{"id":9263,"date":"2026-08-06T09:24:58","date_gmt":"2026-08-06T09:24:58","guid":{"rendered":"https:\/\/quantstrategy.io\/blog\/information-driven-bars-moving-beyond-time-based-financial\/"},"modified":"2026-08-06T09:24:58","modified_gmt":"2026-08-06T09:24:58","slug":"information-driven-bars-moving-beyond-time-based-financial","status":"publish","type":"post","link":"https:\/\/quantstrategy.io\/blog\/information-driven-bars-moving-beyond-time-based-financial\/","title":{"rendered":"Information Driven Bars: Moving Beyond Time-Based Financial Sampling &#8211; Marcos L\u00f3pez de Prado"},"content":{"rendered":"<p><img decoding=\"async\" src=\"https:\/\/quantstrategy.io\/blog\/wp-content\/uploads\/2026\/08\/ticker_city_pexels_5.jpg\" alt=Information Driven Bars: Moving><br \/>\nIn his groundbreaking book <a href=\"https:\/\/quantstrategy.io\/blog\/advances-in-financial-machine-learning-a-comprehensive\">Advances in Financial Machine Learning: A Comprehensive Framework for Modern Quant Trading by Marcos L\u00f3pez de Prado<\/a>, the author identifies <strong>Information Driven Bars: Moving Beyond Time-Based Financial Sampling &#8211; Marcos L\u00f3pez de Prado<\/strong> as a fundamental paradigm shift for quantitative researchers. Traditional time-sampled bars (like 1-minute or 5-minute intervals) fail because markets do not process information at a constant speed. Instead, L\u00f3pez de Prado advocates for sampling data as a function of market activity. By using tick, volume, or dollar bars, traders can achieve better statistical properties, such as near-normality and reduced heteroscedasticity, which are essential for robust machine learning models.<\/p>\n<h2 id=\"the-fundamental-flaw-of-time-based-sampling\">The Fundamental Flaw of Time-Based Sampling<\/h2>\n<p>Standard OHLC (Open, High, Low, Close) bars are sampled at fixed time intervals. However, financial markets are characterized by &#8220;bursty&#8221; information arrival. During periods of high volatility, a 5-minute bar may contain thousands of trades, while during a lunch lull, it may contain none. Sampling by time forces machine learning models to struggle with non-synchronous data. Information-driven bars ensure that every observation processed by your model carries a similar amount of &#8220;weight&#8221; or economic significance, making it easier to apply <a href=\"https:\/\/quantstrategy.io\/blog\/fractionally-differentiated-features-balancing-stationarity\">Fractionally Differentiated Features<\/a> to maintain memory without sacrificing stationarity.<\/p>\n<h2 id=\"practical-types-of-information-bars\">Practical Types of Information Bars<\/h2>\n<p>L\u00f3pez de Prado categorizes these alternative sampling methods into two main groups: standard information bars and more advanced information imbalance bars.<\/p>\n<ul>\n<li><strong>Tick Bars:<\/strong> A new bar is created every <em>N<\/em> transactions. This captures the pace of market participants&#8217; interactions regardless of time.<\/li>\n<li><strong>Volume Bars:<\/strong> A bar is created once a specific number of units (shares, contracts) have been traded. This is often more representative than tick bars because it accounts for the size of each trade.<\/li>\n<li><strong>Dollar Bars:<\/strong> A bar is formed after a fixed dollar amount (or fiat value) has been exchanged. L\u00f3pez de Prado strongly recommends these because they are robust to price fluctuations and corporate actions like stock splits.<\/li>\n<\/ul>\n<h2 id=\"information-imbalance-and-run-bars\">Information Imbalance and Run Bars<\/h2>\n<p>Beyond simple volume or tick counts, <strong>Information Driven Bars: Moving Beyond Time-Based Financial Sampling &#8211; Marcos L\u00f3pez de Prado<\/strong> introduces more sophisticated structures:<\/p>\n<ol>\n<li><strong>Information Imbalance Bars:<\/strong> These bars sample data when the sequence of &#8220;informed&#8221; trades (buys vs. sells) deviates from the expected trend. It detects when one side of the market is exerting unusual pressure.<\/li>\n<li><strong>Information Run Bars:<\/strong> These monitor sequences of trades in the same direction, identifying &#8220;runs&#8221; that suggest institutional accumulation or distribution.<\/li>\n<\/ol>\n<p>Using these bars allows for more effective labeling via <a href=\"https:\/\/quantstrategy.io\/blog\/the-triple-barrier-method-revolutionizing-how-we-label\">The Triple Barrier Method<\/a>, as the barriers are hit based on information flow rather than arbitrary clock time.