Test-035-Montecarlo: Free TradingView Indicator (Pine Script)

Add It to Your TradingView Chart

1Copy the scriptPress Copy script above, or the copy button on the code below.
2Open the Pine EditorOpen a TradingView chart and choose Pine Editor at the bottom of the screen.
3Paste over the templateCreate a new indicator, select all of the template text and paste. Press Save and give it a name.
4Add it to the chartPress Add to chart, then adjust the settings with the gear icon next to its name.

Source Code

145 lines of Pine Script v4 from beta/arrays/test-035-montecarlo.pinescript, published under MPL-2.0 (file header). Keep the author's credit and licence lines when you reuse it.

test-035-montecarlo.pinescript

// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © RicardoSantos

//@version=4
study("test-035-montecarlo", overlay=true, max_bars_back=5000)
//t = ''

//| Line functions:
f_initline()=>line.new(time, 0., time, 0., xloc.bar_time, width=2)
f_line_updater(_id, _x1, _y1, _x2, _y2)=>
    line.set_xy1(id=_id, x=_x1, y=_y1)
    line.set_xy2(id=_id, x=_x2, y=_y2)
    line.set_color(id=_id, color=_y2 > _y1 ? color.teal : color.orange)

var line L00 = f_initline(), var line L10 = f_initline(), var line L20 = f_initline(), var line L30 = f_initline(), var line L40 = f_initline()
var line L01 = f_initline(), var line L11 = f_initline(), var line L21 = f_initline(), var line L31 = f_initline(), var line L41 = f_initline()
var line L02 = f_initline(), var line L12 = f_initline(), var line L22 = f_initline(), var line L32 = f_initline(), var line L42 = f_initline()
var line L03 = f_initline(), var line L13 = f_initline(), var line L23 = f_initline(), var line L33 = f_initline(), var line L43 = f_initline()
var line L04 = f_initline(), var line L14 = f_initline(), var line L24 = f_initline(), var line L34 = f_initline(), var line L44 = f_initline()
var line L05 = f_initline(), var line L15 = f_initline(), var line L25 = f_initline(), var line L35 = f_initline(), var line L45 = f_initline()
var line L06 = f_initline(), var line L16 = f_initline(), var line L26 = f_initline(), var line L36 = f_initline(), var line L46 = f_initline()
var line L07 = f_initline(), var line L17 = f_initline(), var line L27 = f_initline(), var line L37 = f_initline(), var line L47 = f_initline()
var line L08 = f_initline(), var line L18 = f_initline(), var line L28 = f_initline(), var line L38 = f_initline(), var line L48 = f_initline()
var line L09 = f_initline(), var line L19 = f_initline(), var line L29 = f_initline(), var line L39 = f_initline(), var line L49 = f_initline()
f_line_id(_i)=>(_i==00?L00:(_i==01?L01:(_i==02?L02:(_i==03?L03:(_i==04?L04:(_i==05?L05:(_i==06?L06:(_i==07?L07:(_i==08?L08:(_i==09?L09:
     (_i==10?L10:(_i==11?L11:(_i==12?L12:(_i==13?L13:(_i==14?L14:(_i==15?L15:(_i==16?L16:(_i==17?L17:(_i==18?L18:(_i==19?L19:
     (_i==20?L20:(_i==21?L21:(_i==22?L22:(_i==23?L23:(_i==24?L24:(_i==25?L25:(_i==26?L26:(_i==27?L27:(_i==28?L28:(_i==29?L29:
     (_i==30?L30:(_i==31?L31:(_i==32?L32:(_i==33?L33:(_i==34?L34:(_i==35?L35:(_i==36?L36:(_i==37?L37:(_i==38?L38:(_i==39?L39:
     (_i==40?L40:(_i==41?L41:(_i==42?L42:(_i==43?L43:(_i==44?L44:(_i==45?L45:(_i==46?L46:(_i==47?L47:(_i==48?L48:(_i==49?L49:na
     )))))))))) )))))))))) )))))))))) )))))))))) ))))))))))


//| -------------------------------------------------------------------------------|
var float[] seed = array.new_float(size=1, initial_value=42.0)                  //-|
f_prng(_range)=>                                                              //---|
//| pseudo random function:                                                 // O O |
    var float _return = 1.0                                                 //- V -|
    float _seed = array.get(id=seed, index=0)                                //NNNN|
    float _reseed =  271.828                                                  // A |
    _return := ((3.1416 * _return % _seed) * _reseed) % _range                // V |
    array.set(id=seed, index=0, value=(1 + (_seed * 1.618) % _range) + _return) //||
    _return                                                                    // ||
//| -------------------------------------------------------------------------------|


f_decision_tree(_node_weights, _depth)=>
    int[] _path = array.new_int(size=0, initial_value=0)
    int _nweights = array.size(id=_node_weights)
    int _sumweights = ceil(array.sum(id=_node_weights))
    if _nweights > 0 and _depth > 0 and _sumweights == 1
        for _d = 1 to _depth
            for _w = 0 to 999
                int _select_weight_index = int(f_prng(_nweights))
                float _rng = f_prng(1.0)
                float _weight = array.get(id=_node_weights, index=_select_weight_index)
                if _weight >= _rng
                    array.push(id=_path, value=_select_weight_index)
                    break
    _path

