PHP Code:
// This Pine Script™ code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © ChartPrime
//@version=5
indicator(".", max_boxes_count=500, max_lines_count=500, max_bars_back=500, overlay=true)
prng(series float seed, series int _range=100) =>
var float random = 1.0
random := ((3.1415926 * random % seed) * 271.828) % _range
float new_seed = ((seed * 1.618) % _range) + random
[random, new_seed]
method probability(series float[] self, series int idx, series float sum) => self.get(idx) / sum * 100.0
percent (series float end, series float start) => (end - start) / start
add_percent(series float value, series float percent) => (1.0 + percent) * value
log_returns (series float end, series float start) => math.log(end / start)
add_log_returns(series float value, series float log_returns) => math.exp(log_returns) * value
returns (series float end, series float start, simple bool style) => style ? percent( end, start) : log_returns( end, start)
add_returns(series float value, series float returns, series bool style) => style ? add_percent(value, returns) : add_log_returns(value, returns)
method add (series int[] self, series int idx, series int value=1) => self.set(idx, self.get(idx) + value)
method add_to_dist(series int[] self, series float value, series float bin_width) => self.add(int(math.abs(value) / bin_width), 1)
method normal_value( series float[] self,
series int idx,
simple int scale,
simple bool invert=true) =>
float min = self.min()
int normal = int((self.get(idx) - min) * (scale - 1) / (self.max() - min)) + 1
if invert
normal := scale - normal
normal
sinc(series float source, series float bandwidth) =>
float omega = math.pi * source / bandwidth
source != 0.0 ? math.sin(omega) / omega : 1.0
type move
int[] polarity
int[] up
int[] down
float bin_width
float extreme
float drift
string drift_style
bool return_style
float vol
slope(series int length) =>
float sumx = 0.0
float sumy = 0.0
float sumx2 = 0.0
float sumxy = 0.0
for int i = 0 to length - 1
float src = close[i]
int idx_1 = i + 1
sumx += idx_1
sumy += src
sumx2 += math.pow(idx_1, 2)
sumxy += src * idx_1
float slope = -((length * sumxy - sumx * sumy) / (length * sumx2 - sumx * sumx))
standard_drift(series int length) =>
float log_return = math.log(close / close[1])
float log_return2 = math.pow( log_return, 2 )
float vari = 0.0
float mean = 0.0
for int i = 0 to length - 1
mean += log_return[ i]
vari += log_return2[i]
mean /= length
vari /= length * 2
float drift = mean - vari
drift(series int length, simple string style) =>
switch style
"Standard" => standard_drift(length)
"Linear Regression" => slope(length)
=> 0.0 // "None"
volatility_adjustment(series float source, simple int length, simple bool enable) =>
float vol_adj = ta.sma(math.sqrt(math.sum(math.pow(source, 2), length) / (length - 1)), length) * 0.5
if not enable
vol_adj := 0.0
vol_adj
moves(simple int precision,
simple int bins,
simple int lookback,
simple string drift_style,
simple int drift_length,
simple string source,
simple bool return_style,
simple int steps,
simple bool volatility_adj) =>
array<int> polarity = array.new<int>( 2, 0)
array<int> delta_up_dist = array.new<int>(bins + 1, 0)
array<int> delta_down_dist = array.new<int>(bins + 1, 0)
float last = source=="Move" ? close[1] : open
float ret = returns(close, last, return_style)
int rounding = precision + (return_style ? 6 : 2)
float delta = math.round(ret, rounding)
float volatil = volatility_adjustment(ret, steps, volatility_adj)
float extreme = math.max(math.abs(ta.highest(delta[1], lookback)),
math.abs(ta.lowest (delta[1], lookback)))
float drift = drift(drift_length == 0 ? lookback : drift_length, drift_style)
float bin_width = extreme / bins
