ksplice: hot-patch your kernel

ksplice is a tool for patching a running kernel without rebooting. i should check it out some time, especially if gentoo makes a package for it.

investment optimization functions

i already know that an objective function for an investment optimization needs to have more than just the expected value of the rate of return (though i think this should be the o.f. if constraints are applied as parameter boundaries). otherwise, i would be saying that i would tolerate an unlimited increase in any risk metric for a small increase in expected return, and that's not very smart. so, what else do i throw into the o.f.? the sortino ratio looks interesting. i think it makes a good critique of the sharpe ratio: upside variability should not be punished like downside variability. maybe a variant of it that, like the information ratio, uses a benchmark rather than a risk-free asset for active return. the wikipedia articles on these list others, and the upside potential ratio article refers to an article comparing it to sharpe and sortino ratios. value-at-risk and expected shortfall are both easy to compute, given a pdf on returns. i know value-at-risk has taken a beating. but even though expected shortfall has nice mathematical properties as a coherent, spectral risk measure, it is sensitive to errors in the distribution in the tail. i'm nervous about estimating distributions on something that provides few data by definition. hmmm, that makes me wonder... are there financial risk metrics based on extreme value theory? one thing they all have in common, though, is that they are functionals of the rate of return. the rate of return is a function of the time to reach a limit order, the price relative, and the transaction cost. so, given a pdf for time as a function of price relative, and the function defining rate of return, it should be easy to wrap an optimizer around any of the objective functions above. EDIT: i think the upside potential ratio is the same thing as the sortino ratio, differing perhaps only in the context. the information ratio is the same as the sharpe ratio, except that it uses a benchmark rather than a risk-free return for comparison. looks like the sharpe ratio, sortino ratio, value-at-risk, and expected shortfall (and probably many/most others) are not only funtionals of the rate of return pdf, they are functions of first and second partial moments. if i can boil those partial moments down to a small set, it would be possible to define a multi-objective space in which constant-value contours of the various financial objective functions are simple manifolds. the advantage of this is that i could find a pareto-optimal front in that partial moment space, and it would be easy to see not only the trade-offs but also how sensitive the o.f.s are at any point. otoh, maybe it would be just as easy to look at an overlay of all the o.f.s plotted in the control parameter space, and see values/sensitivities that way. at the very least, splitting up the moments into partials would facilitate simultaneous computation.

Elementary Calculus of Financial Mathematics

this might be a book worth reading: Elementary Calculus of Financial Mathematics, by A. J. Roberts December 2008 / xii + 128 pages / Softcover / ISBN 978-0-898716-67-2 List Price $59.00 / SIAM Member Price $41.30 / Order Code MM15 some of the description sounds like stuff i already know, but the idea of seeing solutions to some practical problems is appealing. www.siam.org/books

open source maps

stumbled across of open source map codes: roadnav and roadmap. both use census data for generating street maps, and both can use gps input to show your location. roadnav can compute paths for you, though neither knows about one-ways (it's not in the data). a fork of roadmap is working to possibly remedy that problem, however. might be interesting to put them on my laptop, for when i travel.

list.it information scrap organizer

i'm a big user of google notebooks and i'm always on the lookout for better ways to keep quick, short bit of info organized (and off of the paper that seem to surround me anyway). list.it sounds like an interesting concept, and the folks at csail always seem to know what they're doing.

investment entities

why do tax laws have to be so complicated? i found a site that gives some info on the various types of entities to use as vehicles for active trading, and the tax pros and cons for each. i'm afraid i'll need to deal with this at some point. i guess it might be a choice between hiring a lawyer before or an accountant after. or both.

some books on ica

an information-theoretic approach to neural computing, qa76.87.d47 1996 has intros for both information theory and anns, shows connection between pca and ica. has ref to mdl with supervised learning, but it's the old (not stochastic complexity) mdl. independent component analysis: theory and applications, tk5102.9.l44 1998 time-delayed decorrelation, nonlinear ica, some historical info in preface. separated discussions of methods for sub- and super-gaussian (kurtosis < or > gaussian kurtosis) good illustrations of how ica can solve some problems of pca. looks like some deep math, but intelligible advances in independent component analysis, qa76.87.a378 2000 temporal effects: multivariate time series, ref to financial time series; time-varying mixtures, particle filters nonlinear mapping, analyzing independence assumption, ica on noisy data the time-varying stuff really looks interesting independent component analysis: principles and practice, qa76.87.i516 2000 non-stationary sources, including particle filters nonlinear, though not many equations or examples a lot of material seems very similar to the previous book independent component analysis, qa278.h98 2001 lots of background, justification of ica approach preprocessing and time filtering, comparison of algorithms good overall tutorial financial applications independent component analysis: a tutorial introduction, qa76.87.s78 2004 shorter, more intuitive explanations probably better for getting the basic idea kind of dumbed-down in some ways (i know what lambda looks like!) skip the first 6 chapters if you have a decent math background a lot of the ica books seem connected not only with information theory, which i would expect, but also with other stuff on neural nets, which i didn't necessarily expect. this one might be worth checking out. refers to some generalizations of the ica/pca ideas, with good presentation. more stuff on the group's website.