>"In the battle for talent, quant-focused firms often are reluctant to call themselves hedge funds or even investment firms. Quant firms would rather emphasize their similarities to cutting-edge tech companies in Silicon Valley."
Mirror, mirror, on the Wall [Street,] who's the [quantiest] of them all?
I sense many money managers can't differentiate themselves with current ops and now want "quant headcount" as a differentiator in pitch decks.
Haha, awesome thought that you've brought up here.
I bet we will start to see title inflation as a result as well- that is to say, it is not that more highly skilled quants will be hired, but rather run-of-the-mill employees will be branded and marketed as "quants"
I've noticed this drift everywhere:
-Sales people are now "product evangelists" or "product specialists"
-Operations employees who do accounts payable or receivable are now "Finance Analysts" (seriously)
-HR are now "Human Capital Analysts"
-And virtually everyone is now "Senior" or "Lead" or even "Manager" (of some process, not of necessarily employees)
So, I guess we shouldn't be surprised when someone fresh out of college who more or less guesses on stock picks with the firm's money and some fancy charts are now hailed as "HIGHLY SKILLED QUANTITATIVE ANALYSTS"
Everyone can try to do it, but you need to spend the time and educate yourself like in any other profession or hobby and the first step is to go to eBay and search for historical stock market data, you can buy 20 years of data for less than $100 and you can test all trading ideas for free and without losing a single penny...the barrier for entry is very low, some Python knowledge + Linux machine and the data and off you go...
Dude, seriously...you have to stop peddling this ebay recommendation. No one is going to be competitive with data purchased on an auction website.
In case anyone is wondering where they should actually get data to be competitive, I can personally speak for these two:
* Nanex NxCore
* CBOE Livevol
I haven't purchased data from here, but I have heard it well recommended from people I trust:
* TickData
* QuantQuote
There are likely others, but bear in mind that the higher the data quality, the higher the price. For most strategies you'll likely want vetted intraday data, preferably at minute resolution or lower. Tick data is better, but it's going to be huge (my drives are near 100TB). You can reformat it into custom bar sizes if you have data on the actual trades/quotes.
My point is that data is not competitive. If you give me an example of a strategy you use with that data, I'll illustrate the issues with using data at the resolution ebay sells (to say nothing of things like survivorship bias...).
If you insist on data at this price point, why not just scrape Yahoo Finance? There are literally GitHub projects that obviate this for you.
Please, if you'd like to help the community, just share a good data source... I would never share my winning strategy with some "know it all" on a public forum :-)
It doesn't need to be your actual strategy, whatever. I have no desire to "steal" whatever your system is.
What I am trying to demonstrate here is that your strategy can perform significantly differently if it is using data on different time resolutions. If you find an inefficiency that can be traded by analyzing snapshot data at 1 minute bars, that might turn out to be nothing at all when you look at the tick data. If your strategy is profitable but you don't have real insight into the trades versus just the quotes, you might have unrealistic expectations for fills, or hold times, etc...and if your strategy is profitable, your data is the first way in which you can try to prove it's not correlated with the broader market's regime.
Thanks,but you don't have to prove anything to me... my trading account is my proof that I don't have to compete with the flash boys to make money, so I don't care about tick data...
Alright, could you help me understand what type of data it is that you purchased from ebay? And what type of strategy you're using, at the highest level? Are you trading equities? Derivatives? Can you at least tell me how long you typically hold a position?
I'm not interested in your actual strategy, I just want to understand what you're doing categorically to see if there's any way it can possibly work instead of being attributable to a bull market. You should be able to talk about the type of strategy this is without discussing the actual signal(s) key to the strategy. This is just becoming kafkaesque for me. I'm legitimately shocked no one else has asked you about this just out of basic curiosity about how you arrived at your current methodology. You mentioned Flash Boys and tick data...but HFT is not the only paradigm for which you'd use tick data.
I'm trying to give you the benefit of the doubt here but from my experience none of what you're doing makes sense. You have to understand that the reason I'm giving you so much flak for this is because you're repeatedly recommending people buy financial data from ebay, you're being cagey about why your recommendation is sound, and your recommendation is utterly alien to the way in which people professionally trade using historical data.
If your data from ebay is actually, somehow reliable, then fine; but if it isn't, recommending it for people who are looking for actionable data is irresponsible.
You can come up with "long term" (hold times of weeks I guess) trading ideas with nothing more than close prices right? On the assumption that you only ever trade the auction you'd be getting a reasonable approximation of your actual experience.
