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1. Constrained optimization ("solving") is a very well studied, deep field. I assure you that optimization tools exist in dozens of languages, not just excel. P

by kyzyl 14y ago

1. Constrained optimization ("solving") is a very well studied, deep field. I assure you that optimization tools exist in dozens of languages, not just excel. Perhaps you were searching the wrong corner of google? What are you looking for specifically? 2. The folks doing portfolio optimization are generally finance geeks first, and math geeks second (if at all). Like you, they do not want to implement everything themselves and thus are born libraries like Quantlib[1], which has interfaces to "C#, Objective Caml, Java, Perl, Python, GNU R, Ruby, and Scheme" with other bindings on the way or hacked by someone already. This is not me endorsing qlib, but it is certainly a common choice. 3. There is a market for finance tooling, and there are many smart people rolling around in it as we speak. For example, take a look at rapidquant[2] from (formerly?) LambdaFoundry. 4. It can be tough to find a tool that suits you out of the box, but they do exist. My personal preference is python, but at the moment R and C++ win in terms of financial library support. (hint: rpy2) [1] http://quantlib.org/index.shtml http://quantlib.org/index.shtml [2] http://www.rapidquant.com/ http://www.rapidquant.com/

rglullis 14y ago
Thanks for your response. I think the key word about the constraint solver part is "easy to use". I did find quantlib, but in my specific case I was just looking for an immediate substitute to Excel. Learning how to do minimally productive work with quantlib seemed to be a task to take longer than my budgeted time of 1.5 hours/day for the Coursera class. I do plan on spending some more time setting up an environment with quantlib and ipython notebook. But when I was talking about tooling, perhaps I misused the term. I was thinking in finer-grained things. For instance, there is no "tool" that would just take a table of assets and the covariance matrix to give you the portfolio optimization. In a system like Galaxy, you can develop a "tool" that does exactly that and make that available to researchers. They can then incorporate it to their workflow, get the output and put in the workflow pipeline for other "tools", etc, etc. I haven't seen anything like that for finance.
- kyzyl 14y ago
  Yeah there is some startup time required to get to know the tools. In my experience it's worth it, though. I've seen some absolutely mind boggling spreadsheets trying to do what is a couple of lines of work with numpy/pandas. Spreadsheets are dead simple, but get unwieldy very quickly as your data & problem become more complex. I suppose you're right, Quantopian could turn into a galaxy-like tool, but I think there is a great deal of experimentation that goes on in finance that doesn't fit into the pipeline model very well. As well, a lot of the fitting routines run in finance are quite computationally intensive and wouldn't really work on a framework like quantopian. For example, good luck hooking your risk model into a recurrent neural network on quantopian. That isn't to say that bioinf. is not computationally bound, of course it is, but a lot of what's done is more oriented to data pipelines as opposed to fancy computations (correct me if I'm wrong), which is why a thing like Galaxy fits so well.
  - rglullis 14y ago
    Oh, genomics can be very demanding in their computating power requirements. We had a few applications with the running time reported in days/weeks. But still... with all the talk about cloud computing and PaaS, this is the only real use-case where I see a benefit in tying up a platform to something like EC2. In the case of Galaxy, the developers provided an AMI that you could deploy and use your own account. For the Quantopian case, one of their services could be exactly in doing the managing and scaling of these tools. So, the neural network would be a tool that you'd have access to, and you would define how much power should be allocated when running it.

by kyzyl 14y ago

rglullis 14y ago

Thanks for your response. I think the key word about the constraint solver part is "easy to use". I did find quantlib, but in my specific case I was just looking for an immediate substitute to Excel. Learning how to do minimally productive work with quantlib seemed to be a task to take longer than my budgeted time of 1.5 hours/day for the Coursera class. I do plan on spending some more time setting up an environment with quantlib and ipython notebook. But when I was talking about tooling, perhaps I misused the term. I was thinking in finer-grained things. For instance, there is no "tool" that would just take a table of assets and the covariance matrix to give you the portfolio optimization. In a system like Galaxy, you can develop a "tool" that does exactly that and make that available to researchers. They can then incorporate it to their workflow, get the output and put in the workflow pipeline for other "tools", etc, etc. I haven't seen anything like that for finance.

kyzyl 14y ago
Yeah there is some startup time required to get to know the tools. In my experience it's worth it, though. I've seen some absolutely mind boggling spreadsheets trying to do what is a couple of lines of work with numpy/pandas. Spreadsheets are dead simple, but get unwieldy very quickly as your data & problem become more complex. I suppose you're right, Quantopian could turn into a galaxy-like tool, but I think there is a great deal of experimentation that goes on in finance that doesn't fit into the pipeline model very well. As well, a lot of the fitting routines run in finance are quite computationally intensive and wouldn't really work on a framework like quantopian. For example, good luck hooking your risk model into a recurrent neural network on quantopian. That isn't to say that bioinf. is not computationally bound, of course it is, but a lot of what's done is more oriented to data pipelines as opposed to fancy computations (correct me if I'm wrong), which is why a thing like Galaxy fits so well.
- rglullis 14y ago
  Oh, genomics can be very demanding in their computating power requirements. We had a few applications with the running time reported in days/weeks. But still... with all the talk about cloud computing and PaaS, this is the only real use-case where I see a benefit in tying up a platform to something like EC2. In the case of Galaxy, the developers provided an AMI that you could deploy and use your own account. For the Quantopian case, one of their services could be exactly in doing the managing and scaling of these tools. So, the neural network would be a tool that you'd have access to, and you would define how much power should be allocated when running it.