{"id":9263,"date":"2025-09-07T16:04:55","date_gmt":"2025-09-07T14:04:55","guid":{"rendered":"https:\/\/www.trading-bot.fr\/?page_id=9263"},"modified":"2025-09-07T16:12:21","modified_gmt":"2025-09-07T14:12:21","slug":"simulation-de-monte-carlo-verifier-la-rentabilite-de-vos-reglages-en-trading","status":"publish","type":"page","link":"https:\/\/www.trading-bot.fr\/en\/simulation-de-monte-carlo-verifier-la-rentabilite-de-vos-reglages-en-trading\/","title":{"rendered":"Monte Carlo simulation: Check the profitability of your trading settings"},"content":{"rendered":"<style>\n    \/* Le :root est conserv\u00e9 pour la d\u00e9finition des variables de couleur *\/\n    :root {\n      --bg: #181a20;\n      --panel: #111827;\n      --soft: #1f2937;\n      --muted: #9ca3af;\n      --text: #e5e7eb;\n      --accent: #60a5fa;   \/* bleu clair *\/\n      --accent-2: #34d399; \/* vert *\/\n      --accent-3: #f59e0b; \/* orange *\/\n      --sample: rgba(180,180,180,0.2); \/* gris transparent pour \u00e9chantillons *\/\n      --equity: #ffffff;   \/* blanc pour \u00e9quit\u00e9 *\/\n    }\n    \n    \/* Conteneur principal pour l'int\u00e9gration WordPress *\/\n    .monte-carlo-wrapper {\n        \/* MODIFI\u00c9 : Ancienne couleur des blocs *\/\n        background: #111827; \n        font-family: ui-sans-serif, -apple-system, Segoe UI, Roboto, Helvetica, Arial;\n        color: var(--text);\n        padding: 24px 0;\n    }\n\n    * { box-sizing: border-box; }\n\n    .monte-carlo-wrapper .container { max-width: 1200px; margin: 0 auto; padding: 0 16px; }\n    .monte-carlo-wrapper .grid { display: grid; grid-template-columns: 1fr; gap: 16px; }\n    \/* MODIFI\u00c9 : Ancienne couleur de fond g\u00e9n\u00e9rale *\/\n    .monte-carlo-wrapper .card { background: #181a20; border: 1px solid rgba(148,163,184,.15); border-radius: 16px; box-shadow: 0 10px 30px rgba(0,0,0,.35); }\n    .monte-carlo-wrapper .card-header { padding: 16px 18px; border-bottom: 1px solid rgba(148,163,184,.12); font-weight: 700; }\n    .monte-carlo-wrapper .card-content { padding: 16px 18px; }\n    .monte-carlo-wrapper .row { display: grid; grid-template-columns: 1fr 110px; gap: 10px; align-items: center; margin-bottom: 12px; }\n    .monte-carlo-wrapper .row > label { font-size: 13px; color: var(--muted); }\n    .monte-carlo-wrapper .row > input { width: 100%; padding: 10px 12px; border-radius: 10px; border: 1px solid rgba(148,163,184,.25); background: var(--soft); color: var(--text); outline: none; }\n    .monte-carlo-wrapper .btns { display: flex; gap: 10px; margin-top: 10px; }\n    .monte-carlo-wrapper .btn { border: none; padding: 10px 14px; border-radius: 12px; cursor: pointer; font-weight: 700; }\n    .monte-carlo-wrapper .btn-primary { background: linear-gradient(180deg, #2563eb, #1d4ed8); color: white; }\n    .monte-carlo-wrapper .kpi { display: grid; grid-template-columns: repeat(2, 1fr); gap: 10px; }\n    .monte-carlo-wrapper .kpi .tile { background: rgba(255,255,255,.03); border: 1px solid rgba(148,163,184,.15); border-radius: 12px; padding: 12px; }\n    .monte-carlo-wrapper .kpi .tile .label { font-size: 12px; color: var(--muted); }\n    .monte-carlo-wrapper .kpi .tile .value { font-size: 18px; font-weight: 800; margin-top: 6px; }\n    .monte-carlo-wrapper canvas { width: 100%; height: 420px; display: block; background: #0b1220; border-radius: 12px; border: 1px solid rgba(148,163,184,.15); }\n    .monte-carlo-wrapper .legend { display: flex; gap: 14px; font-size: 12px; color: var(--muted); margin-top: 10px; flex-wrap: wrap; }\n    .monte-carlo-wrapper .legend .dot { width: 10px; height: 10px; display: inline-block; border-radius: 999px; margin-right: 6px; }\n    \n    \/* Style sobre pour la section d'introduction *\/\n    .monte-carlo-wrapper .intro-card h4 {\n      font-size: 16px;\n      color: var(--text);\n      margin-top: 1em;\n      margin-bottom: 0.5em;\n    }\n    .monte-carlo-wrapper .intro-card h4:first-child {\n      margin-top: 0;\n    }\n    .monte-carlo-wrapper .intro-card p, .intro-card li {\n      font-size: 14px;\n      line-height: 1.6;\n      color: var(--muted);\n    }\n    .monte-carlo-wrapper .intro-card ul {\n      padding-left: 20px;\n      margin-top: 0.5em;\n    }\n    .monte-carlo-wrapper .intro-card strong {\n      color: var(--text);\n    }\n<\/style>\n\n<div class=\"monte-carlo-wrapper\">\n  <div class=\"container\">\n    <div class=\"grid\">\n\n      <!-- Section de pr\u00e9sentation ajout\u00e9e -->\n      <div class=\"card intro-card\">\n        <div class=\"card-header\">Monte Carlo simulation: Check the statistical expectation of your settings<\/div>\n        <div class=\"card-content\">\n          <h4>What is it?<\/h4>\n          <p>\n            Monte Carlo simulation is a statistical method that evaluates the range of possible outcomes of a trading strategy. Rather than relying on a single past performance, it generates thousands of potential \"futures\" based on your parameters (success rate, risk, etc.). This provides a probabilistic view of the profitability and robustness of your approach. If the blue line (statistical bad luck) remains below the dotted line over the long term, your profitability is not assured.\n          <\/p>\n          <h4>How do I use this tool?<\/h4>\n          <p>\n            Fill in the fields in the \"Settings\" panel to test your strategy:\n          <\/p>\n          <ul>\n            <li><strong>Initial capital :<\/strong> Your starting capital.<\/li>\n            <li><strong>Risk per trade (%) :<\/strong> The percentage of your current capital that you risk on each position.<\/li>\n            <li><strong>Success rate (%) :<\/strong> The percentage of winning trades in your history.<\/li>\n            <li><strong>Average gain \/ Risk factor (R) :<\/strong> Your average profit in multiples of your risk. If on TradingView the profit factor is 2.5, enter 2 here and 1 below.<\/li>\n            <li><strong>Average loss (R) :<\/strong> Your average loss in multiples of your risk. Leave this value at 1 and adjust the average gain.<\/li>\n            <li><strong>Trades :<\/strong> The number of trades to simulate for each scenario.<\/li>\n            <li><strong>Simulations :<\/strong> The total number of scenarios to be generated. A higher number increases the reliability of the statistics.<\/li>\n            <li><strong>Slippage (% per trade) :<\/strong> Estimated transaction costs (fees, price slippage) for each order.<\/li>\n          <\/ul>\n          <p>\n            Click on \"Run\" to view the results. The graph will display the median scenario (P50), as well as the best (P100) and worst (P0) scenarios from the thousands of simulations.\n          <\/p>\n        <\/div>\n      <\/div>\n\n      <!-- Param\u00e8tres -->\n      <div class=\"card\">\n        <div class=\"card-header\">Parameters<\/div>\n        <div class=\"card-content\">\n          <div class=\"row\"><label>Initial capital (\u20ac)<\/label><input id=\"capital\" type=\"number\" value=\"10000\"\/><\/div>\n          <div class=\"row\"><label>Risk per trade (%)<\/label><input id=\"risk\" type=\"number\" value=\"1\"\/><\/div>\n          <div class=\"row\"><label>Success rate (%)<\/label><input id=\"winrate\" type=\"number\" value=\"50\"\/><\/div>\n          <div class=\"row\"><label>Average profit (Profit factor on TradingView) (R)<\/label><input