I'd caution the HN crowd not to expect production-level quant models out of this, like I'm seeing some doing in the comments already. Kagglers are excellent machine learning practitioners and the models that come out of many competitions are top-notch stuff, often making their way into research papers. But this is a short competition on limited data in a non-real-world scenario. The winning models will be very interesting educational exercises and probably wonderful recruiting material for Jane Street, but won't be the underpinnings of a new fund.
That said, I can't wait to see what comes out of this one. It ticks all of my competitive boxes :)
The problem with predicting absolute levels, is that there is a game theoretic aspect which undermines any mathematical trading strategy as soon as it is public. optimal game theory trading strategies don’t produce great results, and they are relatively trivial to identify. Instead strong profits in market long/short macro positions are mostly created by information advantages, which don’t really make for interesting Kaggle competitions. For example, big profits in macro trading have historically been consistently achieved by front running customer orders, by building timing advantages on top of trading infrastructure, by funding research analysts that inspect operations on the ground, by lobbying for regulations that change market directions and so on.
It’s very hard to tell if a best performing hedge funds that doesn’t have an unfair advantage, that declares its only using quantitative strategies, is in fact just a statistical anomaly with a hollow narrative.
>> The problem with predicting absolute levels, is that there is a game theoretic aspect which undermines any mathematical trading strategy as soon as it is public.
I took finance in Business school, coming from doing a lot of statistical analysis in a research lab. I hated my finance professors and there pseudo science. Pricing formulas work great until they don't. The problem is when they don't, they really don't, in a catastrophic way. Read "When Genius Failed." Real traders know this. But some economists and finance professors act like these mathematical models are describing a predictable physical phenomena.
LTCM in being overly leveraged, relied on other market participants to maintain short term price alignment, which meant it was not arbitrage. Salomon's reduced its role as market-maker, maintaining short term price alignment, which increased short term price anomalies, and thus increased LTCM's vulnerability. The Asian financial crisis increased the frequency and extent of those pricing anomalies, and the subsequent Russian Default crisis did the same. Margin calls were made on LTCM that it couldn't cover, forcing them to close out of their positions at very unprofitable times of the trade strategy.
So I don't think "pseudo-science" is a great description for what those B-School profs are teaching. Rather the pricing formulae are just the beginnings of the financial theory you need to run arbitrage strategies, but they are not sufficient. You need to augment them with a broader picture of market dynamics and capital management, just like you'd need to learn about financial law, financial market technology, and a bunch of other stuff to run a successful market-making desk.
From what I've read elsewhere, if you had managed to hold an LTCM replica portfolio until expiry, you would have actually made money.
Start by reading/running notebooks ("kernels") and winners solutions from this and previous competitions with tabular/time series data, building models and then grid searching, stacking, bagging, boosting 'em up. Here's a bit aged but still useful guide how pros do it: https://mlwave.com/kaggle-ensembling-guide/
If you want something online, check out https://www.quantopian.com/
The reason why relative valuation models are more effective is the same reason why most sports betting models use current odds as an input. Prices contain information but, in my experience, these methods aren't totally effective because they often miss important information about the company itself (big price moves happen because relative valuations are wrong). Value or quality appears to do fundamental work but is often woefully blind (for example, there are proven accounting issues with value strategies...does your average quant understand this? No. Have they ever read a set of accounts? No. They have no hope. None.)
Just imo, I think quant strategies are almost totally worthless beyond liquidity provision (even a strategy like front-running news in FX...humans do this better, and I know people who are still making tons of money doing this). I think there is massive value in that mode of analysis but the people who make the most are always going to be people who know the fundamentals better (I think firms like Marshall Wace that are doing this synthesis will move ahead) because that information is often not in the price at all.
I'm not sure about the rest of your comment, but this is mathematically the correct reason why we don't predict absolute price levels.