I mean, I'm not sure I accept the premise that there's enough information there to find significant alpha - but if you assume that he's using a different data source for that, he can test the trading performance on close prices alone.
Good morning dsacco, I really like your way of thinking based on the other comments you are posting, but I cannot understand why you are so against cheap or free (if we can find it) data? 20 years of OHLCV daily data containing all listed and delisted symbols is perfect for someone who is just starting testing trading ideas like trend following for example, but most importantly will encourage them to think about their money!
Your alternative for people to pay $3000 or more will discourage all of them and will leave them to be victims of financial advisors, companies like fintech startups, internet gurus and buy and hold evangelists. This is why I’ll always promote the $100 eBay data until I find a cheaper or a free version, maybe you can help the community here?
If you are going to use machine learning on the data, though, make sure you know what you are doing unless you are just using someone else's complete package. It's really easy to screw up machine learning.
I recall an example given in a class I took. (I may be misremembering the details, though).
Some people were trying to apply machine learning to currency trading. They had a bunch of data. They normalized the data (a common step in machine learning), and divided it into training and test and validation sets, and trained their model. Everything looked great, and they were getting excellent results on the test set.
When they went live with real money instead of making the nice profit predicted, they lost a lot of money.
Their mistake? They normalized the whole data set up front. What they should have done is split it into the training, test, and validation sets, and normalized each of those individually. Normalizing before splitting compromises the independence of the sets, biasing the learning.
(I must admit I never did quite understand this. Normalizing is optional. As far as I understand if one does an arbitrary transformation on one's data as a whole that should not actually make learning go bad, at least as long as the same transformation is applied to all the input when you go live. So when they did a normalizing step on the whole data set, why wasn't that just like doing any other arbitrary transform? There is serious dark magic here...)
I've read about a similar scenario (possibly the same one?), and you got the important bits right.
You cannot normalize the entire dataset at once; the normalization contains some parity effectively allowing the algorithm to cheat and see the future... but in real life we can't see the future.
The simple example that eliminates some of the "black magic" is that normalizing against the entire set lets the algorithm know what the highest and lowest points across the entire data set are - and knowing the lowest and highest lets the algorithm buy low/sell high for all of the known data.
Without a minute understanding of what's involved, I'd guess that normalizing is a non-local operation, in the sense that the effect of normalizing some x depends on the other elements of your dataset, so that normalization includes the "information" of the rest of the data implicitly.
In real life, that's sort of like having access to the answers when you're given the questions. Being trained in that environment won't do much good.
I think you've got it. The part about normalization across the entire dataset implicitly including information is by definition true (it contains, at a minimum the true min/max). Can't speak to the rest, though, as I'm still learning myself... :)
1. Almost all buyers are institutional funds or individuals with the means to trade as well-informed investors. To put it succinctly, they're in it as serious business, because it's incredibly expensive. They have no incentive to make the edge they just purchased for five - six figures public.
2. These vendors go to various lengths to protect the data, including steganographic "trap streets" to identify the account the data belongs to.
3. There are significant legal liabilities inherent to releasing the data without a redistribution license. You agree to these explicitly by buying the data.
Sorry, I should have clarified my question. Why aren't the exchanges themselves publishing the data onto torrents?
My layman's understanding is the more actors that participate on the exchanges, the more volume, the more the exchanges profit. If this is true, then making the historical trading data would encourage more entrants onto the exchanges, and the exchanges would earn far more than selling the data to a few large institutions.
I think it's not like there are tons of qualified people with a great interest in quant-y things whose only barrier to entry is a lack of data. It's sort of a monopoly+monopsony thing (I guess, this is highly likely wrong) where there's no incentive to expanding sales further because you're already hitting almost the entire market, and entry to the market isn't a problem for potential buyers.
Actually, Nasdaq makes more money on licensing its data than on the rest of its businesses. You can go to each exchange and buy the historical data directly. Generally, this is better for backdating depth of book data than say a Bloomberg in my experience but requires a lot more upfront work (parsing, normalizing). Additionally, any non-hft trader that handles institutional trading needs to pay a small fee (roughly $20/month per exchange) in each system that requires real-time data. Also, little-known fact, no retail trading orders hit the actual exchange. Even institutional investors who are not brokers (like most hedge funds) must execute through a broker/EMS.
I have a hard time seeing how it could be copyrighted, at least if we are talking about comprehensive stock listings organized in the obvious way (e.g., a table of prices organized by date, where the stocks included are chosen by some straightforward criteria).
In the United States I'd expect this to be covered by Feist Publications, Inc., v. Rural Telephone Service Co., 499 U.S. 340 (1991).