if barstate.islast
for int i = 1 to lookback
if close[i] <= last[i]
polarity.add(1)
add_to_dist(delta_down_dist, delta[i], bin_width)
else
polarity.add(0)
add_to_dist(delta_up_dist, delta[i], bin_width)
move.new(polarity, delta_up_dist, delta_down_dist, bin_width, extreme, drift, drift_style, return_style, volatil)
method idx_to_percent(series move self,
series int index,
series float volatil,
simple bool polarity) =>
(polarity ? 1 : -1) * (index * self.bin_width + volatil)
monte_carlo(series move moves, series float seed, simple int steps) =>
array<float> monte = array.new<float>(steps)
float over_seed = seed
int polarity_sum = moves.polarity.sum()
int moves_up_sum = moves.up.sum()
int moves_dn_sum = moves.down.sum()
if moves.up.max() > 0 and moves.down.max() > 0
for int i = 0 to steps - 1
[polarity_random, polarity_seed] = prng(over_seed)
over_seed := polarity_seed
if moves.polarity.probability(0, polarity_sum) >= polarity_random
int moves_up_size_1 = moves.up.size() - 1
[move_random, move_random_seed] = prng(over_seed, moves_up_size_1)
over_seed := move_random_seed
int up_index = int(move_random)
bool move_found = false
while not move_found
if up_index > moves_up_size_1
up_index := 0
if moves.up.get(up_index) == 0
up_index += 1
continue
[move_find_random, move_find_seed] = prng(over_seed)
over_seed := move_find_seed
if moves.up.probability(up_index, moves_up_sum) >= move_find_random
monte.set(i, moves.idx_to_percent(up_index, moves.vol, true))
move_found := true
break
else
up_index += 1
continue
else
int moves_down_size_1 = moves.down.size() - 1
[move_random, move_random_seed] = prng(over_seed, moves_down_size_1)
over_seed := move_random_seed
int down_index = int(move_random)
bool move_found = false
while not move_found
if down_index > moves_down_size_1
down_index := 0
if moves.down.get(down_index) == 0
down_index += 1
continue
[move_find_random, move_find_seed] = prng(over_seed)
over_seed := move_find_seed
if moves.down.probability(down_index, moves_dn_sum) >= move_find_random
monte.set(i, moves.idx_to_percent(down_index, moves.vol, false))
move_found := true
break
else
down_index += 1
continue
[monte, over_seed]
add_drift(series float value, series float drift, series string style) =>
switch style
"Standard" => add_log_returns(value, drift)
"Linear Regression" => value + drift
=> value // "None"
sim(series move moves, series float seed, simple int steps) =>
float simulation = open
[movements, _seed] = monte_carlo(moves, seed, steps)
for int i = 0 to steps - 1
simulation := add_drift(add_returns(simulation, movements.get(i), moves.return_style), moves.drift, moves.drift_style)
[simulation, _seed]
sinc_filter(series array<float> source, simple float length) =>
var float length_1 = length + 1.0
if length > 0.0 and source.size() > 0
int source_size = array.size(source)
int source_size_1 = source_size - 1
array<float> est = array.new<float>(source_size)
for int i = 0 to source_size_1
float sum = 0.0
float sumw = 0.0
for int j = 0 to source_size_1
float weight = sinc(i - j, length_1)
sum += weight * array.get(source, j)
sumw += weight
float current_price = sum / sumw
array.set(est, i, current_price >= 0.0 ? current_price : 0.0)
est
else
source
monte_carlo_distribution(series move moves,
series float seed,
simple int bins,
simple int steps,
simple int sims) =>
array< int > distribution = array.new< int >(bins + 1, 0)
array<float> sim_outcomes = array.new<float>()
var int sims_1 = sims - 1
float sum_stdev = 0.0
float reseed = seed
for int i = 0 to sims_1
[simulation, seed_sim] = sim(moves, reseed, steps)
reseed := seed_sim
sum_stdev += math.pow(simulation - open, 2)
sim_outcomes.push(simulation)
float sim_lowest = sim_outcomes.min()
