Install ======= To get started with Catalyst, you will need to install it in your computer. Like any other piece of software, Catalyst has a number of dependencies (other software on which it depends to run) that you will need to install, as well. We recommend using a software named ``Conda`` that will manage all these dependencies for you, and set up the environment needed to get you up and running as easily as possible. See :ref:`Installing with Conda `. Installing with ``pip`` ----------------------- Installing Catalyst via ``pip`` is slightly more involved than the average Python package. There are two reasons for the additional complexity: 1. Catalyst ships several C extensions that require access to the CPython C API. In order to build the C extensions, ``pip`` needs access to the CPython header files for your Python installation. 2. Catalyst depends on `numpy `_, the core library for numerical array computing in Python. Numpy depends on having the `LAPACK `_ linear algebra routines available. Because LAPACK and the CPython headers are non-Python dependencies, the correctway to install them varies from platform to platform. If you'd rather use a single tool to install Python and non-Python dependencies, or if you're already using `Anaconda `_ as your Python distribution, you can skip to the :ref:`Installing with Conda ` section. Once you've installed the necessary additional dependencies (see below for your particular platform), you should be able to simply run .. code-block:: bash $ pip install enigma-catalyst If you use Python for anything other than Catalyst, we **strongly** recommend that you install in a `virtualenv `_. The `Hitchhiker's Guide to Python`_ provides an `excellent tutorial on virtualenv `_. Here's a summarized version: .. code-block:: bash $ pip install virtualenv $ virtualenv catalyst-venv $ source ./catalyst-venv/bin/activate $ pip install enigma-catalyst Though not required by Catalyst directly, our example algorithms use matplotlib to visually display the results of the trading algorithms. If you wish to run any examples or use matplotlib during development, it can be installed using: .. code-block:: bash $ pip install matplotlib GNU/Linux ~~~~~~~~~ On `Debian-derived`_ Linux distributions, you can acquire all the necessary binary dependencies from ``apt`` by running: .. code-block:: bash $ sudo apt-get install libatlas-base-dev python-dev gfortran pkg-config libfreetype6-dev On recent `RHEL-derived`_ derived Linux distributions (e.g. Fedora), the following should be sufficient to acquire the necessary additional dependencies: .. code-block:: bash $ sudo dnf install atlas-devel gcc-c++ gcc-gfortran libgfortran python-devel redhat-rep-config On `Arch Linux`_, you can acquire the additional dependencies via ``pacman``: .. code-block:: bash $ pacman -S lapack gcc gcc-fortran pkg-config .. Commenting it out until Catalyst fully supports Python 3.X .. .. There are also AUR packages available for installing `Python 3.4 .. `_ (Arch's default python is now .. 3.5, but Catalyst only currently supports 3.4), and `ta-lib .. `_, an optional Catalyst dependency. .. Python 2 is also installable via: .. .. $ pacman -S python2 OSX ~~~ The version of Python shipped with OSX by default is generally out of date, and has a number of quirks because it's used directly by the operating system. For these reasons, many developers choose to install and use a separate Python installation. The `Hitchhiker's Guide to Python`_ provides an excellent guide to `Installing Python on OSX `_, which explains how to install Python with the `Homebrew`_ manager. Assuming you've installed Python with Homebrew, you'll also likely need the following brew packages: .. code-block:: bash $ brew install freetype pkg-config gcc openssl OSX + virtualenv + matplotlib ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ A note about using matplotlib in virtual enviroments on OSX: it may be necessary to run .. code-block:: bash echo "backend: TkAgg" > ~/.matplotlib/matplotlibrc in order to override the default ``macosx`` backend for your system, which may not be accessible from inside the virtual environment. This will allow Catalyst to open matplotlib charts from within a virtual environment, which is useful for displaying the performance of your backtests. To learn more about matplotlib backends, please refer to the `matplotlib backend documentation `_. .. _windows: Windows ~~~~~~~ In Windows, you will need the `Microsoft Visual C++ Compiler for Python 2.7 `_. This package contains the compiler and the set of system headers necessary for producing binary wheels for Python 2.7 packages. If it's not already in your system, download it and install it before proceeding to the next step. For windows, the easiest and best supported way to install Catalyst is to use :ref:`Conda `. Some problems we have encountered installing the **Visual C++ Compiler** mentioned above are as follows: - **The system administrator has set policies to prevent this installation**. In some systems, there is a default *Windows Software Restriction* policy that prevents the installation of some software packages like this one. You'll have to change the Registry to circumvent this: - Click ``Start``, and search for ``regedit`` and launch the ``Registry Editor`` - Navigate to the following folder: ``HKEY_LOCAL_MACHINE\SOFTWARE\Policies\Microsoft\Windows\Installer`` - If there is an entry for ``DisableMSI``, set the Value data to 0. - If there is no such entry, click on the ``Edit`` menu -> ``New`` -> ``DWORD (32-bit) Value`` and enter ``DisableMSI`` as the Name (and by default you get 0 as the Value Data) | - **The installer has encountered an unexpected error installing this package. This may indicate a problem with this package. The error code is 2503.