It's considered a "compilation". Feist addresses this in the links you supplied. However, if you're not used to reading law, this might help explain it in normal English. (Court documents are most definitely not normal English!)
That was why I threw in the mentions of comprehensiveness, obvious organization, and obvious selection. Feist requires at least a minimum degree of creativity in the organization and selection for a compilation to be copyrightable.
A compilation whose selection criteria is, for example, "everything traded on NASDAQ" and whose organization is "order by date" seems unlikely to me to have sufficient creativity to qualify.
I trade options using Livevol data. Good historical options data doesn't come cheaper than about $3,000/year (I'm willing and grateful to be proven wrong, but I've looked).
My question for these new would-be quants is, how do you see your work?
I know there are certain types of smart whizzes who see working for Google/Facebook/Amazon as some sort of intellectual step down, or where in finance the value to society is some handwavy "market-making" argument (depending on how philosophical you get), but presumably these types going into this are also tenure-track, research producing scientists. You could spend your time disproving the Higgs at Cern, or you could optimize pennies (albeit in ever more challenging ways).
Have these Renaissance/TwoSigma type firms really ever delivered social value that isn't a new trading model with a limited shelf life? Is there new tech? Network, hardware, software innovation that spills over into the real world?
You speak as though there's an enormous gulf in producing social value between working at Facebook and working at Renaissance.
Speaking as a physicist who left the field, the vast majority of research physicists are not going to make a significant impact on advancing human knowledge. They're all too busy teaching bored undergrads, competing for underfunded grants, and politicking to get their name onto more papers.
I'd rather do math and make lots of money. Wouldn't you?
I know more than a few dudes who work on Wall Street, most of whom graduated EE or CS from Carnegie Mellon/MIT/Harvard.
Most of them view it as a mercenary job, nothing more. They don't give a shit about finance itself, but they enjoy the money, they enjoy solving problems - especially ones that are seen as impossible - and obviously they also enjoy the deep technical aspects of the job as well. It scratches a lot of different itches and it's a very high prestige gig ("Wall Street", "Wolf of Wall Street", etc.) to boot.
Look at it another way: if they're out in the Valley, they're one of thousands of talented engineers, competing against gender disparity in a very competitive social arena. In NYC, they're young, brash hotshots working less hours for more money with all of the ego, bonuses, and cocaine that goes with it, in an infinitely more exciting city. Tell me which setup you'd rather have.
If you really are a talented machine learning specialist, you don't have too much to compete with in San Francisco. A lot of "ML engineers" and data science people in SF dont actually have the background or expertise to call themselves career machine learning specialists. They take free courses and started using tensorflow once it blew up. I know because I was one of those people (fwiw im going back to school to actually learn the fundamentals now).
Also, I know people in the situation you describe, and its a grind like every other bonus incentivized gig on wall street. People may tell you otherwise, but they're probably sugarcoating. I know a few people who burned out early and started their own companies, and a couple who are continuing the slog for huge bonus payouts. The no free lunch theorem holds true.
For me, I'll finish up this degree and see where things are headed. I'm definitely not set on SV or WS yet, but it'll probably be one or the other.
> very high prestige... Wolf of Wall Street... ego... cocaine
The lifestyle appeals to some, but not all, and Wall Street's "very high prestige" doesn't really exist outside NYC and its immediate neighbors. For most people, finance is associated with greed-fueled, drug-addled sociopaths who have to pay for sex and companionship, since that's what's portrayed in "Wall Street," "Wolf of Wall Street," the news, etc., and they don't associate that with prestige.
There are a lot of cautionary tales about Wall Street for a reason. Some in finance get out alive, others end up washed up or burnt out, barely maintaining employment, battling chronic depression due to years of abusing their brain chemistry. A million dollar bonus would be nice, but the other stuff I can do without.
Someone else mentioned pandas and AQR; I'll also mention BeakerX (Beaker Notebook) which was developed by Two Sigma: https://github.com/twosigma/beakerx
Jane Street is also pretty awesome - they open sourced their in-house standard library for OCaml, called Base: https://github.com/janestreet/base (they use a lot of OCaml).
And to be clear, although Base is a more recent alternative, Core: https://github.com/janestreet/core has been the standard library for Ocaml for a while.
There's nothing handwavy about the liquidity argument. Improving liquidity reduces the cost of trading for almost everyone in the market and thus makes almost everyone just a little wealthier. That means ordinary people, like schoolteachers and custodians, retire with a bit more money, and are a bit more comfortable; it means lots of people can donate just a bit more money to charitable causes without crossing whatever financial safety line they have, &c.
