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Quantopian’s algorithmic trading platform now accepts outside data sets

42 points by jbredeche 14y ago

fawce 14y ago
Here's an example of fetcher in action: https://www.quantopian.com/posts/new-feature-fetcher https://www.quantopian.com/posts/new-feature-fetcher
- wicknicks 14y ago
  Some additional details about Fetcher: https://www.quantopian.com/help https://www.quantopian.com/help (You will need to scroll down to the Fetcher section -- couldn't find anchor links)
  - fawce 14y ago
    here you are: https://www.quantopian.com/help#overview-fetcher https://www.quantopian.com/help#overview-fetcher
spitfire 14y ago
Cool! Now add risk management. It's not sexy, but it is table stakes.
kyzyl 14y ago
Good work! This is an important step. For some people not being able to use their CRSP data (or whatever) is simply a deal-breaker. As someone who once implemented most of the bits that make up the quantopian functionality, I can certainly appreciate the effort you folks are putting in. I believe it's been mentioned a few times now, but have to agree that risk metrics should be pretty high on your priority list. Sharpe/sortino/omega/etc. are great to have, but you really need to be able to build and experiment with variance models. As in all science, a number doesn't mean much without an error estimate :-) GARCH models are pretty popular these days, and they are fairly non-trivial to implement correctly (especially in their vectorized form), particularly for people new to the field. Now for the most part this stuff _can_ be implemented by the users in their algos, but it's often a non-starter when it comes to the more complex stuff like vector autoregressive processes, bootstrap analysis or sane fitting routines.
rglullis 14y ago
Just slightly off-topic, but the quant.ly forum is so dead, and I think the Quantopian guys might chime in here. I started taking the Finance Engineering class on Coursera, and one of the things that did not help me was their reliance of Excel as a way to solve most of the exercises in the problem sets. They even have part of the lectures dedicated to "how to use Excel for X". For solving the problem sets, they mention Matlab, but that's about it. Suffice it to say, with my refusal to buy a license of Excel and LibreOffice not having the requested features, I now found myself having to learn the theory and writing a bunch of small pieces of ad-hoc code to re-implement the formulas. And no, I could not find an easy to use constraint solver, in either Python or Octave. So my task of "learn about Mean Variance Optimization" also included "learn and re-implement a constraint solver in NumPy", and the first lesson I'm actually taking away from this class is "forget about all and any decent tooling if you want to work in Finance." There was another field of work where I experienced something like that, which is bioinformatics. Not to that degree, though. There is still way too much spreadsheet-emailing going on to my taste, but it was recognized as far from ideal. So at the lab there was a big push to adopt Galaxy as part of everyone's workflow. I no longer work over there, but I'm sure that the ad-hoc, sloppy pieces of R and Python code are being replaced by better integrated and more efficient Galaxy tools. Going back to Quantopian... am I too far off to wish that it could become the "Galaxy for Quants and finance students"? Am I too much of a finance n00b to think that it makes sense to have something like Galaxy, but with tools focused on quantitative analysis? Is there a market in developing only these tools, instead of focusing on trading itself? Or maybe this is exactly what these guys want to do, but it's just that they are too early in the development to tell? If so, I'm not exactly looking for work, but if you need a seasoned Python developer with experience in Galaxy and an interest in finance, we should talk...
- 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/

42 points by jbredeche 14y ago

fawce 14y ago

Here's an example of fetcher in action: https://www.quantopian.com/posts/new-feature-fetcher https://www.quantopian.com/posts/new-feature-fetcher

wicknicks 14y ago
Some additional details about Fetcher: https://www.quantopian.com/help https://www.quantopian.com/help (You will need to scroll down to the Fetcher section -- couldn't find anchor links)
- fawce 14y ago
  here you are: https://www.quantopian.com/help#overview-fetcher https://www.quantopian.com/help#overview-fetcher

spitfire 14y ago

Cool! Now add risk management. It's not sexy, but it is table stakes.

kyzyl 14y ago

Good work! This is an important step. For some people not being able to use their CRSP data (or whatever) is simply a deal-breaker. As someone who once implemented most of the bits that make up the quantopian functionality, I can certainly appreciate the effort you folks are putting in. I believe it's been mentioned a few times now, but have to agree that risk metrics should be pretty high on your priority list. Sharpe/sortino/omega/etc. are great to have, but you really need to be able to build and experiment with variance models. As in all science, a number doesn't mean much without an error estimate :-) GARCH models are pretty popular these days, and they are fairly non-trivial to implement correctly (especially in their vectorized form), particularly for people new to the field. Now for the most part this stuff _can_ be implemented by the users in their algos, but it's often a non-starter when it comes to the more complex stuff like vector autoregressive processes, bootstrap analysis or sane fitting routines.

rglullis 14y ago

Just slightly off-topic, but the quant.ly forum is so dead, and I think the Quantopian guys might chime in here. I started taking the Finance Engineering class on Coursera, and one of the things that did not help me was their reliance of Excel as a way to solve most of the exercises in the problem sets. They even have part of the lectures dedicated to "how to use Excel for X". For solving the problem sets, they mention Matlab, but that's about it. Suffice it to say, with my refusal to buy a license of Excel and LibreOffice not having the requested features, I now found myself having to learn the theory and writing a bunch of small pieces of ad-hoc code to re-implement the formulas. And no, I could not find an easy to use constraint solver, in either Python or Octave. So my task of "learn about Mean Variance Optimization" also included "learn and re-implement a constraint solver in NumPy", and the first lesson I'm actually taking away from this class is "forget about all and any decent tooling if you want to work in Finance." There was another field of work where I experienced something like that, which is bioinformatics. Not to that degree, though. There is still way too much spreadsheet-emailing going on to my taste, but it was recognized as far from ideal. So at the lab there was a big push to adopt Galaxy as part of everyone's workflow. I no longer work over there, but I'm sure that the ad-hoc, sloppy pieces of R and Python code are being replaced by better integrated and more efficient Galaxy tools. Going back to Quantopian... am I too far off to wish that it could become the "Galaxy for Quants and finance students"? Am I too much of a finance n00b to think that it makes sense to have something like Galaxy, but with tools focused on quantitative analysis? Is there a market in developing only these tools, instead of focusing on trading itself? Or maybe this is exactly what these guys want to do, but it's just that they are too early in the development to tell? If so, I'm not exactly looking for work, but if you need a seasoned Python developer with experience in Galaxy and an interest in finance, we should talk...

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/