id=\"rwin\" type=\"number\" value=\"2\"\/><\/div>\n          <div class=\"row\"><label>Average loss (R) - Leave on 1<\/label><input id=\"rloss\" type=\"number\" value=\"1\"\/><\/div>\n          <div class=\"row\"><label>Number of Trades to simulate<\/label><input id=\"trades\" type=\"number\" value=\"250\"\/><\/div>\n          <div class=\"row\"><label>Number of possible simulations<\/label><input id=\"sims\" type=\"number\" value=\"2000\"\/><\/div>\n          <div class=\"row\"><label>Slippage + Fees (% per trade) - Set as \"punitive\" by default<\/label><input id=\"slip\" type=\"number\" value=\"0.25\" step=\"0.01\"\/><\/div>\n          <div class=\"btns\">\n            <button class=\"btn btn-primary\" id=\"run\">Launch<\/button>\n          <\/div>\n        <\/div>\n      <\/div>\n\n      <!-- R\u00e9sultats -->\n      <div class=\"card\">\n        <div class=\"card-header\">Results<\/div>\n        <div class=\"card-content\">\n          <div class=\"kpi\">\n            <div class=\"tile\"><div class=\"label\">Median capital<\/div><div class=\"value\" id=\"k-median\">-<\/div><\/div>\n            <div class=\"tile\"><div class=\"label\">Average<\/div><div class=\"value\" id=\"k-mean\">-<\/div><\/div>\n          <\/div>\n          <!-- MODIFICATION : Mise \u00e0 jour de la l\u00e9gende -->\n          <div class=\"legend\">\n            <span><span class=\"dot\" style=\"background: var(--accent)\"><\/span>Worst-case scenario (P0)<\/span>\n            <span><span class=\"dot\" style=\"background: var(--equity)\"><\/span>Median (P50)<\/span>\n            <span><span class=\"dot\" style=\"background: var(--accent-2)\"><\/span>Best scenario (P100)<\/span>\n            <span><span class=\"dot\" style=\"border: 1px solid var(--equity); background: none;\"><\/span>Initial capital<\/span>\n          <\/div>\n          <canvas id=\"chart\" width=\"1000\" height=\"420\"><\/canvas>\n        <\/div>\n      <\/div>\n    <\/div>\n  <\/div>\n<\/div>\n\n<script>\n(function(){\n  \/\/ Utilise un s\u00e9lecteur plus sp\u00e9cifique pour ne pas interf\u00e9rer avec d'autres \u00e9l\u00e9ments de la page WordPress\n  const $ = id => document.querySelector('.monte-carlo-wrapper #' + id);\n\n  function simulate(capital, riskPct, winRate, rWin, rLoss, trades, slipPct){\n    const eq = [capital];\n    for(let t=1;t<=trades;t++){\n      const cur=eq[t-1], risk=cur*riskPct;\n      const win=Math.random()<winRate;\n      const gross = win ? rWin*risk : -rLoss*risk;\n      const slip = cur*slipPct;\n      eq[t]=Math.max(0, cur+gross-slip);\n    }\n    return eq;\n  }\n\n  function percentile(arr,p){\n    arr=[...arr].sort((a,b)=>a-b);\n    const i=(arr.length-1)*p,lo=Math.floor(i),hi=Math.ceil(i);\n    return lo===hi?arr[lo]:arr[lo]+(arr[hi]-arr[lo])*(i-lo);\n  }\n\n  let zoom=1, offsetX=0, offsetY=0, dragging=false, lastX=0, lastY=0;\n\n  function drawChart(p0,p50,p100,samples,capital){\n    const c=$(\"chart\"),ctx=c.getContext(\"2d\");\n    ctx.clearRect(0,0,c.width,c.height);\n    const P={l:50,r:20,t:20,b:30},W=c.width-P.l-P.r,H=c.height-P.t-P.b;\n\n    const all=[...p0,...p100,...samples.flat(),capital];\n    let min=Math.min(...all),max=Math.max(...all);\n    const pad=(max-min)*0.1;min-=pad;max+=pad;\n\n    function xScale(i){return P.l+(i\/samples[0].length)*(W*zoom)+offsetX;}\n    function yScale(v){return P.t+H-( (v-min)\/(max-min) )*H*zoom+offsetY;}\n\n    ctx.strokeStyle=\"rgba(200,200,200,.3)\";\n    ctx.beginPath();ctx.moveTo(P.l,P.t);ctx.lineTo(P.l,P.t+H);ctx.lineTo(P.l+W,P.t+H);ctx.stroke();\n\n    function drawLine(arr,col,width=2){\n      ctx.strokeStyle=col;ctx.lineWidth=width;\n      ctx.beginPath();\n      arr.forEach((v,i)=>{const