)
else
label.set_xy(trace.o_l, box.get_right(candle.body), candle.o)
label.set_text(trace.o_l, str.tostring(candle.o))
if bar_index - candle.c_idx < 5000
if na(trace.c)
trace.c := line.new(candle.c_idx, candle.c, box.get_left(candle.body), candle.c, xloc= xloc.bar_index, color=settings.trace_c_color, style=helper.LineStyle(settings.trace_c_style), width=settings.trace_c_size)
else
line.set_xy1(trace.c, candle.c_idx, candle.c)
line.set_xy2(trace.c, box.get_left(candle.body), candle.c)
if settings.label_show
if na(trace.c_l)
trace.c_l := label.new(box.get_right(candle.body), candle.c, str.tostring(candle.c), textalign = text.align_center, style=label.style_label_left, size=settings.label_size, color=color_transparent, textcolor=settings.label_color)
else
label.set_xy(trace.c_l, box.get_right(candle.body), candle.c)
label.set_text(trace.c_l, str.tostring(candle.c))
if bar_index - candle.h_idx < 5000
if na(trace.h)
trace.h := line.new(candle.h_idx, candle.h, line.get_x1(candle.wick_up), candle.h, xloc= xloc.bar_index, color=settings.trace_h_color, style=helper.LineStyle(settings.trace_h_style), width=settings.trace_h_size)
else
line.set_xy1(trace.h, candle.h_idx, candle.h)
line.set_xy2(trace.h, line.get_x1(candle.wick_up), candle.h)
if settings.label_show
if na(trace.h_l)
trace.h_l := label.new(box.get_right(candle.body), candle.h, str.tostring(candle.h), textalign = text.align_center, style=label.style_label_left, size=settings.label_size, color=color_transparent, textcolor=settings.label_color)
else
label.set_xy(trace.h_l, box.get_right(candle.body), candle.h)
label.set_text(trace.h_l, str.tostring(candle.o))
if bar_index - candle.l_idx < 5000
if na(trace.l)
trace.l := line.new(candle.l_idx, candle.l, line.get_x1(candle.wick_down), candle.l, xloc= xloc.bar_index, color=settings.trace_l_color, style=helper.LineStyle(settings.trace_l_style), width=settings.trace_l_size)
else
line.set_xy1(trace.l, candle.l_idx, candle.l)
line.set_xy2(trace.l, line.get_x1(candle.wick_down), candle.l)
if settings.label_show
if na(trace.l_l)
trace.l_l := label.new(box.get_right(candle.body), candle.l, str.tostring(candle.l), textalign = text.align_center, style=label.style_label_left, size=settings.label_size, color=color_transparent, textcolor=settings.label_color)
else
label.set_xy(trace.l_l, box.get_right(candle.body), candle.l)
label.set_text(trace.l_l, str.tostring(candle.o))
candleSet
int cnt = 0
int last = helper.HTFEnabled()
int offset = settings.offset
if htf1.settings.show and helper.ValidTimeframe(htf1.settings.htf)
bool showTrace = false
if settings.trace_anchor == "First Timeframe"
showTrace := true
if settings.trace_anchor == "Last Timeframe" and settings.max_sets == 1
showTrace := true
htf1.Monitor().Update(offset, showTrace).FindImbalance()
cnt +=1
offset += cnt > 0 ? (htf1.candles.size() * settings.width) + (htf1.candles.size() > 0 ? htf1.candles.size()-1 * settings.buffer : 0) + settings.htf_buffer : 0
if htf2.settings.show and helper.ValidTimeframe(htf2.settings.htf) and cnt < last
bool showTrace = false
if settings.trace_anchor == "First Timeframe" and cnt == 0
showTrace := true
if settings.trace_anchor == "Last Timeframe" and cnt == last-1
showTrace := true
htf2.Monitor().Update(offset, showTrace).FindImbalance()
cnt+=1
offset += cnt > 0 ? (htf2.candles.size() * settings.width) + (htf2.candles.size() > 0 ? htf2.candles.size()-1 * settings.buffer : 0) + settings.htf_buffer : 0
if htf3.settings.show and helper.ValidTimeframe(htf3.settings.htf) and cnt < last
bool showTrace = false
if settings.trace_anchor == "First Timeframe" and cnt == 0
showTrace := true
if settings.trace_anchor == "Last Timeframe" and cnt == last-1
showTrace := true
htf3.Monitor().Update(offset, showTrace).FindImbalance()
cnt+=1
offset += cnt > 0 ? (htf3.candles.size() * settings.width) + (htf3.candles.size() > 0 ? htf3.candles.size()-1 * settings.buffer : 0) + settings.htf_buffer : 0
if htf4.settings.show and helper.ValidTimeframe(htf4.settings.htf) and cnt < last
bool showTrace = false
if settings.trace_anchor == "First Timeframe" and cnt == 0
showTrace := true
if settings.trace_anchor == "Last Timeframe" and cnt == last-1
showTrace := true
htf4.Monitor().Update(offset, showTrace).FindImbalance()
cnt+=1
offset += cnt > 0 ? (htf4.candles.size() * settings.width) + (htf4.candles.size() > 0 ? htf4.candles.size()-1 * settings.buffer : 0) + settings.htf_buffer : 0
if htf5.settings.show and helper.ValidTimeframe(htf5.settings.htf) and cnt < last
bool showTrace = false
if settings.trace_anchor == "First Timeframe" and cnt == 0
showTrace := true
if settings.trace_anchor == "Last Timeframe" and cnt == last-1
showTrace := true
htf5.Monitor().Update(offset, showTrace).FindImbalance()
cnt+=1
offset += cnt > 0 ? (htf5.candles.size() * settings.width) + (htf5.candles.size() > 0 ? htf5.candles.size()-1 * settings.buffer : 0) + settings.htf_buffer : 0
if htf6.settings.show and helper.ValidTimeframe(htf6.settings.htf) and cnt < last
bool showTrace = false
if settings.trace_anchor == "First Timeframe" and cnt == 0
showTrace := true
if settings.trace_anchor == "Last Timeframe"
showTrace := true
htf6.Monitor().Update(offset, showTrace).FindImbalance()
//------------------------------------------------------------------------------------------------//
// This Pine Script™ code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
// ©Mutharasan12
//@version=5
//indicator("Notes, Watermark",overlay = true)
// STICKY NOTES #BEGIN --------------------------------------------------------------------------------------------------------------------------------------------------------//
var StickyNote_settings = "STICKY NOTES --------------------------------------------------"
showStickyNotes = input.bool(false, "Show Sticky Notes?", group = StickyNote_settings)
if showStickyNotes
stickynote01 = input.text_area("Your Note Here","NOTES",group = StickyNote_settings)
tl1 = input.string("top", title = "Sticky Note Position (Vertical)", options = , group = StickyNote_settings)
tl2 = input.string("right", title = "Sticky Note Position (Horizontal)", options = , group = StickyNote_settings)
font_size = input.string("Normal", title = "Sticky Note Text Size", options = , group = StickyNote_settings)
font_type = input.string("Default", title = "Font Type", options = , group = StickyNote_settings)
font_color = input.color(color.rgb(0,0,0,0), title = "Note Color", inline = 'nc', group = StickyNote_settings)
note_color = input.color(color (color.rgb(0,0,0,100)), title = "Note Background", inline='nc', group = StickyNote_settings)
selected_font = font_type == "Default" ? font.family_default : font.family_monospace
selected_size = font_size == "Auto" ? size.auto : font_size == "Huge" ? size.huge : font_size == "Large" ? size.large : font_size == "Normal" ? size.normal : font_size == "Small" ? size.small : font_size == "Tiny" ? size.tiny : na
var table t1 = table.new(tl1 + "_" + tl2, 1, 10)
if barstate.islast
if not na(stickynote01) and stickynote01 != ""
table.cell(t1,0,0, text = stickynote01 , bgcolor = note_color, text_color = font_color, text_halign = text.align_left, text_size = selected_size, text_font_family = selected_font)
// STICKY NOTES #END ----------------------------------------------------------------------------------------------------------------------------------------------------------//
// WATERMARK #BEGIN --
스크립트에서 "马斯克+100万"에 대해 찾기
Moving Average ProjectionDisplays 2-5 moving averages (solid lines) and projects their future trajectory (dashed lines) based on current trend momentum. This helps you anticipate where key MAs are heading and identify potential future support/resistance levels.
Important: Projections show where MAs would move IF the current trend continues—they're not predictions. Market conditions change, so use projections as planning tools, not trading signals.
General Settings
Number of MAs (2-5) controls how many moving averages display on your chart. Start with 2-3 to avoid clutter. Projection Bars (1-100) determines how far into the future to project—use 10-20 for intraday charts and 20-40 for daily charts. Lookback for Slope (2-100) sets the number of bars used to calculate trend slope, where shorter lookbacks are more responsive and longer ones are smoother. The default of 20 works well for most situations.
Individual MA Settings (MA 1-5)
Each MA has four settings: Length sets the period for the MA (common values are 9, 20, 50, 100, and 200), Type lets you choose between SMA, EMA, WMA, HMA, VWMA, or RMA (EMA is most popular), Color sets the historical MA line color, and Projection Color sets the projected line color (usually a lighter or transparent version of the main color).
MA Types Quick Reference: EMA is most popular and responsive to recent prices. SMA gives equal weight to all periods and is the smoothest. HMA is very responsive with low lag. VWMA incorporates volume data.
Quick Setup Examples
Day Trading: 3 MAs (9/21/50 EMA), 10-15 projection bars, 10-15 lookback
Swing Trading: 2 MAs (50/200 EMA), 20-30 projection bars, 20 lookback
Scalping: 2 MAs (9/20 EMA), 5-10 projection bars, 5-10 lookback
How to Use
Trend Identification: An uptrend shows price above rising MAs with projections pointing up. A downtrend shows price below falling MAs with projections pointing down. Consolidation appears as flat MAs with horizontal projections.
Support & Resistance: Rising MA projections act as future dynamic support levels, while falling MA projections act as future dynamic resistance levels.
Anticipating Changes: Watch for projected MA crossovers before they happen. When projections converge, expect volatility or consolidation. Steep projections suggest unsustainable trends, so be cautious. Flat projections indicate ranging markets.
Trade Planning: Check the current trend using MA alignment, then look at projections to gauge trend continuation likelihood. Use projected MA levels for potential targets or stop placement.
Important Tips
When Projections Work Best: Projections are most reliable in stable trending markets with consistent momentum, low volatility environments, and away from major news events.
When to Be Cautious: Use caution during high volatility or choppy price action, around major economic releases, when projections show extreme or parabolic angles, and during trend transitions.
Combine With Other Analysis: Don't trade projections alone. Use them alongside price action, volume, support and resistance levels, and other indicators for confirmation.
Best Practices
Start with 2-3 MAs to avoid chart clutter. Match your projection and lookback bars to your trading timeframe. Use consistent color schemes for quick interpretation. Adjust settings as market conditions change. Always use proper risk management—projections are planning tools, not guarantees.
Troubleshooting
Projections not showing: Check that Projection Bars > 0 and you're viewing the most recent bar
Chart too cluttered: Reduce number of MAs or increase projection color transparency
Projections too volatile: Increase lookback bars or switch to EMA/SMA from HMA
Can't see certain MAs: Verify "Number of MAs" setting includes them (MA 3 won't show if set to 2)
Clock&Flow – Market Pulse IndicatorClock&Flow – Market Pulse Indicator
1) General Purpose
The Market Pulse Indicator is designed to visualize the strength and direction of market flow in a clear, intuitive way.
Unlike common volume or momentum indicators, it blends three essential dimensions — price velocity, normalized volume, and volatility (ATR) — to highlight when market pressure is truly meaningful.
It helps identify genuine liquidity inflows/outflows, potential exhaustion zones, and moments of compression or expansion within the price structure.
2) Data Sources
All data is directly taken from the current chart’s feed on TradingView:
Price (close): to measure relative price change.
Volume: to detect the intensity of market participation (normalized to average).
ATR (Average True Range): to evaluate volatility relative to price levels.
No external data or off-platform sources are used.
3) Logic and Calculation Steps
Price Velocity: calculates the percentage change between the current close and the close N bars ago.
priceChange = (close - close ) / close
Normalized Volume: compares current volume to its moving average over the same period.
volNorm = volume / sma(volume, length)
Normalized Volatility: ATR divided by price to adjust for instrument scale.
atrNorm = atr(length) / close
Combination : multiplies the three components into one raw value that represents market pulse intensity.
rawPulse = priceChange * volNorm * (1 + atrNorm)
Smoothing: a moving average (smoothLen) is applied to create a cleaner and more readable oscillator line.
flowPulse = sma(rawPulse * multiplier, smoothLen)
4) Parameters (Default Settings)
length (20): analysis period for price change, volume, and ATR.
smoothLen (5): smoothing factor; higher values reduce noise.
multiplier (100): scales the output for readability; adjust to fit chart scale.
5) How to Read the Indicator
Market Pulse > 0 (green): net inflow of liquidity; buying pressure dominates.
Market Pulse < 0 (red): net outflow of liquidity; selling pressure dominates.
Near 0: neutral phase; market balance or consolidation.
Sudden peaks: strong bursts of flow — often coincide with news releases or session overlaps.
Confirmations: use as a second-level filter before entering trades or to confirm momentum behind a breakout.
6) Divergences
Divergences between price and Market Pulse are key signals of weakening flow strength:
Bullish divergence: price forms lower lows while Market Pulse forms higher lows → selling pressure is fading; potential reversal or bounce.
Bearish divergence: price forms higher highs while Market Pulse fails to confirm → buying momentum is losing strength; potential correction ahead.
For reliability, look for divergences on higher timeframes (H4, Daily).
On lower timeframes, treat them as early warnings.
7) Typical Use Cases
Breakout confirmation: price breaks resistance with a rising Market Pulse → confirms genuine participation.
False signal filter: price breaks a level but Market Pulse remains flat/negative → likely fake breakout.
Pullback entry: after a breakout, wait for a short retracement and a new positive pulse → safer entry point.
Exit signal: if you’re long and Market Pulse suddenly turns negative with strong volume → consider partial exit or tighter stops.
8) Recommended Timeframes
Intraday / Scalping: 5–30 min charts with length 10–14, smoothLen 3–5.
Swing trading: 1h–4h charts with length 20–50.
Position trading: Daily charts with larger length (50–100) for smoother data.
Always optimize parameters to the specific asset — there are no universal settings.
9) Limitations
This indicator is not a trading system — it’s a decision-support tool.
Results depend on the quality of the volume data available for the symbol.
Performance and sensitivity are influenced by length, smoothing, and multiplier values — always test before live trading.
Use alongside sound risk and money management.
10) Disclaimer
This script is provided for educational purposes only and does not constitute financial advice.
Trading and investing involve significant risk, including the potential loss of capital.
Always test indicators in simulation environments and make independent decisions based on your own analysis and risk tolerance.
Italiano
1) Scopo generale
Flow Pulse è un oscillatore pensato per visualizzare la forza e la direzione del flusso di mercato in modo immediato. Non è un semplice indicatore di volume né una copia di RSI/MACD: combina tre dimensioni fondamentali — variazione di prezzo, volume normalizzato e volatilità — per mettere in evidenza i momenti in cui la pressione dei partecipanti è realmente significativa.
È ideale per identificare: entrate guidate da flussi reali, potenziali esaurimenti, momenti di compressione/espansione del movimento e segnali di conferma per breakout o rimbalzi.
2) Dati utilizzati
L’indicatore usa esclusivamente dati disponibili sulla piattaforma TradingView del grafico corrente:
price (close) — per calcolare la variazione percentuale del prezzo;
volume per misurare l’intensità degli scambi (normalizzato su media);
ATR (Average True Range) — per normalizzare la volatilità rispetto al prezzo;
Tutti i feed (prezzo e volume) sono quelli forniti dall’exchange/fornitore dati collegato al simbolo sul grafico.
3) Logica e passaggi di calcolo
Velocità del prezzo: calcolo della variazione percentuale tra la chiusura corrente e la chiusura N barre fa:
priceChange = (close - close ) / close
— misura la direzione e magnitudine del movimento in termine relativo.
Volume normalizzato: rapporto tra il volume corrente e la media mobile semplice del volume su length barre:
volNorm = volume / sma(volume, length)
— evidenzia volumi anomali rispetto alla media.
Volatilità normalizzata (ATR): rapporto ATR/close per rendere la volatilità comparabile across price levels:
atrNorm = atr(length) / close
Combinazione: il prodotto di questi fattori (con un piccolo offset su ATR) genera un valore grezzo:
rawPulse = priceChange * volNorm * (1 + atrNorm)
— se priceChange e volNorm sono positivi e l’ATR è presente, il rawPulse sarà significativamente positivo.
Smoothing: media mobile semplice (SMA) applicata al rawPulse e moltiplicazione per un fattore scalare (multiplier) per portare il range su livelli leggibili:
flowPulse = sma(rawPulse * multiplier, smoothLen)
4) Parametri esposti (default consigliati)
length (periodo analisi) — default 20: influenza calcolo Δ% e media volumi; allunga la finestra storica.
smoothLen (smussamento) — default 5: smoothing del segnale per ridurre rumore.
multiplier — default 100: fattore di scala per rendere l’oscillatore più leggibile.
5) Interpretazione pratica dei valori
FlowPulse > 0 (verde): predominanza di flusso d’ingresso — pressione d’acquisto. Maggiore il valore, più forte la convinzione (volume + movimento + volatilità).
FlowPulse < 0 (rosso): predominanza di flusso in uscita — pressione di vendita.
Vicino a 0: assenza di flussi netti chiari; mercato piatto o bilanciato.
Picchi repentini: indicano accelerate di flusso — spesso coincidono con rotture, open/close session, news.
Sostegno al trade: usa FlowPulse come conferma prima di entrare su breakout o come avviso di attenzione su esaurimenti.
6) Divergenze (come leggerle)
Le divergenze tra prezzo e FlowPulse sono segnali importanti:
Divergenza rialzista (bullish divergence): prezzo fa nuovi minimi mentre FlowPulse non fa nuovi minimi (o forma minimo relativo più alto) → indica che la spinta di vendita non è supportata da volume/volatilità, possibile inversione/rimbalzo.
Divergenza ribassista (bearish divergence): prezzo fa nuovi massimi mentre FlowPulse non li conferma (o forma massimo relativo più basso) → la spinta d’acquisto è “debole”, possibile esaurimento e inversione.
Note pratiche: cercare divergenze su timeframe maggiori (H4, D) per maggiore attendibilità; sui timeframe minori prendere solo come early warning.
7) Esempi d’uso operativo
Conferma breakout: prezzo rompe resistenza + FlowPulse positivo e crescente → breakout più probabile e con volumi reali.
Filtro per falsi segnali: prezzo rompe ma FlowPulse è piatto/negativo → alto rischio di false breakout.
Entrata per pullback: dopo breakout, attendere un pullback con FlowPulse che torna positivo → ingresso più prudente.
Gestione delle uscite: se sei long e FlowPulse improvvisamente si inverte in negativo su volumi elevati → considerare riduzione posizione o stop.
8) Timeframe consigliati
Intraday / Scalping: M5–M30 con length ridotto (es. 10–14) e smoothLen piccolo.