It's hard to make the same kind of argument about adtech.
No one disputes the quantitative benefits of increasing liquidity. But I've yet to see it quantified in a mathematically rigorous way. Are the speculators and active traders actually generating more value for society as a whole then they're syphoning off? (That's an honest question, I'd really like to know.)
I think that, in terms of social value, the point of diminishing returns for market making and liquidity has long since passed, especially wrt social value for Avg. Joes & Janes.
I def. don't hold Facebook etc in a much higher regard here, but if we're talking about brain drain from socially useful fields, I don't think anyone can credibly argue that finance (esp. HFT et al) provides any meaningful social value.
In fact I'll go one further: by sucking up the best and brightest, high finance is doing a net harm to more socially useful sectors.
I'm not being prescriptive wrt what choices people should make, btw, just speaking in terms of social good.
HFT firms are make money by taking a spread, which is effectively the cost of making a single trade. As they compete and get better and better at there jobs, this spread well be reduced, the amount of money available to pay for the best and brightest programmers will come down, and they'll suck fewer of them away from other parts of the economy.
The point being, the further they go past this 'point of diminishing returns', the less money they can possibly make. To the extent that HFT is a zero sum game, there will always be a limit on how much money they can spend.
(I believe but cannot prove that this limiting effect has already started, HFT firms are consolidating and making thinner margins than they used to)
That's a good point, but would that happen quickly enough to undo whatever brain drain problems the industry creates?
Nearly all hedge funds still take 2/20 for example, and their returns have been questionable for a decade, so idk we have reason to have faith in high finance's ability to self correct.
Every time someone brings up social good, I keep wanting to know what that means. Can we have a truly objective definition of 'social good'? Or is it entirely dependent on the individual person's definition of what that means to them.
I agree that it's not a sufficiently precise term, but I think it's obtuse to suggest it's so ambiguous as to be deceptive or obstructive to discussion.
How's about: an act that is socially valuable has benefits, immediate or otherwise, realized by parties that aren't directly involved in the transaction.
Eg, a blackjack dealer's labor probably only matters to her players and her boss, but a cancer researcher impacts well beyond that. Both can be lucrative, but one is clearly more socially valuable.
i agree with you and i think it is a good point but just one doubt i have that liquidity argument is correct assuming that this doesn't effect the fundamental pricing and cause bubbles (for ex.[oversimplified one] if lot of people followed trend following - it keeps driving bubbles in the long run - although i know counter argument is markets actually become efficient with more trading, but it is hard for me to buy into that 100% - i hope i'm wrong though). Also I think a decent gauge would be how much open-source has been contributed back and how much of it is valuable. Other things are hard to quantify like the example you gave, but I can't prove that you're wrong either.
Also when you develop something like physics, once proved - it always works with very good precision(reason we can launch satellites accurately). Or let's say a software or database that is supposed to work a certain way.
What's being done at quant funds - I can't think of something on those lines that could be useful. Lot of these things don't always work because you're still speculating with large inaccuracy but a slight edge statistically.
I'm not going to comment on the liquidity argument (it's a dead horse in my opinion), but another commenter and I mentioned a few examples of open source contributed by "quant" funds.
Not everyone at Google works on adtech. There are many people working on browsers that have sped up and innovated the web for hundreds of millions of users, or operating systems that are the foundations of many people's lives.
All I see Wall St and Silicon Valley doing is using new found speed and scale for their own benefit while concocting self deluding narratives about how this benefits everyone.
Nothing extraordinary about that.
Thomas isn't being condescending (at least not intentionally) when he uses the term, "ordinary." He would also fall into "ordinary" under his own definition, as he's not in that industry.
His point is that, contrary to the narrative of the "little guy" getting screwed by Wall St, they are actually helped by the liquidity HFT market makers contribute.
Everyone is unsophisticated at something. It's not an insult.
none, their goal is to optimize profits. They haven't gotten to a point where investing in cutting edge tech only research is seen by them as a value add. Also for such projects they stay away from open source collaboration with outside world - so that would be really slow to develop something useful. Hope that will change in future.(very few exceptions - as someone commented pandas - pandas creator at two sigma actually is pushing for open-source, i think it is great but i hope it is not just a gimmick to attract better talent).
To be honest with you, I consider most examples of companies releasing open source software to be commoditizing their complements and streamlining recruiting, not altruism.
The result can be altruistic, I guess, but my point is that judging intentions with regards to open source on the part of large companies is a fool's errand in my opinion.
good point, what do you think about tensorflow, openai gym, python, apache foundation, linux foundation? you gotta have some love for open source if you're a developer. i might not be as well read as you but i would like to be corrected if my thoughts are incoherent.
also i don't think it is about altruism so i agree with you, there's mutual incentive to open source things like tensorflow i.e. to speed up development.
