[HN Gopher] Jane Street Market Prediction ($100k Kaggle competit...
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Jane Street Market Prediction ($100k Kaggle competition)
Author : tosh
Score : 169 points
Date : 2020-11-24 18:25 UTC (4 hours ago)
(HTM) web link (www.kaggle.com)
(TXT) w3m dump (www.kaggle.com)
| usmannk wrote:
| As a frequent Kaggler (perhaps too frequent... it's a bit
| addicting, in a way I'm sure others on HN will understand), I was
| fairly intrigued to see this one pop up in the competition list a
| few days ago. Finance shops have tried their hand at Kaggle
| before, but I think they've normally been out of their domain.
| e.g. Two Sigma recently did a reinforcement learning game
| competition.
|
| 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 :)
| riazrizvi wrote:
| Mathematical analysis of financial markets is more celebrated
| when applied to relative valuation of different assets, rather
| than prediction of the market. Black-scholes, for example,
| applied calculus with an underlying no-arbitrage assumption to
| create a thriving market in option pricing, by giving traders a
| mechanism to reduce risk and thereby reduce bid offer spreads.
| Same in fixed income, mortgage, and credit market assets over
| the years.
|
| 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.
| hogFeast wrote:
| Also, prices aren't stationary. For an equity security, you
| are predicting the price for a company that is compounding
| capital over time. You can predict the price for the company
| at one point, the relative valuation for the company (for
| example, against peer group) may not change in one year but
| that company is investing their capital at X% so you get
| price growth.
|
| 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.
| blovescoffee wrote:
| I've wanted to start learning about this for a while but I'm
| really not sure where to start. I have a degree in CS and
| Math so I'm not a total layman wrt the maths. Do you have any
| suggestions?
| riazrizvi wrote:
| What's your goal?
| georgeecollins wrote:
| This!
|
| >> 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.
| riazrizvi wrote:
| To clarify, the hedge fund LTCM in "When Genius Failed",
| collapsed not because it relied on arbitrage 'pricing
| formulae', rather because it failed to properly execute
| arbitrage trades.
|
| 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.
| milesvp wrote:
| To add to this, unless your model is situated, and can
| purturb the market, it has no way of knowing what happens
| when you flex your muscle. I have a friend who did
| algorithmic trading professionally for a few years, and he
| said it was amazing to watch the data. Said he could see
| other bots come along and poke him, trying to look for
| weaknesses in his algorithm to exploit. I would expect a
| purely formulaic trader to underperform other traders who
| can take advantage of others. It's no different than how
| you have to win a rhoshambo turnament.
| fractionalhare wrote:
| Yes, it's overwhelmingly unlikely that the winning model will
| actually be a competitive trading strategy.
|
| 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. [1]
|
| 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.
| usmannk wrote:
| > 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.
|
| 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.
| rahimnathwani wrote:
| "have no domain expertise in the underlying data. This kind
| of approach to finance has historically performed poorly"
|
| 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.
| fractionalhare wrote:
| I can see why someone would characterize RenTech that way
| but it's not really fair to do so. There is a lot of mythos
| about how Simons hired computer scientists, mathematicians,
| signal processing and NLP experts, etc. When Mercer came
| over from IBM, he definitely contributed a significant
| amount of analytical expertise that was probably
| nonexistent in financial trading at the time (with the
| possible exception of the Ed Thorp diaspora). The
| astrophysicists RenTech hires every year bring new insights
| in ways to model and understand vast amounts of data with
| absurd dimensionality.
|
| 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.
| reese_john wrote:
| How much do you think does their advantage stems from
| having high quality proprietary/alternative datasets?
| ciamac wrote:
| That DB/rentech basket option is a tax avoidance scheme.
| It has nothing to do options pricing and concepts like
| delta, gamma, etc.
| fractionalhare wrote:
| Yeah, yeah. That controversy has been litigated on HN a
| dozen times already, I'm not going to rehash it. Do you
| dispute my primary point here? If so, why?
|
| (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).
| nojito wrote:
| Yup.
|
| https://www.rentec.com/Careers.action?computerProgrammer=
| tru...
|
| They look for programmers with knowledge of Tax and Risk
| Management.
| x87678r wrote:
| They even say in the instructions:
|
| 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.