And the reason why this is mathematically the correct reason is because for a non-stationary process, when you predict into the future, the variance tends to infinity which means taking expectation on any statistical model is useless since the variance is ridiculously wide
This isn't true if you're fast enough. Everybody knows how to do arbitrage but it's still extremely profitable if you're faster than everyone else. HFTs are consistently more profitable than slow trading firms, earning 40%+ returns, it's just a much more capacity-constrained form of trading so absolute returns are lower.
But yeah, it might not be that useful for the funds themselves.
But isn't prediction an inherent part of valuation?
Doesn't need to be. You have wealth you have to store somehwere because it is not being spent now. Can store it as equities, debt (including a bank account), Paper cash money under the mattress, gold bars, BTC, etc etc.
Let's say you have the list of potential places to store it and can get a price/value ratio but with a common term of "X" in the value
- A = 3.1/X
- B = 2.8/X
- C = 1.9/X
- D = 4.2/X
Yes, this is a massive simplification as it ignores diversification benefits and so on but there's the basics of the usefulness of valuation where you don't and can't know the value.
"prediction of the market" here means "Do you think the S&P500 is going up or down this year?" Warren Buffet completely ignores this, takes no view on it. Timing the market is very hard. Assuming the market will reflect the economy and the economy will grow because people work hard, are smart, we invent new things and population increases, then picking the best of what's available is the normal value investors route, a la Buffet as the most famous practioner.
Burton Malkiel's "A Random Walk Down Wall St." is still, IMHO the best summary of all the theory and practise of investing - excluding the quant funds like Renaissance. Maybe he has an update for them? Maybe it's also worth reading if it exists or maybe not. Don't know.
No idea if renaissance time the market or what.
edit: punctuation and the Malkiel recommendation.
Kaggle encourages a domain agnostic approach to modeling, in the sense that participants use sophisticated machine learning and statistical methods but typically have no domain expertise in the underlying data. This kind of approach to finance has historically performed poorly. 
Good quantitative trading is usually backed by a strong fundamental thesis and an interpretable model, which is obtained by cross-pollinating sophisticated math and statistics with domain expertise in some part of finance. That domain expertise might be in different kinds of assets, liquidity or market microstructure, but it's there.
$100k is cheap for Jane Street. If nothing else they have a new recruiting pipeline of people with demonstrable machine learning skills.
1. I would also say this is a poor way to approach statistical analysis in most domains, and usually leads to spurious or overfit results. But the idea that you can just run a model and find patterns in pricing data is especially attractive and insidious.
Yes this is accurate and put very well. This is so much the case that if you have a strong background understanding of the field, the ML part can actually be picked up quite quickly or contributed by someone else. There are a few notable users who are both domain and ML experts and they tend to absolutely clean up in their field. I'm thinking of a couple of med students in particular who are formidable in every medical imaging competition.
I recently read 'The man who solved the market', about Jim Simons and Renaissance Capital. The way the book tells it, looking for patterns without seeking domain expertise (e.g. ignoring fundamental valuation of equities) is exactly what Renaissance did, and it worked out very well.
But all of this has to be utilized in the context of the data. The reality is that you're not going to develop a sophisticated options trading strategy without a strong understanding of what an option (and more generally, a derivative) is. You can't develop a viable statistical arbitrage strategy just by treating market microstructure as a blackbox signal to be solved with e.g. Fourier analysis. You can certainly find an edge in using fundamentally superior methods of analysis, but you still need to know what that data represents in the context of the market.
Don't be fooled: people working at firms like RenTech have a strong understanding of the underlying finance. It's just that they learned it on the job, because the ethos at these firms is that learning fundamental theory in math and statistics is harder than learning fundamental theory in finance. You don't have to take my word for it though. Read about one of the few strategies of RenTech's which has been publicized: https://www.bloomberg.com/opinion/articles/2014-07-22/senate.... Deutsche and RenTech didn't team up on this strategy (to fantastic success) by treating basket options as some kind of blackbox abstraction devoid of delta, gamma, theta and vega.
(Also, even if I agree it was purely intended for tax avoidance, I don't understand why you think that would obviate having to understand how the options work intimately well).