float sim_highest = sim_outcomes.max()
float bin_width = (sim_highest - sim_lowest) / bins
float avg_idx_up = 0.0
float sum_idx_up = 0.0
float avg_idx_dn = 0.0
float sum_idx_dn = 0.0
for int i = 0 to sim_outcomes.size() - 1
float outcome = sim_outcomes.get(i)
int idx = int((outcome - sim_lowest) / bin_width)
if outcome > open
avg_idx_up += idx
sum_idx_up += 1
else
avg_idx_dn += idx
sum_idx_dn += 1
distribution.add(idx)
float avg_up = avg_idx_up / sum_idx_up
float avg_dn = avg_idx_dn / sum_idx_dn
float stdev = math.sqrt(sum_stdev / sims)
[distribution, bin_width, sim_lowest, sim_highest, avg_up, avg_dn, stdev, reseed]
draw_distribution(series move moves,
series float seed,
series bool orientation,
simple int bins,
simple int steps,
simple int simulations,
simple int scale,
simple bool avg_move,
simple float deviation,
simple bool enable_stdev,
simple bool enable_bias,
simple bool background,
simple color max_bear_color,
simple color min_bear_color,
simple color max_bull_color,
simple color min_bull_color,
simple color dev_high_color,
simple color dev_low_color,
simple color text_color,
simple bool extend_lines,
simple bool outcomes_on,
simple bool text_on_dist,
simple string move_help_on,
simple bool max_expected,
simple float smoothing,
simple bool start_line,
simple color start_color) =>
var array< box> dist = array.new< box>()
var array<line> avg_line = array.new<line>()
var string xtend_line = extend_lines ? extend.left : extend.none
const color invisible = #00000000
int offset = steps
int direction = orientation ? 1 : -1
int position = orientation ? 0 : offset
int width = (scale + offset) * direction
[_monte, bin_width, lowest_sim, highest_sim, avg_idx_up, avg_idx_down, stdev, _seed] = monte_carlo_distribution(moves, seed, bins, steps, simulations)
array<float> monte = sinc_filter(_monte, smoothing)
float max_freq = monte.max()
float half_bin = bin_width * 0.5
float variation = stdev * deviation
float dev_up = open + variation
float dev_down = math.max(0.0, open - variation)
float avg_up = avg_idx_up * bin_width + lowest_sim
float avg_down = avg_idx_down * bin_width + lowest_sim
if avg_line.size() > 0
for int i = avg_line.size() - 1 to 0
avg_line.get(i).delete()
avg_line.remove(i)
if dist.size() > 0
for int i = dist.size() - 1 to 0
dist.get(i).delete()
dist.remove(i)
int x2 = outcomes_on ? width + bar_index :
orientation ? offset + bar_index :
-1 + bar_index
if max_expected
float expected = monte.indexof(max_freq) * bin_width + lowest_sim
color expected_color = expected > open ? min_bull_color : min_bear_color
//avg_line.push(line.new(bar_index + position, expected, x2, expected, color=expected_color, style=line.style_solid, extend=xtend_line, width=2))
if enable_stdev
//avg_line.push(line.new(bar_index + position, dev_up, x2, dev_up, color=dev_high_color, style=line.style_dotted, extend=xtend_line, width=2))
//avg_line.push(line.new(bar_index + position, dev_down, x2, dev_down, color=dev_low_color, style=line.style_dotted, extend=xtend_line, width=2))
if move_help_on == "Deviation Range"
or move_help_on == "Both"
avg_line.push(line.new(bar_index, open, bar_index + offset, dev_up, color=dev_high_color, style=line.style_arrow_right))
avg_line.push(line.new(bar_index, open, bar_index + offset, dev_down, color=dev_low_color, style=line.style_arrow_right))
if avg_move
//avg_line.push(line.new(bar_index + position, avg_up, x2, avg_up, color=min_bull_color, style=line.style_dashed, extend=xtend_line, width=2))
//avg_line.push(line.new(bar_index + position, avg_down, x2, avg_down, color=min_bear_color, style=line.style_dashed, extend=xtend_line, width=2))
if move_help_on == "Expected"
or move_help_on == "Both"