| Spinnaker_ wrote:
| We should also remember that Jane Street is primarily an ETF
| market maker. Their main business isn't betting on prices of
| stocks or managing a portfolio.
|
| 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.
| npmisdown wrote:
| I'm sorry, if you could build a model to predict markets, why
| will you post in to Kaggle to get $40k in prize instead of
| applying this model to your own broker account?
| tikhonj wrote:
| It is _much_ harder to turn a model into a profitable trading
| strategy than people realize. Apart from transaction costs,
| risk management and market impact there are also a lot of small
| operational details which can make or break your execution. One
| example I vaguely recall was that the details of how a specific
| foreign exchange conducted its closing auction could make a
| substantial difference to a strategy that involved executing
| there alongside other trading venues.
|
| 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 :).
| hogFeast wrote:
| I believe what you are referring to is the fix. Foreign
| exchange markets, that I am aware of, do not have closing
| auctions.
|
| 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.
| nstj wrote:
| A "foreign exchange" not "foreign exchange market"
| hogFeast wrote:
| Ah, same principle. I have heard of many similar stories.
| madrafi wrote:
| well because quant trading isn't about import xgboost, you need
| a sustainable infra to handle api failovers, bad data... not
| even going to mention risk management which is 50% of what
| quant trading is about. the data provided is anonymized but
| would probably be a mix of laggard measurements (moving
| averages, rsi...) and maybe some flow data... quant trading
| isn't really about finding "secret stuff" most profitable
| strats you can deploy can be based on stat-arb, basis trading
| or even just delta-neutral funding farming and such
| homie wrote:
| Mostly because it's impossible to accurately predict the market
| - and this is just a competition to see who can build the best
| model.
| thegjp210 wrote:
| HFT firms aren't trying to predict "the market" as a whole -
| just small eddies of it. A typical example of this is arbing
| names at the bottom of index fund rebalances. Speed is
| important mostly to make sure someone else doesn't hit the
| arb first.
| mbesto wrote:
| It's a good question. The basic answer is not everyone has
| capital and risk, but they may have the time and intellect.
| 1helloworld1 wrote:
| Isn't it pretty well known in the finance world that using stale
| public information to predict the market is a fool's errand?
|
| 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".
| Tinyyy wrote:
| That's not necessarily true.
| 2-tpg wrote:
| Using purely historical price data it is harrowingly difficult.
| There are 130 anonymized features, so that's unlikely to be
| only price data. It could include information on the order
| book, correlated assets, fundamentals, vectorized/embedded
| text, etc.
|
| 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.
| Traster wrote:
| One thing we need to be clear about is that you're not aiming
| to be better than average. You're aiming to make a profit.
| There are probably hundreds of thousands of day traders,
| there are probably <100 market makers and tradingfirms (far
| less than that for a some specific products) and you'll
| probably find 99% of the day traders aren't making systematic
| profits. There are lots of strategies that are much better
| than average and still worse than putting your cash in a
| bank.
| 2-tpg wrote:
| You can aim for both. If you just aim for profit, then you
| can get lucky with just average, or even random, betting.
| If you find a weighted coinflip (which is not impossible),
| provided by how many times you can flip that coin, you will
| see steady systematic profits. Of course, majority of day
| traders are getting owned by the big players, and they
| would do better doing more reasoned and long-term
| investments. Most day traders are not even using predictive
| models though.
| Traster wrote:
| On that point - it's pretty clear that this Kaggle
| competition is highly likely to result in a decent number
| of submissions that make more money through luck, than
| other make through strategy.
| minimaxir wrote:
| Granted, a typical Kaggle metagame-that-is-technically-against-
| the-rules is to use data from outside the dataset, which is one
| of the reasons winners have to be validated.
| usmannk wrote:
| Generally this is allowed if you publish the data you're
| bringing in. They even create a sponsored thread for it in
| most competitions.
| justjonathan wrote:
| From: kaggle.com/c/jane-street-market-
| prediction/overview/code-requirements
|
| "Freely & publicly available external data is allowed,
| including pre-trained models"
| blhack wrote:
| >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)
|
| Planet labs will sell you all of that data, in case people
| reading along here are curious.