x=xScale(i),y=yScale(v);i?ctx.lineTo(x,y):ctx.moveTo(x,y);});\n      ctx.stroke();\n    }\n\n    ctx.lineWidth=1;\n    ctx.strokeStyle=\"rgba(180, 180, 180, 0.2)\";\n    samples.forEach(path=>{\n      ctx.beginPath();\n      path.forEach((v,i)=>{const x=xScale(i),y=yScale(v);i?ctx.lineTo(x,y):ctx.moveTo(x,y);});\n      ctx.stroke();\n    });\n\n    drawLine(p0,\"#60a5fa\",2.2);\n    drawLine(p50,\"#ffffff\",3);\n    drawLine(p100,\"#34d399\",2.2);\n\n    ctx.strokeStyle=\"rgba(255,255,255,0.5)\";ctx.lineWidth=2;\n    ctx.setLineDash([5, 5]);\n    ctx.beginPath();\n    ctx.moveTo(xScale(0),yScale(capital));\n    ctx.lineTo(xScale(samples[0].length-1),yScale(capital));\n    ctx.stroke();\n    ctx.setLineDash([]);\n  }\n\n  function compute(){\n    const cap=+$(\"capital\").value,\n          risk=+$(\"risk\").value\/100,\n          wr=+$(\"winrate\").value\/100,\n          rw=+$(\"rwin\").value,\n          rl=+$(\"rloss\").value,\n          tr=+$(\"trades\").value,\n          sims=+$(\"sims\").value,\n          slip=+$(\"slip\").value\/100;\n\n    const paths=[], finals=[];\n    for(let i=0;i<sims;i++){const e=simulate(cap,risk,wr,rw,rl,tr,slip);paths.push(e);finals.push(e[e.length-1]);}\n    const byStep=Array.from({length:tr+1},(_,t)=>paths.map(p=>p[t]).sort((a,b)=>a-b));\n    \n    const p0=byStep.map(s=>percentile(s,0)),\n          p50=byStep.map(s=>percentile(s,.5)),\n          p100=byStep.map(s=>percentile(s,1));\n\n    const med=percentile(finals,.5),\n          mean=finals.reduce((a,b)=>a+b,0)\/finals.length;\n    $(\"k-median\").textContent=\"\u20ac\"+med.toFixed(0);\n    $(\"k-mean\").textContent=\"\u20ac\"+mean.toFixed(0);\n\n    const samples=paths.slice(0,30);\n    drawChart(p0,p50,p100,samples,cap);\n  }\n  \n  \/\/ S'assure que le bouton est bien trouv\u00e9 avant d'ajouter l'\u00e9couteur d'\u00e9v\u00e9nement\n  const runButton = $(\"run\");\n  if (runButton) {\n      runButton.addEventListener(\"click\",()=>{zoom=1;offsetX=offsetY=0;compute();});\n  }\n\n  const canvas=$(\"chart\");\n  if (canvas) {\n      canvas.addEventListener(\"wheel\",e=>{e.preventDefault();zoom*=e.deltaY<0?1.1:0.9;compute();});\n      canvas.addEventListener(\"mousedown\",e=>{dragging=true;lastX=e.clientX;lastY=e.clientY;});\n      window.addEventListener(\"mouseup\",()=>dragging=false);\n      window.addEventListener(\"mousemove\",e=>{\n        if(dragging){offsetX+=e.clientX-lastX;offsetY+=e.clientY-lastY;lastX=e.clientX;lastY=e.clientY;compute();}\n      });\n  }\n\n  \/\/ Lance un premier calcul au chargement\n  compute();\n})();\n<\/script>","protected":false},"excerpt":{"rendered":"<p>Monte Carlo simulation: Check the statistical expectation of your settings What is it? Monte Carlo simulation is a statistical method that evaluates the range...<\/p>","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"elementor_header_footer","meta":{"pmpro_default_level":"","footnotes":""},"class_list":["post-9263","page","type-page","status-publish","hentry","pmpro-has-access"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Simulation de Monte-Carlo : V\u00e9rifier la rentabilit\u00e9 de vos r\u00e9glages en Trading -Trading Bot -<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.trading-bot.fr\/en\/simulation-de-monte-carlo-verifier-la-rentabilite-de-vos-reglages-en-trading\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Simulation de Monte-Carlo : V\u00e9rifier la rentabilit\u00e9 de vos r\u00e9glages en Trading -Trading Bot -\" \/>\n<meta property=\"og:description\" content=\"Simulation de Monte-Carlo : V\u00e9rifiez l&rsquo;esp\u00e9rance statistique de vos r\u00e9glages Qu&rsquo;est-ce que c&rsquo;est ? 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