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    <title>Maschinelles-Lernen on VikoFintech - FinTech in Focus</title>
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      <title>Why ML Trading Strategies Collapse When Markets Get Volatile (And What You Can Do About It)</title>
      <link>https://vikofintech.com/en/posts/ml-trading-strategien-scheitern-hohe-volatilitaet/</link>
      <pubDate>Sun, 05 Apr 2026 11:04:47 +0100</pubDate>
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      <description>&lt;h1 id=&#34;why-ml-trading-strategies-collapse-when-markets-get-volatile-and-what-you-can-do-about-it&#34;&gt;Why ML Trading Strategies Collapse When Markets Get Volatile (And What You Can Do About It)&lt;/h1&gt;
&lt;h2 id=&#34;tldr&#34;&gt;TL;DR&lt;/h2&gt;
&lt;p&gt;ML-based trading strategies have a well-known Achilles&amp;rsquo; heel: high volatility periods. The r/algotrading community on Reddit is actively debating this exact problem, with a thread generating substantial discussion around why models that perform beautifully in calm markets suddenly fall apart when things get choppy. The core issue isn&amp;rsquo;t bad code or bad data — it&amp;rsquo;s something more fundamental to how machine learning works. Understanding the &amp;ldquo;why&amp;rdquo; is the first step to building strategies that actually hold up when you need them most.&lt;/p&gt;</description>
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