Is there an easy-ish way I can generate "dummy" share price data for the purposes of data visualisation techniques etc.? Essentially I want to have something like the "Adventure Works" of price data.

I don't want to use actual price data of a real company due to issues over ownership/distribution of the data. But something that looks similar to a "typical" history of share prices (over 1 year, say) in terms of its data points is what I'm after.

I considered random deviations from a "start" price, is there a better way?

  • $\begingroup$ (In particular, the issue of what is "stock price-like behaviour" is a financial rather than statistical question.) $\endgroup$ – Silverfish Feb 2 '16 at 11:58
  • $\begingroup$ Ok, is there a way to "migrate" the Q? $\endgroup$ – seventyeightist Feb 2 '16 at 11:59
  • $\begingroup$ I have flagged the question for moderator attention so a Mod will have a look and can decide whether it's appropriate to migrate. For future reference you can use the "flag" button at the bottom of your post for that, but you can't directly migrate the question yourself. $\endgroup$ – Silverfish Feb 2 '16 at 12:01

So you want to simulate a price path, right? Googeling this you will find code in the programming language of your choice.

The usual starting point for share price simulation is the Geometric Brownian motion (GBM) model. It assumes log-normal prices. It turns out that models with jumps often relfect reality closer. Such models are called Lévy models and the GBM is a special case. That's where you should start.

Looking for similar questions here, you find a lot too. E.g. This one.

  • $\begingroup$ Thanks, I didn't think to post here (the original Q was migrated from "stats" (Cross Validated)) as I didn't think that pseudo-share prices would be accepted here.. $\endgroup$ – seventyeightist Feb 7 '16 at 18:23
  • $\begingroup$ This is not "pseudo" .. just check out simulation and you will see that his is part of the theory. Sometimes you have to simulate the price process e.g. if you want to price path dependent options ... this is in the core of finance. $\endgroup$ – Ric Feb 8 '16 at 7:12

I don't know enough about share prices to answer your question, but the UCI Machine Learning Repository is an good resource for real data that you'll likely be able to use for any purpose, as long as you cite them.

From their website:

The UCI Machine Learning Repository is a collection of databases, domain theories, and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms. The archive was created as an ftp archive in 1987 by David Aha and fellow graduate students at UC Irvine. Since that time, it has been widely used by students, educators, and researchers all over the world as a primary source of machine learning data sets. As an indication of the impact of the archive, it has been cited over 1000 times, making it one of the top 100 most cited "papers" in all of computer science.

The citation policy:

If you publish material based on databases obtained from this repository, then, in your acknowledgements, please note the assistance you received by using this repository. This will help others to obtain the same data sets and replicate your experiments.


A few data sets have additional citation requests. These requests can be found on the bottom of each data set's web page.

If you filter by "business" there are few datasets which look like they might contain the right type of information. It might be worth the cost of citing them in order to use them (only necessary if making your public, I believe) because they are free to use for any purpose otherwise, as far as I can see.


Another fascinating option: the so-called random walk which is, in layman's terms the simulation of a random and unpredictable path. More information on Wikipedia and an implementation experiment in Python.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.