They look for programmers with knowledge of Tax and Risk Management.
However, RenTech probably has the cleanest data warehouse of any firm. It was revealed that they have PHDs who do nothing but sort data into databases.
I've only taken a quick look at the data, but the problem doesn't seem to be focused on their core competencies, but instead is much more general.
How can you tell? All the features are completely anonymized.
Admittedly, this challenge far oversimplifies the depth of the quantitative problems Jane Streeters work on daily, and Jane Street is happy with the performance of its existing trading model for this particular question.
If you prove such a model out, get licensed (SEC, FINRA) and start soliciting to manage assets.
Disclaimer: Not investment advice. Not a lawyer, not your fiduciary.
>Admittedly, this challenge far oversimplifies the depth of the quantitative problems Jane Streeters work on daily, and Jane Street is happy with the performance of its existing trading model for this particular question. However, there’s nothing like a good puzzle, and this challenge will hopefully serve as a fun introduction to a type of data science problem that a Jane Streeter might tackle on a daily basis.
Sounds like it's just for fun/recruiting rather than trying to crowd source new strategies -- I'm sure if they were looking to crowd source strats they'd pay a whole lot more than 40k for first place
Having said that you also want to find the (vastly more in number) people who can take someone else's idea and actually implement it.
To get one candidate and come out superior, acceptance rate should be 1%. (i.e. 99 failures). But if there are 50 leads from the program, and you convert a fifth, that's 10 candidates for a cost / successful recruit of $10k which means you have 10% acceptance rate to break even.
Hmm, back of the envelope seems to do all right as a strat. Relatively cheap. I recall the last time we were hiring, we projected cost per hire at $35k with the bulk of that actually being the recruiter referral fee.
According to https://news.efinancialcareers.com/ch-en/307393/jane-street-... "Last year, Jane Street's graduate hires straight from college were said to be paid a $200k annual base salary, plus a $100k sign-on bonus, plus a $100k-$150k guaranteed performance bonus."
According to random people in reddit https://www.reddit.com/r/cscareerquestions/comments/69k0ap/d... "somebody said they got an offer from JS for $150k + $50k/yr "performance"-based bonus"
Both may be true.
I get how maybe "bonus" can count as "not salary" by being paid as lump sum. But "guaranteed" and "performance" going together? What's the deal here, even?!
After interviewing with a bunch of similar finance shops over the past year, I think whoever reported that info omitted one important thing: the "guaranteed" part of "guaranteed performance bonus" applies to the first year of employment only (and it isn't a secret, every single recruiter and hiring manager made that very explicit and clear).
With this context in mind, it makes perfect sense, because you don't know how things work at the new company, you will have lots of learning to do, you are not sure of how performance bonus is structured or what goes into it, you don't know the details of how to affect it in your favor, etc. With that in mind, it makes sense for those companies to reassure candidates that you have a nice headstart by guaranteeing your performance bonus for the first year, to allow you to focus on learning all of the things relevant to your position without having to stress much about the performance bonus.
I know salary (2 years out of date). I don't know oppo cost.
Any alpha that is not fully correlated to existing alpha is worth its weight in gold for an organization with the size, sophistication and complexity of JS. That's part of the reason why efforts such as 2Sigma's Alpha Studio exist: https://alphastudio.com/
hell, the thing about us hackers is I can KNOW it's a dud deal, yet part of me still wants to give it a go because it's a problem and it's right there!
If you want to work at Jane Street, go work for Jane Street. If you want to build your own models and run your own shop, the tools exist for you to do that without Jane Street (although there’s probably some amount of value learning the ropes there while they pay you, if that’s your thing).
My comments in thread are primarily around not having someone’s work exploited by sophisticated hedge/prop trading/investment professionals, which I’ve seen happen more than once, and for which you have no recourse.
Jane Street is a high frequency firm. In this domain execution & infrastructure matters as much if not more than your algo secret sauce.