Swing trading: H1–H4 con length 20–50.
Position trading: D1 con length maggiore per filtrare rumore.
Testa i parametri sul tuo asset e timeframe; nessun parametro è universale.
9) Limitazioni e avvertenze
L’indicatore non è un sistema di trading completo: è un tool di informazione e timing.
Dipende dalla qualità dei dati di volume del simbolo: su alcuni titoli/mercati (es. alcuni ETF, Forex su certi broker) il volume può essere parziale o non rappresentativo.
I valori di margine/multiplier e smoothing influenzano sensibilmente sensibilità e falsi segnali: backtest e ottimizzazione sono raccomandati.
Non usare il solo FlowPulse per entrare su leva elevata senza gestione del rischio12) Disclaimer da inserire
Disclaimer: Questo indicatore è fornito solo a scopo didattico e non costituisce consulenza finanziaria. L’uso comporta rischi: valuta sempre la gestione del rischio e testa su conto demo prima dell’applicazione in reale.
lower_tfLibrary "lower_tf"
█ OVERVIEW
This library is an enhanced (opinionated) version of the library originally developed by PineCoders contained in lower_tf .
It is a Pine Script® programming tool for advanced lower-timeframe selection and intra-bar analysis.
█ CONCEPTS
Lower Timeframe Analysis
Lower timeframe analysis refers to the analysis of price action and market microstructure using data from timeframes shorter than the current chart period. This technique allows traders and analysts to gain deeper insights into market dynamics, volume distribution, and the price movements occurring within each bar on the chart. In Pine Script®, the request.security_lower_tf() function allows this analysis by accessing intrabar data.
The library provides a comprehensive set of functions for accurate mapping of lower timeframes, dynamic precision control, and optimized historical coverage using request.security_lower_tf().
█ IMPROVEMENTS
The original library implemented ten precision levels. This enhanced version extends that to twelve levels, adding two ultra-high-precision options:
Coverage-Based Precision (Original 5 levels):
1. "Covering most chart bars (least precise)"
2. "Covering some chart bars (less precise)"
3. "Covering fewer chart bars (more precise)"
4. "Covering few chart bars (very precise)"
5. "Covering the least chart bars (most precise)"
Intrabar-Count-Based Precision (Expanded from 5 to 7 levels):
6. "~12 intrabars per chart bar"
7. "~24 intrabars per chart bar"
8. "~50 intrabars per chart bar"
9. "~100 intrabars per chart bar"
10. "~250 intrabars per chart bar"
11. "~500 intrabars per chart bar" ← NEW
12. "~1000 intrabars per chart bar" ← NEW
The key enhancements in this version include:
1. Extended Precision Range: Adds two ultra-high-precision levels (~500 and ~1000 intrabars) for advanced microstructure analysis requiring maximum granularity.
2. Market-Agnostic Implementation: Eliminates the distinction between crypto/forex and traditional markets, removing the mktFactor variable in favor of a unified, predictable approach across all asset classes.
3. Explicit Precision Mapping: Completely refactors the timeframe selection logic using native Pine Script® timeframe properties ( timeframe.isseconds , timeframe.isminutes , timeframe.isdaily , timeframe.isweekly , timeframe.ismonthly ) and explicit multiplier-based lookup tables. The original library used minute-based calculations with market-dependent conditionals that produced inconsistent results. This version provides deterministic, predictable mappings for every chart timeframe, ensuring consistent precision behavior regardless of asset type or market hours.
An example of the differences can be seen side-by-side in the chart below, where the original library is on the left and the enhanced version is on the right:
█ USAGE EXAMPLE
// This Pine Script® code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
// © andre_007
//@version=6
indicator("lower_tf Example")
import andre_007/lower_tf/1 as LTF
import PineCoders/Time/5 as PCtime
//#region ———————————————————— Example code
// ————— Constants
color WHITE = color.white
color GRAY = color.gray
string LTF1 = "Covering most chart bars (least precise)"
string LTF2 = "Covering some chart bars (less precise)"
string LTF3 = "Covering less chart bars (more precise)"
string LTF4 = "Covering few chart bars (very precise)"
string LTF5 = "Covering the least chart bars (most precise)"
string LTF6 = "~12 intrabars per chart bar"
string LTF7 = "~24 intrabars per chart bar"
string LTF8 = "~50 intrabars per chart bar"
string LTF9 = "~100 intrabars per chart bar"
string LTF10 = "~250 intrabars per chart bar"
string LTF11 = "~500 intrabars per chart bar"
string LTF12 = "~1000 intrabars per chart bar"
string TT_LTF = "This selection determines the approximate number of intrabars analyzed per chart bar. Higher numbers of
intrabars produce more granular data at the cost of less historical bar coverage, because the maximum number of
available intrabars is 200K.
\n\nThe first five options set the lower timeframe based on a specified relative level of chart bar coverage.
The last five options set the lower timeframe based on an approximate number of intrabars per chart bar."
string TAB_TXT = "Uses intrabars at the {0} timeframe.\nAvg intrabars per chart bar:
{1,number,#.#}\nChart bars covered: {2} of {3} ({4,number,#.##}%)"
string ERR_TXT = "No intrabar information exists at the {1}{0}{1} timeframe."
// ————— Inputs
string ltfModeInput = input.string(LTF3, "Intrabar precision", options = , tooltip = TT_LTF)
bool showInfoBoxInput = input.bool(true, "Show information box ")
string infoBoxSizeInput = input.string("normal", "Size ", inline = "01", options = )
string infoBoxYPosInput = input.string("bottom", "↕", inline = "01", options = )
string infoBoxXPosInput = input.string("right", "↔", inline = "01", options = )
color infoBoxColorInput = input.color(GRAY, "", inline = "01")
color infoBoxTxtColorInput = input.color(WHITE, "T", inline = "01")
// ————— Calculations
// @variable A "string" representing the lower timeframe for the data request.
// NOTE:
// This line is a good example where using `var` in the declaration can improve a script's performance.
// By using `var` here, the script calls `ltf()` only once, on the dataset's first bar, instead of redundantly
// evaluating unchanging strings on every bar. We only need one evaluation of this function because the selected
// timeframe does not change across bars in this script.
var string ltfString = LTF.ltf(ltfModeInput, LTF1, LTF2, LTF3, LTF4, LTF5, LTF6, LTF7, LTF8, LTF9, LTF10, LTF11, LTF12)
// @variable An array containing all intrabar `close` prices from the `ltfString` timeframe for the current chart bar.
array intrabarCloses = request.security_lower_tf(syminfo.tickerid, ltfString, close)
// Calculate the intrabar stats.
= LTF.ltfStats(intrabarCloses)
int chartBars = bar_index + 1
// ————— Visuals
// Plot the `avgIntrabars` and `intrabars` series in all display locations.
plot(avgIntrabars, "Average intrabars", color.silver, 6)
plot(intrabars, "Intrabars", color.blue, 2)
// Plot the `chartBarsCovered` and `chartBars` values in the Data Window and the script's status line.
plot(chartBarsCovered, "Chart bars covered", display = display.data_window + display.status_line)
plot(chartBars, "Chart bars total", display = display.data_window + display.status_line)
// Information box logic.
if showInfoBoxInput
// @variable A single-cell table that displays intrabar information.
var table infoBox = table.new(infoBoxYPosInput + "_" + infoBoxXPosInput, 1, 1)
// @variable The span of the `ltfString` timeframe formatted as a number of automatically selected time units.
string formattedLtf = PCtime.formattedNoOfPeriods(timeframe.in_seconds(ltfString) * 1000)
// @variable A "string" containing the formatted text to display in the `infoBox`.
string txt = str.format(
TAB_TXT, formattedLtf, avgIntrabars, chartBarsCovered, chartBars, chartBarsCovered / chartBars * 100, "'"
)
// Initialize the `infoBox` cell on the first bar.
if barstate.isfirst
table.cell(
infoBox, 0, 0, txt, text_color = infoBoxTxtColorInput, text_size = infoBoxSizeInput,
bgcolor = infoBoxColorInput
)
// Update the cell's text on the latest bar.
else if barstate.islast
table.cell_set_text(infoBox, 0, 0, txt)
// Raise a runtime error if no intrabar data is available.
if ta.cum(intrabars) == 0 and barstate.islast
runtime.error(str.format(ERR_TXT, ltfString, "'"))
//#endregion
█ EXPORTED FUNCTIONS
ltf(userSelection, choice1, choice2, ...)
Returns the optimal lower timeframe string based on user selection and current chart timeframe. Dynamically calculates precision to balance granularity with historical coverage within the 200K intrabar limit.
ltfStats(intrabarValues)
Analyzes an intrabar array returned by request.security_lower_tf() and returns statistics: number of intrabars in current bar, total chart bars covered, and average intrabars per bar.
█ CREDITS AND LICENSING
Original Concept : PineCoders Team
Original Lower TF Library :
License : Mozilla Public License 2.0
COT Index v.2COT Index v.2 Indicator
( fix for extreme values)
📊 Overview
The COT (Commitment of Traders) Index Indicator transforms raw COT data into normalized indices ranging from 0-100, with extensions to 120 and -20 for extreme market conditions. This powerful tool helps traders analyze institutional positioning and market sentiment by tracking the net long positions of three key market participant groups.
🎯 What It Does
This indicator converts weekly CFTC Commitment of Traders data into easy-to-read oscillator format, showing:
Commercial Index (Blue Line) - Smart money/hedgers positioning
NonCommercial Index (Orange Line) - Large speculators/funds positioning
Nonreportable Index (Red Line) - Small traders positioning
📈 Key Features
Smart Scaling Algorithm
0-100 Range: Normal market conditions based on recent price action
120 Level: Extreme bullish positioning (above historical maximum)
-20 Level: Extreme bearish positioning (below historical minimum)
Dual Time Frame Analysis
Short Period (26 weeks default): For current market scaling
Historical Period (156 weeks default): For extreme condition detection
Flexible Data Sources
Futures Only reports
Futures and Options combined reports
Automatic symbol detection with manual overrides for HG and LBR
🔧 Customizable Settings
Data Configuration
Adjustable lookback periods for both current and historical analysis
Report type selection (Futures vs Futures & Options)
Display Options
Toggle individual trader categories on/off
Customizable reference lines (overbought/oversold levels)
Optional 0/100 boundary lines
Adjustable line widths and colors
Reference Levels
Upper Bound: 120 (extreme bullish)
Overbought: 80 (default)
Midline: 50 (neutral)
Oversold: 20 (default)
Lower Bound: -20 (extreme bearish)
💡 Trading Applications
Contrarian Signals
High Commercial Index + Low NonCommercial Index = Potential bullish reversal
Low Commercial Index + High NonCommercial Index = Potential bearish reversal
Market Sentiment Analysis
Track institutional vs retail positioning divergences
Identify extreme market conditions requiring attention
Monitor smart money accumulation/distribution patterns
Confirmation Tool
Use alongside technical analysis for trade confirmation
Validate breakouts with positioning data
Assess market structure changes
📊 Visual Elements
Status Table: Displays current settings and symbol information
Color-Coded Lines: Easy identification of each trader category
Reference Levels: Clear overbought/oversold boundaries
Extreme Indicators: Visual cues for unusual market conditions
⚠️ Important Notes
COT data is released weekly on Fridays (Tuesday data)
Best suited for weekly and daily timeframes
Requires symbols with available CFTC data
Works automatically for most futures contracts
🎯 Best Practices
Use in conjunction with price action analysis
Look for divergences between price and positioning
Pay special attention to extreme readings (120/-20 levels)
Consider all three indices together for complete market picture
Allow for data lag (3-day delay from CFTC)
This indicator is ideal for swing traders, position traders, and anyone interested in understanding the positioning dynamics of professional vs retail market participants.
Central Limit Theorem Reversion IndicatorDear TV community, let me introduce you to the first-ever Central Limit Theorem indicator on TradingView.
The Central Limit Theorem is used in statistics and it can be quite useful in quant trading and understanding market behaviors.
In short, the CLT states: "When you take repeated samples from any population and calculate their averages, those averages will form a normal (bell curve) distribution—no matter what the original data looks like."
In this CLT indicator, I use statistical theory to identify high-probability mean reversion opportunities in the markets. It calculates statistical confidence bands and z-scores to identify when price movements deviate significantly from their expected distribution, signaling potential reversion opportunities with quantifiable probability levels.
Mathematical Foundation
The Central Limit Theorem (CLT) says that when you average many data points together, those averages will form a predictable bell-curve pattern, even if the original data is completely random and unpredictable (which often is in the markets). This works no matter what you're measuring, and it gets more reliable as you use more data points.
Why using it for trading?
Individual price movements seem random and chaotic, but when we look at the average of many price movements, we can actually predict how they should behave statistically. This lets us spot when prices have moved "too far" from what's normal—and those extreme moves tend to snap back (mean reversion).
Key Formula:
Z = (X̄ - μ) / (σ / √n)
Where:
- X̄ = Sample mean (average return over n periods)
- μ = Population mean (long-term expected return)
- σ = Population standard deviation (volatility)
- n = Sample size
- σ/√n = Standard error of the mean
How I Apply CLT
Step 1: Calculate Returns
Measures how much price changed from one bar to the next (using logarithms for better statistical properties)
Step 2: Average Recent Returns
Takes the average of the last n returns (e.g., last 100 bars). This is your "sample mean."
Step 3: Find What's "Normal"
Looks at historical data to determine: a) What the typical average return should be (the long-term mean) and b) How volatile the market usually is (standard deviation)
Step 4: Calculate Standard Error
Determines how much sample averages naturally vary. Larger samples = smaller expected variation.
Step 5: Calculate Z-Score
Measures how unusual the current situation is.
Step 6: Draw Confidence Bands
Converts these statistical boundaries into actual price levels on your chart, showing where price is statistically expected to stay 95% and 99% of the time.
Interpretation & Usage
The Z-Score:
The z-score tells you how statistically unusual the current price deviation is:
|Z| < 1.0 → Normal behavior, no action
|Z| = 1.0 to 1.96 → Moderate deviation, watch closely
|Z| = 1.96 to 2.58 → Significant deviation (95%+), consider entry
|Z| > 2.58 → Extreme deviation (99%+), high probability setup
The Confidence Bands
- Upper Red Bands: 95% and 99% overbought zones → Expect mean reversion downward as the price is not likely to cross these lines.
- Center Gray Line: Statistical expectation (fair value)
- Lower Blue Bands: 95% and 99% oversold zones → Expect mean reversion upward
Trading Logic:
- When price exceeds the upper 95% band (z-score > +1.96), there's only a 5% probability this is random noise → Strong sell/short signal
- When price falls below the lower 95% band (z-score < -1.96), there's a 95% statistical expectation of upward reversion → Strong buy/long signal
Background Gradient
The background color provides real-time visual feedback:
- Blue shades: Oversold conditions, expect upward reversion
- Red shades: Overbought conditions, expect downward reversion
- Intensity: Darker colors indicate stronger statistical significance
Trading Strategy Examples
Hypothetically, this is how the indicator could be used:
- Long: Z-score < -1.96 (below 95% confidence band)
- Short: Z-score > +1.96 (above 95% confidence band)
- Take profit when price returns to center line (Z ≈ 0)
Input Parameters
Sample Size (n) - Default: 100
Lookback Period (m) - Default: 100
You can also create alerts based on the indicator.
Final notes:
- The indicator uses logarithmic returns for better statistical properties
- Converts statistical bands back to price space for practical use
- Adaptive volatility: Bands automatically widen in high volatility, narrow in low volatility
- No repainting: yay! All calculations use historical data only
Feedback is more than welcome!
Henri
cd_correlation_analys_Cxcd_correlation_analys_Cx
General:
This indicator is designed for correlation analysis by classifying stocks (487 in total) and indices (14 in total) traded on Borsa İstanbul (BIST) on a sectoral basis.
Tradingview's sector classifications (20) have been strictly adhered to for sector grouping.
Depending on user preference, the analysis can be performed within sectors, between sectors, or manually (single asset).
Let me express my gratitude to the code author, @fikira, beforehand; you will find the reason for my thanks in the context.
Details:
First, let's briefly mention how this indicator could have been prepared using the classic method before going into details.
Classically, assets could be divided into groups of forty (40), and the analysis could be performed using the built-in function:
ta.correlation(source1, source2, length) → series float.
I chose sectoral classification because I believe there would be a higher probability of assets moving together, rather than using fixed-number classes.
In this case, 21 arrays were formed with the following number of elements:
(3, 11, 21, 60, 29, 20, 12, 3, 31, 5, 10, 11, 6, 48, 73, 62, 16, 19, 13, 34 and indices (14)).
However, you might have noticed that some arrays have more than 40 elements. This is exactly where @Fikira's indicator came to the rescue. When I examined their excellent indicator, I saw that it could process 120 assets in a single operation. (I believe this was the first limit overrun; thanks again.)
It was amazing to see that data for 3 pairs could be called in a single request using a special method.
You can find the details here:
When I adapted it for BIST, I found it sufficient to call data for 2 pairs instead of 3 in a single go. Since asset prices are regular and have 2 decimal places, I used a fixed multiplier of $10^8$ and a fixed decimal count of 2 in Fikira's formulas.
With this method, the (high, low, open, close) values became accessible for each asset.
The summary up to this point is that instead of the ready-made formula + groups of 40, I used variable-sized groups and the method I will detail now.
Correlation/harmony/co-movement between assets provides advantages to market participants. Coherent assets are expected to rise or fall simultaneously.
Therefore, to convert co-movement into a mathematical value, I defined the possible movements of the current candle relative to the previous candle bar over a certain period (user-defined). These are:
Up := high > high and low > low
Down := high < high and low < low
Inside := high <= high and low >= low
Outside := high >= high and low <= low and NOT Inside.
Ignore := high = low = open = close
If both assets performed the same movement, 1 was added to the tracking counter.
If (Up-Up), (Down-Down), (Inside-Inside), or (Outside-Outside), then counter := counter + 1.
If the period length is 100 and the counter is 75, it means there is 75% co-movement.
Corr = counter / period ($75/100$)
Average = ta.sma(Corr, 100) is obtained.
The highest coefficients recorded in the array are presented to the user in a table.
From the user menu options, the user can choose to compare:
• With assets in its own sector
• With assets in the selected sector
• By activating the confirmation box and manually entering a single asset for comparison.
Table display options can be adjusted from the Settings tab.
In the attached examples:
Results for AKBNK stock from the Finance sector compared with GARAN stock from the same sector:
Timeframe: Daily, Period: 50 => Harmony 76% (They performed the same movement in 38 out of 50 bars)
Comment: Opposite movements at swing high and low levels may indicate a change in the direction of the price flow (SMT).
Looking at ASELS from the Electronic Technology sector over the last 30 daily candles, they performed the same movements by 40% with XU100, 73.3% (22/30) with XUTEK (Technology Index), and 86.9% according to the averages.
Comment: It is more appropriate to follow ASELS stock with XUTEK (Technology index) instead of the general index (XU100). Opposite movements at swing high and low levels may indicate a change in the direction of the price flow (SMT).
Again, when ASELS stock is taken on H1 instead of daily, and the length is 100 instead of 30, the harmony rate is seen to be 87%.
Please share your thoughts and criticisms regarding the indicator, which I prepared with a bit of an educational purpose specifically for BIST.