<\/p>\n<h2 id=\"actionable-insights-for-implementation\">Actionable Insights for Implementation<\/h2>\n<p>Implementing these bars requires high-quality tick data. To get started, practitioners should follow these steps:<\/p>\n<table>\n<thead>\n<tr>\n<th>Step<\/th>\n<th>Action<\/th>\n<th>Reasoning<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>1<\/td>\n<td>Calculate Average Daily Dollar Volume<\/td>\n<td>Determine the appropriate &#8220;threshold&#8221; for your Dollar Bars to ensure roughly 50-100 bars per day.<\/td>\n<\/tr>\n<tr>\n<td>2<\/td>\n<td>Test for Normality<\/td>\n<td>Compare the returns of time bars vs. dollar bars using a Jarque-Bera test. Information bars usually appear more Gaussian.<\/td>\n<\/tr>\n<tr>\n<td>3<\/td>\n<td>Apply ML Models<\/td>\n<td>Train your models on these bars and use <a href=\"https:\/\/quantstrategy.io\/blog\/meta-labeling-strategies-reducing-false-positives-in\">Meta-Labeling Strategies<\/a> to filter signals.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2 id=\"case-studies-and-examples\">Case Studies and Examples<\/h2>\n<h3 id=\"example-1-flash-crashes-and-volatility-events\">Example 1: Flash Crashes and Volatility Events<\/h3>\n<p>During the 2010 Flash Crash, time-based bars were essentially useless, as the price moved so fast that a 1-minute bar contained massive, unmanageable price swings. However, Dollar Bars would have accelerated, producing dozens of bars during the crash. This allows a model trained on information bars to &#8220;see&#8221; the microstructure collapse in real-time and potentially trigger <a href=\"https:\/\/quantstrategy.io\/blog\/optimal-bet-sizing-integrating-ml-predictions-with-risk\">Optimal Bet Sizing<\/a> adjustments to reduce exposure before the bottom is reached.<\/p>\n<h3 id=\"example-2-low-liquidity-overnight-sessions\">Example 2: Low-Liquidity Overnight Sessions<\/h3>\n<p>In futures markets like the E-mini S&#038;P 500, the overnight session has very low volume compared to the RTH (Regular Trading Hours). Time bars create many &#8220;flat&#8221; bars with zero volatility, which confuses ML algorithms. Dollar bars effectively compress the overnight session into just a few bars, while expanding the RTH into many bars. This ensures the model spends its &#8220;learning capacity&#8221; on periods where actual trading activity is happening, rather than noise.<\/p>\n<h2 id=\"integrating-with-the-broader-ml-pipeline\">Integrating with the Broader ML Pipeline<\/h2>\n<p>Information-driven bars are just the first step. Once you have sampled your data correctly, you can apply <a href=\"https:\/\/quantstrategy.io\/blog\/structural-breaks-and-regime-detection-in-financial-machine\">Structural Breaks and Regime Detection<\/a> more accurately because the &#8220;time&#8221; in your model now moves in sync with the market. Furthermore, when evaluating your strategy, you must use <a href=\"https:\/\/quantstrategy.io\/blog\/purged-k-fold-cross-validation-the-gold-standard-for\">Purged K-Fold Cross-Validation<\/a> to ensure that information leakage doesn&#8217;t occur across these custom-sampled intervals.<\/p>\n<h2 id=\"conclusion\">Conclusion<\/h2>\n<p>Mastering <strong>Information Driven Bars: Moving Beyond Time-Based Financial Sampling &#8211; Marcos L\u00f3pez de Prado<\/strong> is a prerequisite for any modern quant trader. By abandoning the arbitrary constraints of the clock, you align your data processing with the way markets actually function. This leads to more stable statistical features, better model performance, and a deeper understanding of market microstructure. These concepts form the bedrock of the strategies found in <a href=\"https:\/\/quantstrategy.io\/blog\/advances-in-financial-machine-learning-a-comprehensive\">Advances in Financial Machine Learning: A Comprehensive Framework for Modern Quant Trading by Marcos L\u00f3pez de Prado<\/a>, providing the necessary foundation for advanced techniques like <a href=\"https:\/\/quantstrategy.io\/blog\/ensemble-methods-in-finance-bagging-and-boosting-for-robust\">Ensemble Methods<\/a> and <a href=\"https:\/\/quantstrategy.io\/blog\/clustered-feature-importance-solving-multicollinearity-in\">Clustered Feature Importance<\/a>.<\/p>\n<h2 id=\"faq-information-driven-bars\">FAQ: Information Driven Bars<\/h2>\n<ul>\n<li><strong>Why are Dollar Bars preferred over Volume Bars?<\/strong> Dollar bars are more robust to price changes. If a stock doubles in price, the same amount of capital would trade half the number of shares; Volume bars would distort this, whereas Dollar bars remain consistent.