avg_line.push(line.new(bar_index, open, bar_index + offset, avg_up, color=min_bull_color, style=line.style_arrow_right))
avg_line.push(line.new(bar_index, open, bar_index + offset, avg_down, color=min_bear_color, style=line.style_arrow_right))
if start_line
avg_line.push(line.new(bar_index + position, open, x2, open, color=start_color, extend=xtend_line))
float polarity = 0.0
float monte_sum = monte.sum()
for int i = 0 to monte.size() - 1
float top = (1 + i) * bin_width + lowest_sim - half_bin
float bottom = i * bin_width + lowest_sim - half_bin
float center = math.avg(top, bottom)
int value = monte.normal_value(i, scale)
float prob = monte.probability(i, monte_sum)
if center < open
polarity += prob
color grad_color = if center >= open
float upper = highest_sim - half_bin
color.from_gradient(center, open, upper, min_bull_color, max_bull_color)
else
float lower = lowest_sim + bin_width * 0.5
color.from_gradient(center, lower, open, max_bear_color, min_bear_color)
if prob > 0.0 and outcomes_on
int left = bar_index + (value + offset) * direction
string txt = text_on_dist ? str.tostring(math.round(prob, 2)) + "%" : ""
dist.push(box.new(left, top, bar_index + width, bottom, text=txt, border_color=invisible, bgcolor=grad_color, text_color=text_color))
if background
int left = bar_index + position
int right = outcomes_on ? (value + offset) * direction + bar_index :
orientation ? offset + bar_index :
-1 + bar_index
var string ext = extend_lines ? (orientation ? extend.left : extend.right) : extend.none
color bg_color = color.from_gradient(monte.get(i), 0.0, max_freq, color.new(grad_color, 99), color.new(grad_color, 75))
dist.push(box.new(left, top, right, bottom, invisible, bgcolor=bg_color, extend=ext))
if enable_bias
int left = outcomes_on ? width + bar_index :
orientation ? offset + bar_index :
-1 + bar_index
int right = left + 2 * direction
float bottom = lowest_sim - half_bin
float div = (highest_sim + half_bin - (lowest_sim - half_bin)) * (polarity / 100.0)
dist.push(box.new(left, highest_sim + half_bin, right, div + bottom, border_color=invisible, bgcolor=min_bull_color, text="🢁\n\n" + str.tostring(math.round(100.0 - polarity, 2)) + "%", text_color=text_color))
dist.push(box.new(left, bottom + div, right, bottom, border_color=invisible, bgcolor=min_bear_color, text=str.tostring(math.round( polarity, 2)) + "%" + "\n\n🢃", text_color=text_color))
_seed
string source = input.string("Candle", "Derivative Source", options=["Candle", "Move"], tooltip="Candle uses the difference between Close and Open. Move uses the difference between the current close and the previous close.")
string controls = "Primary Controls"
int simulations = input.int ( 500, "Number of Simulations", group=controls, minval= 50, step= 50, tooltip="This adjusts the number of simulations the indicator will preform. A larger number is alway preferable but isn't always feasible. The larger the number the more accurate the indicator will be.")
int lookback = input.int ( 500, "Lookback", group=controls, minval= 50, step= 50, tooltip="This determines the number of historical candles to take into account. A larger number will allow for more detail in the simulation but will also make it less relevant.")
int steps = input.int ( 10, "Steps Into Future", group=controls, minval= 2, step= 1, tooltip="This determines the number of steps into the future to predict. The further into the future you go, the more resources it will utilize. potentially resulting in the indicator not loading.")
int bins = input.int ( 10, "Outcome Granularity", group=controls, minval= 10, step= 5, tooltip="This is the number of rows in the final distribution of simulations. This determines the granularity of the final result.")
