dylan ツ
@demian_ai • 29,850 subscribers
growth @nebiustf @nebiusai // ex @Scaleway // from silicon to token, inference and anything in between. Views are my own - not financial advice
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10 days ago, i gave my LLM $10k to trade with and a ton of research tools as an experiment the system is built on top of what i built with and uses: - X signals + X websearch(manual selection of top voices + automated search) - Perplexity research - Tavily - Company filings - Earnings, analyst targets etc - News - Technical and catalysts - private reports I limited the scope to 223 stocks in AI infra, energy and robotics. But I didn’t ask it for "stock picks", I asked it to run a portfolio. Every session, MiniMax M3, running through Nebius Token Factory, must decide what to buy, hold, add, trim or avoid. For every stock, it produces: → A target weight → 1, 5 and 20 session forecasts → A stop loss → A profit taking ladder → A time stop → A rationale, counterargument and invalidation trigger Then a deterministic risk engine gets the final word. It checks concentration, correlations, basket exposure and position size. Every call is timestamped, every trade fills at the next observed close, and costs are included. Nothing gets rewritten after the fact. The most interesting trade so far? $AMD The bot started building the position on September 15. A few sessions later, AMD had jumped 9.9% in one day on 2.3x normal volume. The position was up 22.6%. The thesis was still improving. But RSI had reached 72.9. The analyst consensus target had effectively caught up with the price. And the AI basket was already at its exposure limit. The model said hold, but the risk engine trimmed the position anyway. That is exactly the behavior I wanted! It liked the company but refused to let conviction become concentration. The portfolio it built includes: Compute: Micron, Nvidia, Broadcom, TSMC, Monolithic Power Power: Siemens Energy, BWXT, Caterpillar, First Solar, Mitsubishi Heavy, ABB, Cameco Robotics: HIWIN, Novanta, Tesla Early results, from a clean $10,000 start: Portfolio value: $10,471.69 Return: +4.72% Ahead of its benchmark: +4.31 points Executed trades: 114 Cash: 10% Very early. Most positions have only four or five observed sessions. And the first version already taught me something: The bot found too many “good” ideas. It opened 63 positions, often in tiny amounts. So I kept the same account and the full history, but changed the policy. It is now concentrating toward 25 positions, with larger minimum entries and one in, one out replacements. The experiment isn’t really wether or not a LLM can predict stocks, it’s more "can a model turn a mountain of messy info into repeatable, constrained and fully auditable decisions?" Now I can finally measure it will share more as the experiemnt continues... (fictive money, not financial advice, just a fun experiment)
dylan ツ129,541 次观看 • 6 天前
great shoutout from Chamath Palihapitiya on the all in pod today. "you need to go to a vendor that you trust, could be Nebius" perfect recap of why, if your data actually matters, an open source inference path like Nebius Token Factory is the way to go his point, in plain english: you paste a protein design, a deal strategy, a process that is the company into a shared chat api. The vendor says “zero data retention", but that usually means best efforts, not a hard guarantee. then someone hits like / thumbs-up, or the terms allow training on “insights,” not names, or the next model version just… knows the shape of the answer you thought was private (like in the Navier Stokes case) the scary leak ends up being the solution pattern showing up somewhere you don’t control. so the move isn’t “never use ai”, it’s stop putting crown jewel work on a shared frontier api stand it up on your terms: open weight models, inference on hardware that’s you can control, a vendor that provisions your stack, the lane Chamath pointed at (aws class, Nebius Token Factory, Fireworks, etc.) Boards are late to this. “We signed zdr” is starting to look thin next to “do we actually control where this runs.” That’s when the CIO who took the easy api deal becomes the problem
dylan ツ35,507 次观看 • 19 天前
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