Happy trading.
pine script tradingbot - many ema oscillator## 🧭 **Many EMA Oscillator (TradingView Pine Script Indicator)**
*A multi-layer EMA differential oscillator for trend strength and momentum analysis*
---
### 🧩 **Overview**
The **Many EMA Oscillator** is a **TradingView Pine Script indicator** designed to help traders visualize **trend direction**, **momentum strength**, and **multi-timeframe EMA alignment** in one clean oscillator panel.
It’s a **custom EMA-based trend indicator** that shows how fast or slow different **Exponential Moving Averages (EMAs)** are expanding or contracting — helping you identify **bullish and bearish momentum shifts** early.
This **Pine Script EMA indicator** is especially useful for traders looking to combine multiple **EMA signals** into one **momentum oscillator** for better clarity and precision.
---
### ⚙️ **How It Works**
1. **Multiple EMA Layers:**
The indicator calculates seven **EMAs** (default: 20, 50, 100, 150, 200, 300) and applies a **smoothing filter** using another EMA (default smoothing = 20).
This removes short-term noise and gives a smoother, professional-grade momentum reading.
2. **EMA Gap Analysis:**
The oscillator measures the **difference between consecutive EMAs**, revealing how trend layers are separating or converging.
```
diff1 = EMA(20) - EMA(50)
diff2 = EMA(50) - EMA(100)
diff3 = EMA(100) - EMA(150)
diff4 = EMA(150) - EMA(200)
diff5 = EMA(200) - EMA(300)
```
These gaps (or “differentials”) show **trend acceleration or compression**, acting like a **multi-EMA MACD system**.
3. **Color-Coded Visualization:**
Each differential (`diff1`–`diff5`) is plotted as a **histogram**:
- 🟢 **Green bars** → EMAs expanding → bullish momentum growing
- 🔴 **Red bars** → EMAs contracting → bearish momentum or correction
This gives a clean, compact view of **trend strength** without cluttering your chart.
4. **Automatic Momentum Signals:**
- **🟡 Up Triangle** → All EMA gaps increasing → strong bullish trend alignment
- **⚪ Down Triangle** → All EMA gaps decreasing → trend weakening or bearish transition
---
### 📊 **Inputs**
| Input | Default | Description |
|-------|----------|-------------|
| `smmoth_emas` | 20 | Smoothing factor for all EMAs |
| `Length2`–`Length7` | 20–300 | Adjustable EMA periods |
| `Length21`, `Length31`, `Length41`, `Length51` | Optional | For secondary EMA analysis |
---
### 🧠 **Interpretation Guide**
| Observation | Meaning |
|--------------|----------|
| Increasing green bars | Trend acceleration and bullish continuation |
| Decreasing red bars | Trend exhaustion or sideways consolidation |
| Yellow triangles | All EMA layers aligned bullishly |
| White triangles | All EMA layers aligned bearishly |
This **EMA oscillator for TradingView** simplifies **multi-EMA trading strategies** by showing alignment strength in one place.
It works great for **swing traders**, **scalpers**, and **trend-following systems**.
---
### 🧪 **Best Practices for Use**
- Works on **all TradingView timeframes** (1m, 5m, 1h, 1D, etc.)
- Suitable for **stocks, forex, crypto, and indices**
- Combine with **RSI**, **MACD**, or **price action** confirmation
- Excellent for detecting **EMA compression zones**, **trend continuation**, or **momentum shifts**
- Can be used as part of a **multi-EMA trading strategy** or **trend strength indicator setup**
---
### 💡 **Why It Stands Out**
- 100% built in **Pine Script v6**
- Optimized for **smooth EMA transitions**
- Simple color-coded momentum visualization
- Professional-grade **multi-timeframe trend oscillator**
This is one of the most **lightweight and powerful EMA oscillators** available for TradingView users who prefer clarity over clutter.
---
### ⚠️ **Disclaimer**
This indicator is published for **educational and analytical purposes only**.
It does **not provide financial advice**, buy/sell signals, or investment recommendations.
Always backtest before live use and trade responsibly.
---
### 👨💻 **Author**
Developed by **@algo_coders**
Built in **Pine Script v6** on **TradingView**
Licensed under the (mozilla.org)
NASDAQ Trading System with PivotsThis TradingView indicator, designed for the 30-minute NASDAQ (^IXIC) chart, guides QQQ options trading using a trend-following strategy. It plots a 20-period SMA (blue) and a 100-period SMA (red), with an optional 250-period SMA (orange) inspired by rauItrades' NASDAQ SMA outfit. A bullish crossover (20 SMA > 100 SMA) triggers a green "BUY" triangle below the bar, signaling a potential long position in QQQ, while a bearish crossunder (20 SMA < 100 SMA) shows a red "SELL" triangle above, indicating a short or exit. The background colors green (bullish) or red (bearish) for trend bias. Orange circles (recent highs) and purple circles (recent lows) mark support/resistance levels using 5-bar pivot points.
Hidden Impulse═══════════════════════════════════════════════════════════════════
HIDDEN IMPULSE - Multi-Timeframe Momentum Detection System
═══════════════════════════════════════════════════════════════════
OVERVIEW
Hidden Impulse is an advanced momentum oscillator that combines the Schaff Trend Cycle (STC) and Force Index into a comprehensive multi-timeframe trading system. Unlike standard implementations of these indicators, this script introduces three distinct trading setups with specific entry conditions, multi-timeframe confirmation, and trend filtering.
═══════════════════════════════════════════════════════════════════
ORIGINALITY & KEY FEATURES
This indicator is original in the following ways:
1. DUAL-TIMEFRAME STC ANALYSIS
Standard STC implementations work on a single timeframe. This script
simultaneously analyzes STC on both your trading timeframe and a higher
timeframe, providing trend context and filtering out low-probability signals.
2. FORCE INDEX INTEGRATION
The script combines STC with Force Index (volume-weighted price momentum)
to confirm the strength behind price moves. This combination helps identify
when momentum shifts are backed by genuine buying/selling pressure.
3. THREE DISTINCT TRADING SETUPS
Rather than generic overbought/oversold signals, the indicator provides
three specific, rule-based setups:
- Setup A: Classic trend-following entries with multi-timeframe confirmation
- Setup B: Divergence-based reversal entries (highest probability)
- Setup C: Mean-reversion bounce trades at extreme levels
4. INTELLIGENT FILTERING
All signals are filtered through:
- 50 EMA trend direction (prevents counter-trend trades)
- Higher timeframe STC alignment (ensures macro trend agreement)
- Force Index confirmation (validates volume support)
═══════════════════════════════════════════════════════════════════
HOW IT WORKS - TECHNICAL EXPLANATION
SCHAFF TREND CYCLE (STC) CALCULATION:
The STC is a cyclical oscillator that combines MACD concepts with stochastic
smoothing to create earlier and smoother trend signals.
Step 1: Calculate MACD
- Fast MA = EMA(close, Length1) — default 23
- Slow MA = EMA(close, Length2) — default 50
- MACD Line = Fast MA - Slow MA
Step 2: First Stochastic Smoothing
- Apply stochastic calculation to MACD
- Stoch1 = 100 × (MACD - Lowest(MACD, Smoothing)) / (Highest(MACD, Smoothing) - Lowest(MACD, Smoothing))
- Smooth result with EMA(Stoch1, Smoothing) — default 10
Step 3: Second Stochastic Smoothing
- Apply stochastic calculation again to the smoothed stochastic
- This creates the final STC value between 0-100
The dual stochastic smoothing makes STC more responsive than MACD while
being smoother than traditional stochastics.
FORCE INDEX CALCULATION:
Force Index measures the power behind price movements by incorporating volume:
Force Raw = (Close - Close ) × Volume
Force Index = EMA(Force Raw, Period) — default 13
Interpretation:
- Positive Force Index = Buying pressure (bulls in control)
- Negative Force Index = Selling pressure (bears in control)
- Force Index crossing zero = Momentum shift
- Divergences with price = Weakening momentum (reversal signal)
TREND FILTER:
A 50-period EMA serves as the trend filter:
- Price above EMA50 = Uptrend → Only LONG signals allowed
- Price below EMA50 = Downtrend → Only SHORT signals allowed
This prevents counter-trend trading which accounts for most losing trades.
═══════════════════════════════════════════════════════════════════
THE THREE TRADING SETUPS - DETAILED
SETUP A: CLASSIC MOMENTUM ENTRY
Concept: Enter when STC exits oversold/overbought zones with trend confirmation
LONG CONDITIONS:
1. Higher timeframe STC > 25 (macro trend is up)
2. Primary timeframe STC crosses above 25 (momentum turning up)
3. Force Index crosses above 0 OR already positive (volume confirms)
4. Price above 50 EMA (local trend is up)
SHORT CONDITIONS:
1. Higher timeframe STC < 75 (macro trend is down)
2. Primary timeframe STC crosses below 75 (momentum turning down)
3. Force Index crosses below 0 OR already negative (volume confirms)
4. Price below 50 EMA (local trend is down)
Best for: Trending markets, continuation trades
Win rate: Moderate (60-65%)
Risk/Reward: 1:2 to 1:3
───────────────────────────────────────────────────────────────────
SETUP B: DIVERGENCE REVERSAL (HIGHEST PROBABILITY)
Concept: Identify exhaustion points where price makes new extremes but
momentum (Force Index) fails to confirm
BULLISH DIVERGENCE:
1. Price makes a lower low (LL) over 10 bars
2. Force Index makes a higher low (HL) — refuses to follow price down
3. STC is below 25 (oversold condition)
Trigger: STC starts rising AND Force Index crosses above zero
BEARISH DIVERGENCE:
1. Price makes a higher high (HH) over 10 bars
2. Force Index makes a lower high (LH) — refuses to follow price up
3. STC is above 75 (overbought condition)
Trigger: STC starts falling AND Force Index crosses below zero
Why this works: Divergences signal that the current trend is losing steam.
When volume (Force Index) doesn't confirm new price extremes, a reversal
is likely.
Best for: Reversal trading, range-bound markets
Win rate: High (70-75%)
Risk/Reward: 1:3 to 1:5
───────────────────────────────────────────────────────────────────
SETUP C: QUICK BOUNCE AT EXTREMES
Concept: Catch rapid mean-reversion moves when price touches EMA50 in
extreme STC zones
LONG CONDITIONS:
1. Price touches 50 EMA from above (pullback in uptrend)
2. STC < 15 (extreme oversold)
3. Force Index > 0 (buyers stepping in)
SHORT CONDITIONS:
1. Price touches 50 EMA from below (pullback in downtrend)
2. STC > 85 (extreme overbought)
3. Force Index < 0 (sellers stepping in)
Best for: Scalping, quick mean-reversion trades
Win rate: Moderate (55-60%)
Risk/Reward: 1:1 to 1:2
Note: Use tighter stops and quick profit-taking
═══════════════════════════════════════════════════════════════════
HOW TO USE THE INDICATOR
STEP 1: CONFIGURE TIMEFRAMES
Primary Timeframe (STC - Primary Timeframe):
- Leave empty to use your current chart timeframe
- This is where you'll take trades
Higher Timeframe (STC - Higher Timeframe):
- Default: 30 minutes
- Recommended ratios:
* 5min chart → 30min higher TF
* 15min chart → 1H higher TF
* 1H chart → 4H higher TF
* Daily chart → Weekly higher TF
───────────────────────────────────────────────────────────────────
STEP 2: ADJUST STC PARAMETERS FOR YOUR MARKET
Default (23/50/10) works well for stocks and forex, but adjust for:
CRYPTO (volatile):
- Length 1: 15
- Length 2: 35
- Smoothing: 8
(Faster response for rapid price movements)
STOCKS (standard):
- Length 1: 23
- Length 2: 50
- Smoothing: 10
(Balanced settings)
FOREX MAJORS (slower):
- Length 1: 30
- Length 2: 60
- Smoothing: 12
(Filters out noise in 24/7 markets)
───────────────────────────────────────────────────────────────────
STEP 3: ENABLE YOUR PREFERRED SETUPS
Toggle setups based on your trading style:
Conservative Trader:
✓ Setup B (Divergence) — highest win rate
✗ Setup A (Classic) — only in strong trends
✗ Setup C (Bounce) — too aggressive
Trend Trader:
✓ Setup A (Classic) — primary signals
✓ Setup B (Divergence) — for entries on pullbacks
✗ Setup C (Bounce) — not suitable for trending
Scalper:
✓ Setup C (Bounce) — quick in-and-out
✓ Setup B (Divergence) — high probability scalps
✗ Setup A (Classic) — too slow
───────────────────────────────────────────────────────────────────
STEP 4: READ THE SIGNALS
ON THE CHART:
Labels appear when conditions are met:
Green labels:
- "LONG A" — Setup A long entry
- "LONG B DIV" — Setup B divergence long (best signal)
- "LONG C" — Setup C bounce long
Red labels:
- "SHORT A" — Setup A short entry
- "SHORT B DIV" — Setup B divergence short (best signal)
- "SHORT C" — Setup C bounce short
IN THE INDICATOR PANEL (bottom):
- Blue line = Primary timeframe STC
- Orange dots = Higher timeframe STC (optional)
- Green/Red bars = Force Index histogram
- Dashed lines at 25/75 = Entry/Exit zones
- Background shading = Oversold (green) / Overbought (red)
INFO TABLE (top-right corner):
Shows real-time status:
- STC values for both timeframes
- Force Index direction
- Price position vs EMA
- Current trend direction
- Active signal type
═══════════════════════════════════════════════════════════════════
TRADING STRATEGY & RISK MANAGEMENT
ENTRY RULES:
Priority ranking (best to worst):
1st: Setup B (Divergence) — wait for these
2nd: Setup A (Classic) — in confirmed trends only
3rd: Setup C (Bounce) — scalping only
Confirmation checklist before entry:
☑ Signal label appears on chart
☑ TREND in info table matches signal direction
☑ Higher timeframe STC aligned (check orange dots or table)
☑ Force Index confirming (check histogram color)
───────────────────────────────────────────────────────────────────
STOP LOSS PLACEMENT:
Setup A (Classic):
- LONG: Below recent swing low
- SHORT: Above recent swing high
- Typical: 1-2 ATR distance
Setup B (Divergence):
- LONG: Below the divergence low
- SHORT: Above the divergence high
- Typical: 0.5-1.5 ATR distance
Setup C (Bounce):
- LONG: 5-10 pips below EMA50
- SHORT: 5-10 pips above EMA50
- Typical: 0.3-0.8 ATR distance
───────────────────────────────────────────────────────────────────
TAKE PROFIT TARGETS:
Conservative approach:
- Exit when STC reaches opposite level
- LONG: Exit when STC > 75
- SHORT: Exit when STC < 25
Aggressive approach:
- Hold until opposite signal appears
- Trail stop as STC moves in your favor
Partial profits:
- Take 50% at 1:2 risk/reward
- Let remaining 50% run to target
───────────────────────────────────────────────────────────────────
WHAT TO AVOID:
❌ Trading Setup A in sideways/choppy markets
→ Wait for clear trend or use Setup B only
❌ Ignoring higher timeframe STC
→ Always check orange dots align with your direction
❌ Taking signals against the major trend
→ If weekly trend is down, be cautious with longs
❌ Overtrading Setup C
→ Maximum 2-3 bounce trades per session
❌ Trading during low volume periods
→ Force Index becomes unreliable
═══════════════════════════════════════════════════════════════════
ALERTS CONFIGURATION
The indicator includes 8 alert types:
Individual setup alerts:
- "Setup A - LONG" / "Setup A - SHORT"
- "Setup B - DIV LONG" / "Setup B - DIV SHORT" ⭐ recommended
- "Setup C - BOUNCE LONG" / "Setup C - BOUNCE SHORT"
Combined alerts:
- "ANY LONG" — fires on any long signal
- "ANY SHORT" — fires on any short signal
Recommended alert setup:
- Create "Setup B - DIV LONG" and "Setup B - DIV SHORT" alerts
- These are the highest probability signals
- Set "Once Per Bar Close" to avoid false alerts
═══════════════════════════════════════════════════════════════════
VISUALIZATION SETTINGS
Show Labels on Chart:
Toggle on/off the signal labels (green/red)
Disable for cleaner chart once you're familiar with the indicator
Show Higher TF STC:
Toggle the orange dots showing higher timeframe STC
Useful for visual confirmation of multi-timeframe alignment
Info Panel:
Cannot be disabled — always shows current status
Positioned top-right to avoid chart interference
═══════════════════════════════════════════════════════════════════
EXAMPLE TRADE WALKTHROUGH
SETUP B DIVERGENCE LONG EXAMPLE:
1. Market Context:
- Price in downtrend, below 50 EMA
- Multiple lower lows forming
- STC below 25 (oversold)
2. Divergence Formation:
- Price makes new low at $45.20
- Force Index refuses to make new low (higher low forms)
- This indicates selling pressure weakening
3. Signal Trigger:
- STC starts turning up
- Force Index crosses above zero
- Label appears: "LONG B DIV"
4. Trade Execution:
- Entry: $45.50 (current price at signal)
- Stop Loss: $44.80 (below divergence low)
- Target 1: $47.90 (STC reaches 75) — risk/reward 1:3.4
- Target 2: Opposite signal or trail stop
5. Trade Management:
- Price rallies to $47.20
- STC reaches 68 (approaching target zone)
- Take 50% profit, move stop to breakeven
- Exit remaining at $48.10 when STC crosses 75
Result: 3.7R gain
═══════════════════════════════════════════════════════════════════
ADVANCED TIPS
1. MULTI-TIMEFRAME CONFLUENCE
For highest probability trades, wait for:
- Primary TF signal
- Higher TF STC aligned (>25 for longs, <75 for shorts)
- Even higher TF trend in same direction (manual check)
2. VOLUME CONFIRMATION
Watch the Force Index histogram:
- Increasing bar size = Strengthening momentum
- Decreasing bar size = Weakening momentum
- Use this to gauge signal strength
3. AVOID THESE MARKET CONDITIONS
- Major news events (Force Index becomes erratic)
- Market open first 30 minutes (volatility spikes)
- Low liquidity instruments (Force Index unreliable)
- Extreme trending days (wait for pullbacks)
4. COMBINE WITH SUPPORT/RESISTANCE
Best signals occur near:
- Key horizontal levels
- Fibonacci retracements
- Previous day's high/low
- Psychological round numbers
5. SESSION AWARENESS
- Asia session: Use lower timeframes, Setup C works well
- London session: Setup A and B both effective
- New York session: All setups work, highest volume
═══════════════════════════════════════════════════════════════════
INDICATOR WINDOWS LAYOUT
MAIN CHART:
- Price action
- 50 EMA (green/red)
- Signal labels
- Info panel
INDICATOR WINDOW:
- STC oscillator (blue line, 0-100 scale)
- Higher TF STC (orange dots, optional)
- Force Index histogram (green/red bars)
- Reference levels (25, 50, 75)
- Background zones (green oversold, red overbought)
═══════════════════════════════════════════════════════════════════
PERFORMANCE OPTIMIZATION
For best results:
Backtesting:
- Test on your specific instrument and timeframe
- Adjust STC parameters if win rate < 55%
- Record which setup works best for your market
Position Sizing:
- Risk 1-2% per trade
- Setup B can use 2% risk (higher win rate)
- Setup C should use 1% risk (lower win rate)
Trade Frequency:
- Setup B: 2-5 signals per week (be patient)
- Setup A: 5-10 signals per week
- Setup C: 10+ signals per week (scalping)
═══════════════════════════════════════════════════════════════════
CREDITS & REFERENCES
This indicator builds upon established technical analysis concepts:
Schaff Trend Cycle:
- Developed by Doug Schaff (1996)
- Original concept published in Technical Analysis of Stocks & Commodities
- Implementation based on standard STC formula
Force Index:
- Developed by Dr. Alexander Elder
- Described in "Trading for a Living" (1993)
- Classic volume-momentum indicator
The multi-timeframe integration, three-setup system, and specific
entry conditions are original contributions of this indicator.