float smoothing = input.float(1.75, "Outcome Smoothing", group=controls, minval=0.0, step=0.25, tooltip="Smooth the distribution of the outcomes. When its set to 0 it will not apply smoothing.")
int granularity = input.int ( 25, "Returns Granularity", group=controls, minval= 5, step= 5, tooltip="This adjusts the granularity of the distribution of price movements. A larger number can be more accurate but it will sacrifice loading time.")
string adjustments = "Additional Adjustments"
int drift_length = input.int ( 34, "Drift", group=adjustments, inline="drift", minval=0)
string drift_style = input.string( "Linear Regression", "Style:", group=adjustments, inline="drift", options=["Linear Regression", "Standard", "None"], tooltip="Drift adds trend to the simulation. Without drift, the simulation will assume a trendless market. Linear Regression uses linear regression to determine trend while Standard Drift uses a volatility degraded average return.\n\nBoth are excellent options and are generally very similar. Set this to 0 to use the same window size as the lookback. A shorter period will use the short term trend while a longer period will use a longer term trend.")
bool volatility_adj = input.bool ( true, "Volatility Adjust", group=adjustments, tooltip="Adjust the simulation to include the current volatility of returns. This will prevent the simulation from underestimating the variance in movements.")
bool return_style = input.string("Log Returns", "Returns Style", group=adjustments, options=["Percent", "Log Returns"], tooltip="Percent uses % change for the calculations while Log Returns uses log returns for the calculations.") == "Percent"
int precision = input.int ( 1, "Precision", group=adjustments, minval=1, tooltip="Precision lets you adjust the level of rounding for the price movements. A smaller number will be faster but it will sacrifice accuracy.")
bool wait_for_steps = input.bool ( true, "Update Every Bar", group=adjustments, tooltip="Enable this to update the prediction on every bar.")
string visuals = "Visual Preferences"
bool orientation = input.string("Right", "Side of Chart", group=visuals, options=["Right", "Left"], tooltip="Choose which side of the chart you want the indicator on.") == "Right"
string move_help_on = input.string( "Both", "Move Visualization", group=visuals, options=["Expected", "Deviation Range", "Both", "None"])
bool avg_move = input.bool ( true, "Most Likely Move", group=visuals, tooltip="This plots the most likely up and down move for the current estimation period. These may be used as targets for your trade.")
bool enable_stdev = input.bool ( true, "Standard Deviation", group=visuals, inline="stdev")
float deviation = input.float ( 1.0, "", group=visuals, inline="stdev", minval=0.0, step=0.5, tooltip="This is the range in which most of the simulations fall between. It acts as a prediction level for a volatile move.")
bool enable_bias = input.bool ( true, "Most Likely Direction", group=visuals, tooltip="This shows you the sum of up and down probabilities. It allows you to quickly see what the likelihood of a move in either direction is.")
bool max_expected = input.bool ( true, "Max Probability Zone", group=visuals, tooltip="This highlights the area with the largest outcome probability.")
bool outcomes_on = input.bool ( false, "Outcome Distribution", group=visuals, inline="dist")
int scale = input.int ( 10, "", group=visuals, inline="dist", minval=10, tooltip="Enable to display the distribution of outcomes. Adjust this changes the horizontal length of the distribution.")
bool text_on_dist = input.bool ( false, "Distribution Text", group=visuals, tooltip="Enable outcome probabilities to be show in the distribution.")
bool background = input.bool ( false, "Background", group=visuals, tooltip="Display a background of the distribution for the duration of the prediction interval.")
bool start_line = input.bool ( false, "Starting Line", group=visuals, tooltip="Show the starting point of the simulation.")
bool extend_lines = input.bool ( false, "Extend Lines", group=visuals, tooltip="Extend the lines and background.")
