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Jane Street Market Prediction ($100k Kaggle competition)
91–100 of 217 posts
Re: Jane Street Market Prediction ($100k Kaggle competition)
#92Jane 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)?
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.
Re: Jane Street Market Prediction ($100k Kaggle competition)
#93I'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?
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 involv…
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.
Re: Jane Street Market Prediction ($100k Kaggle competition)
#94I'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?
It's a good question. The basic answer is not everyone has capital and risk, but they may have the time and intellect.
Re: Jane Street Market Prediction ($100k Kaggle competition)
#95Isn'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".
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).
Re: Jane Street Market Prediction ($100k Kaggle competition)
#96Earlier quoted context omitted.
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 involv…
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…
A “foreign exchange” not “foreign exchange market”
Re: Jane Street Market Prediction ($100k Kaggle competition)
#97Earlier quoted context omitted.
>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, an…
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
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.
Re: Jane Street Market Prediction ($100k Kaggle competition)
#98These are leetcode equivalent for data science and quant. So invest time only interested.
Re: Jane Street Market Prediction ($100k Kaggle competition)
#99Earlier quoted context omitted.
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…
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…
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.
Re: Jane Street Market Prediction ($100k Kaggle competition)
#100Sine 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.
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.
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.