═══════════════════════════════════════════════════════════════════
DISCLAIMER
This indicator is a technical analysis tool and does not guarantee profits.
Past performance is not indicative of future results. Always:
- Use proper risk management
- Test on demo account first
- Combine with fundamental analysis
- Never risk more than you can afford to lose
═══════════════════════════════════════════════════════════════════
SUPPORT & QUESTIONS
If you find this indicator helpful, please:
- Leave a like and comment
- Share your feedback and results
- Report any bugs or issues
For questions about usage or optimization for specific markets,
feel free to comment below.
═════════════════════════════════════════════════════════════
Range Oscillator (Zeiierman)█ Overview
Range Oscillator (Zeiierman) is a dynamic market oscillator designed to visualize how far the price is trading relative to its equilibrium range. Instead of relying on traditional overbought/oversold thresholds, it uses adaptive range detection and heatmap coloring to reveal where price is trading within a volatility-adjusted band.
The oscillator maps market movement as a heat zone, highlighting when the price approaches the upper or lower range boundaries and signaling potential breakout or mean-reversion conditions.
Highlights
Adaptive range detection based on ATR and weighted price movement.
Heatmap-driven coloring that visualizes volatility pressure and directional bias.
Clear transition zones for detecting trend shifts and equilibrium points.
█ How It Works
⚪ Range Detection
The indicator identifies a dynamic price range using two main parameters:
Minimum Range Length: The number of bars required to confirm that a valid range exists.
Range Width Multiplier: Expands or contracts the detected range proportionally to the ATR (Average True Range).
This approach ensures that the oscillator automatically adapts to both trending and ranging markets without manual recalibration.
⚪ Weighted Mean Calculation
Instead of a simple moving average, the script calculates a weighted equilibrium mean based on the size of consecutive candle movements:
Larger price changes are given greater weight, emphasizing recent volatility.
⚪ Oscillator Formula
Once the range and equilibrium mean are defined, the oscillator computes:
Osc = 100 * (Close - Mean) / RangeATR
This normalizes price distance relative to the dynamic range size — producing consistent readings across volatile and quiet periods.
█ Heatmap Logic
The Range Oscillator includes a built-in heatmap engine that color-codes each oscillator value based on recent price interaction intensity:
Strong Bullish Zones: Bright green — price faces little resistance upward.
Weak Bullish Zones: Muted green — uptrend continuation but with minor hesitation.
Transition Zones: Blue — areas of uncertainty or trend shift.
Weak Bearish Zones: Maroon — downtrend pressure but soft momentum.
Strong Bearish Zones: Bright red — strong downside continuation with low resistance.
Each color band adapts dynamically using:
Number of Heat Levels: Controls granularity of the heatmap.
Minimum Touches per Level: Defines how reactive or “sensitive” each color zone is.
█ How to Use
⚪ Trend & Momentum Confirmation
When the oscillator stays above +0 with green coloring, it suggests sustained bullish pressure.
Similarly, readings below –0 with red coloring, it suggests sustained bearish pressure.
⚪ Range Breakouts
When the oscillator line breaks above +100 or below –100, the price is exceeding its normal volatility range, often signaling breakout potential or exhaustion extremes.
⚪ Mean Reversion Trades
Look for the oscillator to cross back toward zero after reaching an extreme. These transitions (often marked by blue tones) can identify early reversals or range resets.
⚪ Divergence
Use oscillator peaks and troughs relative to price action to spot hidden strength or weakness before the next move.
█ Settings
Minimum Range Length: Number of bars needed to confirm a valid range.
Range Width Multiplier: Expands or contracts range width based on ATR.
Number of Heat Levels: Number of gradient bands used in the oscillator.
Minimum Touches per Level: Sensitivity threshold for when a zone becomes “hot.”
-----------------
Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
PDB 4 MA + Candle Strength/Weakness Detector
4MA Strength & Reversal Detector
Unlock the power of momentum with this advanced 4 Moving Average system (20, 50, 100, 200) designed to pinpoint market strength and early reversal zones with precision.
How It Works:
- Bearish Reversal: Triggered when all moving averages align (20 < 50 < 100 < 200) and bearish reversal candles appear — highlighting potential tops.
- Bullish Reversal: Triggered when all moving averages align (200 < 100 < 50 < 20) and bullish reversal candles form — marking potential bottoms
:Best For:
⚡ Scalpers and day traders using 1–5 minute timeframes
📈 Identifying momentum shifts and trend exhaustion early
Tip: Combine this with volume or RSI for stronger confirmation and fewer false signals.
Mythical EMAs + Dynamic VWAP BandThis indicator titled "Mythical EMAs + Dynamic VWAP Band." It overlays several volatility-adjusted Exponential Moving Averages (EMAs) on the chart, along with a Volume Weighted Average Price (VWAP) line and a dynamic band around it.
Additionally, it uses background coloring (clouds) to visualize bullish or bearish trends, with intensity modulated by the price's position relative to the VWAP.
The EMAs are themed with mythical names (e.g., Hermes for the 9-period EMA), but this is just stylistic flavoring and doesn't affect functionality.
I'll break it down section by section, explaining what each part does, how it works, and its purpose in the context of technical analysis. This indicator is designed for traders to identify trends, momentum, and price fairness relative to volume-weighted averages, with volatility adjustments to make the EMAs more responsive in volatile markets.
### 1. **Volatility Calculation (ATR)**
```pine
atrLength = 14
volatility = ta.atr(atrLength)
```
- **What it does**: Calculates the Average True Range (ATR) over 14 periods (a common default). ATR measures market volatility by averaging the true range (the greatest of: high-low, |high-previous close|, |low-previous close|).
- **Purpose**: This volatility value is used later to dynamically adjust the EMAs, making them more sensitive in high-volatility conditions (e.g., during market swings) and smoother in low-volatility periods. It helps the indicator adapt to changing market environments rather than using static EMAs.
### 2. **Custom Mythical EMA Function**
```pine
mythical_ema(src, length, base_alpha, vol_factor) =>
alpha = (2 / (length + 1)) * base_alpha * (1 + vol_factor * (volatility / src))
ema = 0.0
ema := na(ema ) ? src : alpha * src + (1 - alpha) * ema
ema
```
- **What it does**: Defines a custom function to compute a modified EMA.
- It starts with the standard EMA smoothing factor formula: `2 / (length + 1)`.
- Multiplies it by a `base_alpha` (a user-defined multiplier to tweak responsiveness).
- Adjusts further for volatility: Adds a term `(1 + vol_factor * (volatility / src))`, where `vol_factor` scales the impact, and `volatility / src` normalizes ATR relative to the source price (making it scale-invariant).
- The EMA is then calculated recursively: If the previous EMA is NA (e.g., at the start), it uses the current source value; otherwise, it weights the current source by `alpha` and the prior EMA by `(1 - alpha)`.
- **Purpose**: This creates "adaptive" EMAs that react faster in volatile markets (higher alpha when volatility is high relative to price) without overreacting in calm periods. It's an enhancement over standard EMAs, which use fixed alphas and can lag in choppy conditions. The mythical theme is just naming—functionally, it's a volatility-weighted EMA.
### 3. **Calculating the EMAs**
```pine
ema9 = mythical_ema(close, 9, 1.2, 0.5) // Hermes - quick & nimble
ema20 = mythical_ema(close, 20, 1.0, 0.3) // Apollo - short-term foresight
ema50 = mythical_ema(close, 50, 0.9, 0.2) // Athena - wise strategist
ema100 = mythical_ema(close, 100, 0.8, 0.1) // Zeus - powerful oversight
ema200 = mythical_ema(close, 200, 0.7, 0.05) // Kronos - long-term patience
```
- **What it does**: Applies the custom EMA function to the close price with varying lengths (9, 20, 50, 100, 200 periods), base alphas (decreasing from 1.2 to 0.7 for longer periods to make shorter ones more responsive), and volatility factors (decreasing from 0.5 to 0.05 to reduce volatility influence on longer-term EMAs).
- **Purpose**: These form a multi-timeframe EMA ribbon:
- Shorter EMAs (e.g., 9 and 20) capture short-term momentum.
- Longer ones (e.g., 200) show long-term trends.
- Crossovers (e.g., short EMA crossing above long EMA) can signal buy/sell opportunities. The volatility adjustment makes them "mythical" by adding dynamism, potentially improving signal quality in real markets.
### 4. **VWAP Calculation**
```pine
vwap_val = ta.vwap(close) // VWAP based on close price
```
- **What it does**: Computes the Volume Weighted Average Price (VWAP) using the built-in `ta.vwap` function, anchored to the close price. VWAP is the average price weighted by volume over the session (resets daily by default in Pine Script).
- **Purpose**: VWAP acts as a benchmark for "fair value." Prices above VWAP suggest bullishness (buyers in control), below indicate bearishness (sellers dominant). It's commonly used by institutional traders to assess entry/exit points.
### 5. **Plotting EMAs and VWAP**
```pine
plot(ema9, color=color.fuchsia, title='EMA 9 (Hermes)')
plot(ema20, color=color.red, title='EMA 20 (Apollo)')
plot(ema50, color=color.orange, title='EMA 50 (Athena)')
plot(ema100, color=color.aqua, title='EMA 100 (Zeus)')
plot(ema200, color=color.blue, title='EMA 200 (Kronos)')
plot(vwap_val, color=color.yellow, linewidth=2, title='VWAP')
```
- **What it does**: Overlays the EMAs and VWAP on the chart with distinct colors and titles for easy identification in TradingView's legend.
- **Purpose**: Visualizes the EMA ribbon and VWAP line. Traders can watch for EMA alignments (e.g., all sloping up for uptrend) or price interactions with VWAP.
### 6. **Dynamic VWAP Band**
```pine
band_pct = 0.005
vwap_upper = vwap_val * (1 + band_pct)
vwap_lower = vwap_val * (1 - band_pct)
p1 = plot(vwap_upper, color=color.new(color.yellow, 0), title="VWAP Upper Band")
p2 = plot(vwap_lower, color=color.new(color.yellow, 0), title="VWAP Lower Band")
fill_color = close >= vwap_val ? color.new(color.green, 80) : color.new(color.red, 80)
fill(p1, p2, color=fill_color, title="Dynamic VWAP Band")
```
- **What it does**: Creates a band ±0.5% around the VWAP.
- Plots the upper/lower bands with full transparency (color opacity 0, so lines are invisible).
- Fills the area between them dynamically: Semi-transparent green (opacity 80) if close ≥ VWAP (bullish bias), red if below (bearish bias).
- **Purpose**: Highlights deviations from VWAP visually. The color change provides an at-a-glance sentiment indicator—green for "above fair value" (potential strength), red for "below" (potential weakness). The narrow band (0.5%) focuses on short-term fairness, and the fill makes it easier to spot than just the line.
### 7. **Trend Clouds with VWAP Interaction**
```pine
bullish = ema9 > ema20 and ema20 > ema50
bearish = ema9 < ema20 and ema20 < ema50
bullish_above_vwap = bullish and close > vwap_val
bullish_below_vwap = bullish and close <= vwap_val
bearish_below_vwap = bearish and close < vwap_val
bearish_above_vwap = bearish and close >= vwap_val
bgcolor(bullish_above_vwap ? color.new(color.green, 50) : na, title="Bullish Above VWAP")
bgcolor(bullish_below_vwap ? color.new(color.green, 80) : na, title="Bullish Below VWAP")
bgcolor(bearish_below_vwap ? color.new(color.red, 50) : na, title="Bearish Below VWAP")
bgcolor(bearish_above_vwap ? color.new(color.red, 80) : na, title="Bearish Above VWAP")
```
- **What it does**: Defines trend conditions based on EMA alignments:
- Bullish: Shorter EMAs stacked above longer ones (9 > 20 > 50, indicating upward momentum).
- Bearish: The opposite (downward momentum).
- Sub-conditions combine with VWAP: E.g., bullish_above_vwap is true only if bullish and price > VWAP.
- Applies background colors (bgcolor) to the entire chart pane:
- Strong bullish (above VWAP): Green with opacity 50 (less transparent, more intense).
- Weak bullish (below VWAP): Green with opacity 80 (more transparent, less intense).
- Strong bearish (below VWAP): Red with opacity 50.
- Weak bearish (above VWAP): Red with opacity 80.
- If no condition matches, no color (na).
- **Purpose**: Creates "clouds" for trend visualization, enhanced by VWAP context. This helps traders confirm trends—e.g., a strong bullish cloud (darker green) suggests a high-conviction uptrend when price is above VWAP. The varying opacity differentiates signal strength: Darker for aligned conditions (trend + VWAP agreement), lighter for misaligned (potential weakening or reversal).
### Overall Indicator Usage and Limitations
- **How to use it**: Add this to a TradingView chart (e.g., stocks, crypto, forex). Look for EMA crossovers, price bouncing off EMAs/VWAP, or cloud color changes as signals. Bullish clouds with price above VWAP might signal buys; bearish below for sells.
- **Strengths**: Combines momentum (EMAs), volume (VWAP), and volatility adaptation for a multi-layered view. Dynamic colors make it intuitive.
- **Limitations**:
- EMAs lag in ranging markets; volatility adjustment helps but doesn't eliminate whipsaws.
- VWAP resets daily (standard behavior), so it's best for intraday/session trading.
- No alerts or inputs for customization (e.g., changeable lengths)—it's hardcoded.
- Performance depends on the asset/timeframe; backtest before using.
- **License**: Mozilla Public License 2.0, so it's open-source and modifiable.
Tristan's Devil Mark (Short / Long, with W%R)The Devil’s Mark indicator is a visual tool designed to help traders identify potential short and long opportunities based on candle structure and market momentum. It combines price action analysis with the Williams %R (W%R) oscillator to highlight candles with high potential for reversal or continuation.
Can be used on any timeline, from scalping day trades to swing trades on daily and higher timelines. Know that the higher the timeline the less likely the indicator will show. (Asia and London sessions tend to show many indicators. I find this more useful for NY session.)
How the script works
Candle Structure Conditions
Short (Sell) Wedge: Plotted above green candles that have no bottom wick, indicating that inside that candle there was strong upward momentum without downside hesitation .
Long (Buy) Wedge: Plotted below red candles that have no top wick, indicating that inside that candle there was strong downward momentum without upside hesitation .
These candles are visually emphasized as wedges to mark potential turning points.
Williams %R Filter
The indicator uses Williams %R to measure overbought and oversold conditions:
Proximity to 0 (nearZeroThresh): Determines how close W%R must be to 0 (overbought) to trigger a Sell Wedge. This acts as a “Sell sensitivity” filter.
Proximity to -100 (nearHundredThresh): Determines how close W%R must be to -100 (oversold) to trigger a Buy Wedge. This acts as a “Buy sensitivity” filter.
When the candle meets both the candle structure and the W%R condition, the wedge is plotted in purple (“Within W%R Range”).
When the "ignore W%R filter" toggle is on, all eligible candles are plotted regardless of W%R. Wedges that normally would not meet W%R criteria are plotted in light purple (“Outside W%R Range”) to distinguish them. #YOLO (🚫 I recommend leaving "Ignore W%R Filter" OFF)
Settings Explained
Williams %R Length: The number of bars used to calculate the W%R oscillator. Shorter lengths make it more sensitive; longer lengths smooth the readings.
Proximity to 0 / 100: Controls how “strict” the indicator is in requiring overbought or oversold W%R conditions to trigger. Lower values mean closer to extreme zones, higher values are more permissive.
Ignore W%R Toggle: Option to show Devil’s Marks on every eligible candle regardless of W%R. Useful for visualizing purely price-action-based signals.
What the trader sees
Purple wedges: Candles meeting both candle structure and W%R conditions.
Light purple wedges: Candles meeting candle structure but ignored W%R (when toggle is on). #YOLO (🚫 I recommend leaving "Ignore W%R Filter" OFF)
Short opportunities are wedges above bars (green candles with no bottom wick).
Long opportunities are wedges below bars (red candles with no top wick).
Trading Insight
The Devil’s Mark is a momentum and reversal alert tool:
Look for purple downward-pointing wedges when W%R is near overbought. This is a potential shorting opportunity. Buying at the close of that candle may improve your short trades.
Look for purple upward-pointing wedges when W%R is near oversold. This is a potential
long opportunity. Buying at the close of that candle may improve your long trades.
Light purple wedges show the same price-action cues without W%R confirmation—useful for aggressive traders who want every potential setup. #YOLO #YMMV #noFullPort
Settings / Security
The “Output values” checkbox appears for each plotted series (like a plot or plotshape) and controls whether the series will also be exposed numerically in the Data Window or used by other indicators/scripts.
Here’s what it means in practice:
1. Checked (true)
The series values (like candle high, low, or any computed value) are exported to the Data Window and can be read by other scripts using request.security() or ta functions.
Example: You can see the exact numerical value of each plotted point in the Data Window when you hover over the chart.
Useful if you want to backtest or reference these plotted values programmatically.
2. Unchecked (false)
The series is plotted visually only.
The numeric values are hidden from the Data Window and cannot be accessed by other scripts.
Makes the chart cleaner if you don’t need the numeric outputs.
Hellenic EMA Matrix - Α Ω PremiumHellenic EMA Matrix - Alpha Omega Premium
Complete User Guide
Table of Contents
Introduction
Indicator Philosophy
Mathematical Constants
EMA Types
Settings
Trading Signals
Visualization
Usage Strategies
FAQ
Introduction
Hellenic EMA Matrix is a premium indicator based on mathematical constants of nature: Phi (Phi - Golden Ratio), Pi (Pi), e (Euler's number). The indicator uses these universal constants to create dynamic EMAs that adapt to the natural rhythms of the market.
Key Features:
6 EMA types based on mathematical constants
Premium visualization with Neon Glow and Gradient Clouds
Automatic Fast/Mid/Slow EMA sorting
STRONG signals for powerful trends
Pulsing Ribbon Bar for instant trend assessment
Works on all timeframes (M1 - MN)
Indicator Philosophy
Why Mathematical Constants?
Traditional EMAs use arbitrary periods (9, 21, 50, 200). Hellenic Matrix goes further, using universal mathematical constants found in nature:
Phi (1.618) - Golden Ratio: galaxy spirals, seashells, human body proportions
Pi (3.14159) - Pi: circles, waves, cycles
e (2.71828) - Natural logarithm base: exponential growth, radioactive decay
Markets are also a natural system composed of millions of participants. Using mathematical constants allows tuning into the natural rhythms of market cycles.
