Tell HN: Want to build a finance site? Come try our equity data API.
18 comments
Flagged. If you want the feedback of the community you have to at least have the backbone to post a damn URL. Advertising spam like this with some unbounded claim to email so we can "work something out" is an abuse of this system, IMO. Post a link or don't ask for help.
The API is currently not exposed to the public internet. I'll keep your feedback in mind for the future.
Hey, I understand. It's hard to do this sort of stuff, especially inside large companies. However, posting an anonymous "call for participation" of sorts is a serious put-off. At a minimum you need to post a link to the company you represent. Preferably you should post a link to the API itself or some documentation of the API. It doesn't have to be public, private beta's happen all the time and people ask for participation here--but it's done transparently. Personally, I'm a user of the type of data you're talking about. This is what I do. Why should I waste time investigating it more until I know what I'm dealing with? I've talked to all the major data vendors and know what they offer--are you offering something new? I just don't know because you won't tell me, and I'm not going to start emailing around wasting my time until I've atleast vetted that it is something that works for what I do.
Fair enough. Thanks for the feedback.
http://fundamentals.morningstar.com/
http://fundamentals.morningstar.com/
Yahoo data sources:
http://finance.yahoo.com/exchanges
So Telekurs (http://www.telekurs-financial.com) or Interactive Data Rts (http://www.interactivedata-rts.com).
http://finance.yahoo.com/exchanges
So Telekurs (http://www.telekurs-financial.com) or Interactive Data Rts (http://www.interactivedata-rts.com).
You're "one of the leading providers of financial data" and you won't say who you are?
I sent you an email, but I do agree with lrm242 that it would be great if you could publicly give us some more information here. Openness is awesome and stealthiness is overrated.
How "fresh" are stock markets data? Does those include EU market too? Italy, in particular? thank you
Yes, we have EU including Italy.
Personally I think this is pretty cool and appreciate the posting, although I would prefer some sort of link.
Do you have historical stock market data (going more than thirty years back)? Or just recent data?
Do you provide real time data? Last sale? Bid-offer? Full book? Quotes or streams?
Sounds interesting, at the same time, mighty sketchy. Are you going to cut us off from the data once we develop something interesting, and run off with the idea and sell if yourself?
No thanks..
No thanks..
you looking for any particular type of user (company), or are hacker types ok?
Hacker types are most welcome. Show us what you can build.
"Show us what you can build."
Okay:
(1) As we know well from J. Doob's work in martingale theory and there the Doob decomposition, every stochastic process is the sum of a martingale and a predictable process.
So, using the data, estimate both processes. Then use the predictable part of make money?
(2) Also, from the martingale convergence theorem, every martingale either converges almost surely to some random variable or runs off to infinity. The interesting case is the convergence in which case the predictable part is more interesting. Look at the real data and test for and try to find convergence.
(3) For i = 1, 2, ..., let X(i) be the change in the price each time 'tick' i. So, each X(i) is a real valued random variable. Assume that it has an expectation and that E[X(i)^2] is finite.
If the set of all X(i) is independent and if all the X(i) have the same distribution, then, by an elementary version of the central limit theorem, for any i and for not very large n,
Y = X(i) + X(i + 1) + ... X(i + n)
will have a distribution that is quite accurately Gaussian. Since it is accepted, e.g., in the Jim Simons talk at
http://paul.kedrosky.com/archives/2011/01/james_simons_sp.ht...
that the tails of the distribution are 'fat', then at least one of independent or identically distributed has to fail. I vote for independence fails. Then there should be some predictability. Look for it.
(4) If convert dollars to yen to marks to francs to pounds to dollars, then should come out even. Similarly for any such currency trades. But without trading to enforce these relationships, they need not hold in which case there will be arbitrage opportunities. Look at the currency data and see if there are some significant arbitrage opportunities.