What good is kagglers' favorites giant boosted bagged lightgbm/xgboost/neural ensembles which will take seconds to predict, when my FPGA or ASIC running in the same rack as exchange's matching engine can make a million trades in the meantime with a much simpler strategy involving nothing beyond freshman math (take a cue in XTX Markets' firm name, for example)
And in the longer timeframes, the edge is more often than not in the data than training algo.
A portion of the profits these market makers collect are rebated to RH, which, alongside cash sweep and RH Gold, comprise the bulk of their revenue.
RH makes money by keeping trade volumes and user engagement high. They don't care if you make or lose money on their platform, as long as the trades keep happening.
Market prediction is low signal-to-noise problem, what's much more likely the contest will be an exercise in overfitting and the final shake up will be yuuuge.
I understand that there are more models being thrown at the solution space, but if you think about people at jane street, while they don't have 3000 employees working on these, they've got a few hundred, and then if you think about the _time_ they spend on (the real, not this kaggle competition simplified stuff) models, I think there's a good chance that they'll have a significant advantage in man-hours (it's a full time job).
Also, while there is a high standard to people doing kaggle competitions, I think you can easily discard >90% of them as not being super competitive, then your 3000 models becomes 300, and those 300 have less than 1/3 of the time spent on them that jane street-ers are spending on their models.
(I realise it's not an apples to apples comparison, as the models they're working on are different since this is a toy example, but you said "will outperform anything that Jane Street _could_ come up with)
Jane Street's hiring standards exceeds FAANG's.
This is a hiring/branding strategy. Good luck to them.
As someone who has interviewed thrice and passed once, I disagree. Jane Street is extremely good at marketing themselves; before I ever worked there I received over $500 worth of "swag" (including a free iPad) just for attending talks and hiring events. The official recruiting strategy is grassroots where recruiters have anonymous reddit/hn/blind accounts to praise the company semi-anonymously. So you have a bunch of accounts posing as real employees or students talking up the company and the prestige of working there.
They sell exclusivity and mystery, but they definitely pay top of market salaries. I don't personally believe the actual interviews are any harder than top FAANG companies but they did seem to recruit exclusively from FAANG and/or top engineering schools.
I'd say the standard is arguably a lot higher, the things being tested are fairly different, more focussed on raw reasoning rather than specific techniques.
The main thing I'd say is that in general the standard of interviewing for FAANG isn't actually as high as people think it is.
Problems required knowledge of basic stats, general problem solving, critical thinking/reasoning, and all of it was wrapped in a nice package. Mind you, when I say "general problem solving", I don't mean brain teasers like "how many piano tuners there are in Manhattan" (I don't like that kind of problems at all).
I don't remember the details, but one problem was about estimating the most optimal move in a poker game given a specific situation at the table. The rules were explained in a very relevant and simple way to those not familiar with poker, which I wasn't at the time, and I didn't find that my lack of poker knowledge affected my ability to solve that problem at all (which is already an impressive feat, major credit to those who wrote that specific problem that way).
Zero memory of the rest of the problems, but most of them were relying on just basic stats and reasoning skills/critical thinking, no bs, and they were written/designed in such a way that it was interesting. The kind of a problem you would randomly see online in the middle of some discussion and won't be able to resist the temptation to stay glued to your computer screen until you solve it.
I expect in a constrained environment, someone on Kaggle will beat Jane Street's solution.
They have high hiring standard, but I suspect their 100s of smart engineers can't compete with the rest of the world.
Unless you have some kind of specialized non-public data (e.g satellite images of number of cars parked outside parking malls, number of cargo ships moving in and out), trying to predict the market with historical data does worse than "Just give me some monkeys, darts and a dart board".
Besides, I bet you can train monkeys to do (slightly) better than blindfolded random throwing. Even with public data (replace satellite images with Youtube mentions, or number of links moving into a company website) it is very possible to do better than average guessing on quite a lot of assets (especially smaller and newer markets).