| cultus wrote:
| I'm sure they've got more/better data in production. There
| seems to be some arbitrage that can be shaved off the edges for
| players with innovative enough strategies and good and timely
| enough data.
| logicslave wrote:
| Theres actually still money to be made in small scale
| strategies. Sophisticated funds are running billions. They cant
| focus on strategies that only work for 100-500k. This is where
| big returns can be made. Even warren buffet will say, if he was
| only managing 1 million, he would get 100% a year returns.
| xapata wrote:
| That doesn't make sense unless there are very few viable
| small scale strategies, at which point they'd probably be
| difficult to identify. Your assertion might have been true
| before computers were able to help someone manage many
| strategies simultaneously.
| smabie wrote:
| no not really. you can use public information to give you an
| edge. And I say this as a person who trades and develops models
| at a _vety_ successful market making firm.
|
| 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.
| hogFeast wrote:
| None of that information is non-public (you can find cargo ship
| data online for free), none of it is particularly valuable (you
| are looking for information, it is hard to know how much
| information is in cargo ship movement...it depends), and most
| non-quant hedge funds have been doing stuff like this for
| decades (i.e. hiring people to stand outside a retailer's
| stores and count customers)...most of this stuff is less useful
| than people think (again, you need information, data with
| intent).
|
| 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).
| master_yoda_1 wrote:
| These are leetcode equivalent for data science and quant. So
| invest time only interested.
| baobabKoodaa wrote:
| Market prediction with anonymized feature set, sounds like
| Numerai: https://numer.ai/
| toomuchtodo wrote:
| Any model superior to what Jane Street is running is worth vastly
| more than the prizes they're offering.
|
| 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.
| [deleted]
| csomar wrote:
| You are ignoring execution, infrastructure and real-market
| conditions. The model is just one part of the game.
| georgeek wrote:
| Such competitions might have two goals in mind: recruiting and
| signal diversification. The recruiting angle is obvious.
|
| 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/
| usmannk wrote:
| You don't have to (and certainly won't) beat all of Jane
| Street. The goal is to beat everyone else on Kaggle. A still
| difficult but much more accomplishable task.
| toomuchtodo wrote:
| Yeah, I'm arguing to not disclose the model. It's worth far
| more held close.
|
| 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.
| Spinnaker_ wrote:
| A backtested model is similar to a great startup idea.
| There is a huge amount of work to be done before it is
| worth much.
| jjallen wrote:
| Assuming it passes some absolute measure(s) of quality. You
| could be the best on Kaggle and still have a mediocre
| model.
| usmannk wrote:
| My point is the winning model of the competition will not
| be worth anything beyond the prize. It will only be the
| best amongst other kagglers, almost none of whom are domain
| experts in finance. It will not stand a chance in "prod".
| lostcolony wrote:
| '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'
| nv-vn wrote:
| >Jane Street has spent decades developing their own trading
| models and machine learning solutions to identify profitable
| opportunities and quickly decide whether to execute trades.
| These models help Jane Street trade thousands of financial
| products each day across 200 trading venues around the world.
|
| >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
| tcbawo wrote:
| This contest seems like the equivalent of the "inventor's
| hotline" infomercial. If it identifies one promising new
| approach that they can iterate on, it has probably paid for
| itself. It also serves as a good PR and recruiting tool. The
| prize is probably designed to bring in clever non-
| professionals. It's a win-win for Jane Street
| Traster wrote:
| If someone has a good idea, you don't want the idea, you
| want the person. If you take the idea, at best you'll split
| the market with the person who had the idea. At worst
| they'll iterate and you'll get nothing. Far better to find
| people who have the skills to develop an idea.
|
| 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.
| amznthrwaway wrote:
| I sincerely doubt they think they'll get actionable ideas.
| It seems like a fun recruiting play from a company that
| takes pride in hiring non-traditional talent.
| elil17 wrote:
| The inventors hotline infomercial is a scam where they get
| you to pay for expensive patent filing, consulting, and
| marketing packages. They never intend to actually use any
| of the inventions.
| b20000 wrote:
| why would they need to pay more? the nature of smart people
| is to undervalue themselves and to not negotiate, so as long
| as smart people keep doing that other people can take
| advantage of that.