<\/li>\n<li><strong>How do Information Bars help with backtest overfitting?<\/strong> By producing more stationary and Gaussian returns, they reduce the likelihood of a model picking up on noise. To further protect your strategy, you should also calculate <a href=\"https:\/\/quantstrategy.io\/blog\/the-probability-of-backtest-overfitting-lessons-from-marcos\">The Probability of Backtest Overfitting<\/a>.<\/li>\n<li><strong>What is the main challenge in implementing these bars?<\/strong> The primary challenge is the data requirement; you need high-resolution tick data to construct accurate Information Driven Bars, which can be expensive and computationally intensive to store.<\/li>\n<li><strong>Can I use these bars for intraday trading?<\/strong> Yes, they are specifically designed for intraday trading to better capture the varying speeds of the market throughout the trading session.<\/li>\n<li><strong>How do Imbalance Bars detect informed trading?<\/strong> They monitor the cumulative signed flow of trades. If the &#8220;imbalance&#8221; of buys over sells exceeds a dynamically calculated threshold, it suggests informed participants are active, triggering a new bar.<\/li>\n<li><strong>Do these bars work with Meta-Labeling?<\/strong> Absolutely. Using Information Driven Bars provides a cleaner input for the primary model, while <a href=\"https:\/\/quantstrategy.io\/blog\/meta-labeling-strategies-reducing-false-positives-in\">Meta-Labeling<\/a> helps filter the resulting signals for higher precision.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"In his groundbreaking book Advances in Financial Machine Learning: A Comprehensive Framework for Modern Quant Trading by Marcos&hellip;\n","protected":false},"author":1,"featured_media":9262,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[69,17,11],"tags":[],"class_list":{"0":"post-9263","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-book-bites","8":"category-ml_ai_models","9":"category-technical_indicators"},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.9.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Information Driven Bars: Moving Beyond Time-Based Financial Sampling - Marcos L\u00f3pez de Prado - Learn Quant Trading | QuantStrategy.io<\/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:\/\/quantstrategy.io\/blog\/information-driven-bars-moving-beyond-time-based-financial\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Information Driven Bars: Moving Beyond Time-Based Financial Sampling - Marcos L\u00f3pez de Prado - Learn Quant Trading | QuantStrategy.io\" \/>\n<meta property=\"og:description\" content=\"In his groundbreaking book Advances in Financial Machine Learning: A Comprehensive Framework for Modern Quant Trading by Marcos&hellip;\" \/>\n<meta property=\"og:url\" content=\"https:\/\/quantstrategy.io\/blog\/information-driven-bars-moving-beyond-time-based-financial\/\" \/>\n<meta property=\"og:site_name\" content=\"Learn Quant Trading | QuantStrategy.io\" \/>\n<meta property=\"article:published_time\" content=\"2026-08-06T09:24:58+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/quantstrategy.io\/blog\/wp-content\/uploads\/2026\/08\/ticker_city_pexels_5.jpg\" \/>\n<meta name=\"author\" content=\"QuantStrategy.io Team\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"QuantStrategy.io Team\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"5 minutes\" \/>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Information Driven Bars: Moving Beyond Time-Based Financial Sampling - Marcos L\u00f3pez de Prado - Learn Quant Trading | QuantStrategy.io","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/quantstrategy.io\/blog\/information-driven-bars-moving-beyond-time-based-financial\/","og_locale":"en_US","og_type":"article","og_title":"Information Driven Bars: Moving Beyond Time-Based Financial Sampling - Marcos L\u00f3pez de Prado - Learn Quant Trading | QuantStrategy.io","og_description":"In his groundbreaking book Advances in Financial Machine Learning: A Comprehensive Framework for Modern Quant Trading by Marcos&hellip;","og_url":"https:\/\/quantstrategy.io\/blog\/information-driven-bars-moving-beyond-time-based-financial\/","og_site_name":"Learn Quant Trading | QuantStrategy.io","article_published_time":"2026-08-06T09:24:58+00:00","og_image":[{"url":"https:\/\/quantstrategy.io\/blog\/wp-content\/uploads\/2026\/08\/ticker_city_pexels_5.jpg"}],"author":"QuantStrategy.io Team","twitter_card":"summary_large_image","twitter_misc":{"Written by":"QuantStrategy.io Team","Est. reading time":"5 