<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Quant on Subhajai Benchadhikul</title><link>https://subhajai.benchadhikul.com/tags/quant/</link><description>Recent content in Quant on Subhajai Benchadhikul</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 26 Jun 2026 07:00:00 +0700</lastBuildDate><atom:link href="https://subhajai.benchadhikul.com/tags/quant/index.xml" rel="self" type="application/rss+xml"/><item><title>The Overfitting Trap: Why Your AI Strategy Wins on Paper and Bleeds Live</title><link>https://subhajai.benchadhikul.com/posts/2026-06-26-trading-ai-overfitting-trap/</link><pubDate>Fri, 26 Jun 2026 07:00:00 +0700</pubDate><guid>https://subhajai.benchadhikul.com/posts/2026-06-26-trading-ai-overfitting-trap/</guid><description>An AI trading model that nails the backtest can still lose money the moment it meets a real market. The gap is usually overfitting — and there are ways to catch it before your capital does.</description></item></channel></rss>