Mathematical Constants
Phi (Phi) - Golden Ratio
Phi = 1.618033988749895
Properties:
Phi² = Phi + 1 = 2.618
Phi³ = 4.236
Phi⁴ = 6.854
Application: Ideal for trending movements and Fibonacci corrections
Pi (Pi) - Pi Number
Pi = 3.141592653589793
Properties:
2Pi = 6.283 (full circle)
3Pi = 9.425
4Pi = 12.566
Application: Excellent for cyclical markets and wave structures
e (Euler) - Euler's Number
e = 2.718281828459045
Properties:
e² = 7.389
e³ = 20.085
e⁴ = 54.598
Application: Suitable for exponential movements and volatile markets
EMA Types
1. Phi (Phi) - Golden Ratio EMA
Description: EMA based on the golden ratio
Period Formula:
Period = Phi^n × Base Multiplier
Parameters:
Phi Power Level (1-8): Power of Phi
Phi¹ = 1.618 → ~16 period (with Base=10)
Phi² = 2.618 → ~26 period
Phi³ = 4.236 → ~42 period (recommended)
Phi⁴ = 6.854 → ~69 period
Recommendations:
Phi² or Phi³ for day trading
Phi⁴ or Phi⁵ for swing trading
Works excellently as Fast EMA
2. Pi (Pi) - Circular EMA
Description: EMA based on Pi for cyclical movements
Period Formula:
Period = Pi × Multiple × Base Multiplier
Parameters:
Pi Multiple (1-10): Pi multiplier
1Pi = 3.14 → ~31 period (with Base=10)
2Pi = 6.28 → ~63 period (recommended)
3Pi = 9.42 → ~94 period
Recommendations:
2Pi ideal as Mid or Slow EMA
Excellently identifies cycles and waves
Use on volatile markets (crypto, forex)
3. e (Euler) - Natural EMA
Description: EMA based on natural logarithm
Period Formula:
Period = e^n × Base Multiplier
Parameters:
e Power Level (1-6): Power of e
e¹ = 2.718 → ~27 period (with Base=10)
e² = 7.389 → ~74 period (recommended)
e³ = 20.085 → ~201 period
Recommendations:
e² works excellently as Slow EMA
Ideal for stocks and indices
Filters noise well on lower timeframes
4. Delta (Delta) - Adaptive EMA
Description: Adaptive EMA that changes period based on volatility
Period Formula:
Period = Base Period × (1 + (Volatility - 1) × Factor)
Parameters:
Delta Base Period (5-200): Base period (default 20)
Delta Volatility Sensitivity (0.5-5.0): Volatility sensitivity (default 2.0)
How it works:
During low volatility → period decreases → EMA reacts faster
During high volatility → period increases → EMA smooths noise
Recommendations:
Works excellently on news and sharp movements
Use as Fast EMA for quick adaptation
Sensitivity 2.0-3.0 for crypto, 1.0-2.0 for stocks
5. Sigma (Sigma) - Composite EMA
Description: Composite EMA combining multiple active EMAs
Composition Methods:
Weighted Average (default):
Sigma = (Phi + Pi + e + Delta) / 4
Simple average of all active EMAs
Geometric Mean:
Sigma = fourth_root(Phi × Pi × e × Delta)
Geometric mean (more conservative)
Harmonic Mean:
Sigma = 4 / (1/Phi + 1/Pi + 1/e + 1/Delta)
Harmonic mean (more weight to smaller values)
Recommendations:
Enable for additional confirmation
Use as Mid EMA
Weighted Average - most universal method
6. Lambda (Lambda) - Wave EMA
Description: Wave EMA with sinusoidal period modulation
Period Formula:
Period = Base Period × (1 + Amplitude × sin(2Pi × bar / Frequency))
Parameters:
Lambda Base Period (10-200): Base period
Lambda Wave Amplitude (0.1-2.0): Wave amplitude
Lambda Wave Frequency (10-200): Wave frequency in bars
How it works:
Period pulsates sinusoidally
Creates wave effect following market cycles
Recommendations:
Experimental EMA for advanced users
Works well on cyclical markets
Frequency = 50 for day trading, 100+ for swing
Settings
Matrix Core Settings
Base Multiplier (1-100)
Multiplies all EMA periods
Base = 1: Very fast EMAs (Phi³ = 4, 2Pi = 6, e² = 7)
Base = 10: Standard (Phi³ = 42, 2Pi = 63, e² = 74)
Base = 20: Slow EMAs (Phi³ = 85, 2Pi = 126, e² = 148)
Recommendations by timeframe:
M1-M5: Base = 5-10
M15-H1: Base = 10-15 (recommended)
H4-D1: Base = 15-25
W1-MN: Base = 25-50
Matrix Source
Data source selection for EMA calculation:
close - closing price (standard)
open - opening price
high - high
low - low
hl2 - (high + low) / 2
hlc3 - (high + low + close) / 3
ohlc4 - (open + high + low + close) / 4
When to change:
hlc3 or ohlc4 for smoother signals
high for aggressive longs
low for aggressive shorts
Manual EMA Selection
Critically important setting! Determines which EMAs are used for signal generation.
Use Manual Fast/Slow/Mid Selection
Enabled (default): You select EMAs manually
Disabled: Automatic selection by periods
Fast EMA
Fast EMA - reacts first to price changes
Recommendations:
Phi Golden (recommended) - universal choice
Delta Adaptive - for volatile markets
Must be fastest (smallest period)
Slow EMA
Slow EMA - determines main trend
Recommendations:
Pi Circular (recommended) - excellent trend filter
e Natural - for smoother trend
Must be slowest (largest period)
Mid EMA
Mid EMA - additional signal filter
Recommendations:
e Natural (recommended) - excellent middle level
Pi Circular - alternative
None - for more frequent signals (only 2 EMAs)
IMPORTANT: The indicator automatically sorts selected EMAs by their actual periods:
Fast = EMA with smallest period
Mid = EMA with middle period
Slow = EMA with largest period
Therefore, you can select any combination - the indicator will arrange them correctly!
Premium Visualization
Neon Glow
Enable Neon Glow for EMAs - adds glowing effect around EMA lines
Glow Strength:
Light - subtle glow
Medium (recommended) - optimal balance
Strong - bright glow (may be too bright)
Effect: 2 glow layers around each EMA for 3D effect
Gradient Clouds
Enable Gradient Clouds - fills space between EMAs with gradient
Parameters:
Cloud Transparency (85-98): Cloud transparency
95-97 (recommended)
Higher = more transparent
Dynamic Cloud Intensity - automatically changes transparency based on EMA distance
Cloud Colors:
Phi-Pi Cloud:
Blue - when Pi above Phi (bullish)
Gold - when Phi above Pi (bearish)
Pi-e Cloud:
Green - when e above Pi (bullish)
Blue - when Pi above e (bearish)
2 layers for volumetric effect
Pulsing Ribbon Bar
Enable Pulsing Indicator Bar - pulsing strip at bottom/top of chart
Parameters:
Ribbon Position: Top / Bottom (recommended)
Pulse Speed: Slow / Medium (recommended) / Fast
Symbols and colors:
Green filled square - STRONG BULLISH
Pink filled square - STRONG BEARISH
Blue hollow square - Bullish (regular)
Red hollow square - Bearish (regular)
Purple rectangle - Neutral
Effect: Pulsation with sinusoid for living market feel
Signal Bar Highlights
Enable Signal Bar Highlights - highlights bars with signals
Parameters:
Highlight Transparency (88-96): Highlight transparency
Highlight Style:
Light Fill (recommended) - bar background fill
Thin Line - bar outline only
Highlights:
Golden Cross - green
Death Cross - pink
STRONG BUY - green
STRONG SELL - pink
Show Greek Labels
Shows Greek alphabet letters on last bar:
Phi - Phi EMA (gold)
Pi - Pi EMA (blue)
e - Euler EMA (green)
Delta - Delta EMA (purple)
Sigma - Sigma EMA (pink)
When to use: For education or presentations
Show Old Background
Old background style (not recommended):
Green background - STRONG BULLISH
Pink background - STRONG BEARISH
Blue background - Bullish
Red background - Bearish
Not recommended - use new Gradient Clouds and Pulsing Bar
Info Table
Show Info Table - table with indicator information
Parameters:
Position: Top Left / Top Right (recommended) / Bottom Left / Bottom Right
Size: Tiny / Small (recommended) / Normal / Large
Table contents:
EMA list - periods and current values of all active EMAs
Effects - active visual effects
TREND - current trend state:
STRONG UP - strong bullish
STRONG DOWN - strong bearish
Bullish - regular bullish
Bearish - regular bearish
Neutral - neutral
Momentum % - percentage deviation of price from Fast EMA
Setup - current Fast/Slow/Mid configuration
Trading Signals
Show Golden/Death Cross
Golden Cross - Fast EMA crosses Slow EMA from below (bullish signal) Death Cross - Fast EMA crosses Slow EMA from above (bearish signal)
Symbols:
Yellow dot "GC" below - Golden Cross
Dark red dot "DC" above - Death Cross
Show STRONG Signals
STRONG BUY and STRONG SELL - the most powerful indicator signals
Conditions for STRONG BULLISH:
EMA Alignment: Fast > Mid > Slow (all EMAs aligned)
Trend: Fast > Slow (clear uptrend)
Distance: EMAs separated by minimum 0.15%
Price Position: Price above Fast EMA
Fast Slope: Fast EMA rising
Slow Slope: Slow EMA rising
Mid Trending: Mid EMA also rising (if enabled)
Conditions for STRONG BEARISH:
Same but in reverse
Visual display:
Green label "STRONG BUY" below bar
Pink label "STRONG SELL" above bar
Difference from Golden/Death Cross:
Golden/Death Cross = crossing moment (1 bar)
STRONG signal = sustained trend (lasts several bars)
IMPORTANT: After fixes, STRONG signals now:
Work on all timeframes (M1 to MN)
Don't break on small retracements
Work with any Fast/Mid/Slow combination
Automatically adapt thanks to EMA sorting
Show Stop Loss/Take Profit
Automatic SL/TP level calculation on STRONG signal
Parameters:
Stop Loss (ATR) (0.5-5.0): ATR multiplier for stop loss
1.5 (recommended) - standard
1.0 - tight stop
2.0-3.0 - wide stop
Take Profit R:R (1.0-5.0): Risk/reward ratio
2.0 (recommended) - standard (risk 1.5 ATR, profit 3.0 ATR)
1.5 - conservative
3.0-5.0 - aggressive
Formulas:
LONG:
Stop Loss = Entry - (ATR × Stop Loss ATR)
Take Profit = Entry + (ATR × Stop Loss ATR × Take Profit R:R)
SHORT:
Stop Loss = Entry + (ATR × Stop Loss ATR)
Take Profit = Entry - (ATR × Stop Loss ATR × Take Profit R:R)
Visualization:
Red X - Stop Loss
Green X - Take Profit
Levels remain active while STRONG signal persists
Trading Signals
Signal Types
1. Golden Cross
Description: Fast EMA crosses Slow EMA from below
Signal: Beginning of bullish trend
How to trade:
ENTRY: On bar close with Golden Cross
STOP: Below local low or below Slow EMA
TARGET: Next resistance level or 2:1 R:R
Strengths:
Simple and clear
Works well on trending markets
Clear entry point
Weaknesses:
Lags (signal after movement starts)
Many false signals in ranging markets
May be late on fast moves
Optimal timeframes: H1, H4, D1
2. Death Cross
Description: Fast EMA crosses Slow EMA from above
Signal: Beginning of bearish trend
How to trade:
ENTRY: On bar close with Death Cross
STOP: Above local high or above Slow EMA
TARGET: Next support level or 2:1 R:R
Application: Mirror of Golden Cross
3. STRONG BUY
Description: All EMAs aligned + trend + all EMAs rising
Signal: Powerful bullish trend
How to trade:
ENTRY: On bar close with STRONG BUY or on pullback to Fast EMA
STOP: Below Fast EMA or automatic SL (if enabled)
TARGET: Automatic TP (if enabled) or by levels
TRAILING: Follow Fast EMA
Entry strategies:
Aggressive: Enter immediately on signal
Conservative: Wait for pullback to Fast EMA, then enter on bounce
Pyramiding: Add positions on pullbacks to Mid EMA
Position management:
Hold while STRONG signal active
Exit on STRONG SELL or Death Cross appearance
Move stop behind Fast EMA
Strengths:
Most reliable indicator signal
Doesn't break on pullbacks
Catches large moves
Works on all timeframes
Weaknesses:
Appears less frequently than other signals
Requires confirmation (multiple conditions)
Optimal timeframes: All (M5 - D1)
4. STRONG SELL
Description: All EMAs aligned down + downtrend + all EMAs falling
Signal: Powerful bearish trend
How to trade: Mirror of STRONG BUY
Visual Signals
Pulsing Ribbon Bar
Quick market assessment at a glance:
Symbol Color State
Filled square Green STRONG BULLISH
Filled square Pink STRONG BEARISH
Hollow square Blue Bullish
Hollow square Red Bearish
Rectangle Purple Neutral
Pulsation: Sinusoidal, creates living effect
Signal Bar Highlights
Bars with signals are highlighted:
Green highlight: STRONG BUY or Golden Cross
Pink highlight: STRONG SELL or Death Cross
Gradient Clouds
Colored space between EMAs shows trend strength:
Wide clouds - strong trend
Narrow clouds - weak trend or consolidation
Color change - trend change
Info Table
Quick reference in corner:
TREND: Current state (STRONG UP, Bullish, Neutral, Bearish, STRONG DOWN)
Momentum %: Movement strength
Effects: Active visual effects
Setup: Fast/Slow/Mid configuration
Usage Strategies
Strategy 1: "Golden Trailing"
Idea: Follow STRONG signals using Fast EMA as trailing stop
Settings:
Fast: Phi Golden (Phi³)
Mid: Pi Circular (2Pi)
Slow: e Natural (e²)
Base Multiplier: 10
Timeframe: H1, H4
Entry rules:
Wait for STRONG BUY
Enter on bar close or on pullback to Fast EMA
Stop below Fast EMA
Management:
Hold position while STRONG signal active
Move stop behind Fast EMA daily
Exit on STRONG SELL or Death Cross
Take Profit:
Partially close at +2R
Trail remainder until exit signal
For whom: Swing traders, trend followers
Pros:
Catches large moves
Simple rules
Emotionally comfortable
Cons:
Requires patience
Possible extended drawdowns on pullbacks
Strategy 2: "Scalping Bounces"
Idea: Scalp bounces from Fast EMA during STRONG trend
Settings:
Fast: Delta Adaptive (Base 15, Sensitivity 2.0)
Mid: Phi Golden (Phi²)
Slow: Pi Circular (2Pi)
Base Multiplier: 5
Timeframe: M5, M15
Entry rules:
STRONG signal must be active
Wait for price pullback to Fast EMA
Enter on bounce (candle closes above/below Fast EMA)
Stop behind local extreme (15-20 pips)
Take Profit:
+1.5R or to Mid EMA
Or to next level
For whom: Active day traders
Pros:
Many signals
Clear entry point
Quick profits
Cons:
Requires constant monitoring
Not all bounces work
Requires discipline for frequent trading
Strategy 3: "Triple Filter"
Idea: Enter only when all 3 EMAs and price perfectly aligned
Settings:
Fast: Phi Golden (Phi³)
Mid: e Natural (e²)
Slow: Pi Circular (3Pi)
Base Multiplier: 15
Timeframe: H4, D1
Entry rules (LONG):
STRONG BUY active
Price above all three EMAs
Fast > Mid > Slow (all aligned)
All EMAs rising (slope up)
Gradient Clouds wide and bright
Entry:
On bar close meeting all conditions
Or on next pullback to Fast EMA
Stop:
Below Mid EMA or -1.5 ATR
Take Profit:
First target: +3R
Second target: next major level
Trailing: Mid EMA
For whom: Conservative swing traders, investors
Pros:
Very reliable signals
Minimum false entries
Large profit potential
Cons:
Rare signals (2-5 per month)
Requires patience
Strategy 4: "Adaptive Scalper"
Idea: Use only Delta Adaptive EMA for quick volatility reaction
Settings:
Fast: Delta Adaptive (Base 10, Sensitivity 3.0)
Mid: None
Slow: Delta Adaptive (Base 30, Sensitivity 2.0)
Base Multiplier: 3
Timeframe: M1, M5
Feature: Two different Delta EMAs with different settings
Entry rules:
Golden Cross between two Delta EMAs
Both Delta EMAs must be rising/falling
Enter on next bar
Stop:
10-15 pips or below Slow Delta EMA
Take Profit:
+1R to +2R
Or Death Cross
For whom: Scalpers on cryptocurrencies and forex
Pros:
Instant volatility adaptation
Many signals on volatile markets
Quick results
Cons:
Much noise on calm markets
Requires fast execution
High commissions may eat profits
Strategy 5: "Cyclical Trader"
Idea: Use Pi and Lambda for trading cyclical markets
Settings:
Fast: Pi Circular (1Pi)
Mid: Lambda Wave (Base 30, Amplitude 0.5, Frequency 50)
Slow: Pi Circular (3Pi)
Base Multiplier: 10
Timeframe: H1, H4
Entry rules:
STRONG signal active
Lambda Wave EMA synchronized with trend
Enter on bounce from Lambda Wave
For whom: Traders of cyclical assets (some altcoins, commodities)
Pros:
Catches cyclical movements
Lambda Wave provides additional entry points
Cons:
More complex to configure
Not for all markets
Lambda Wave may give false signals
Strategy 6: "Multi-Timeframe Confirmation"
Idea: Use multiple timeframes for confirmation
Scheme:
Higher TF (D1): Determine trend direction (STRONG signal)
Middle TF (H4): Wait for STRONG signal in same direction
Lower TF (M15): Look for entry point (Golden Cross or bounce from Fast EMA)
Settings for all TFs:
Fast: Phi Golden (Phi³)
Mid: e Natural (e²)
Slow: Pi Circular (2Pi)
Base Multiplier: 10
Rules:
All 3 TFs must show one trend
Entry on lower TF
Stop by lower TF
Target by higher TF
For whom: Serious traders and investors
Pros:
Maximum reliability
Large profit targets
Minimum false signals
Cons:
Rare setups
Requires analysis of multiple charts
Experience needed
Practical Tips
DOs
Use STRONG signals as primary - they're most reliable
Let signals develop - don't exit on first pullback
Use trailing stop - follow Fast EMA
Combine with levels - S/R, Fibonacci, volumes
Test on demo before real
Adjust Base Multiplier for your timeframe
Enable visual effects - they help see the picture
Use Info Table - quick situation assessment
Watch Pulsing Bar - instant state indicator
Trust auto-sorting of Fast/Mid/Slow
DON'Ts
Don't trade against STRONG signal - trend is your friend
Don't ignore Mid EMA - it adds reliability
Don't use too small Base Multiplier on higher TFs
Don't enter on Golden Cross in range - check for trend
Don't change settings during open position
Don't forget risk management - 1-2% per trade
Don't trade all signals in row - choose best ones
Don't use indicator in isolation - combine with Price Action
Don't set too tight stops - let trade breathe
Don't over-optimize - simplicity = reliability
Optimal Settings by Asset
US Stocks (SPY, AAPL, TSLA)
Recommendation:
Fast: Phi Golden (Phi³)
Mid: e Natural (e²)
Slow: Pi Circular (2Pi)
Base: 10-15
Timeframe: H4, D1
Features:
Use on daily for swing
STRONG signals very reliable
Works well on trending stocks
Forex (EUR/USD, GBP/USD)
Recommendation:
Fast: Delta Adaptive (Base 15, Sens 2.0)
Mid: Phi Golden (Phi²)
Slow: Pi Circular (2Pi)
Base: 8-12
Timeframe: M15, H1, H4
Features:
Delta Adaptive works excellently on news
Many signals on M15-H1
Consider spreads
Cryptocurrencies (BTC, ETH, altcoins)
Recommendation:
Fast: Delta Adaptive (Base 10, Sens 3.0)
Mid: Pi Circular (2Pi)
Slow: e Natural (e²)
Base: 5-10
Timeframe: M5, M15, H1
Features:
High volatility - adaptation needed
STRONG signals can last days
Be careful with scalping on M1-M5
Commodities (Gold, Oil)
Recommendation:
Fast: Pi Circular (1Pi)
Mid: Phi Golden (Phi³)
Slow: Pi Circular (3Pi)
Base: 12-18
Timeframe: H4, D1
Features:
Pi works excellently on cyclical commodities
Gold responds especially well to Phi
Oil volatile - use wide stops
Indices (S&P500, Nasdaq, DAX)
Recommendation:
Fast: Phi Golden (Phi³)
Mid: e Natural (e²)
Slow: Pi Circular (2Pi)
Base: 15-20
Timeframe: H4, D1, W1
Features:
Very trending instruments
STRONG signals last weeks
Good for position trading
Alerts
The indicator supports 6 alert types:
1. Golden Cross
Message: "Hellenic Matrix: GOLDEN CROSS - Fast EMA crossed above Slow EMA - Bullish trend starting!"