I use Tensorflow, but it's a fantastic example of my point. Google open sourced Tensorflow because the real strategic advantage in machine learning is data and talent. By commoditizing the software used for machine learning, they broaden the market for their expensive, high margin data, and lower the barrier for talent to enter the industry which they can hire.
Anyone have hard numbers on if/by how much quants outperform old fashioned techniques like flags and finding stocks that tend to go the opposite direction from the one you're interested in? I don't know the terminology for all this but I know there are limits due to uncertainty so even the best algorithms may not do much better that someone guessing. Do quants do 10% better, 2x better, 10x better?
My Dad and I had a relatively lucky streak where we doubled our money day trading Apple on margin after the September 29, 2000 dot bomb:
He saved quite a bit of money by being able to trade 1000 shares each time. Since the stock swung +/- 2% a couple of days a week it was pretty easy to make 2% most days, 5% on a good day. It was like a casino where the odds were 2% in your favor, with a ratchet that just sold when it was about to go down. Then you just guess the shape of the day’s heartbeat. He wouldn't even let me sell short because he felt it was unethical, so we only gained half what we could have which was agonizing to endure. I pleaded for him to get out because he was up a couple years of my wage at the time and my gut was screaming at me that something wasn't right. Then we lost all the gains the day 9/11 happened. We sold a few weeks later and ended up breaking even. Then Apple went to 500 over the next couple years with splits thrown in to boot.
I wanted to try day trading myself but they changed the law in 2001 so you had to have $25,000 to trade on margin, so only the wealthy could get twice the gains:
I personally don’t buy any of the standard advice about risk because it’s more risky to keep your money in a bank and only get a couple percent a year. I felt so miserable about the whole experience that I worked a bunch of dead end jobs and ran up my credit cards over the next several years. It’s heartbreaking to know how hard typical folks in the world work to make $100 a day when day trading $50,000 can easily earn $1000. But those folks don’t have $50,000 so are locked out. I guess in my heart it felt like stealing, or at the very least finding yourself in the universe where you won and thinking you somehow earned your survivor bias. So I don’t do it anymore, and I’m even hesitant to invest because politics are so volatile right now. Sorry to digress, I should have stopped at the first paragraph.
I don't do this, but there is a place without any rules, 24/7 trading, wild volatility, and tons of dumb money. This place is cryptocurrencies. Supposedly it's a lot easier to trade in these markets, less competition, less money needed. I used to laugh at my brother, but he turned $20k into $300k, so it is possible.
Well, crypto markets have much better odds than the lottery right now. Even without understanding the nitty gritties, if you just diversify in the top 10-20ish coins, and set sensible stop losses, you are in a very good position to come out ahead.
The Dutch tulip bulb market also had great odds in 1635. How did that work out?
Stop loss orders are ineffective when markets lose liquidity and experience sharp price discontinuities.
Sorry to digress, I should have stopped at the first paragraph.
No, it's good digression, and it brings back fun and painful memories, as I made and lost money at that time.
Trading on margin is something rookies should avoid. It's the easiest way to lose money.
The thing I don't understand is this noise about shorting being unethical. You have more to lose than anybody in the game, and you're not breaking any rules.
But even with all of those advantages, you didn't make $1000 a day. You broke even and almost lost money. In order compare to someone making $100/day you need to make money consistently for a long term. No one can consistently turn $50k into $365k/year because if you keep making bets like that, you eventually lose just as big as you win.
> Anyone have hard numbers on if/by how much quants outperform old fashioned techniques
From the article, "In the past five years, quant-focused hedge funds gained about 5.1% a year on average. The average hedge fund rose 4.3% a year in the same period."
Meaningless for telling us the distribution of returns across all hedge funds.(It is very likely a Pareto distribution with, or even more skewed, with a small percentage of funds making the majority of returns.)
Interestingly, what these averages do tell us is that the average hedge fund return is not much, if at all, better than the average index fund return, with the index fund return graph being much more uniform in variance.
And without knowing the distribution, we have no idea whether the difference in quant funds vs non-quant funds is distinguishable from zero. Whenever someone tells you the difference between two averages and nothing more, you should assume they are "lying" with statistics.
> I wanted to try day trading myself but they changed the law in 2001 so you had to have $25,000 to trade on margin, so only the wealthy could get twice the gains: https://en.wikipedia.org/wiki/Pattern_day_trader
This is how the rich get richer and the poor get poorer. The rich can trade on margin, pay less tax (capital gain tax is lower than even income tax), and have many more other opportunities. The middle class is shrinking for a reason.