Most hedge funds, even with specialized expensive non-public data, are not magical unicorns. Their quants really may just run a gradient boosting machine and leave it at that. Some hedge funds even prefer linear methods, because this lowers risk through lower variance. Such models can be beaten by experienced Kagglers for sure. For one, I did.
Also, most of this stuff isn't in the price. Lots of people are collecting new data, it is definitely becoming more widespread but the actual synthesis is tricky (most people who are quants do not understand fundamentals, and most fundamental analysis don't understand data...most firms are swirling in a perfect storm of ignorance).
Alternately, if Jolanda doesn't need to make this opportunity her full-time job, then the investment company can pay her $500k to manage many of these small opportunities. After all, there must be many opportunities. Otherwise only 1 of the HN readers would be able to exploit it. In which case, again, it doesn't really exist at all.
If it's relatively easy to make money investing on a small scale, that means there are many potential strategies that don't require someone's full attention. If there are many potential strategies that don't require full attention, then someone skilled can go after many of those potential strategies and transform the small-scale into a large-scale opportunity. Since we've already established that these large-scale opportunities are hard ...
And yes, there is easy money laying around if you know what youre doing. There are dollar bills laying on the ground for smaller scale strats.
> economics has nothing to do with investing, and is worthless
Is it worthless because it has nothing to do with investing, or has nothing to do with investing because it's worthless? Further, why do you think the Federal Reserve hires so many economists if economics is worthless?
> And yes, there is easy money laying around if you know what youre doing. There are dollar bills laying on the ground for smaller scale strats. People just arent taking about them
Isn't that like saying, "Winning chess tournaments is easy if you know what you're doing."? If something requires expertise, then it isn't easy. If finding these smaller scale strategies requires expertise, then the expert probably has better things to do with their time. I suppose that holds to arguing with idiots on Hacker News.
Also, the FED is in the process of destroying the US dollar and hyper inflating all the assets. Everyone but the capitalists are getting poorer.
The "Chief Economists" of many major US banks dont even hold economics degrees. People arent rational actors that you can plug into a math equation.
"If finding these smaller scale strategies requires expertise, then the expert probably has better things to do with their time. I suppose that holds to arguing with idiots on Hacker News."
Theres alot of friction of getting data to analyze, developing a strategy, executing the strategy, paying fees, managing risk, hedging. If you know how to do all that, you are probably already employed in that capacity or you run your own fund. But yes, if youre smart you can find an edge. Theres a ton of small man shops in NYC with 5 million in capital where everyone makes 600-1M a year.
> easy money laying around
There's a bit of dissonance between the way you've characterized this process in different comments. That's my point.
"Hey, there's a dollar lying on the ground!"
"Can't be. Someone would have picked it up."
> destroying the US dollar and hyper inflating all the assets
Sounds like a good strategy to bring manufacturing back to the US. They've said the new policy is high employment.
Found the efficient market hypothesis believer, classic failing of economic theory.
Theres no alpha in the stock market!!!11!! If there was the completely efficient allocation of human capital, access of information, opportunity cost would strip it out so fast!!!
Alternative data no one else can get easily certainly has tremendous value though.
Of course, predicting one or two seconds into the future (my primary concern) is easier than days or years, so there's that.
"Freely & publicly available external data is allowed, including pre-trained models"
Planet labs will sell you all of that data, in case people reading along here are curious.
The payoff for getting these operational details right or wrong is massively asymmetrical. If you get everything right, you'll only do as well as your model lets you. But if you get anything wrong, you run a real chance of losing far more money than you could have hoped to make!
Even just validating your strategy on historical data (ie back-testing) is harder than it sounds. If you make a mistake that leaks information to the code you're testing, you can end up with a much rosier return and risk profile than you really have. Another way to lose money when you go put your model into action.
If you get over these challenges and run your strategy successfully for a while, other market participants are going to start adjusting against it and you have to adjust in turn. You can't just "set and forget".
I should note that I am far from an expert on any of this, though! I just know enough to not trade with serious money—my real savings are all in index funds I don't touch, thank you very much :).