When: Fast EMA crosses Slow EMA from below
2. Death Cross
Message: "Hellenic Matrix: DEATH CROSS - Fast EMA crossed below Slow EMA - Bearish trend starting!"
When: Fast EMA crosses Slow EMA from above
3. STRONG BULLISH
Message: "Hellenic Matrix: STRONG BULLISH SIGNAL - All EMAs aligned for powerful uptrend!"
When: All conditions for STRONG BUY met (first bar)
4. STRONG BEARISH
Message: "Hellenic Matrix: STRONG BEARISH SIGNAL - All EMAs aligned for powerful downtrend!"
When: All conditions for STRONG SELL met (first bar)
5. Bullish Ribbon
Message: "Hellenic Matrix: BULLISH RIBBON - EMAs aligned for uptrend"
When: EMAs aligned bullish + price above Fast EMA (less strict condition)
6. Bearish Ribbon
Message: "Hellenic Matrix: BEARISH RIBBON - EMAs aligned for downtrend"
When: EMAs aligned bearish + price below Fast EMA (less strict condition)
How to Set Up Alerts:
Open indicator on chart
Click on three dots next to indicator name
Select "Create Alert"
In "Condition" field select needed alert:
Golden Cross
Death Cross
STRONG BULLISH
STRONG BEARISH
Bullish Ribbon
Bearish Ribbon
Configure notification method:
Pop-up in browser
Email
SMS (in Premium accounts)
Push notifications in mobile app
Webhook (for automation)
Select frequency:
Once Per Bar Close (recommended) - once on bar close
Once Per Bar - during bar formation
Only Once - only first time
Click "Create"
Tip: Create separate alerts for different timeframes and instruments
FAQ
1. Why don't STRONG signals appear?
Possible reasons:
Incorrect Fast/Mid/Slow order
Solution: Indicator automatically sorts EMAs by periods, but ensure selected EMAs have different periods
Base Multiplier too large
Solution: Reduce Base to 5-10 on lower timeframes
Market in range
Solution: STRONG signals appear only in trends - this is normal
Too strict EMA settings
Solution: Try classic combination: Phi³ / Pi×2 / e² with Base=10
Mid EMA too close to Fast or Slow
Solution: Select Mid EMA with period between Fast and Slow
2. How often should STRONG signals appear?
Normal frequency:
M1-M5: 5-15 signals per day (very active markets)
M15-H1: 2-8 signals per day
H4: 3-10 signals per week
D1: 2-5 signals per month
W1: 2-6 signals per year
If too many signals - market very volatile or Base too small
If too few signals - market in range or Base too large
4. What are the best settings for beginners?
Universal "out of the box" settings:
Matrix Core:
Base Multiplier: 10
Source: close
Phi Golden: Enabled, Power = 3
Pi Circular: Enabled, Multiple = 2
e Natural: Enabled, Power = 2
Delta Adaptive: Enabled, Base = 20, Sensitivity = 2.0
Manual Selection:
Fast: Phi Golden
Mid: e Natural
Slow: Pi Circular
Visualization:
Gradient Clouds: ON
Neon Glow: ON (Medium)
Pulsing Bar: ON (Medium)
Signal Highlights: ON (Light Fill)
Table: ON (Top Right, Small)
Signals:
Golden/Death Cross: ON
STRONG Signals: ON
Stop Loss: OFF (while learning)
Timeframe for learning: H1 or H4
5. Can I use only one EMA?
No, minimum 2 EMAs (Fast and Slow) for signal generation.
Mid EMA is optional:
With Mid EMA = more reliable but rarer signals
Without Mid EMA = more signals but less strict filtering
Recommendation: Start with 3 EMAs (Fast/Mid/Slow), then experiment
6. Does the indicator work on cryptocurrencies?
Yes, works excellently! Especially good on:
Bitcoin (BTC)
Ethereum (ETH)
Major altcoins (SOL, BNB, XRP)
Recommended settings for crypto:
Fast: Delta Adaptive (Base 10-15, Sensitivity 2.5-3.0)
Mid: Pi Circular (2Pi)
Slow: e Natural (e²)
Base: 5-10
Timeframe: M15, H1, H4
Crypto market features:
High volatility → use Delta Adaptive
24/7 trading → set alerts
Sharp movements → wide stops
7. Can I trade only with this indicator?
Technically yes, but NOT recommended.
Best approach - combine with:
Price Action - support/resistance levels, candle patterns
Volume - movement strength confirmation
Fibonacci - retracement and extension levels
RSI/MACD - divergences and overbought/oversold
Fundamental analysis - news, company reports
Hellenic Matrix:
Excellently determines trend and its strength
Provides clear entry/exit points
Doesn't consider fundamentals
Doesn't see major levels
8. Why do Gradient Clouds change color?
Color depends on EMA order:
Phi-Pi Cloud:
Blue - Pi EMA above Phi EMA (bullish alignment)
Gold - Phi EMA above Pi EMA (bearish alignment)
Pi-e Cloud:
Green - e EMA above Pi EMA (bullish alignment)
Blue - Pi EMA above e EMA (bearish alignment)
Color change = EMA order change = possible trend change
9. What is Momentum % in the table?
Momentum % = percentage deviation of price from Fast EMA
Formula:
Momentum = ((Close - Fast EMA) / Fast EMA) × 100
Interpretation:
+0.5% to +2% - normal bullish momentum
+2% to +5% - strong bullish momentum
+5% and above - overheating (correction possible)
-0.5% to -2% - normal bearish momentum
-2% to -5% - strong bearish momentum
-5% and below - oversold (bounce possible)
Usage:
Monitor momentum during STRONG signals
Large momentum = don't enter (wait for pullback)
Small momentum = good entry point
10. How to configure for scalping?
Settings for scalping (M1-M5):
Base Multiplier: 3-5
Source: close or hlc3 (smoother)
Fast: Delta Adaptive (Base 8-12, Sensitivity 3.0)
Mid: None (for more signals)
Slow: Phi Golden (Phi²) or Pi Circular (1Pi)
Visualization:
- Gradient Clouds: ON (helps see strength)
- Neon Glow: OFF (doesn't clutter chart)
- Pulsing Bar: ON (quick assessment)
- Signal Highlights: ON
Signals:
- Golden/Death Cross: ON
- STRONG Signals: ON
- Stop Loss: ON (1.0-1.5 ATR, R:R 1.5-2.0)
Scalping rules:
Trade only STRONG signals
Enter on bounce from Fast EMA
Tight stops (10-20 pips)
Quick take profit (+1R to +2R)
Don't hold through news
11. How to configure for long-term investing?
Settings for investing (D1-W1):
Base Multiplier: 20-30
Source: close
Fast: Phi Golden (Phi³ or Phi⁴)
Mid: e Natural (e²)
Slow: Pi Circular (3Pi or 4Pi)
Visualization:
- Gradient Clouds: ON
- Neon Glow: ON (Medium)
- Everything else - to taste
Signals:
- Golden/Death Cross: ON
- STRONG Signals: ON
- Stop Loss: OFF (use percentage stop)
Investing rules:
Enter only on STRONG signals
Hold while STRONG active (weeks/months)
Stop below Slow EMA or -10%
Take profit: by company targets or +50-100%
Ignore short-term pullbacks
12. What if indicator slows down chart?
Indicator is optimized, but if it slows:
Disable unnecessary visual effects:
Neon Glow: OFF (saves 8 plots)
Gradient Clouds: ON but low quality
Lambda Wave EMA: OFF (if not using)
Reduce number of active EMAs:
Sigma Composite: OFF
Lambda Wave: OFF
Leave only Phi, Pi, e, Delta
Simplify settings:
Pulsing Bar: OFF
Greek Labels: OFF
Info Table: smaller size
13. Can I use on different timeframes simultaneously?
Yes! Multi-timeframe analysis is very powerful:
Classic scheme:
Higher TF (D1, W1) - determine global trend
Wait for STRONG signal
This is our trading direction
Middle TF (H4, H1) - look for confirmation
STRONG signal in same direction
Precise entry zone
Lower TF (M15, M5) - entry point
Golden Cross or bounce from Fast EMA
Precise stop loss
Example:
W1: STRONG BUY active (global uptrend)
H4: STRONG BUY appeared (confirmation)
M15: Wait for Golden Cross or bounce from Fast EMA → ENTRY
Advantages:
Maximum reliability
Clear timeframe hierarchy
Large targets
14. How does indicator work on news?
Delta Adaptive EMA adapts excellently to news:
Before news:
Low volatility → Delta EMA becomes fast → pulls to price
During news:
Sharp volatility spike → Delta EMA slows → filters noise
After news:
Volatility normalizes → Delta EMA returns to normal
Recommendations:
Don't trade at news release moment (spreads widen)
Wait for STRONG signal after news (2-5 bars)
Use Delta Adaptive as Fast EMA for quick reaction
Widen stops by 50-100% during important news
Advanced Techniques
Technique 1: "Divergences with EMA"
Idea: Look for discrepancies between price and Fast EMA
Bullish divergence:
Price makes lower low
Fast EMA makes higher low
= Possible reversal up
Bearish divergence:
Price makes higher high
Fast EMA makes lower high
= Possible reversal down
How to trade:
Find divergence
Wait for STRONG signal in divergence direction
Enter on confirmation
Technique 2: "EMA Tunnel"
Idea: Use space between Fast and Slow EMA as "tunnel"
Rules:
Wide tunnel - strong trend, hold position
Narrow tunnel - weak trend or consolidation, caution
Tunnel narrowing - trend weakening, prepare to exit
Tunnel widening - trend strengthening, can add
Visually: Gradient Clouds show this automatically!
Trading:
Enter on STRONG signal (tunnel starts widening)
Hold while tunnel wide
Exit when tunnel starts narrowing
Technique 3: "Wave Analysis with Lambda"
Idea: Lambda Wave EMA creates sinusoid matching market cycles
Setup:
Lambda Base Period: 30
Lambda Wave Amplitude: 0.5
Lambda Wave Frequency: 50 (adjusted to asset cycle)
How to find correct Frequency:
Look at historical cycles (distance between local highs)
Average distance = your Frequency
Example: if highs every 40-60 bars, set Frequency = 50
Trading:
Enter when Lambda Wave at bottom of sinusoid (growth potential)
Exit when Lambda Wave at top (fall potential)
Combine with STRONG signals
Technique 4: "Cluster Analysis"
Idea: When all EMAs gather in narrow cluster = powerful breakout soon
Cluster signs:
All EMAs (Phi, Pi, e, Delta) within 0.5-1% of each other
Gradient Clouds almost invisible
Price jumping around all EMAs
Trading:
Identify cluster (all EMAs close)
Determine breakout direction (where more volume, higher TFs direction)
Wait for breakout and STRONG signal
Enter on confirmation
Target = cluster size × 3-5
This is very powerful technique for big moves!
Technique 5: "Sigma as Dynamic Level"
Idea: Sigma Composite EMA = average of all EMAs = magnetic level
Usage:
Enable Sigma Composite (Weighted Average)
Sigma works as dynamic support/resistance
Price often returns to Sigma before trend continuation
Trading:
In trend: Enter on bounces from Sigma
In range: Fade moves from Sigma (trade return to Sigma)
On breakout: Sigma becomes support/resistance
Risk Management
Basic Rules
1. Position Size
Conservative: 1% of capital per trade
Moderate: 2% of capital per trade (recommended)
Aggressive: 3-5% (only for experienced)
Calculation formula:
Lot Size = (Capital × Risk%) / (Stop in pips × Pip value)
2. Risk/Reward Ratio
Minimum: 1:1.5
Standard: 1:2 (recommended)
Optimal: 1:3
Aggressive: 1:5+
3. Maximum Drawdown
Daily: -3% to -5%
Weekly: -7% to -10%
Monthly: -15% to -20%
Upon reaching limit → STOP trading until end of period
Position Management Strategies
1. Fixed Stop
Method:
Stop below/above Fast EMA or local extreme
DON'T move stop against position
Can move to breakeven
For whom: Beginners, conservative traders
2. Trailing by Fast EMA
Method:
Each day (or bar) move stop to Fast EMA level
Position closes when price breaks Fast EMA
Advantages:
Stay in trend as long as possible
Automatically exit on reversal
For whom: Trend followers, swing traders
3. Partial Exit
Method:
50% of position close at +2R
50% hold with trailing by Mid EMA or Slow EMA
Advantages:
Lock profit
Leave position for big move
Psychologically comfortable
For whom: Universal method (recommended)
4. Pyramiding
Method:
First entry on STRONG signal (50% of planned position)
Add 25% on pullback to Fast EMA
Add another 25% on pullback to Mid EMA
Overall stop below Slow EMA
Advantages:
Average entry price
Reduce risk
Increase profit in strong trends
Caution:
Works only in trends
In range leads to losses
For whom: Experienced traders
Trading Psychology
Correct Mindset
1. Indicator is a tool, not holy grail
Indicator shows probability, not guarantee
There will be losing trades - this is normal
Important is series statistics, not one trade
2. Trust the system
If STRONG signal appeared - enter
Don't search for "perfect" moment
Follow trading plan
3. Patience
STRONG signals don't appear every day
Better miss signal than enter against trend
Quality over quantity
4. Discipline
Always set stop loss
Don't move stop against position
Don't increase risk after losses
Beginner Mistakes
1. "I know better than indicator"
Indicator says STRONG BUY, but you think "too high, will wait for pullback"
Result: miss profitable move
Solution: Trust signals or don't use indicator
2. "Will reverse now for sure"
Trading against STRONG trend
Result: stops, stops, stops
Solution: Trend is your friend, trade with trend
3. "Will hold a bit more"
Don't exit when STRONG signal disappears
Greed eats profit
Solution: If signal gone - exit!
4. "I'll recover"
After losses double risk
Result: huge losses
Solution: Fixed % risk ALWAYS
5. "I don't like this signal"
Skip signals because of "feeling"
Result: inconsistency, no statistics
Solution: Trade ALL signals or clearly define filters
Trading Journal
What to Record
For each trade:
1. Entry/exit date and time
2. Instrument and timeframe
3. Signal type
Golden Cross
STRONG BUY
STRONG SELL
Death Cross
4. Indicator settings
Fast/Mid/Slow EMA
Base Multiplier
Other parameters
5. Chart screenshot
Entry moment
Exit moment
6. Trade parameters
Position size
Stop loss
Take Profit
R:R
7. Result
Profit/Loss in $
Profit/Loss in %
Profit/Loss in R
8. Notes
What was right
What was wrong
Emotions during trade
Lessons
Journal Analysis
Analyze weekly:
1. Win Rate
Win Rate = (Profitable trades / All trades) × 100%
Good: 50-60%
Excellent: 60-70%
Exceptional: 70%+
2. Average R
Average R = Sum of all R / Number of trades
Good: +0.5R
Excellent: +1.0R
Exceptional: +1.5R+
3. Profit Factor
Profit Factor = Total profit / Total losses
Good: 1.5+
Excellent: 2.0+
Exceptional: 3.0+
4. Maximum Drawdown
Track consecutive losses
If more than 5 in row - stop, check system
5. Best/Worst Trades
What was common in best trades? (do more)
What was common in worst trades? (avoid)
Pre-Trade Checklist
Technical Analysis
STRONG signal active (BUY or SELL)
All EMAs properly aligned (Fast > Mid > Slow or reverse)
Price on correct side of Fast EMA
Gradient Clouds confirm trend
Pulsing Bar shows STRONG state
Momentum % in normal range (not overheated)
No close strong levels against direction
Higher timeframe doesn't contradict
Risk Management
Position size calculated (1-2% risk)
Stop loss set
Take profit calculated (minimum 1:2)
R:R satisfactory
Daily/weekly risk limit not exceeded
No other open correlated positions
Fundamental Analysis
No important news in coming hours
Market session appropriate (liquidity)
No contradicting fundamentals
Understand why asset is moving
Psychology
Calm and thinking clearly
No emotions from previous trades
Ready to accept loss at stop
Following trading plan
Not revenging market for past losses
If at least one point is NO - think twice before entering!
Learning Roadmap
Week 1: Familiarization
Goals:
Install and configure indicator
Study all EMA types
Understand visualization
Tasks:
Add indicator to chart
Test all Fast/Mid/Slow settings
Play with Base Multiplier on different timeframes
Observe Gradient Clouds and Pulsing Bar
Study Info Table
Result: Comfort with indicator interface
Week 2: Signals
Goals:
Learn to recognize all signal types
Understand difference between Golden Cross and STRONG
Tasks:
Find 10 Golden Cross examples in history
Find 10 STRONG BUY examples in history
Compare their results (which worked better)
Set up alerts
Get 5 real alerts
Result: Understanding signals
Week 3: Demo Trading
Goals:
Start trading signals on demo account
Gather statistics
Tasks:
Open demo account
Trade ONLY STRONG signals
Keep journal (minimum 20 trades)
Don't change indicator settings
Strictly follow stop losses
Result: 20+ documented trades
Week 4: Analysis
Goals:
Analyze demo trading results
Optimize approach
Tasks:
Calculate win rate and average R
Find patterns in profitable trades
Find patterns in losing trades
Adjust approach (not indicator!)
