<?xml version="1.0" encoding="UTF-8"?><rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title><![CDATA[Topics tagged with quant]]></title><description><![CDATA[A list of topics that have been tagged with quant]]></description><link>https://board.circlewithadot.net/tags/quant</link><generator>RSS for Node</generator><lastBuildDate>Fri, 15 May 2026 06:02:46 GMT</lastBuildDate><atom:link href="https://board.circlewithadot.net/tags/quant.rss" rel="self" type="application/rss+xml"/><pubDate>Invalid Date</pubDate><ttl>60</ttl><item><title><![CDATA[This is interesting.]]></title><description><![CDATA[This is interesting. As Quants we can distinguish problem classes suited for1. causal inference. global macro, CPI, treasury yield, central bank liquidity, game theory crisis resolution models, Q-Learning, SARSA etc.2. point in time inference. Signals, Alpha generationhttps://www.pitinference.com/#problem I'd think that Point In Time inference has many more use cases besides Alpha generation. Because you lose the lookahead bias problem. In general.#model #quant #finance]]></description><link>https://board.circlewithadot.net/topic/c3269dac-badd-4f39-adce-a90c94a17f14/this-is-interesting.</link><guid isPermaLink="true">https://board.circlewithadot.net/topic/c3269dac-badd-4f39-adce-a90c94a17f14/this-is-interesting.</guid><dc:creator><![CDATA[windsheep@infosec.exchange]]></dc:creator><pubDate>Invalid Date</pubDate></item></channel></rss>