I have heard of some quants trading foreign exchange markets, agreeing to trade at the fix with their counter-party, and not realising that traders often manipulate the fix resulting in the quant's strategy appearing not to work. It is almost comical (I worked in finance but not in FX, everyone knew this was going on for decades before the SEC starting fining people) that someone who managed money was making this error.
You are 100% correct about all the other stuff. Lots of issues with "production"...that is why financial firms employ traders/risk people/etc. Most people who trade themselves tend to go for lower-frequency strategies that they can implement personally. I actually don't think there are huge barriers, smaller investors have a huge advantage (when you trade at scale, the market moves against you) but you have to work with what you have and realise that you will get crushed if you try to replicate what someone with more money is doing.
Also, data. Data is expensive, and a huge fixed cost.
Nothing foreign-exchange-specific there although, now that you mention it, dealing with different currencies is another problem you can run into with strategies.
There's also heterogeneous data sources to aggregate and consolidate, each with their own way of measuring things. e.g. You can see how different states and countries are tracking Covid related stats, they all have their own metrics and interpretations. Some even change the way they report overnight. Companies will similarly report their data in different ways.
Data cleansing is its own science and art for this reason, quite separately from developing any algos on it. It's a practical problem that's easy to overlook when you're just looking at ML transformations from input to output data sets.
But, even if I’d implemented it perfectly, and even if the algorithm has survived the financial crash, it would’ve only worked if I could trade for free, and other people copying the algorithm would probably have made it stop working.
My point is when it comes to investing, inaction IS action too. Therefore an investment algorithm does not need to beat the market. It just needs to beat doing nothing.
Consider that if you have earnings you can put money into an IRA account and then trade with it almost for free.
It wasn’t great outside of the spreadsheet.
I forget the exact numbers, but imagine: making 0.1% per transaction sounds amazing until you find out the transaction fee is 1%.
> Consider that if you have earnings you can put money into an IRA account and then trade with it almost for free.
This was the UK in 2005 — no IRAs , and I received 5% on my current account around then.
 Actually it’s worse than that — if you went into a UK bank in the early 2000s and said “IRA”, I’d expect the armed response unit to be called.
Iirc, last I checked, bitmex cost 0.075% to take liquidity, and paid 0.025 to give liquidity (which should be noted, is more than a tick - so, market making on bitmex is free money in an unpredictable market - which has made it technically expensive)
INET had a similar incentive structure before they were bought by Nasdaq, as did BATS when it started - it’s a common way to jumpstart liquidity in a new exchange.
Robinhood and IB will let you trade for free today (with other hidden and hard to quantify costs related to their order flow transactions instead)
So there’s no general solution - the details keep changing - but there’s likely a way to make nice profit of 0.1%.
Because things aren't that simple. I find this argument very similar to that of devs who complain "I wrote this piece of code that made my company $3mil in revenue over the past year, but I only got paid a fraction of that, i am getting ripped off, oh woe poor me". If you could do that, you would have made it on your own and made that much money already.
Turns out, other people at the company are actually doing tons of work to make it possible to make that much revenue off your code. Same applies here. It isn't just as simple as having one good model at a single point in time to be able to make tons of money off it, there are a lot of other people doing their own work at those finance shops that make it possible for those models to bring in tons of money.
At this point this is just a widest/deepest neural net competition on some unknown bunch of features.
> Freely & publicly available external data is allowed
So I presume it would be fair to fetch and leverage additional data on your own.
Best in class engineering and internet scale problems? Nope, you aren't going to find that at any hedge fund. They are much more like small start up cultures. Speed and results are favored over a mature engineering culture and maintainable code.
Want to have the potential to make a large direct impact and make a crap load of money? Well then, a hedge fund may be a good fit.
> Best in class engineering and internet scale problems? Nope
This is mostly true apart from a few specific teams and projects. I think most passionate engineers would find the work uninspiring.