Write trading plan
Result: Trading plan on 1 page
Month 2: Improvement
Goals:
Deepen understanding
Add additional techniques
Tasks:
Study multi-timeframe analysis
Test combinations with Price Action
Try advanced techniques (divergences, tunnels)
Continue demo trading (minimum 50 trades)
Achieve stable profitability on demo
Result: Win rate 55%+ and Profit Factor 1.5+
Month 3: Real Trading
Goals:
Transition to real account
Maintain discipline
Tasks:
Open small real account
Trade minimum lots
Strictly follow trading plan
DON'T increase risk
Focus on process, not profit
Result: Psychological comfort on real
Month 4+: Scaling
Goals:
Increase account
Become consistently profitable
Tasks:
With 60%+ win rate can increase risk to 2%
Upon doubling account can add capital
Continue keeping journal
Periodically review and improve strategy
Share experience with community
Result: Stable profitability month after month
Additional Resources
Recommended Reading
Technical Analysis:
"Technical Analysis of Financial Markets" - John Murphy
"Trading in the Zone" - Mark Douglas (psychology)
"Market Wizards" - Jack Schwager (trader interviews)
EMA and Moving Averages:
"Moving Averages 101" - Steve Burns
Articles on Investopedia about EMA
Risk Management:
"The Mathematics of Money Management" - Ralph Vince
"Trade Your Way to Financial Freedom" - Van K. Tharp
Trading Journals:
Edgewonk (paid, very powerful)
Tradervue (free version + premium)
Excel/Google Sheets (free)
Screeners:
TradingView Stock Screener
Finviz (stocks)
CoinMarketCap (crypto)
Conclusion
Hellenic EMA Matrix is a powerful tool based on universal mathematical constants of nature. The indicator combines:
Mathematical elegance - Phi, Pi, e instead of arbitrary numbers
Premium visualization - Neon Glow, Gradient Clouds, Pulsing Bar
Reliable signals - STRONG BUY/SELL work on all timeframes
Flexibility - 6 EMA types, adaptation to any trading style
Automation - auto-sorting EMAs, SL/TP calculation, alerts
Key Success Principles:
Simplicity - start with basic settings (Phi/Pi/e, Base=10)
Discipline - follow STRONG signals strictly
Patience - wait for quality setups
Risk Management - 1-2% per trade, ALWAYS
Journal - document every trade
Learning - constantly improve skills
Remember:
Indicator shows probability, not guarantee
Important is series statistics, not one trade
Psychology more important than technique
Quality more important than quantity
Process more important than result
Acknowledgments
Thank you for using Hellenic EMA Matrix - Alpha Omega Premium!
The indicator was created with love for mathematics, markets, and beautiful visualization.
Wishing you profitable trading!
Guide Version: 1.0
Date: 2025
Compatibility: Pine Script v6, TradingView
"In the simplicity of mathematical constants lies the complexity of market movements"
Smart Structure Pro - Market Structure & Smart Money Concepts═══════════════════════════════════════════════════════════════════════════════
SMART STRUCTURE PRO
═══════════════════════════════════════════════════════════════════════════════
A comprehensive market structure analysis tool that identifies institutional trading
patterns and smart money concepts for improved trade timing and decision-making.
═══════════════════════════════════════════════════════════════════════════════
📊 WHAT IT DOES
═══════════════════════════════════════════════════════════════════════════════
This indicator automatically detects and visualizes key market structure elements:
🔹 BOS (Break of Structure)
- Identifies trend continuation patterns
- Marks when price breaks above previous highs (bullish) or below previous lows (bearish)
- Confirms trend strength and momentum
🔹 CHoCH (Change of Character)
- Detects potential trend reversals
- Alerts when market structure shifts from bullish to bearish or vice versa
- Helps identify early reversal opportunities
🔹 Order Blocks
- Highlights institutional entry zones
- Identifies the last opposite candle before a structure break
- Shows areas where smart money likely entered positions
🔹 Fair Value Gaps (FVG)
- Detects price imbalances and inefficiencies
- Shows areas where price moved rapidly leaving gaps
- Often act as support/resistance when retested
🔹 Liquidity Zones
- Marks swing high and low levels
- Identifies areas where stop losses likely cluster
- Shows potential stop hunt and liquidity grab zones
═══════════════════════════════════════════════════════════════════════════════
🎯 HOW TO USE
═══════════════════════════════════════════════════════════════════════════════
BULLISH SETUP:
1. Wait for Bullish CHoCH (trend reversal signal) or BOS ↑ (continuation)
2. Look for price to pull back into an Order Block or Fair Value Gap
3. Enter long when price bounces from these zones
4. Place stop loss below the Order Block
5. Target the next liquidity zone or resistance level
BEARISH SETUP:
1. Wait for Bearish CHoCH (trend reversal signal) or BOS ↓ (continuation)
2. Look for price to retrace into an Order Block or Fair Value Gap
3. Enter short when price rejects from these zones
4. Place stop loss above the Order Block
5. Target the next liquidity zone or support level
DASHBOARD INTERPRETATION:
• Trend: Current market direction (Bullish/Bearish)
• Volume: Confirmation strength (High volume = stronger signals)
• Signal: Latest structure break detected
• Key High/Low: Critical levels for the current trend
• Position: Price location (Premium = expensive, Discount = cheap)
═══════════════════════════════════════════════════════════════════════════════
⚙️ SETTINGS GUIDE
═══════════════════════════════════════════════════════════════════════════════
STRUCTURE DETECTION:
• Pivot Length (Default: 10)
- Lower values = More signals but potentially weaker
- Higher values = Fewer signals but stronger/more reliable
- Recommended: 8-12 for intraday, 10-15 for higher timeframes
• Structure Line Extension
- Visual preference for how far lines extend
- Does not affect signal detection
SMART MONEY CONCEPTS:
• Order Block Extension: How long OB boxes remain visible
• FVG Extension: How long gap boxes remain visible
• Min FVG Size: Filter out small gaps (0 = show all)
- Set to 10-20% to reduce noise
- Set to 0 to see all gaps
VOLUME FILTER:
• Volume Confirmation (Recommended: ON)
- Filters weak signals without volume support
- Reduces false breakouts
• Volume Multiplier (Default: 1.5)
- Higher = Stricter filtering (fewer but stronger signals)
- Lower = More signals (but may include weak ones)
DISPLAY:
• Dashboard: Toggle information panel
• Trend Background: Subtle color tint showing current trend
• Dashboard Position: Choose corner placement
═══════════════════════════════════════════════════════════════════════════════
🔔 ALERTS
═══════════════════════════════════════════════════════════════════════════════
Available alert conditions:
✓ Bullish BOS - Uptrend continuation confirmed
✓ Bearish BOS - Downtrend continuation confirmed
✓ Bullish CHoCH - Reversal to uptrend detected
✓ Bearish CHoCH - Reversal to downtrend detected
✓ Structure Break - Any significant market structure change
To set up alerts:
1. Click the "⏰" alert icon
2. Select "Smart Structure Pro"
3. Choose your desired condition
4. Configure notification method
5. Click "Create"
═══════════════════════════════════════════════════════════════════════════════
⚠️ IMPORTANT DISCLOSURES
═══════════════════════════════════════════════════════════════════════════════
REPAINTING BEHAVIOR:
• Pivot points WILL repaint until confirmed (this is by design and unavoidable)
• Structure breaks (BOS/CHoCH) use CLOSED candles and do NOT repaint after confirmation
• Order Blocks and FVGs are drawn on confirmed signals and do NOT repaint
• All signals wait for candle close before triggering
BEST PRACTICES:
• Use on higher timeframes (15min+) for more reliable signals
• Combine with other analysis (support/resistance, volume profile, etc.)
• Wait for candle close confirmation before acting on signals
• Use proper risk management - this is not a standalone trading system
• Backtest on your preferred instrument and timeframe
PERFORMANCE:
• Limited to 100 boxes, 100 lines, 100 labels for optimal performance
• Older objects automatically removed as new ones appear
• Works on all markets (Forex, Crypto, Stocks, Indices, Commodities)
═══════════════════════════════════════════════════════════════════════════════
📚 CONCEPTS EXPLAINED
═══════════════════════════════════════════════════════════════════════════════
MARKET STRUCTURE:
Market structure refers to the pattern of price movements creating swing highs
and lows. Understanding structure helps identify trend direction and potential
reversal points.
SMART MONEY CONCEPTS:
These are trading techniques based on tracking institutional order flow and
understanding where large players (banks, funds, institutions) enter and exit
positions.
ORDER BLOCKS:
The last opposing candle before a strong directional move. Institutions often
leave unfilled orders in these zones, which can act as support/resistance when
price returns.
FAIR VALUE GAPS:
Areas where price moved so quickly that it left an imbalance. These gaps often
get "filled" as price returns to find equilibrium, creating trading opportunities.
═══════════════════════════════════════════════════════════════════════════════
🎓 EDUCATIONAL VALUE
═══════════════════════════════════════════════════════════════════════════════
This indicator helps traders:
✓ Understand market structure mechanics
✓ Identify institutional trading patterns
✓ Improve trade timing and entry precision
✓ Recognize trend continuation vs reversal
✓ Learn smart money concepts through visualization
═══════════════════════════════════════════════════════════════════════════════
📋 TECHNICAL DETAILS
═══════════════════════════════════════════════════════════════════════════════
• Version: 1.0.0
• Pine Script Version: 5
• Indicator Type: Overlay
• No Repainting: Structure breaks use confirmed candles
• Performance Optimized: Limited drawing objects
• Works On: All markets and timeframes
• Alerts: Yes, fully customizable
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👤 AUTHOR
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Created by: Zakaria Safri
Original Work: All code and concepts are original implementations
Based On: ICT (Inner Circle Trader) educational concepts
License: © 2024 Zakaria Safri - Personal Use Only
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⚖️ DISCLAIMER
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This indicator is for educational and informational purposes only. It does not
constitute financial advice. Trading involves substantial risk of loss. Past
performance does not guarantee future results. Always conduct your own research
and consult with a licensed financial advisor before making trading decisions.
The author is not responsible for any losses incurred from using this indicator.
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If you find this indicator helpful, please:
👍 Like and favorite
⭐ Leave a review
📢 Share with other traders
💬 Comment with feedback or suggestions
Happy Trading! 📈
Chart Fusion Line SND Detection by TitikSona🧭 Overview
Fusion Line Momentum Analyzer is a momentum visualization tool that introduces a unified model of oscillator fusion.
It blends Fast and Slow Stochastics with RSI into one adaptive curve, designed to eliminate conflicting signals between different momentum sources.
Instead of reading three separate oscillators, the Fusion Line provides a consolidated view of strength and exhaustion zones in a single framework.
This approach helps analysts detect aligned momentum shifts with greater clarity and less noise, without repainting or lagging methods.
⚙️ Core Concept
Traditional oscillators often provide conflicting readings when volatility changes.
To solve this, the Fusion Line averages three normalized components:
Fast Stochastic (12,3,3) — reacts quickly to short-term momentum spikes.
Slow Stochastic (100,8,8) — filters long-term momentum context.
RSI (26) — measures internal strength between buying and selling pressure.
Each is rescaled to a 0–100 range, then averaged into a single curve called the Fusion Line.
A secondary Signal Line (SMA 9) is added to visualize directional confirmation.
This combination aims to preserve responsiveness from the fast components while maintaining structural stability from the slow and RSI layers.
🌈 Features
Unified momentum curve combining stochastic and RSI dynamics.
Automatic bias shading to highlight dominant trend direction.
Real-time percentage strength meter (visual intensity).
Configurable alert triggers on key momentum zones (20/80).
Clean chart display without unnecessary elements or overlays.
📘 Interpretation
Rising Fusion Line → indicates strengthening bullish momentum.
Falling Fusion Line → indicates strengthening bearish pressure.
Fusion values below 20 → potential oversold recovery.
Fusion values above 80 → possible exhaustion or reversal zone.
Mid-zone movement → reflects equilibrium or sideways momentum.
These readings should always be combined with higher timeframe structure or volume confirmation for context.
⚙️ Default Parameters
Fast Stochastic (12,3,3)
Slow Stochastic (100,8,8)
RSI Length (26)
Signal Line Smoothing (9)
All values can be adjusted to adapt to asset volatility or timeframe conditions.
⚠️ Disclaimer
This indicator is a research and visualization tool, not a signal generator.
It does not predict price movement or guarantee performance.
Use for analytical purposes only and combine with your own trading framework.
👨💻 Developer
Created by TitikSona — Research & Fusion Concept Designer
Built using Pine Script v6
Type: Open-source educational script
💬 Short Description
Fusion-based momentum visualization combining Double Stochastic and RSI into one adaptive line for clearer, noise-free momentum analysis.
SMA RibbonThis indicator overlays multiple Simple Moving Averages (SMAs) on the price chart to help visualize both short- and long-term market trends. It includes five configurable SMA lines — 10, 21, 50, 100, and 200 periods by default — each plotted with distinct colors for quick differentiation.
Short-term averages (10 and 21) highlight near-term momentum, while medium- and long-term averages (50, 100, and 200) provide broader trend context and identify potential areas of dynamic support or resistance.
Users can easily adjust the period lengths and line thickness through the settings panel to fit different timeframes or trading styles.
Features
Plots 5 configurable SMAs (default: 10, 21, 50, 100, 200)
Adjustable line width and colors for visual clarity
Works seamlessly on any timeframe and instrument
Useful for identifying trend direction, strength, and key support/resistance zones
[Fune]-Trend Technology🌊 - Trend Technology
“Flow with the trend — read every wave.”
🎯 Concept
Micro EMA (White) – Short-term pulse
Mid EMA (Aqua) – Medium-term direction
Macro EMA (Orange) – Long-term flow
Mid- to long-term references:
100 EMA = Yellow (trend balance)
300 EMA = Blue (structural anchor)
In addition, the PLR (Periodic Linear Regression) reveals the cyclical rhythm of the market trend — a recurring regression curve that reflects the underlying heartbeat of price movement.
📊 Trend Logic Summary
Condition Color Meaning Action
Mid > Macro 🟢 Green background Bullish trend Look for long opportunities
Mid < Macro 🔴 Red background Bearish trend Look for short opportunities
PLR slope > 0 📈 Upward bias Confirms bullish momentum
PLR slope < 0 📉 Downward bias Confirms bearish momentum
Micro EMA (White) dominant ⚪ White background Neutral / Resting phase Stand aside and wait
🧭 Trading Guidance
🟢 Long Setup: Green background + PLR slope upward + price above 100/300 EMA
🔴 Short Setup: Red background + PLR slope downward + price below 100/300 EMA
⚪ No Trade: White background, EMAs converging, or PLR slope flattening
⚓ Philosophy of
“ (The Boat) is a vessel sailing across the ocean of the market.
The EMAs are its sails, the PLR its compass.
The trader holds the helm, while the divine wind guides the waves.
Only those who move with the current — not against it —
will one day reach the state of ‘mindless clarity.’”
Squeeze Weekday Frequency [CHE] Squeeze Weekday Frequency — Tracks historical frequency of low-volatility squeezes by weekday to inform timing of low-risk setups.
Summary
This indicator monitors periods of unusually low volatility, defined as when the average true range falls below a percentile threshold, and tallies their occurrences across each weekday. By aggregating these counts over the chart's history, it reveals patterns in squeeze frequency, helping traders avoid or target specific days for reduced noise. The approach uses persistent counters to ensure accurate daily tallies without duplicates, providing a robust view of weekday biases in volatility regimes.
Motivation: Why this design?
Traders often face inconsistent signal quality due to varying volatility patterns tied to the trading calendar, such as quieter mid-week sessions or busier Mondays. This indicator addresses that by binning low-volatility events into weekday buckets, allowing users to spot recurring low-activity days where trends may develop with less whipsaw. It focuses on historical aggregation rather than real-time alerts, emphasizing pattern recognition over prediction.
What’s different vs. standard approaches?
- Reference baseline: Traditional volatility trackers like simple moving averages of range or standalone Bollinger Band squeezes, which ignore temporal distribution.
- Architecture differences:
- Employs array-based persistent counters for each weekday to accumulate events without recounting.
- Includes duplicate prevention via day-key tracking to handle sparse data.
- Features on-demand sorting and conditional display modes for focused insights.
- Practical effect: Charts show a persistent table of ranked weekdays instead of transient plots, making it easier to glance at biases like higher squeezes on Fridays, which reduces the need for manual logging and highlights calendar-driven edges.
How it works (technical)
The indicator first computes the average true range over a specified lookback period to gauge recent volatility. It then ranks this value against its own history within a sliding window to identify squeezes when the rank drops below the threshold. Each bar's timestamp is resolved to a weekday using the selected timezone, and a unique day identifier is generated from the date components.
On detecting a squeeze and valid price data, it checks against a stored last-marked day for that weekday to avoid multiple counts per day. If it's a new occurrence, the corresponding weekday counter in an array increments. Total days and data-valid days are tracked separately for context.
At the chart's last bar, it sums all counters to compute shares, sorts weekdays by their squeeze proportions, and populates a table with the selected subset. The table alternates row colors and highlights the peak weekday. An info label above the final bar summarizes totals and the top day. Background shading applies a faint red to squeeze bars for visual confirmation. State persists via variable arrays initialized once, ensuring counts build incrementally without resets.
Parameter Guide
ATR Length — Sets the lookback for measuring average true range, influencing squeeze sensitivity to short-term swings. Default: 14. Trade-offs/Tips: Shorter values increase responsiveness but raise false positives in chop; longer smooths for stability, potentially missing early squeezes.
Percentile Window (bars) — Defines the history length for ranking the current ATR, balancing recent relevance with sample size. Default: 252. Trade-offs/Tips: Narrower windows adapt faster to regime shifts but amplify noise; wider ones stabilize ranks yet lag in fast markets—aim for 100-500 bars on daily charts.
Squeeze threshold (PR < x) — Determines the cutoff for low-volatility classification; lower values flag rarer, tighter squeezes. Default: 10.0. Trade-offs/Tips: Tighter thresholds (under 5) yield fewer but higher-quality signals, reducing clutter; looser (over 20) captures more events at the cost of relevance.
Timezone — Selects the reference for weekday assignment; exchange default aligns with asset's session. Default: Exchange. Trade-offs/Tips: Use custom for cross-market analysis, but verify alignment to avoid offset errors in global pairs.
Show — Toggles the results table visibility for quick on/off of the display. Default: true. Trade-offs/Tips: Disable in multi-indicator setups to save screen space; re-enable for periodic reviews.
Pos — Positions the table on the chart pane for optimal viewing. Default: Top Right. Trade-offs/Tips: Bottom options suit long-term charts; test placements to avoid overlapping price action.
Font — Adjusts text size in the table for readability at different zooms. Default: normal. Trade-offs/Tips: Smaller fonts fit more data but strain eyes on small screens; larger for presentations.
Dark — Applies a dark color scheme to the table for contrast against chart backgrounds. Default: true. Trade-offs/Tips: Toggle false for light themes; ensures legibility without manual recoloring.
Display — Filters table rows to show all, top three, or bottom three weekdays by squeeze share. Default: All. Trade-offs/Tips: Use "Top 3" for focus on high-frequency days in active trading; "All" for full audits.
Reading & Interpretation
Red-tinted backgrounds mark individual squeeze bars, indicating current low-volatility conditions. The table's summary row shows the highest squeeze count, its percentage of total events, and the associated weekday in teal. Detail rows list selected weekdays with their absolute counts, proportional shares, and a left arrow for the peak day—higher percentages signal days where squeezes cluster, suggesting potential for calmer trend development. The info label reports overall days observed, valid data days, and reiterates the top weekday with its count. Drifting counts toward zero on a weekday imply rarity, while elevated ones point to habitual low-activity sessions.