| heipei wrote:
| True, and I don't think they expect models that are superior to
| their own, I'd look at this as a hiring / marketing tool. Plus,
| even if you had a model that from a pure engineering standpoint
| was able to match Jane Street's approach, the model would not
| work without the wealth of proprietary (and expensive) data
| sources that Jane Street is sure to ingest, so you still
| couldn't just go out and do it yourself without some serious
| upfront investment first to get the same data. That is assuming
| all data they use is even available commercially, which I doubt
| as well. There are probably data sources that only become
| available to you through personal relationships with the right
| folks at the right places.
| uponcoffee wrote:
| To me, this seems more like a funnel for recruiting
| renewiltord wrote:
| With an engineer phone screen and three on-site interviews,
| that's 4 hours of engineer time. $150/hr compensation per
| engineer, so cost is roughly 2x, $300/hr. So $1.2k to run a
| candidate through the pipeline post-initial-qualification.
|
| 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.
| aaronblohowiak wrote:
| I think you are significantly underestimating the cost per
| hour of jane street employees
| renewiltord wrote:
| I have first party information, two years out of date. Do
| you have contradictory first party information? Yes/no
| will be sufficient for me to adjust my priors and I will
| be grateful.
| throwaway378692 wrote:
| Package for just-graduated SWEs is about $400k.
| [deleted]
| renewiltord wrote:
| Thank you.
| theptip wrote:
| The correct metric is not what the employee's salary is,
| but the opportunity cost of their time. If all your
| engineers are working on urgent stuff, and each engineer
| adds in $1-2m of revenue a year, then the cost to your
| business of taking them off feature work to do recruiting
| is not $150/hr.
| renewiltord wrote:
| Yes, of course. Reasoning for not using that is as
| follows: if conservative estimates yield a yes, you don't
| need to assume more.
|
| I know salary (2 years out of date). I don't know oppo
| cost.
| kgwgk wrote:
| Disclaimer: I have no first hand info.
|
| 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.c
| om/r/cscareerquestions/comments/69k0ap/d... "somebody
| said they got an offer from JS for $150k + $50k/yr
| "performance"-based bonus"
|
| Both may be true.
| renewiltord wrote:
| Thank you.
| paxys wrote:
| Why do you think this competition will result in a model
| superior to what Jane Street is running?
| 2-tpg wrote:
| The winner model will likely outperform anything that Jane
| Street could come up with this 130 feature set. With 3000+
| competitors, the top 10 will likely be superior to what Jane
| Street can do in-house. Then an ensemble of the top solutions
| will be the best possible model anyone can come up with.
| ACow_Adonis wrote:
| That's a general problem with a lot of kaggle-esque comps: but
| then I don't discount the number of unemployed or
| intellectually curious very intelligent people out there, even
| if they're doing the equivalent of pushing down against the
| value of my wage/ bargaining power and doing the datascience
| equivalent of working for reputation/recruitment.
|
| 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!
| [deleted]
| ampdepolymerase wrote:
| Quantopian tried this. Didn't work out. Now they have been
| acquihired by Robin Hood.
| huac wrote:
| part of what makes trading hard, and especially quantitative
| trading, is the necessary infrastructure. not just the obvious
| stuff like execution but also the infra to manage backtesting,
| risk sizing and management, etc. big firms offer this and lots
| of data and allow researchers to focus on the small parts where
| they can become subject domain experts.
| basicneo wrote:
| By a similar argument to https://danluu.com/sounds-easy/ , no one
| will beat Jane Street in a weekend.
|
| Jane Street's hiring standards exceeds FAANG's.
|
| This is a hiring/branding strategy. Good luck to them.
| 2-tpg wrote:
| I actually give it 48 hours before the top 3 equals what Jane
| Street can do in-house on this exact same dataset. A week
| before the reasonable plateau is reached, and a month or so
| before the absolute most information is squeezed out.
| Goosee wrote:
| Sine this is a competition, does someone mind explaining the
| reason for people to publicly post notebooks in the "Notebooks"
| section?
|
| Seems counter-intuitive to provide competitors with free
| information, unless you are trying to throw them off.