I think in general though a small company (JS has 900 employees, so I'm guessing around 50-100 devs) simply can't hyper optimize their entire engineering stack to the same extent that a large FAANG can. It's far too wasteful. And I'm talking about tooling, infra, and overall process, not just the code. Code review is the minimum any competent engineering org should be doing.
It depends what you mean by "optimizing their engineering stack". They certainly do put a lot of effort into tooling, by necessity since historically there hasn't been much available for OCaml. For an example of stuff going beyond the open source work to make OCaml usable for large projects, see https://blog.janestreet.com/putting-the-i-back-in-ide-toward...
Obviously there is a lot of infrastructure that you need at a FAANG and not at a company with a few hundred devs. But any trading company that doesn't want to pull a Knight Capital needs to make sure their software is correct and reliable (probably to a greater extent than most of a FAANG).
Apart from the OCaml compiler, everything else is fairly typical of the spectrum of roles you can find at the very large high frequency firms. And mid-sized firms are similar yet again, minus the basic research. I would say it is definitely worth working in this industry if anything above sounds interesting to you.
I'm not familiar with this area so I'm probably missing something obvious.
If you have a model that outperforms the market, why on Earth would you give it away for 40k rather than use it yourself and make millions?
If you are really good so that whereas others can only predict the future 0.1s but you can predict the future 5s with the same accuracy, then sure, you could trade from home over the Internet, and if you are much more accurate than others, especially when the market moves a lot, you don't need low fees to be competitive.
That's why I list patenting it after getting rich.
The quants don't work for themselves because they're number crunchers and need the financial knowledge that the trading/portfolio managers have. Either way, the main reasons they don't work for themselves is risk and access to capital.
This is from section A subsection 1 of the competition rules, just for your information. Competition sponsor is Jane Street obviously. If you manage to build a model that can generate returns that would be sufficient to them, you rather trade with it yourself, or use it as a proof-of-work for recruiting interviews.
This is from HackerNews' legal page:
> By uploading any User Content you hereby grant and will grant Y Combinator and its affiliated companies a nonexclusive, worldwide, royalty free, fully paid up, transferable, sublicensable, perpetual, irrevocable license to copy, display, upload, perform, distribute, store, modify and otherwise use your User Content for any Y Combinator-related purpose in any form, medium or technology now known or later developed.
Firm performance is excellent but IMHO the only reason to work there as an engineer is for marginally more money.
Seems counter-intuitive to provide competitors with free information, unless you are trying to throw them off.
The best techniques, certainly coming from teams, are hardly ever published as Notebooks. But yes, many winning teams will eventually incorporate some of the information in the Notebooks, if only to hedge against the others doing the same.
Buying overpriced loss making investments is the fastest way to lose cash.
https://numer.ai is an entire hedge fund built around an anonymous ML prediction tournament. They solicit predictions, trade them, and reward the best performing ones. IIRC They’ve paid millions in prizes over the last few years.
They also recently introduced Numerai Signals, where they pay for the performance of actual training data. So you can make money providing datasets that perform well.
Personally I’m put off by the language used in the video.
> Stake on your model to earn cryptocurrency
No thanks. I prefer dollars in my bank account.
Staking was introduced to reward actual market performance instead of performance on holdout data, and to prevent spam.
Cryptocurrency is the method of payment because it’s a global competition, and that’s the only way to pay everyone across countries. Originally rewards were in Bitcoin but switched to an ethereum token to facilitate staking.
Ideally, a trading competition should penalize risky investments. But this is hard to do retrospectively, especially when evaluating algorithms.
They are quite known if only for the fact they practically adopted OCaml, which is quite impressive considering their size. I highly recommend checking out some of their talks on youtube like this one: https://www.youtube.com/watch?v=gXdMFxGdako
This is all largely thanks to HFT. Robinhood is only viable because big HFT firms are willing to pay dearly for the privilege to serve retail order flow
Mostly it seems like they scrape pennies off out of the market to enrich the people who work there (they have no outside investors afaik), so imo they are pretty neutral. Not a bad place to be, lots of companies are negative.
Why would they need to?