{"id":219355,"date":"2025-03-04T11:59:09","date_gmt":"2025-03-04T16:59:09","guid":{"rendered":"https:\/\/ibkrcampus.com\/campus\/?p=219355"},"modified":"2025-04-17T16:50:22","modified_gmt":"2025-04-17T20:50:22","slug":"confessions-of-a-recovering-engineer-or-why-engineers-make-bad-traders","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/confessions-of-a-recovering-engineer-or-why-engineers-make-bad-traders\/","title":{"rendered":"Confessions of a Recovering Engineer (or Why Engineers Make Bad Traders)"},"content":{"rendered":"\n<p><em>The article &#8220;Confessions of a Recovering Engineer (or Why Engineers Make Bad Traders)&#8221; was originally posted on <a href=\"https:\/\/robotwealth.com\/confessions-of-a-recovering-engineer-or-why-engineers-make-bad-traders\/#more-109214\">Robot Wealth<\/a> blog.<\/em><\/p>\n\n\n\n<p>Here\u2019s something that might shock you:<\/p>\n\n\n\n<p><em>Engineers make the&nbsp;<strong>worst<\/strong>&nbsp;traders.<\/em><\/p>\n\n\n\n<p>I\u2019m speaking from experience here.<\/p>\n\n\n\n<p>As an engineer who transitioned to trading, it wasn\u2019t until I stopped thinking like an engineer that I started to make progress.<\/p>\n\n\n\n<p>You can read all about this in my&nbsp;<a href=\"https:\/\/robotwealth.com\/case-study\/\" target=\"_blank\" rel=\"noreferrer noopener\">case study<\/a>, by the way.<\/p>\n\n\n\n<p>Engineers like me are trained to solve implementation problems. We build bridges based on well-understood physics.<\/p>\n\n\n\n<p>The challenge isn\u2019t understanding gravity; it\u2019s designing an efficient structure given a specific set of constraints.<\/p>\n\n\n\n<p><em>Engineering solves implementation problems.<\/em><\/p>\n\n\n\n<p>And when I first approached trading, I did what engineers do: I assumed I was looking at an implementation problem, and I jumped straight into solving it.<\/p>\n\n\n\n<p>I built elaborate backtesting frameworks, optimized parameters, and created complicated machine learning systems before I even understood what edge I was trying to capture.<\/p>\n\n\n\n<p><em>This approach is completely backwards for trading.<\/em><\/p>\n\n\n\n<p>Here\u2019s the key point:<\/p>\n\n\n\n<p><strong><em>In trading, you\u2019re not starting with a well-understood phenomenon like you do in engineering.<\/em><\/strong><\/p>\n\n\n\n<p>You\u2019re starting with an observation of some market effect. Something you\u2019ve noticed yourself, read about somewhere, or heard from other traders.<\/p>\n\n\n\n<p>But you don\u2019t know anything about this phenomenon!<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>What causes it?<\/li>\n\n\n\n<li>How consistent has it been?<\/li>\n\n\n\n<li>How strong is it?<\/li>\n\n\n\n<li>Is it very noisy?<\/li>\n\n\n\n<li>Is it slow to converge?<\/li>\n\n\n\n<li>Under what circumstances might it disappear?<\/li>\n<\/ul>\n\n\n\n<p>How can you engineer a solution for a problem you don\u2019t understand?<\/p>\n\n\n\n<p>You can\u2019t. At least, not very effectively.<\/p>\n\n\n\n<p>Instead, you should design little data analysis experiments that gradually reveal clues about the thing you observed.<\/p>\n\n\n\n<p>In this respect, trading is more like a science project.<\/p>\n\n\n\n<p>There\u2019s a little art in there as well, just as there is in any good science \u2013 creative thinking can help you design good experiments and ask the right questions.<\/p>\n\n\n\n<p>But the only engineering is in the implementation of a trading strategy, and that\u2019s the last piece of the puzzle. You wouldn\u2019t even&nbsp;<strong><em>think&nbsp;<\/em><\/strong>about this until you understood the basis of your edge.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><em>You wouldn\u2019t build a bridge without a deep understanding of structural mechanics.<\/em><\/li>\n\n\n\n<li><em>And you shouldn\u2019t build a trading strategy without a deep understanding of your edge.<\/em><\/li>\n<\/ul>\n\n\n\n<p>Good trading research looks nothing like what most engineers initially attempt:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Think about potential causal effects \u2013 don\u2019t assume this is settled and well-understood.