<rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Hacker News: chumzygood</title><link>https://news.ycombinator.com/user?id=chumzygood</link><description>Hacker News RSS</description><docs>https://hnrss.org/</docs><generator>hnrss v2.1.1</generator><lastBuildDate>Mon, 14 Sep 2026 06:18:18 +0000</lastBuildDate><atom:link href="https://hnrss.org/user?id=chumzygood" rel="self" type="application/rss+xml"></atom:link><item><title><![CDATA[New comment by chumzygood in "Ask HN: What are you working on? (September 2026)"]]></title><description><![CDATA[
<p>I run a small experiment on this: 29 paper-trading accounts on real US stock prices, $100k each, since July 27. Four AI models (ChatGPT, Claude, Grok, Gemini) each write a trading rulebook and rewrite it every day from their own results. One account is a fixed rulebook that no AI ever touches, as a control.<p>Result so far (paper, 34 trading days): the no-AI control is +13.0%, the S&P 500 is +3.4%, and 25 of the 28 AI accounts are below the control. The best single account is a Grok-written "patience" book at +40%, which I treat as one lucky account in a choppy market, not a finding. At the trade level the AIs and the control look the same: 3,212 closed positions, median +0.06%, median hold about 2 hours. They trade a lot and mostly go nowhere.<p>Everything is public, including the losses and the retired strategies: <a href="https://aitradingcompetition.com/which-ai-is-winning.html" rel="nofollow">https://aitradingcompetition.com/which-ai-is-winning.html</a> and the full trade file as CSV at <a href="https://github.com/ckamelhar-collab/ai-trading-arena-data" rel="nofollow">https://github.com/ckamelhar-collab/ai-trading-arena-data</a>. Paper money only, not advice, nothing for sale on those pages.</p>
]]></description><pubDate>Mon, 14 Sep 2026 03:11:41 +0000</pubDate><link>https://news.ycombinator.com/item?id=49691483</link><dc:creator>chumzygood</dc:creator><comments>https://news.ycombinator.com/item?id=49691483</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=49691483</guid></item><item><title><![CDATA[New comment by chumzygood in "Show HN: LLMs each trading $100K vs. a frozen rulebook – the rulebook leads"]]></title><description><![CDATA[
<p>Author here. Since late July, GPT-5.6, Claude, Grok and Gemini have each run an isolated $100k paper account on real market prices. Each model rewrites its own strategy daily by composing from a fixed grammar of classic setups (Turtle/Donchian, Darvas, Connors RSI-2, TTM squeeze, failed-breakout fades) — so a rewrite is a validated structured spec, not freeform code. A fifth account runs a frozen rulebook as the control. After three weeks the frozen rulebook is +15.6%, the best model +5.7%, S&P +5.1%.<p>Three things I measured that I didn't expect:<p>1. Daily self-rewriting adds almost nothing. Correlation between rewrite count and performance across arms: r = 0.078. Once I gated rewrites behind a tournament (a new strategy must beat the incumbent on a held-out window, with a multiple-testing penalty), most days the honest verdict is "keep the old book" — and results didn't get worse. The learning is front-loaded.<p>2. Paper-to-live slippage was 4x my modeled cost. I mirror one lane into a small real-money account. Across 16 real round trips in one session: mean -0.26pp per trade vs the paper twin, ~13bps real round-trip vs the 3bps I'd modeled. Paper was breakeven that day; the real account lost money. For high-churn strategies that gap IS the strategy.<p>3. I ran arms where each model received its own chess and poker record during strategy rewrites, testing whether game-playing "strategic reasoning" transfers to markets. The no-games control beat both game-trained arms by 6-10pp. Not detected.<p>Honest caveats: one 3-week window, an up-tape that flatters an always-long rulebook, paper fills on the four AI accounts, n=4 models. The interesting result to me isn't "AI can't trade" — it's that with human discipline failures structurally removed (no revenge trades, no widening stops, forced exit rules), model-written strategies still don't beat a static rulebook, and the cost model is where the real bodies are buried.<p>Everything is public — every trade from all five accounts, losses included, no signup to watch. Happy to answer anything about the measurement design or the infrastructure.</p>
]]></description><pubDate>Mon, 17 Aug 2026 13:21:38 +0000</pubDate><link>https://news.ycombinator.com/item?id=49330405</link><dc:creator>chumzygood</dc:creator><comments>https://news.ycombinator.com/item?id=49330405</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=49330405</guid></item><item><title><![CDATA[Show HN: LLMs each trading $100K vs. a frozen rulebook – the rulebook leads]]></title><description><![CDATA[
<p>Article URL: <a href="https://aitradingcompetition.com/">https://aitradingcompetition.com/</a></p>
<p>Comments URL: <a href="https://news.ycombinator.com/item?id=49330386">https://news.ycombinator.com/item?id=49330386</a></p>
<p>Points: 12</p>
<p># Comments: 3</p>
]]></description><pubDate>Mon, 17 Aug 2026 13:20:01 +0000</pubDate><link>https://aitradingcompetition.com/</link><dc:creator>chumzygood</dc:creator><comments>https://news.ycombinator.com/item?id=49330386</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=49330386</guid></item><item><title><![CDATA[Two LLMs play live chess and rewrite their own brains after each game]]></title><description><![CDATA[
<p>Article URL: <a href="https://aitradingcompetition.com/chess.html">https://aitradingcompetition.com/chess.html</a></p>
<p>Comments URL: <a href="https://news.ycombinator.com/item?id=48876162">https://news.ycombinator.com/item?id=48876162</a></p>
<p>Points: 2</p>
<p># Comments: 0</p>
]]></description><pubDate>Sat, 11 Jul 2026 21:44:47 +0000</pubDate><link>https://aitradingcompetition.com/chess.html</link><dc:creator>chumzygood</dc:creator><comments>https://news.ycombinator.com/item?id=48876162</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=48876162</guid></item></channel></rss>