This is ridiculous. Poor people aren't going to get rich by day trading on 2x margin. And if someone in the US seriously wants to get into margin trading it's not that tough to scrape together $25k by getting a second job and living frugally for a couple years.
Also if you have decent credit you can simply take out an unsecured personal loan for $25k and deposit that in your trading account. This is effectively the same thing as trading on margin.
Why do the poor have to get a second job in order to get 2x margin? Why do they have to jump through the hoop when the rich don't have to? If the poor have $2,000, why cannot they use margin as leverage? The opportunities are not equal there.
If the margin trading limit was lower then the news would be full of sob stories about how greedy brokerages let unsophisticated people get in over their heads and then took their life's savings on a margin call. Sometimes stocks go down.
It is not fair for everyone. But this is an artificial, unneeded hoop that could be easily removed. If we set it at $25,000, does that prevent "unsophisticated people take their life's savings on a margin call"?
You're speaking of long term capital gains. Day trading results in short term gains which are taxed at regular income rates. Margin accounts are also generally foolish; a way to lose money. They are not usually at favorable rates.
The rate for margin is yearly 8% for my brokerage account. The NASDAQ has gone up 15% since Trump got elected. If you use it wisely, it is great leverage. The poor don't even get to use it. They are completely shut out of the opportunity.
How much leverage do you think a poor person should take for speculating in the stock market? 15% annual return on nothing is still nothing. For most poor, doubling salary would have dramatically more impact than doubling their savings account.
This says how disconnected you are from the reality. Many Americans don't even make $100 a day. $10,000 investment plus $10,000 margin multiplied by 15% is significant for many people to buy food and pay rent.
Oh boy. Your criticism is that I overestimate wages and your counter-example is a $10,000 investment? "Nearly half of Americans would have trouble finding $400 to pay for an emergency" (https://www.theatlantic.com/magazine/archive/2016/05/my-secr...). How are these sub-$100 per day folks going to scrape together $10,000 for investing?
Anywhere below $25,000 does not get the opportunity to use the margin. $10,000 is still far from hoop. $100 per day will get you $2,500 per month conservatively. For a few months of tight pants, you will be able to save enough for $10,00-. You are missing the point here. It is about the principle. Everybody is created equal and should get equal opportunities (at least in terms of law).
I agree with the principle of equal opportunity, but I disagree with the implementation. Providing the "opportunity" to buy stocks on margin to people with less than $25k in their accounts is more like selling cigarettes than selling education.
Just today I noticed a sign posted in the window of my corner store, "Customers of [convenience] store won $1,490,823 from [state] lottery in 2016!" I wish it were required to post the total paid as well. It'd be much better for the public if the sign read, "People in your neighborhood paid $3 million to the state lottery and only won $1 million back."
Similarly, if the brokerage is going to offer margin accounts, they should post easy to understand statistics on how much customers earn and spend. A nice histogram showing returns with sliders to select a bucket for initial amounts invested and proportion bought on margin.
Enforcing free information access is another way that governments can help provide equal opportunity.
There's a plot where they differentiate "quant hedge funds" from "other hedge funds". How are they defining this? (To what extent is this just a matter of quant being sexy and more firms describing themselves as quant?)
It's about whether the computer does the analysis or not, but that's just a stand-in. The real difference is whether and idea or investment strategy can be systematized to the point where it's automated. That implies that there's no emotion or personal bias in most individual trades.
Contrast that to humans doing the picking where there's a fair amount of "lick the finger and hold it up to see which way the wind is blowing"
The other commenters are correct-ish about "quant" meaning something like, "computers and technology." But for the most part it's now a marketing term. Discretionary/fundamental hedge funds also use data pretty extensively, and they also use trading algorithms. It's largely about technology culture, not simply the way in which trades are chosen (or not, as it were).
For the most part, execution does not require high math, but the modeling required to find arbitrage or alpha does.
So for example, managing an index weight fund is simple on the face of it but when and how you choose to rebalance your portfolio can affect your drift. This is the part that requires high math. This is even more important if you're talking a billion dollar fund as opposed to a 100,000 personal account.
for index funds the core problems are estimating risk and return, minimizing effect of uncertainty and std err on portfolio tracking, and fast optimization for large portfolios (>1000 assets).
these are fairly long term problems so stochastic calculus not too useful here. portfolio risk is a very classic stats field.
it's not really clear that the math is "high" from the above comment but portfolio management, even the really boring sounding ones like index replication, uses the entire gamut of "high mathematics": robust and Bayesian statistics, non-parametric statistics, extreme value statistics, signal processing, numerical optimization, etc.
