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101 posts

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@mipt_filipp

it’s not just stocks - it’s equities

London, England Katılım Eylül 2012
367 Takip Edilen61 Takipçiler
privet
privet@mipt_filipp·
@JFPuget we do not yet have fully open source models, right? only open weight ones.
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JFPuget 🇫🇷🇺🇦🇨🇦🇬🇱
I thought this was random B.S. till I saw that the author works at OpenAI. We knew Anthropic FUD about open source. Now we have OpenAI FUD. I get that these companies feel threatened by Open Source models. I would be if I were in their shoes. The answer is to provide better model and better user experience. FUD and intimidation won't work.
Dean W. Ball@deanwball

Some observations on Kimi: 1. It's a very good model! I don't think its performance can be explained away by distillation or anything like that. In agentic coding sessions, it seems pretty much on par with the best public models of Q1 2026. In my fairly limited use, it also seemed very token hungry. It's not obvious to me that this model is actually that cheap to run. 2. I am personally surprised the Chinese state continues to allow the open sourcing of models this good, given potential risks. To be clear, I *myself* might be fine with models presenting this level of marginal risk being open weight, but I am surprised that China is fine with it. I suspect the reason they are is 75% explained by strategic blindness/lack of AGI-pilledness (the CCP is very Yann Lecun-y in its views of AI). The other 25% or so is their lack of compute for customer inference (making China's open-weight strategy an unintended byproduct of US export controls) and the normal Chinese strategy of aggressive exports. For the companies, as opposed to the government, the decision to open source is partially ideological and partially because they are behind, and they know that very few people would pay for sub-frontier models from China. 3. Open-weight models are inherently decelerationist, and I'm continually surprised to see the so-called "accelerationists" so excited about open-weight models. I suspect the reason they are is that they know open-weight models are effectively ungovernable, and they simply like the overall cloak of ungovernability open-weight models create over the whole of AI. It's not a bad strategy; it reminds me of James Scott's recounting of the hill people in "the art of not being governed." Still, in the end, open-weight models deter further AI capex. 4. One probable outcome of an open-weight-model-dominant world is full AI communism, which is precisely what China proposes: rather than a market product, AI is a "public good" which will ultimately be provided by the state as a kind of "digital public infrastructure." This future strikes me as a dystopian hellscape, but I've never met an open-weight models advocate who doesn't ultimately concede this is where things end. You'd be surprised how many 'accelerationists' lobbied me, while I was in government, to support an eleven or twelve-figure federally funded data center so that startups could train models at a subsidy and then give them away for free. There was no other way for AI to progress, they said. Perhaps this is the logical end state of things. Nonetheless, I find myself surprised to see supposed accelerationists excited about such an outcome. I think many of them just don't know what they're doing. Many accelerationists do not view the creation and serving of frontier models as a legitimate business. 5. I would guess that the Trump Administration will at some point realize that their best strategy here would be to create large amounts of regulatory risk around the use of open-weight Chinese models. You don't need to "ban open source" (one of the dumber motifs of AI policy discussion). You just need to direct every agency to issue soft law that creates FUD. "A Federal Reserve Advisory Bulletin found that there may be backdoors in Chinese AI models." It needn't be that well justified. You just create enough regulatory risk that every regulated enterprise backs off. You probably don't want to create so much regulatory risk that you scare off the hyperscalers from serving Chinese models; this will just drive startups to sketchier providers. There's a happy middle ground here. I'd assume they will do some version of this. 6. It's probably true that open-weight models of this capability make the world a bit more dangerous, but not so much more that you'll really notice. At some point the models will be capable enough that you will notice. "A nonliving, invisible, dangerous, and infinitely self-replicating agent escaped from a Chinese lab," you say? Color me shocked.