string coloring = "Color Preferences"
color max_bear_color = input.color(#FFA0CC, "Bearish Colors ", group=coloring, inline="bear")
color min_bear_color = input.color(#FF22CC, "", group=coloring, inline="bear")
color max_bull_color = input.color(#00FFFF, "Bullish Colors ", group=coloring, inline="bull")
color min_bull_color = input.color(#0066FF, "", group=coloring, inline="bull")
color dev_high_color = input.color(#FFB617, "Deviation Color - High:", group=coloring, inline="dev" )
color dev_low_color = input.color(#2672ff, "Low:", group=coloring, inline="dev" )
color start_color = input.color(#7E7E7E, "Start Line Color", group=coloring)
color text_color = input.color(#FFFFFF, "Text Color", group=coloring)
move rand_moves = moves(precision, granularity, lookback, drift_style, drift_length, source, return_style, steps, volatility_adj)
var float seed = 1
[_, re_seed] = prng(seed)
seed := re_seed
if barstate.islast
var int counter = -1
if counter == -1
int bar_index_1 = bar_index + 1
if bar_index_1 < lookback
runtime.error('Please use a "Lookback" input setting that is less than: ' + str.tostring(bar_index))
if bar_index_1 < drift_length
runtime.error('Please use a "Drift" input setting that is less than: ' + str.tostring(bar_index))
counter += 1
bool once = counter == 0 ? true : barstate.isconfirmed
bool wait = not wait_for_steps ? 0 == counter % steps and once :
0 == counter or barstate.isconfirmed
if wait
seed := draw_distribution(rand_moves, seed, orientation, bins, steps, simulations, scale, avg_move, deviation, enable_stdev, enable_bias,
background, max_bear_color, min_bear_color, max_bull_color, min_bull_color, dev_high_color, dev_low_color, text_color,
extend_lines, outcomes_on, text_on_dist, move_help_on, max_expected, smoothing, start_line, start_color)
/////////////////////////////////
//@version=5
// Inputs
a = input(2, title='Key Vaule. \'This changes the sensitivity\'')
c = input(10, title='ATR Period')
h = input(true, title='Signals from Heikin Ashi Candles')
xATR = ta.atr(c)
nLoss = a * xATR
src = h ? request.security(ticker.heikinashi(syminfo.tickerid), timeframe.period, close, lookahead=barmerge.lookahead_off) : close
xATRTrailingStop = 0.0
iff_1 = src > nz(xATRTrailingStop[1], 0) ? src - nLoss : src + nLoss
iff_2 = src < nz(xATRTrailingStop[1], 0) and src[1] < nz(xATRTrailingStop[1], 0) ? math.min(nz(xATRTrailingStop[1]), src + nLoss) : iff_1
xATRTrailingStop := src > nz(xATRTrailingStop[1], 0) and src[1] > nz(xATRTrailingStop[1], 0) ? math.max(nz(xATRTrailingStop[1]), src - nLoss) : iff_2
pos = 0
iff_3 = src[1] > nz(xATRTrailingStop[1], 0) and src < nz(xATRTrailingStop[1], 0) ? -1 : nz(pos[1], 0)
pos := src[1] < nz(xATRTrailingStop[1], 0) and src > nz(xATRTrailingStop[1], 0) ? 1 : iff_3
xcolor = pos == -1 ? color.red : pos == 1 ? color.green : color.blue
ema = ta.ema(src, 1)
above = ta.crossover(ema, xATRTrailingStop)
below = ta.crossover(xATRTrailingStop, ema)
buy = src > xATRTrailingStop and above
sell = src < xATRTrailingStop and below
barbuy = src > xATRTrailingStop
barsell = src < xATRTrailingStop
plotshape(buy, title='Buy', text='L', style=shape.labelup, location=location.belowbar, color=color.new(color.green, 0), textcolor=color.new(color.white, 0), size=size.tiny)
plotshape(sell, title='Sell', text='S', style=shape.labeldown, location=location.abovebar, color=color.new(color.red, 0), textcolor=color.new(color.white, 0), size=size.tiny)
