{"version":"1.0","provider_name":"StrategyQuant","provider_url":"https:\/\/strategyquant.com\/ja","title":"Monte Carlo - Simulate Parameter Jitter - StrategyQuant","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"J8IukwDHxE\"><a href=\"https:\/\/strategyquant.com\/ja\/codebase\/monte-carlo-simulate-parameter-jitter\/\">\u30e2\u30f3\u30c6\u30ab\u30eb\u30ed \u2013 \u30d1\u30e9\u30e1\u30fc\u30bf\u30b8\u30c3\u30bf\u30fc\u306e\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/strategyquant.com\/ja\/codebase\/monte-carlo-simulate-parameter-jitter\/embed\/#?secret=J8IukwDHxE\" width=\"600\" height=\"338\" title=\"&#8220;Monte Carlo &#8211; Simulate Parameter Jitter&#8221; &#8212; StrategyQuant\" data-secret=\"J8IukwDHxE\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script>\n\/*! This file is auto-generated *\/\n!function(d,l){\"use strict\";l.querySelector&&d.addEventListener&&\"undefined\"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!\/[^a-zA-Z0-9]\/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),o=l.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),c=new RegExp(\"^https?:$\",\"i\"),i=0;i<o.length;i++)o[i].style.display=\"none\";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute(\"style\"),\"height\"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):\"link\"===t.message&&(r=new URL(s.getAttribute(\"src\")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener(\"message\",d.wp.receiveEmbedMessage,!1),l.addEventListener(\"DOMContentLoaded\",function(){for(var e,t,s=l.querySelectorAll(\"iframe.wp-embedded-content\"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute(\"data-secret\"))||(t=Math.random().toString(36).substring(2,12),e.src+=\"#?secret=\"+t,e.setAttribute(\"data-secret\",t)),e.contentWindow.postMessage({message:\"ready\",secret:t},\"*\")},!1)))}(window,document);\n\/\/# sourceURL=https:\/\/strategyquant.com\/wp-includes\/js\/wp-embed.min.js\n<\/script>","description":"In the real world of trading, market conditions are constantly evolving. Volatility shifts, liquidity fluctuates, and the data feed itself might have minute variations from tick to tick. Consequently, even a well-optimized strategy might not perform exactly as predicted by a backtest, as its core parameters or indicator calculations could experience slight \"jitter\" or instability when faced with live conditions. This Monte Carlo simulation is designed to test how resilient your strategy is to such minor, unpredictable deviations from its perfect backtest behavior."}