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privet@mipt_filipp·
@retiremeplease @aarushshah04 on a "very senior QR/PM" level there are absolutely useful recruiters. Especially useful for deal negotiation (this goes much further than guarantees and such, btw).
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Depressed Mathematician
Depressed Mathematician@retiremeplease·
Third party recruiters and headhunters in the quant industry are some of the most disrespectful and frustrating people I have ever come across in my life.
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privet@mipt_filipp·
@QFEX do you have a public dashboard with statistics (daily volume, open interest, funding, etc.)? a-la hyperliquid’s one
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QFEX
QFEX@QFEX·
We're just getting started.
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privet@mipt_filipp·
@systematicls @Unknown_Keys they had huge drawdown - exactly at this point returns were removed (though, not to discount the fact that recent performance is good)
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sysls
sysls@systematicls·
Alpha drop: Numerai forums. Numerai obfuscates all features before giving you data. feature_1... feature_1050, zero semantic meaning. Can't lean on fundamentals or papers, forced to discover pure statistical relationships. The forum is people trying to crack why things work without being able to anchor to conventional wisdom. Forced creativity at scale. Some threads: why recent <1yr models beat long history, why neutralization backfires, handling regime shifts when you can't see what actually changed. Good hunting ground if you want to see statistical thinking divorced from "but Fama-French says value works".
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privet@mipt_filipp·
@systematicls So, this is not entirely true. Consider, e.g., GQS team. This is an elite business, one of the best worldwide. But that does not necessarily translate to people from this organization being able to build a new pod in MLP-like organization.
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sysls@systematicls·
Ernest P Chan's books were a useful and clear introduction for a beginner to mess around but you're not going to make money off any resources that are widely available. Don't want to be crass here, but the best resource for someone who wants to understand statarb is to join the best statarb team you can find. The way to join the best statarb team you can find is to have as many "good projects" as possible on your resume that you can use to showcase talent/interest/passion; then aggressively cold email / call / message.
Michael M. Yalovenko@Yalovenko

@systematicls What are the best books, papers on this topic? Preferably the ones that go from theory to practice. I am grateful 🙏 in advance.

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sysls@systematicls·
Been thinking about this from @__paleologo‘s MIT talk. Every firm considered to be at the top of their quadrants (prop/hf/taking/making) has won monopolistic profits in one niche area/style of the markets. It’s not impossible to make money from markets; but it does feel like uncontested returns is in secular decline, even for fairly complex investment processes. If one is starting out and has the explicit goal of creating something world class, where does one go?
sysls tweet media
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privet@mipt_filipp·
@gunesevitan Why do you think trees are not good for such datasets?
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ltrd@ltrd_·
If you are Gotbit spin-off then you have those relationships. If you start from scratch then it is not that easy I would say. Especially if you want to play legit game and do not do wash-trading. Their method is pretty simple - they are super aggressive with marketing and proposing you huge volume doing wash-trades. Under the hood it is basically the business of taking risk of doing wash-trading and eventually end up in jail or facing some other problems, but you can have decent money within couple of months from starting your company.
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privet@mipt_filipp·
@gunesevitan Their fund got a huge drawdown sometime in 2023, since that they stopped posting returns. But rumors are they recovered.
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privet@mipt_filipp·
@sasuke___420 sounds like my firm, but I know what people are working on
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sasuke⚡420
sasuke⚡420@sasuke___420·
none of these people work on anything i work on. i don't even know who they are
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sasuke⚡420@sasuke___420·
the guy in the desk to my right loudly said "gentlemen" and "come on, for god's sake" to try to get the people to the left of my desk to shut the fuck up, but they will not
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privet@mipt_filipp·
@dalibali2 3k apartment decade ago is what now costs 6 or even more. In London you can get quite non trivial place for this.
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dalibali
dalibali@dalibali2·
Found out one of my guys who I pay well (>1M) has lived in the same 3K a month apartment with gf for the last decade. Pretty admirable tbh.
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Mario Filho
Mario Filho@mariofilhoml·
Two mindset shifts you might have to make to get the most of neural nets (if your experience is mostly with tree ensembles): - No more throwing every feature under the sun and it figures out which are good. - Can't care only about Val loss, Training loss also matters a lot
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privet@mipt_filipp·
@__paleologo HRT is far from underrated and rarely talked about. In certain circles everyone talks about them
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sysls
sysls@systematicls·
Land of the guiseppes, and it was beautiful, and delicious! It was mind blowing to see thousands of people flock to appreciate work done thousands of years ago.
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privet@mipt_filipp·
@mnskymoment I think you can ask for passing through some good amount of costs, and then be paid on some matrix of return and “ir” (here benchmark kicks in). So in good years everyone is super happy, in bad years he is not unhappy.
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privet@mipt_filipp·
@__paleologo Some bullshit list. They did not even include Moscow here 🤨
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Gappy (Giuseppe Paleologo)
Gappy (Giuseppe Paleologo)@__paleologo·
First: I would like how these people rationally combine rankings. It has applications to stock selection too. Second: Bologna>Milan and Osaka>Turin or Rome.
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privet@mipt_filipp·
@ltrd_ Wash and Korean please
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ltrd
ltrd@ltrd_·
OK, the reaction for last analysis surprised me. It seems that I have a lot more to share you in next weeks. What breakdown in-depth analysis would you love to see?
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privet retweetledi
Bojan Tunguz
Bojan Tunguz@tunguz·
Make NeurIPS NIPS again.
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