> the modeling required to find arbitrage or alpha does
Not really. Even if everyone is just throwing darts, half of the players will beat the market, and the more players you have the more extreme the outliers will be. Of course, the converse is also true, but no one pays attention to the losers. Both Vegas and Wall Street prosper from this same principle.
Virtu only lost money trading one day out of 1278 trading days between 2009 and 2014. In the most uncharitable analysis (1278/2; or the lost day happened in the middle), they had a 0.5^639 chance of doing that.
Maybe you disagree with 0.5 per day. Let's make it 0.9!
...But that's still 5.7 x 10^-30. How many firms do we need to exist for this to emerge by chance?
This and website bug bounties being sold on the black market are my two HN crusades. I have heard darts, I have heard coin flipping; I hear all manner of analogies from people who stubbornly insist or strongly imply that consistently, purposely beating the market is infeasible in principle.
But no one ever does a modicum of analysis to support whatever trite analogy that's thrown out, they just wave their hands and exclaim, "statistics!".
So here is something actually quantifiable. If you have something quantifiable to refute my analysis of your analogy in turn, please share it by all means.
> Virtu only lost money one day out of 1278 trading days
Lost money trading. If they made $1 trading that counted though I assure you that day was a loser from a business perspective. I also have not lost money trading in 3 years, simply by not trading at all.
It doesn't really detract from your broader point, which I don't have a strong opinion on but that stat is a pet peeve of mine as it's fairly meaningless.
This is massively incorrect. If Virtu only made $1 trading, they would still make massive amount of $$$ via exchange rebates, as they are a designated market maker. In fact, they aren't the only market maker that does this. A market maker guarantees they'll take trades on both sides of the book, so long as their strategies aren't grotesquely losing, they'll always come out ahead due to exchange rebates.
Source: I worked for Virtu's predecessor Madison Tyler for 4 years and for Virtu after it merged with Madison Tyler for ~8 months and have first hand experience with HFT.
I actually think that exchange rebates reinforce my point. I've seen algos that would dump trading profit to get their trade levels into particular rebate levels for instance. Those algos would look bad under the metric of "never lost money trading" but look good under the metric of "holistic profitability".
I just am annoyed when people site the former when talking about Virtu, both using it pejoratively or in praise of the firm, because I don't think its a terribly useful thing to say. We can judge Virtu the same way we judge every other firm on the planet, by actual profits, there is no need to come up with a new metric for them.
In fairness, while I agree with your second paragraph, Virtu's example is a very neat case study for the limited dialectic purpose of demonstrating trading consistency, not necessarily profits. I have found that using profits for this particular debate isn't convincing for others, even if it shows a fuller picture; the actual win rate for trades is a simple and quantifiable point to refute comparisons to e.g. coin flipping because it shows that they are capable of doing something over and over in a market that is claimed to be resistant to such a thing.
But when people are talking about coin flipping, they are talking about real profit, not trade PnL. If your overhead to get into a trade is more expensive than the PnL on that trade, your consistent ability to make that trade is not a good thing, and that negative should be counted against your win rate wrt things like the efficient market hypothesis.
Again, I actually don't have strong opinions about coin flipping and trading being equivalent (and I think Virtu is likely a bad place to have that conversation vs a hedge fund because its not really an investment firm its an execution one) but I think claiming trading PnL refutes it is wrong.
For whatever it's worth and on the off chance that this short-circuits a really common message board pathology, and allowing that I'm only commenting here because I read all of Kasey's comments: he's got HFT experience too.
I think he's just saying that particular metric is missing context: a lot of crappy businesses could claim never to have lost a dollar trading, while still as businesses losing tens or hundreds of millions of dollars chasing, and not finding, profits.
I don't think he's arguing specifically that Virtu lost money.
Ah fair enough, in that case I'd agree, but it didn't appear to be what he was saying in the comment I responded to. I've got going on 10 years of HFT experience as a Software Eng / Systems monkey and try to at least set the record straight. If you don't understand simple HFT economics such as Maker/Taker rebates, you have absolutely no business whatsoever commenting on market making firms, especially Virtu, which is fundamentally (until they close their KCG acquisition and do execution for more banks at least) a very large and very sophisticated Designated Market Maker.
I'm only responding to you, because you're my fav HN personality and tend to have a lot of interesting things to say at ChiSec :)
Fair enough, that's correct :) But as you said, it doesn't detract from the point, even if we shift the definition a bit. The likelihood is still clearly beyond what would emerge by chance.