Practical Workflows & Combinations
- Trend following: Scan for squeezes on high-frequency weekdays as entry filters, confirming with higher highs or lower lows in the structure; pair with momentum oscillators to time breaks.
- Exits/Stops: On low-squeeze days, widen stops for breathing room, tightening them during peak squeeze periods to guard against false breaks—use the table's percentages as a regime proxy.
- Multi-asset/Multi-TF: Defaults work across forex and indices on hourly or daily frames; for stocks, adjust percentile window to 100 for shorter histories. Scale thresholds up by 5-10 points for high-vol assets like crypto to maintain signal sparsity.
Behavior, Constraints & Performance
- Repaint/confirmation: Counts update only on confirmed bars via day-key changes, with no future references—live bars may shade red tentatively but tallies finalize at session close.
- security()/HTF: Not used, so no higher-timeframe repaint risks; all computations stay in the chart's resolution.
- Resources: Relies on a fixed-size array of seven elements and small loops for sorting and table fills, capped at 5000 bars back—efficient for most charts but may slow on very long intraday histories.
- Known limits: Ignores weekends and holidays implicitly via data presence; early chart bars lack full percentile context, leading to initial undercounting; assumes continuous sessions, so gaps in data (e.g., news halts) skew totals.
Sensible Defaults & Quick Tuning
Start with the built-in values for broad-market daily charts: ATR at 14, window at 252, threshold at 10. For noisier environments, lower the threshold to 5 and shorten the window to 100 to prioritize rare squeezes. If too few events appear, raise the threshold to 15 and extend ATR to 20 for broader capture. To combat overcounting in sparse data, widen the window to 500 while keeping others stock—monitor the info label's data-days count before trusting patterns.
What this indicator is—and isn’t
This serves as a statistical overlay for spotting calendar-based volatility biases, aiding in session selection and filter design. It is not a standalone signal generator, predictive model, or risk manager—integrate it with price action, volume, and broader strategy rules for decisions.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
Squeeze Hour Frequency [CHE]Squeeze Hour Frequency (ATR-PR) — Standalone — Tracks daily squeeze occurrences by hour to reveal time-based volatility patterns
Summary
This indicator identifies periods of unusually low volatility, defined as squeezes, and tallies their frequency across each hour of the day over historical trading sessions. By aggregating counts into a sortable table, it helps users spot hours prone to these conditions, enabling better scheduling of trading activity to avoid or target specific intraday regimes. Signals gain robustness through percentile-based detection that adapts to recent volatility history, differing from fixed-threshold methods by focusing on relative lowness rather than absolute levels, which reduces false positives in varying market environments.
Motivation: Why this design?
Traders often face uneven intraday volatility, with certain hours showing clustered low-activity phases that precede or follow breakouts, leading to mistimed entries or overlooked calm periods. The core idea of hourly squeeze frequency addresses this by binning low-volatility events into 24 hourly slots and counting distinct daily occurrences, providing a historical profile of when squeezes cluster. This reveals time-of-day biases without relying on real-time alerts, allowing proactive adjustments to session focus.
What’s different vs. standard approaches?
- Reference baseline: Classical volatility tools like simple moving average crossovers or fixed ATR thresholds, which flag squeezes uniformly across the day.
- Architecture differences:
- Uses persistent arrays to track one squeeze per hour per day, preventing overcounting within sessions.
- Employs custom sorting on ratio arrays for dynamic table display, prioritizing top or bottom performers.
- Handles timezones explicitly to ensure consistent binning across global assets.
- Practical effect: Charts show a persistent table ranking hours by squeeze share, making intraday patterns immediately visible—such as a top hour capturing over 20 percent of total events—unlike static overlays that ignore temporal distribution, which matters for avoiding low-liquidity traps in crypto or forex.
How it works (technical)
The indicator first computes a rolling volatility measure over a specified lookback period. It then derives a relative ranking of the current value against recent history within a window of bars. A squeeze is flagged when this ranking falls below a user-defined cutoff, indicating the value is among the lowest in the recent sample.
On each bar, the local hour is extracted using the selected timezone. If a squeeze occurs and the bar has price data, the count for that hour increments only if no prior mark exists for the current day, using a persistent array to store the last marked day per hour. This ensures one tally per unique trading day per slot.
At the final bar, arrays compile counts and ratios for all 24 hours, where the ratio represents each hour's share of total squeezes observed. These are sorted ascending or descending based on display mode, and the top or bottom subset populates the table. Background shading highlights live squeezes in red for visual confirmation. Initialization uses zero-filled arrays for counts and negative seeds for day tracking, with state persisting across bars via variable declarations.
No higher timeframe data is pulled, so there is no repaint risk from external fetches; all logic runs on confirmed bars.
Parameter Guide
ATR Length — Controls the lookback for the volatility measure, influencing sensitivity to short-term fluctuations; shorter values increase responsiveness but add noise, longer ones smooth for stability — Default: 14 — Trade-offs/Tips: Use 10-20 for intraday charts to balance quick detection with fewer false squeezes; test on historical data to avoid over-smoothing in trending markets.
Percentile Window (bars) — Sets the history depth for ranking the current volatility value, affecting how "low" is defined relative to past; wider windows emphasize long-term norms — Default: 252 — Trade-offs/Tips: 100-300 bars suit daily cycles; narrower for fast assets like crypto to catch recent regimes, but risks instability in sparse data.
Squeeze threshold (PR < x) — Defines the cutoff for flagging low relative volatility, where values below this mark a squeeze; lower thresholds tighten detection for rarer events — Default: 10.0 — Trade-offs/Tips: 5-15 percent for conservative signals reducing false positives; raise to 20 for more frequent highlights in high-vol environments, monitoring for increased noise.
Timezone — Specifies the reference for hourly binning, ensuring alignment with market sessions — Default: Exchange — Trade-offs/Tips: Set to "America/New_York" for US assets; mismatches can skew counts, so verify against chart timezone.
Show Table — Toggles the results display, essential for reviewing frequencies — Default: true — Trade-offs/Tips: Disable on mobile for performance; pair with position tweaks for clean overlays.
Pos — Places the table on the chart pane — Default: Top Right — Trade-offs/Tips: Bottom Left avoids candle occlusion on volatile charts.
Font — Adjusts text readability in the table — Default: normal — Trade-offs/Tips: Tiny for dense views, large for emphasis on key hours.
Dark — Applies high-contrast colors for visibility — Default: true — Trade-offs/Tips: Toggle false in light themes to prevent washout.
Display — Filters table rows to focus on extremes or full list — Default: All — Trade-offs/Tips: Top 3 for quick scans of risky hours; Bottom 3 highlights safe low-squeeze periods.
Reading & Interpretation
Red background shading appears on bars meeting the squeeze condition, signaling current low relative volatility. The table lists hours as "H0" to "H23", with columns for daily squeeze counts, percentage share of total squeezes (summing to 100 percent across hours), and an arrow marker on the top hour. A summary row above details the peak count, its share, and the leading hour. A label at the last bar recaps total days observed, data-valid days, and top hour stats. Rising shares indicate clustering, suggesting regime persistence in that slot.
Practical Workflows & Combinations
- Trend following: Scan for hours with low squeeze shares to enter during stable regimes; confirm with higher highs or lower lows on the 15-minute chart, avoiding top-share hours post-news like tariff announcements.
- Exits/Stops: Tighten stops in high-share hours to guard against sudden vol spikes; use the table to shift to conservative sizing outside peak squeeze times.
- Multi-asset/Multi-TF: Defaults work across crypto pairs on 5-60 minute timeframes; for stocks, widen percentile window to 500 bars. Combine with volume oscillators—enter only if squeeze count is below average for the asset.
Behavior, Constraints & Performance
Logic executes on closed bars, with live bars updating counts provisionally but finalizing on confirmation; table refreshes only at the last bar, avoiding intrabar flicker. No security calls or higher timeframes, so no repaint from external data. Resources include a 5000-bar history limit, loops up to 24 iterations for sorting and totals, and arrays sized to 24 elements; labels and table are capped at 500 each for efficiency. Known limits: Skips hours without bars (e.g., weekends), assumes uniform data availability, and may undercount in sparse sessions; timezone shifts can alter profiles without warning.
Sensible Defaults & Quick Tuning
Start with ATR Length at 14, Percentile Window at 252, and threshold at 10.0 for broad crypto use. If too many squeezes flag (noisy table), raise threshold to 15.0 and narrow window to 100 for stricter relative lowness. For sluggish detection in calm markets, drop ATR Length to 10 and threshold to 5.0 to capture subtler dips. In high-vol assets, widen window to 500 and threshold to 20.0 for stability.
What this indicator is—and isn’t
This is a historical frequency tracker and visualization layer for intraday volatility patterns, best as a filter in multi-tool setups. It is not a standalone signal generator, predictive model, or risk manager—pair it with price action, news filters, and position sizing rules.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
Thanks to Duyck
for the ma sorter
Aladin Pair Trading System v1Aladin Pair Trading System v1
What is This Indicator?
The Aladin Pair Trading System is a sophisticated tool designed to help traders identify profitable opportunities by comparing two related stocks that historically move together. Think of it as finding when one twin is running ahead or lagging behind the other - these moments often present trading opportunities as they tend to return to moving together.
Who Should Use This?
Beginners: Learn about statistical arbitrage and pair trading
Intermediate Traders: Execute mean-reversion strategies with confidence
Advanced Traders: Fine-tune parameters for optimal pair relationships
Portfolio Managers: Implement market-neutral strategies
💡 What is Pair Trading?
Imagine two ice cream shops next to each other. They usually have similar customer traffic because they're in the same area. If one day Shop A is packed while Shop B is empty, you might expect this imbalance to correct itself soon.
Pair trading works the same way:
You find two stocks that normally move together (like TCS and Infosys)
When one stock moves too far from the other, you trade expecting them to realign
You buy the lagging stock and sell the leading stock
When they come back together, you profit from both sides
Key Features
1. Z-Score Analysis
What it is: A statistical measure showing how far the price relationship has deviated from normal
What it means:
Z-Score near 0 = Normal relationship
Z-Score at +2 = Stock A is expensive relative to Stock B (Sell A, Buy B)
Z-Score at -2 = Stock A is cheap relative to Stock B (Buy A, Sell B)
2. Multiple Timeframe Analysis
Long-term Z-Score (300 bars): Shows the big picture trend
Short-term Z-Score (100 bars): Shows recent movements
Signal Z-Score (20 bars): Generates quick trading signals
3. Statistical Validation
The indicator checks if the pair is suitable for trading:
Correlation (must be > 0.7): Confirms the stocks move together
1.0 = Perfect positive correlation
0.7 = Strong correlation
Below 0.7 = Warning: pair may not be reliable
ADF P-Value (should be < 0.05): Tests if the relationship is stable
Low value = Good for pair trading
High value = Relationship may be random
Cointegration: Confirms long-term equilibrium relationship
YES = Pair tends to revert to mean
NO = Pair may drift apart permanently
Visual Elements Explained
Chart Zones (Color-Coded Areas)
Yellow Zone (-1.5 to +1.5)
Normal Zone: Relationship is stable
Action: Wait for better opportunities
Blue Zone (±1.5 to ±2.0)
Entry Zone: Deviation is significant
Action: Prepare for potential trades
Green/Red Zone (±2.0 to ±3.0)
Opportunity Zone: Strong deviation
Action: High-probability trade setups
Beyond ±3.0
Risk Limit: Extreme deviation
Action: Either maximum opportunity or structural break
Signal Arrows
Green Arrow Up (Buy A + Sell B):
Stock A is undervalued relative to B
Buy Stock A, Short Stock B
Red Arrow Down (Sell A + Buy B):
Stock A is overvalued relative to B
Sell Stock A, Buy Stock B
Settings Guide
Symbol Inputs
Pair Symbol (Symbol B): Choose the second stock to compare
Default: NSE:INFY (Infosys)
Example pairs: TCS/INFY, HDFCBANK/ICICIBANK, RELIANCE/ONGC
Z-Score Parameters
Long Z-Score Period (300): Historical context
Short Z-Score Period (100): Recent trend
Signal Period (20): Trading signals
Z-Score Threshold (2.0): Entry trigger level
Higher = Fewer but stronger signals
Lower = More frequent signals
Statistical Parameters
Correlation Period (240): How many bars to check correlation
Hurst Exponent Period (50): Measures mean-reversion tendency
Probability Lookback (100): Historical probability calculations
Trading Parameters
Entry Threshold (0.0): Minimum Z-score for entry
Risk Threshold (1.5): Warning level
Risk Limit (3.0): Maximum deviation to trade
How to Use (Step-by-Step)
Step 1: Choose Your Pair
Add the indicator to your chart (this becomes Stock A)
In settings, select Stock B (the comparison stock)
Choose stocks from the same sector for best results
Step 2: Verify Pair Quality
Check the Statistics Table (top-right corner):
✅ Correlation > 0.70 (Green = Good)
✅ ADF P-value < 0.05 (Green = Good)
✅ Cointegrated = YES (Green = Good)
If all three are green, the pair is suitable for trading!
Step 3: Wait for Signals
BUY SIGNAL (Green Arrow Up)
Z-Score crosses above -2.0
Action: Buy Stock A, Sell Stock B
Exit: When Z-Score returns to 0
SELL SIGNAL (Red Arrow Down)
Z-Score crosses below +2.0
Action: Sell Stock A, Buy Stock B
Exit: When Z-Score returns to 0
Step 4: Risk Management
Yellow Zone: Monitor only
Blue Zone: Prepare for entry
Green/Red Zone: Active trading zone
Beyond ±3.0: Maximum risk - use caution
⚠️ Important Warnings
Not All Pairs Work: Always check the statistics table first
Market Conditions Matter: Correlation can break during market stress
Use Stop Losses: Set stops at Z-Score ±3.5 or beyond
Position Sizing: Trade both legs with appropriate hedge ratios
Transaction Costs: Factor in brokerage and slippage for both stocks
Example Trade
Scenario: TCS vs INFOSYS
Correlation: 0.85 ✅
Z-Score: -2.3 (TCS is cheap vs INFY)
Action to be taken:
Buy 1lot of TCS Future
Sell 1lot of INFOSYS Future
Expected Outcome:
As Z-Score moves toward 0, TCS outperforms INFOSYS
Close both positions when Z-Score crosses 0
Profit from the convergence
Best Practices
Test Before Trading: Use paper trading first
Sector Focus: Choose pairs from the same industry
Monitor Statistics: Check correlation daily
Avoid News Events: Don't trade pairs during earnings/major news
Size Appropriately: Start small, scale with experience
Be Patient: Wait for high-quality setups (±2.0 or beyond)
What Makes This Indicator Unique?
Multi-timeframe Z-Score analysis: Three different perspectives
Statistical validation: Built-in correlation and cointegration tests
Visual risk zones: Easy-to-understand color-coded areas
Real-time statistics: Live pair quality monitoring
Beginner-friendly: Clear signals with educational zones
Technical Background
The indicator uses:
Engle-Granger Cointegration Test: Validates pair relationship
ADF (Augmented Dickey-Fuller) Test: Tests stationarity
Pearson Correlation: Measures linear relationship
Z-Score Normalization: Standardizes deviations
Log Returns: Handles price differences properly
Support & Community
For questions, suggestions, or to share your pair trading experiences:
Comment below the indicator
Share your successful pair combinations
Report any issues for quick fixes
Disclaimer
This indicator is for educational and informational purposes only. It does not constitute financial advice. Pair trading involves risk, including the risk of loss.
Always:
Do your own research
Understand the risks
Trade with money you can afford to lose
Consider consulting a financial advisor
📌 Quick Reference Card
Z-ScoreInterpretationAction-3.0 to -2.0A very cheap vs BStrong Buy A, Sell B-2.0 to -1.5A cheap vs BBuy A, Sell B-1.5 to +1.5Normal rangeHold/Wait+1.5 to +2.0A expensive vs BSell A, Buy B+2.0 to +3.0A very expensive vs BStrong Sell A, Buy B
Good Pair Statistics:
Correlation: > 0.70
ADF P-value: < 0.05
Cointegration: YES
Version: 1.0
Last Updated: 10th October 2025
Compatible: TradingView Pine Script v6
Happy Trading!
Williams Alligator Spread Oscillator (WASO)Short description (About box)
Williams Alligator Spread Oscillator (WASO) converts Bill Williams’ Alligator into a 0–100 oscillator that measures the average distance between Lips/Teeth/Jaw relative to ATR. High = expansion/trend (default), low = compression/range — making sideways markets easier to spot. Includes adaptive normalization, configurable thresholds, background shading, and alerts.
Full description (Description field)
What it does
The Williams Alligator Spread Oscillator (WASO) transforms Bill Williams’ Alligator into a single, adaptive 0–100 scale. It computes the average pairwise distance among the Alligator lines (Lips/Teeth/Jaw), normalizes it by ATR and a rolling min–max window, and smooths the result. This makes the signal robust across symbols and timeframes and explicitly improves detection of sideways (ranging) conditions by highlighting compression regimes.
Why it helps
Sideways detection made easier: Low WASO marks compressed regimes that commonly align with consolidation/range phases, helping you identify chop and plan breakout strategies.
Trend/expansion clarity: High WASO indicates the Alligator lines are widening relative to volatility, pointing to trending or expanding conditions.
You can flip the direction if you prefer “High = Range.”
How it is calculated (plain English)
Smooth price with RMA (SMMA-like) to get Jaw, Teeth, Lips.
Compute the average pairwise distance between these three lines.
Divide by ATR to remove price-scale effects.
Normalize with a rolling min–max window to map values to 0–100.
Optionally apply EMA smoothing to the oscillator.
Key settings
Jaw/Teeth/Lips Lengths: Alligator periods (SMMA-like via ta.rma).
ATR Length: Volatility benchmark for scaling.
Normalization Lookback: Longer = steadier; shorter = more responsive.
Smoothing (EMA): Evens out noise.
High Value = Large Spread (Trend): Toggle to invert semantics.
Upper/Lower Thresholds: 70/30 are practical starting points.
Signals / interpretation
Sideways / Compression (easier to spot):
Default direction: WASO below Lower Threshold (e.g., <30).
With inverted direction OFF: WASO above Upper Threshold (e.g., >70).
Trend / Expansion:
Default direction: WASO above Upper Threshold (e.g., >70).
With inverted direction OFF: WASO below Lower Threshold (e.g., <30).
Midline (50): Neutral zone; flips around 50 can hint at regime shifts.
Alerts included
Range Start (sideways/compression)
Trend Start (expansion/trend)
Notes & limitations
This implementation omits the classic forward shift of Alligator lines to keep signals usable on live bars.
If market behavior shifts (very quiet or very volatile), tune Lookback and ATR Length.
Combine WASO with breakout levels or momentum filters for entries/exits.
Credits & disclaimer
Inspired by Bill Williams’ Alligator.
For educational purposes only. Not financial advice.
Release Notes (v1.0):
Initial release of Williams-Alligator Spread Oscillator (WASO) with ATR-based scaling and adaptive 0–100 normalization.
Direction toggle (High = Trend by default), adjustable thresholds, background shading, and two alert conditions.
