![]() ![]() Note: if your user does not have administrator rights or if you are using Vista you might have to install all aforementioned packages as administrator. The lighter version miniconda is also a good alternative. The easiest way to install the developmental version on Windows and MacOS machines is to rely on a all inclusive scientific python distribution, like anaconda. On Debian/Ubuntu you can use apt-get from a terminal to download them without using synaptic. After all the dependencies are satisfied you can proceed downloading python source from git. To install from git open your shell (terminal), move to the folder where you want pySolo to be installed and issue the command: git clone the development version on Windows and MacOS On Ubuntu start the Synaptic Package Manager and download the following packages: Installation of pySolo on most linux platforms is straightforward as all required python packages and dependencies are available as. ![]() Installing the source (development version) or installing on linux systems different from Ubuntu See the Using Python for Science page in the pySolo developer manual for a comprehensive list of useful links. Python is a not-compiled language, meaning that you need to have a working copy of it installed on your computer in order to be able to run PySolo. Python and the accessories libraries can be a great resource for your scientific computing, beside giving you the possibility to run pySolo. PySolo has been successfully tested on the following systems: For this reason, it can run on a multitude of operative systems (all Microsoft Windows, most Unix or unix-like systems, and Macintosh OS X) PySolo has been written entirely in python using a wxWidget platform independent library for construction of the GUI. The binary version will work out of the box as it does not require Python or any other library to run. In this case just proceed downloading the binary file (setup.exe) from the Download page and execute it as Administrator on your machine. On Windows machines it is possible to install a full version of pySolo as compiled binary. Just install the package from AUR (see Download) If you want to volunteer and do it on my behalf, please write me a note. Maintaining packages for Ubuntu is way too much work for me. Installing latest stable version Ubuntu Linux It comes as pre-compiled binary for windows or as deb package for Ubuntu linux: installation of the precompiled packages on Windows or Ubuntu linux is straight forward but adoption of the development version requires a bit more work. The stable version is updated every now and then. The development version is latest available, updated daily or weekly and it does require python to run. With stochastic programming, we solve an approximated problem whose solution is much easier to describe.Two version of pySolo are downloadable at any time: a development version, also known as nightly build, and a stable version. The constraint and objective function can be linearized such that the solution is valid for an interval of $\lambda$.Īs a result, the (approximated) efficient frontier has only finite number of portfolios. In another word, in practice, samples of distribution is more available than its corresponding parametric form.Īnother benefit comes with the linearization of the problem. In Bayesian inference, it is a common practice to directly simulate the posterior predictiveĭistribution with Markov Chain Monte Carlo, where the posterior density function is known only The input for PyMCEF is just Monte Carlo simulated returns, and no knowledge about the underlying distribution function is needed for this package to work. PyMCEF is implemented in this way, and there is huge advantage in terms of flexibility. In this case, we replace the expectation in the risk or reward function with statistical mean and work with a stochastic programming problem. Suppose the returns of all the asserts is the random vector: Not surprisingly, the reward function is the just the expected return of the whole portfolio. We haven't defined the formulas for the risk and reward functions in the above optimization problem. ![]()
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