<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Insights on Vectopian</title><link>https://vectopian.com/insights/</link><description>Recent content in Insights on Vectopian</description><generator>Hugo</generator><language>en-us</language><atom:link href="https://vectopian.com/insights/index.xml" rel="self" type="application/rss+xml"/><item><title>The Kelly Criterion: how much to bet, and what over-betting costs you</title><link>https://vectopian.com/insights/kelly-criterion-how-much-to-bet/</link><pubDate>Wed, 24 Jun 2026 00:00:00 +0000</pubDate><guid>https://vectopian.com/insights/kelly-criterion-how-much-to-bet/</guid><description>A good signal tells you what to buy. It never tells you how much. Kelly answers that second question, with the proof worked out step by step and an interactive simulation showing how the same winning bet destroys a bankroll when you size it too big.</description></item><item><title>The last gate: designing due diligence into order execution</title><link>https://vectopian.com/insights/safe-order-execution-layers/</link><pubDate>Sat, 07 Mar 2026 00:00:00 +0000</pubDate><guid>https://vectopian.com/insights/safe-order-execution-layers/</guid><description>Where algo order execution goes wrong, a three layer decide / plan / gate design, and the safeguard categories that keep due diligence real before money moves.</description></item><item><title>Intraday fills and VWAP: chasing a perfect-execution ceiling</title><link>https://vectopian.com/insights/intraday-fills-with-vwap/</link><pubDate>Mon, 02 Feb 2026 00:00:00 +0000</pubDate><guid>https://vectopian.com/insights/intraday-fills-with-vwap/</guid><description>Early days of Ruby, our flagship Nasdaq-100 algo: fixed-time fills looked calm, but the session told a different story — and that gap changed how we think about execution.</description></item><item><title>Preparing data for backtests: How we have designed DataKitchen</title><link>https://vectopian.com/insights/preparing-data-for-backtests/</link><pubDate>Fri, 26 Dec 2025 00:00:00 +0000</pubDate><guid>https://vectopian.com/insights/preparing-data-for-backtests/</guid><description>Most backtests fail in the data first — splits, sparse bars, fantasy fills, vendor disagreements. How we designed DataKitchen to clean that mess before AlgoLab runs a Walk-Forward.</description></item></channel></rss>