| minkzilla wrote:
| Not a frequent Kaggle user but from what I've seen the ones
| posted in notebooks are baseline examples. The things anyone
| who is competitive enough to win has probably thought of and
| dismissed or could implement themselves in half an hour and
| iterate from there.
| mjn wrote:
| As a current example: the highest-scoring entry that has an
| associated notebook at the moment is basically a clean
| example of how to apply XGBoost [1] to this dataset. XGBoost
| ends up being tried in nearly every Kaggle competition, so
| the person isn't giving away many secrets there.
|
| [1] https://xgboost.readthedocs.io/en/latest/
| 2-tpg wrote:
| Posting notebooks can get you upvotes, which contribute towards
| becoming a Kaggle (Grand)Master. It is also a good way to "win"
| some attention and goodwill, without spending months trying to
| actually win the competition itself. Publishing Notebooks also
| helps you improve your coding/presentation skills, for a
| popular notebook needs to be useful for a wide audience (or
| fairly competitive).
|
| 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.
| [deleted]
| x87678r wrote:
| Jane Street is big in OCaml and CompSci worlds but the only guy I
| know who worked there did ETF arbitrage/redemption/creation which
| has to be one of the most boring businesses on the street. Is it
| worth working there (aside from the salaries)?
| nvarsj wrote:
| It depends what you're looking for.
|
| 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.
| b20000 wrote:
| I never heard of Jane street. I visited the website and saw a
| bunch of people crammed into an open office like chickens in a
| breeding factory. That is not attractive to me. Competitions like
| this just seem to me like a cheap way to outsource problem
| solving without having to pay anyone. Like bringing someone in
| for an interview, letting them solve your problem, and then
| telling them they are not a good fit while profiting of their
| work. Same idea, different package.
| ArtWomb wrote:
| So, if I parsed the training data correctly, one's output
| algorithm is completely agnostic to any actual market conditions.
| It's merely learned on the anonymized feature set of 130
| variables. Making it qualitatively no different that any other
| abstract ML forecasting problem. There's no considerations of
| market microstructure, news driven events, leverage, etc?
| mawise wrote:
| In their "code requirements" section they say:
|
| > Freely & publicly available external data is allowed
|
| So I presume it would be fair to fetch and leverage additional
| data on your own.
| yodsanklai wrote:
| Just curious, how well-known is jane street outside of the OCaml
| world?
| nwsm wrote:
| Often mentioned on Blind as one of the places top engineers can
| be paid handsomely.
| reducesuffering wrote:
| Well known to Ivy-League undergrads and FAANG engineers for
| having some of the highest paying jobs available.
| smabie wrote:
| it's well known as a top tier market making firm. They're not
| as special as they are made out to be, though. They just have
| good marketing.
| blatchcorn wrote:
| Just give me some monkeys, darts and a dart board
| bigdict wrote:
| This would be much more interesting if the features weren't
| anonymized.
|
| At this point this is just a widest/deepest neural net
| competition on some unknown bunch of features.
| rrjjww wrote:
| I was just about to post the same thing. I'm a statistician in
| my day job and the raw math is only one part of building a
| model. Human judgement (while often flawed) can be key to
| improving actual model performance.
| arcticbull wrote:
| Stonks only go up what more do you need to know?
| bklyn11201 wrote:
| Is this a just a hiring competition under a different name? If
| you can beat their model, obviously you deserve a 100k signing
| bonus and a very generous compensation package.
| sjg007 wrote:
| Doing well on Kaggle is basically a resume builder and probably
| would get you into jobs you wouldn't otherwise get. Not much
| different than topcoder in that sense. Some people can make a
| living just winning competitions.
| usmannk wrote:
| You don't beat their model, just those of everyone else in the
| competition. A very different game.
| hnracer wrote:
| You can't beat their model because they don't include all the
| data they use in the competition dataset. It's a highly
| sanitized and simplified toy problem used for hiring and
| marketing. They don't even tell you what the features are so
| it's impossible to use domain expertise to constrain the
| fitting problem (this is a critical component to building
| profitable trading models because of the signal to noise ratio
| in the data)
| lwigo wrote:
| Whats the over / under on someone entering the inverse of what
| WSB does?
| x87678r wrote:
| Pump in 2020, dump in 2021.
| afrojack123 wrote:
| Nothing like free code monkey data scientists.
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