//|----------------------------------------------------------------------------||
//| find the price change distribution:
int data_size = input(500)

var float max_range = 0.0
var float min_range = 0.0

float range = change(close)
//float avg_range = cum(range) / (bar_index + 1)

if bar_index > 1
    max_range := max(range, max_range)
    min_range := min(range, min_range)
else
    max_range := range
    min_range := range

int rate = int((range - min_range) / (max_range - min_range) * 100)


// var float[] node_weights = array.new_float(size=0, initial_value=0)
// if barstate.isfirst
//     array.push(id=node_weights, value=0.25)
//     array.push(id=node_weights, value=0.4)
//     array.push(id=node_weights, value=0.35)

int depth = input(10)
int simulations = input(10)

var int[] paths = array.new_int(size=0, initial_value=0)


if barstate.ishistory[1] and barstate.islast and bar_index > data_size
    
    var float[] node_weights = array.new_float(size=0, initial_value=0.)

    var int[] distribution_counters = array.new_int(size=101, initial_value=1)

    for _i = 0 to data_size
        array.set(id=distribution_counters, index=rate[_i], value=array.get(id=distribution_counters, index=rate[_i]) + 1)

    int _total = array.sum(id=distribution_counters)
    
    for _i = 0 to array.size(id=distribution_counters)-1
        array.push(id=node_weights, value=array.get(id=distribution_counters, index=_i) / _total)
        
    //t := t + tostring(node_weights) + '\n'

    // run simulations:
    for _s = 0 to simulations-1
        _path = f_decision_tree(node_weights, depth)
        
        for _d = 0 to depth-1
            for _w = 0 to array.size(id=node_weights) - 1
                int _v = array.get(id=_path, index=_d)
                if _v == _w
                    array.push(id=paths, value=_w)

    // Find the best fit path:
    int[] _path_bestfit = array.new_int(size=0, initial_value=0)
    
    for _d = 0 to depth-1
        int[] _weight_counters = array.new_int(size=array.size(id=node_weights), initial_value=0)
        
        for _s = 0 to simulations-1
            int _i = array.get(id=paths, index=_d + _s * depth)
            int _new_count = array.get(id=_weight_counters, index=_i) + 1
            array.set(id=_weight_counters, index=_i, value=_new_count)
        
        int _best_fit_weight = array.indexof(id=_weight_counters, value=array.max(id=_weight_counters))
        array.push(id=_path_bestfit, value=_best_fit_weight)
        //t := t + tostring(_best_fit_weight) + ', '
    
    //draw the path
    int _time_shift = time-time[1]
    float _previous_price = close
    for _i = 0 to array.size(id=_path_bestfit)-1
        float _new_price = _previous_price + min_range + ((max_range - min_range) / 100) * array.get(id=_path_bestfit, index=_i)
        f_line_updater(f_line_id(_i), time + _time_shift * _i, _previous_price, time + _time_shift * (_i + 1), _new_price)
        //t := t + tostring(_new_price) + ' --- ' + tostring(_time_shift) + '\n'
        _previous_price := _new_price
        
// var label la = label.new(bar_index, 0.0, '')
// label.set_xy(id=la, x=bar_index, y=0.0)
// label.set_text(id=la, text=t)

Overview

A custom pseudo-random function updates a stored seed and returns values inside a chosen range. Inputs set the historical data size, the depth of each path, and how many simulations run. Line endpoints are updated in time and price, and each line is colored teal when the second point is higher than the first and orange otherwise. No trading signals, entries, exits, or risk rules appear in the visible code.

Fifty overlay chart lines
Custom pseudo-random number generator
Data size, depth and simulation inputs
Teal or orange line coloring by direction

Details

Published by

ricardosantos79 on GitHub

Code credit

RicardoSantos, as written in the script's header

Type

Indicator, Pine Script v4

Draws

On the price chart

Markets

Any symbol

Timeframes

Any timeframe

Risk Notes

No repainting, lookahead or stacked entries were found in its code or README. That says nothing about how well it works: test it first.

File

beta/arrays/test-035-montecarlo.pinescript

Repository

github.com/ricardosantos79/pinescript. See its other scripts in the library

Settings

The inputs you can change from the gear icon on the chart. Defaults are the author's.

Setting	Type	Default	Variable
data_size	`int`	`500`	`data_size`
depth	`int`	`10`	`depth`
simulations	`int`	`10`	`simulations`

Third-party code. ForexR did not write, test or endorse this script, and the summary above was generated automatically from its code and README. Indicators describe the past, and a strategy's backtest can look better than live trading, especially when a script repaints. Check it in the Strategy Tester and on a demo account before you rely on it.

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