////////
// This Pine Script™ code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © Seckin42
//@version=5
source99 = close
windowsize = input(title="Window Size", defval=25)
offset = input.float(title="Offset", defval=0.85)
sigma = input.float(title="Sigma", defval=6)
plot(ta.alma(source99, windowsize, offset, sigma))
atrPeriod = input.int(10, "ATR Length", minval = 1)
factor = input.float(3.0, "Factor", minval = 0.01, step = 0.01)
[supertrend, direction] = ta.supertrend(factor, atrPeriod)
supertrend := barstate.isfirst ? na : supertrend
upTrend = plot(direction < 0 ? supertrend : na, "Up Trend", color = color.green, style = plot.style_linebr)
downTrend = plot(direction < 0 ? na : supertrend, "Down Trend", color = color.red, style = plot.style_linebr)
bodyMiddle = plot(barstate.isfirst ? na : (open + close) / 2, "Body Middle",display = display.none)
//////
//@version=5
// Dr. Abhiram's Golden Strategy //@version=5
// srflip2011
// --- inputs
source55 = close
TITLE = input(false, title='Turn on Alerts & Enable Background Color options are based on EMA1 & EMA2 Crossovers, (This button does nothing)')
turnon2 = input(true, title='Turn on Alerts?')
colorbars = input(true, title = "Color Bars?")
colorbars2 = input(true,title = "Color Bar when price closes under / above Ema 1 & 2?" , inline = "1")
barc1 = input (color.rgb(0, 17, 255), "+", inline = "1")
barc2 = input(color.rgb(212, 4, 248), "-", inline ="1")
turnon = input(true, title='Turn on Ema 1 & 2?')
//backgroundcolor = input(false, title='Enable Background Color?')
//Ema 1 & 2
len1 = input.int(10, minval=1, title='EMA1')
len2 = input.int(21, minval=1, title='EMA2')
ema1 = ta.ema(source55, len1)
ema2 = ta.ema(source55, len2)
//---
cond1 = ta.crossover(close,ema1) and ta.crossover(close,ema2)
cond2 = ta.crossunder(close,ema1) and ta.crossunder(close,ema2)
barcolor(cond1 ? color.rgb(0, 17, 255) : cond2 ? color.rgb(212, 4, 248): na)
//-- Candle Color
colbar = ema1 > ema2 ? color.rgb(15, 250, 23) : color.rgb(255, 0, 0)
barcolor(colorbars ? colbar :na)
// Ema Cross
mylong = ta.crossover(ema1, ema2)
myshort = ta.crossunder(ema1, ema2)
first = ta.ema(close, len1)
sec = ta.ema(close, len2)
// Calculations
last_long = float(na)
last_short = float(na)
last_long := mylong ? time : nz(last_long[1])
last_short := myshort ? time : nz(last_short[1])
in_long = last_long > last_short ? 2 : 0
in_short = last_short > last_long ? 2 : 0
condlongx = in_long
condlong = ta.crossover(condlongx, 1.9)
condlongclose = ta.crossunder(condlongx, 1.9)
condshortx = in_short
condshort = ta.crossover(condshortx, 1.9)
condshortclose = ta.crossover(condshortx, 1.9)
// Color Fill
fcolor = first > sec ? #0aff68 : first < sec ? #ff0a5a : #cccccc
// Plots
plotshape(turnon2 ? condlong : na, title='Breakout', color=color.new(#112f16, 0), location=location.belowbar, style=shape.labelup, text='L', textcolor=color.new(color.white, 0), size=size.small, offset=1)
plotshape(turnon2 ? condshort : na, title='Breakdown', color=color.new(#9d0d0d, 0), style=shape.labeldown, text='S', textcolor=color.new(color.white, 0), size=size.small, offset=1)
/////
selamlar....
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Bronz Üye
Originally Posted by @yörük@
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