Starting tomorrow at 12pm PT, Claude subscriptions will no longer cover usage on third-party tools like OpenClaw. You can still use these tools with your Claude login via extra usage bundles (now available at a discount), or with a Claude API key.

Most beautiful message you will listen today. From a true legend.

Leading Stocks & Themes Fiber Optics ~ $LITE $CIEN $COMM $VIAV $AAOI $CLS $MKSI $TTMI $FN $APH $AVGO $CSCO $GLW $AEIS $LUMN $COHR Data Storage ~ $MU $STX $WDC $SNDK $PSTG Solar ~ $FSLR $NXT $RUN $SHLS $CSIQ $DQ $JKS Mining-Gold/Silver ~ $PAAS $ARMN $EQX $ORLA $AEM $AU $B $FSM $GFI $HL $IAG $KGC $NEM $NGD Medical/Biotech ~ LLY ARGX INCY LQDA ARQT AXSM MDGL INSM NTRA NKTR JAZZ TMDX GH GMED GRAL Nat Gas ~ EQT AR EXE CRK Aerospace/Defense ~ ONDS CRS FTAI KTOS EMBJ Construction ~ AGX PWR MTZ STRL Chemicals ~ SQM SEI ALB Software ~ APP MDB U APPN DOCN SNOW FROG FSLY TWLO NBIS PATH Semiconductors ~ ALAB CRDO AVGO AMD LRCX AMAT KLAC ASML MTSI AXTI TSEM INTC ADI Power Equipment ~ VRT BW ENS ROK EOSE NVT Alternative Energy ~ BE CWEN FLNC LEU CEG GEV TE AMPX Coal ~ HCC HNRG BTU Mortgage ~ RKT Miners ~ IREN GLXY HUT CIFR APLD BKKT CLSK WULF CORZ Internet Content ~ RDDT GOOGL Machinery ~ SYM FLS MIR CAT The market showed a massive improvement in breadth last week, with strength spreading across all sectors.

I have a story for you guys. This is a TRUE story, with some minor touch ups to anonymize our characters. I once employed a man who had all kinds of degrees and accolades to his name. Let's call our man... Valerie. --- Valerie was quite literally in possession of multiple masters, all from top universities, in very technical and challenging topics (e.g. physics, computer science, etc). AND he had a PhD! Of course, he also had the CFA AND the FRM. Very impressive, right? You'd ask Valerie why he would bring himself to spend 10 years studying to get all of these certificates and he would tell you, UNironically, that it was because he LOVED being a quant. And being a quant is a DEEP, and WIDE field; so he thought the best way forward was to learn all the ways in which the field was WIDE and DEEP. Quants use computer science, signal processing, math? I'm going to get masters in all of them! The field is moving towards deep neural networks? That's what my PhD is going to be based on! --- See, the problem was that... Valerie spent so long trying to get all these degrees and accolades that he forgot to actually do the thing he was passionate about... Which was to be a quant! And whilst he was out there trying to get all these fancy certificates to append to the back of his name, people half his age were actually hacking together solutions that worked and testing them in the battlefields of the markets. He was, of course, getting GPA 4/4 whilst burning tens and thousands of dollars a year doing so. "I'm making an INVESTMENT", he would say. --- AND... it was truly sad. Because Valerie got so used to clean, neat nature of academia that he never really, truly understood the stochastic nature of markets and research. For him there was always an answer, even when there were none. There was always a global optima, and he would always find it, even if he had to force the data to reveal one. --- So, all in all, Valerie was quite literally useless. And we had to let Valerie go. But I sometimes wonder what Valerie is up to. --- Anyway, the point of all this... Is that all I want is for you is to not be Valerie. And to learn something that actually makes sense and that you can use to try out an idea. So, in the spirit of that, let me tell you that when we are first testing ideas, we should first test the ideas on the simplest representation that we think is able to model our hypothesis. Why? Parsimony, of course. Simple representations give you the opportunity to see if the underlying mechanism is so strong that no further complexity is needed to approximate the true, hidden function. So it is a long, long way of testing before we eventually get to conformal predictions, if ever, and a much further ways along before we start to represent our data as images and calculate convolutions on them. -- Is there merit in the complex? Absolutely - there is more than sufficient evidence in our industry to show that complexity not only works, but it can be a moat. Take XTX for example, no one would accuse them of having simple, linear models with single variables. But their namesake is literally homage to the mighty linear regression. So.. think about that, fellow quant enjoyoor beginning his journey in this very fun, very wide and very dark, deep, forest.