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/quantstrategy.io\/blog\/information-driven-bars-moving-beyond-time-based-financial\/#article","isPartOf":{"@id":"https:\/\/quantstrategy.io\/blog\/information-driven-bars-moving-beyond-time-based-financial\/"},"author":{"name":"QuantStrategy.io Team","@id":"https:\/\/quantstrategy.io\/blog\/#\/schema\/person\/63aef420d635f0dc50f9ba974f6c95d1"},"headline":"Information Driven Bars: Moving Beyond Time-Based Financial Sampling &#8211; Marcos L\u00f3pez de Prado","datePublished":"2026-08-06T09:24:58+00:00","dateModified":"2026-08-06T09:24:58+00:00","mainEntityOfPage":{"@id":"https:\/\/quantstrategy.io\/blog\/information-driven-bars-moving-beyond-time-based-financial\/"},"wordCount":1065,"publisher":{"@id":"https:\/\/quantstrategy.io\/blog\/#organization"},"articleSection":["Book Bites","ML And AI Models","Technical Indicators"],"inLanguage":"en-US"},{"@type":"WebPage","@id":"https:\/\/quantstrategy.io\/blog\/information-driven-bars-moving-beyond-time-based-financial\/","url":"https:\/\/quantstrategy.io\/blog\/information-driven-bars-moving-beyond-time-based-financial\/","name":"Information Driven Bars: Moving Beyond Time-Based Financial Sampling - Marcos L\u00f3pez de Prado - Learn Quant Trading | QuantStrategy.io","isPartOf":{"@id":"https:\/\/quantstrategy.io\/blog\/#website"},"datePublished":"2026-08-06T09:24:58+00:00","dateModified":"2026-08-06T09:24:58+00:00","breadcrumb":{"@id":"https:\/\/quantstrategy.io\/blog\/information-driven-bars-moving-beyond-time-based-financial\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/quantstrategy.io\/blog\/information-driven-bars-moving-beyond-time-based-financial\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/quantstrategy.io\/blog\/information-driven-bars-moving-beyond-time-based-financial\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/quantstrategy.io\/blog\/"},{"@type":"ListItem","position":2,"name":"Information Driven Bars: Moving Beyond Time-Based Financial Sampling &#8211; Marcos L\u00f3pez de Prado"}]},{"@type":"WebSite","@id":"https:\/\/quantstrategy.io\/blog\/#website","url":"https:\/\/quantstrategy.io\/blog\/","name":"QuantStrategy.io - blog","description":"Blog","publisher":{"@id":"https:\/\/quantstrategy.io\/blog\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/quantstrategy.io\/blog\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/quantstrategy.io\/blog\/#organization","name":"QuantStrategy.io","url":"https:\/\/quantstrategy.io\/blog\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/quantstrategy.io\/blog\/#\/schema\/logo\/image\/","url":"https:\/\/quantstrategy.io\/blog\/wp-content\/uploads\/2023\/11\/qs_io_logo-80.png","contentUrl":"https:\/\/quantstrategy.io\/blog\/wp-content\/uploads\/2023\/11\/qs_io_logo-80.png","width":80,"height":80,"caption":"QuantStrategy.io"},"image":{"@id":"https:\/\/quantstrategy.io\/blog\/#\/schema\/logo\/image\/"}},{"@type":"Person","@id":"https:\/\/quantstrategy.io\/blog\/#\/schema\/person\/63aef420d635f0dc50f9ba974f6c95d1","name":"QuantStrategy.io Team","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/quantstrategy.io\/blog\/#\/schema\/person\/image\/","url":"https:\/\/secure.gravatar.com\/avatar\/23922b0b6b220e6e9aca4c738eace72e744af8c32a4b3ee7ca8d7bbb8fc8d5b2?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/23922b0b6b220e6e9aca4c738eace72e744af8c32a4b3ee7ca8d7bbb8fc8d5b2?s=96&d=mm&r=g","caption":"QuantStrategy.io Team"},"sameAs":["https:\/\/quantstrategy.io\/blog"],"url":"https:\/\/quantstrategy.io\/blog\/author\/razmik_davtyan\/"}]}},"_links":{"self":[{"href":"https:\/\/quantstrategy.io\/blog\/wp-json\/wp\/v2\/posts\/9263","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/quantstrategy.io\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/quantstrategy.io\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/quantstrategy.io\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/quantstrategy.io\/blog\/wp-json\/wp\/v2\/comments?post=9263"}],"version-history":[{"count":0,"href":"https:\/\/quantstrategy.io\/blog\/wp-json\/wp\/v2\/posts\/9263\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/quantstrategy.io\/blog\/wp-json\/wp\/v2\/media\/9262"}],"wp:attachment":[{"href":"https:\/\/quantstrategy.io\/blog\/wp-json\/wp\/v2\/media?parent=9263"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/quantstrategy.io\/blog\/wp-json\/wp\/v2\/categories?post=9263"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/quantstrategy.io\/blog\/wp-json\/wp\/v2\/tags?post=9263"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}