There's such a thing as real alpha and also just luck. In a market with so many players, you would also expect that random chance would predict many people winning consistently year after year.
This is not to say that all such funds can be explained this way. But most of the successful ones likely yes
For those who don't know this trick, click the web link and go to the site via google (which is what the web link does) and more often than not the paywall disappears and you can read the article (at least consistently for WSJ articles this works).
I do wish the web link was on the index page instead of the comments page so it was faster/easier/more obvious to use it.
Have you tried from a mobile device or using a mobile browser's user agent? Seems like a lot of work but right now a lot of companies care only about mobile views and want to show growth in it-- so there's some reason to believe it would work for mobile but not for desktop. Simply because the company doesn't want to send away mobile traffic so their metrics look good.
Ditto. This is a tough situation. WSJ articles are generally high quality and probably worth paying for, but if there's an unbypassable paywall then there's no reason to feature it on HN.
We could use it as a prompt. Maybe the comments could be a zone to talk about quants and wall street. That might be useful, but it'd have to be explicit.
EDIT: I've actually wished for prompts on HN, for what it's worth. Other sites typically have ways of starting conversations without articles, and Ask HN's usually die except for a few lucky ones. Usually you end up in a situation where you want to talk about X but have to wait until a tangentially related comment is posted, and then sort of shoehorn it in awkwardly. Being able to spin up a "Quant thread" would be nice without a specific topic or question.
Regardless of its status on HN, I should consider subscribing. It looks like it's about $400/year for a digital subscription. A little over a dollar a day is very reasonable. And it looks like there are specials going on right now: $198/year for the first year; or $99 for six months; or $1 for two months. https://buy.wsj.com/wsjusmemorial17/
As for using the headline as a prompt for discussion, I'm quite sure that's all that drives many commenters on HN :)
I appreciate your comment and I mean absolutely no ill will by my response, but in my opinion, $400 a year (or ~$1 a day) is not even remotely close to a reasonable price, for two reasons:
1) my friends and I write to each other every day with more nuanced, researched, investigative journalism/analysis than the WSJ could ever hope to put out. I frequently release anonymous pastebins of my (hopefully impartial, unlike WSJ) analysis for free as a public service and I hope someone promotes them
2) the amount you quoted, $400, is somewhere around 1-2 months worth of total income for easily a billion people in this world. $400 would be considered an excellent monthly salary in a variety of countries, such as Thailand, Vietnam, Bosnia, Serbia, Albania, Ukraine.
So for likely the majority of the world, $400 would be almost unjustifiably high for words on a page that don't really offer any profitable or immediately actionable ideas
The value of something across the span of milliseconds has no meaning. At this temporal level of resolution it's just a gamble no different from games like poker. Why deploy brain power on trying to extract this value? What a waste of intelligence.
If you haven not been keeping up, since about sometime in the mid-1980's acquisition of Money became the defining virtue of goodness and success. That's all that matters now.
The problem is that it's more like sports than math/science: a tug-of-war (zero-sum game) instead of win-win.
You're pitting your intelligence against the intelligence of other quants (not against the complexity of some natural system or something). You have 3 phds? they bring in 4. You bring in 6? they bring in 10. You bring the processing down to 50ms, they go 45ms; you go 5ms, they go 2ms. The game never ends.
It's even more depressing when you realize that, even if you win, you're essentially propping up, what I like to call, a "fluff market", instead of adding-value the way Silicon Valley and the manufacturing industry does.
This made me wonder if it would render well in emacs using eww. Surprisingly well rendered, actually. Odd that it doesn't have any pictures. But easy to read.
There are people who believe paywalls are the least-bad solution compared to a world where journalists can only make money from advertising (thwarted by adblock) or writng corporate puff pieces and lowbrow clickbait.
Paywalls can be frustrating. As far as I'm aware, the web links for WSJ no longer work, and I intermittently have issues with FT. Are there others you have issues with?
Other than those, NYT. It's usually not too bad but iirc they are dropping support for work arounds. It also seems to be posted with very high regularity and volume here.
I'd like a flag, but I don't want it to hide paywalled articles, but rather to just make them a different color. That way I can skip the link and just focus on the discussion.
I think this would cut down on most of the meta-discussion that happens every time a WSJ or FT link gets posted.
Mirror, mirror, on the Wall [Street,] who's the [quantiest] of them all?
I sense many money managers can't differentiate themselves with current ops and now want "quant headcount" as a differentiator in pitch decks.