(5) Imagine a simulation of currency values: Someone sells $1 billion dollars for pounds. Now other currency values have to change. In this simulation, the rule is, to get the currency values back for no arbitrage, can only make trades of currency pairs, one at a time. So, are doing crude two dimensional iterations to get back to a multi-dimensional 'equilibrium', and that can't be very fast. Or as we know well from non-linear minimization, such iterations tend to oscillate; we try to damp the oscillations with conjugate gradients or quasi-Newton iteration, but, still, there is a lot of oscillation. Also, we do not have accurate global definitions of all the 'supply-demand' curves. So, there should be some 'dynamics' getting back to equilibrium and some predictability. Maybe there will be some multidimensional 'ringing' that would result in some predictability. Look for it.
(6) Take some possibly related real valued stochastic processes and do a real time, distribution-free, multidimensional hypothesis test where the null hypothesis is that the processes are acting as usual. When can soundly reject that hypothesis, take a position that will make money when the process becomes 'usual' again. Where to get a family of distribution-free, multidimensional hypothesis tests? Glad you asked. Use the data to test this idea.
For "hacking", this is applied math, especially about stochastic processes, and not 'hacking'.
Okay:
(1) As we know well from J. Doob's work in martingale theory and there the Doob decomposition, every stochastic process is the sum of a martingale and a predictable process.
So, using the data, estimate both processes. Then use the predictable part of make money?
(2) Also, from the martingale convergence theorem, every martingale either converges almost surely to some random variable or runs off to infinity. The interesting case is the convergence in which case the predictable part is more interesting. Look at the real data and test for and try to find convergence.
(3) For i = 1, 2, ..., let X(i) be the change in the price each time 'tick' i. So, each X(i) is a real valued random variable. Assume that it has an expectation and that E[X(i)^2] is finite.
If the set of all X(i) is independent and if all the X(i) have the same distribution, then, by an elementary version of the central limit theorem, for any i and for not very large n,
Y = X(i) + X(i + 1) + ... X(i + n)
will have a distribution that is quite accurately Gaussian. Since it is accepted, e.g., in the Jim Simons talk at
http://paul.kedrosky.com/archives/2011/01/james_simons_sp.ht...
that the tails of the distribution are 'fat', then at least one of independent or identically distributed has to fail. I vote for independence fails. Then there should be some predictability. Look for it.
(4) If convert dollars to yen to marks to francs to pounds to dollars, then should come out even. Similarly for any such currency trades. But without trading to enforce these relationships, they need not hold in which case there will be arbitrage opportunities. Look at the currency data and see if there are some significant arbitrage opportunities.
(5) Imagine a simulation of currency values: Someone sells $1 billion dollars for pounds. Now other currency values have to change. In this simulation, the rule is, to get the currency values back for no arbitrage, can only make trades of currency pairs, one at a time. So, are doing crude two dimensional iterations to get back to a multi-dimensional 'equilibrium', and that can't be very fast. Or as we know well from non-linear minimization, such iterations tend to oscillate; we try to damp the oscillations with conjugate gradients or quasi-Newton iteration, but, still, there is a lot of oscillation. Also, we do not have accurate global definitions of all the 'supply-demand' curves. So, there should be some 'dynamics' getting back to equilibrium and some predictability. Maybe there will be some multidimensional 'ringing' that would result in some predictability. Look for it.
(6) Take some possibly related real valued stochastic processes and do a real time, distribution-free, multidimensional hypothesis test where the null hypothesis is that the processes are acting as usual. When can soundly reject that hypothesis, take a position that will make money when the process becomes 'usual' again. Where to get a family of distribution-free, multidimensional hypothesis tests? Glad you asked. Use the data to test this idea.
For "hacking", this is applied math, especially about stochastic processes, and not 'hacking'.
We're one of the leading providers of financial data (we provide Yahoo! Finance with stock data). Through our APIs you can get access to global fundamental data (balance sheet, income statement, cash flow, ratios, etc...), prices, corporate actions, earning call transcripts, ownership data, executive compensation, and much more.
I'm excited to make our data available more widely and look forward to seeing what the people build with it.
EDIT: to incorporate LRM's feedback: http://fundamentals.morningstar.com/