<\/li>\n\n\n\n<li>Start with simple but maximally data-efficient analysis (think scatter plots and factor plots) \u2013 not complicated simulations of trading rules.<\/li>\n\n\n\n<li>Make small, testable hypotheses. Design experiments that gradually reveal the nature of your observed phenomenon. Ask \u201cWhat would I see in the data if this were true?\u201d \u2013 not \u201cWhat trading rules should I design?\u201d<\/li>\n\n\n\n<li>Try to disprove your ideas quickly. Be paranoid you\u2019re wrong \u2013 not confident you\u2019re right.<\/li>\n\n\n\n<li>Focus on understanding the phenomenon \u2013 not the machine learning framework you\u2019ll use to trade it.<\/li>\n<\/ol>\n\n\n\n<p>Another trap we engineers fall into is our attraction to precision and determinism. We fail to appreciate the probabilistic nature of edge exploitation \u2013 that short-term results are dominated by randomness, long-term results by skill.<\/p>\n\n\n\n<p>We hear about the markets being \u201clow signal to noise.\u201d And we understand this intellectually, but we don\u2019t appreciate what it means for our trading.<\/p>\n\n\n\n<p>This leads us to think we can build a perfect system for capturing a single edge to deliver that up-and-to-the-right equity curve.<\/p>\n\n\n\n<p>But it turns out this is a waste of time for retail traders, given the variance of the edges we can realistically compete in.<\/p>\n\n\n\n<p>Instead, embrace the mayhem of any single strategy (be OK with it having high variance) and focus on the bigger picture:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Finding additional things to trade<\/li>\n\n\n\n<li>Obsessing over your exposures<\/li>\n\n\n\n<li>Looking at how each component contributes to the whole to produce something extraordinary at the portfolio level.<\/li>\n<\/ul>\n\n\n\n<p>In fact, the ability to trade multiple edges is your biggest edge as a systematic trader. So embrace it!<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-summary\">Summary<\/h2>\n\n\n\n<p>If engineers unlearned some of their engineering training, they\u2019d make much better traders:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Research before implementation<\/strong>: Unlike engineering, trading requires scientific exploration of your edge before building any strategy. Understand the phenomenon first.<\/li>\n\n\n\n<li><strong>Simpler analysis is better<\/strong>: Use direct, data-efficient methods to understand market effects rather than complicated backtests.<\/li>\n\n\n\n<li><strong>Embrace the mayhem<\/strong>: Engineers\u2019 desire for precision and determinism conflicts with markets\u2019 inherently noisy, probabilistic nature.<\/li>\n\n\n\n<li><strong>Portfolio thinking beats obsessing over a single edge<\/strong>: Your greatest edge as a systematic trader is the ability to trade multiple uncorrelated strategies, not perfecting any single approach.<\/li>\n\n\n\n<li><strong>Disprove quickly<\/strong>: Be paranoid you\u2019re wrong rather than confident you\u2019re right. Design experiments to challenge your hypotheses, not confirm them.<\/li>\n<\/ul>\n\n\n\n<p>When you first observe a market effect, don\u2019t be an engineer.<\/p>\n\n\n\n<p><em>Instead, unleash your inner scientist.<\/em><\/p>\n\n\n\n<p>Only when you understand the phenomenon and are ready for implementation should you let an engineer loose on the problem.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Good trading research looks nothing like what most engineers initially attempt.<\/p>\n","protected":false},"author":271,"featured_media":184555,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[339,338,341],"tags":[4873,1006,494,1002],"contributors-categories":[13676],"class_list":{"0":"post-219355","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science","8":"category-ibkr-quant-news","9":"category-quant-development","10":"tag-backtesting","11":"tag-fintech","12":"tag-quant","13":"tag-research","14":"contributors-categories-robot-wealth"},"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.9 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Confessions of a Recovering Engineer (or Why Engineers Make Bad 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