** We have observed this when trying to install a package without enough administrator permissions. Even when you are logged in as an Administrator, you have to explictily install this package with administrator privileges: - Click ``Start`` and find ``CMD`` or ``Command Prompt`` - Right click on it and choose ``Run as administrator`` - ``cd`` into the folder where you downloaded ``VCForPython27.msi`` - Run ``msiexec /i VCForPython27.msi`` Amazon Linux AMI ~~~~~~~~~~~~~~~~ The packages ``pip`` and ``setuptools`` that come shipped by default are very outdated. Thus, you first need to run: .. code-block:: bash pip install --upgrade pip setuptools The default installation is also missing the C and C++ compilers, which you install by: .. code-block:: bash sudo yum install gcc gcc-c++ Then you should follow the regular installation instructions outlined at the beginning of this page. Troubleshooting ``pip`` Install ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ **Issue**: Package enigma-catalyst cannot be found **Solution**: Make sure you have the most up-to-date version of pip installed, by running: .. code-block:: bash pip install --upgrade pip On Windows, the recommended command is: .. code-block:: bash python -m pip install --upgrade pip ---- **Issue**: Package enigma-catalyst cannot still be found, even after upgrading pip (see above), with an error similar to: .. code-block:: bash Downloading/unpacking enigma-catalyst Could not find a version that satisfies the requirement enigma-catalyst (from versions: 0.1.dev9, 0.2.dev2, 0.1.dev4, 0.1.dev5, 0.1.dev3, 0.2.dev1, 0.1.dev8, 0.1.dev6) Cleaning up... No distributions matching the version for enigma-catalyst **Solution**: In some systems (this error has been reported in Ubuntu), pip is configured to only find stable versions by default. Since Catalyst is in alpha version, pip cannot find a matching version that satisfies the installation requirements. The solution is to include the `--pre` flag to include pre-release and development versions: .. code-block:: bash pip install --pre enigma-catalyst ---- **Issue**: Package enigma-catalyst fails to install because of outdated setuptools **Solution**: Upgrade to the most up-to-date setuptools package by running: .. code-block:: bash pip install --upgrade pip setuptools ---- **Issue**: Missing required packages **Solution**: Download `requirements.txt `_ (click on the *Raw* button and Right click -> Save As...) and use it to install all the required dependencies by running: .. code-block:: bash pip install -r requirements.txt ---- **Issue**: Installation fails with error: ``fatal error: Python.h: No such file or directory`` **Solution**: Some systems (this issue has been reported in Ubuntu) require `python-dev` for the proper build and installation of package dependencies. The solution is to install python-dev, which is independent of the virtual environment. In Ubuntu, you would need to run: .. code-block:: bash sudo apt-get install python-dev .. _conda: Installing with ``conda`` ------------------------- Another way to install Catalyst is via the ``conda`` package manager, which comes as part of Continuum Analytics' `Anaconda `_ distribution. The primary advantage of using Conda over ``pip`` is that conda natively understands the complex binary dependencies of packages like ``numpy`` and ``scipy``. This means that ``conda`` can install Catalyst and its dependencies without requiring the use of a second tool to acquire Catalyst's non-Python dependencies. For Windows, you will need the *Microsoft Visual C++ Compiler for Python 2.7*. Follow the instructions on the :ref:`Windows` section and come back here. For instructions on how to install ``conda``, see the `Conda Installation Documentation `_. Alternatively, you can install MiniConda, which is a smaller footprint (fewer packages and smaller size) than its big brother Anaconda, but it still contains all the main packages needed. To install MiniConda, you can follow these steps: 1. Download `MiniConda `_. Select Python 2.7 for your Operating System. 2. Install MiniConda. See the `Installation Instructions `_ if you need help. 3. Ensure the correct installation by running ``conda list`` in a Terminal window, which should print the list of packages installed with Conda. Once either Conda or MiniConda has been set up you can install Catalyst: 1. Download the file `python2.7-environment.yml `_. 2. Open a Terminal window and enter [``cd/dir``] into the directory where you saved the above ``python2.7-environment.yml`` file. 3. Install using this file. This step can take about 5-10 minutes to install. .. code-block:: bash conda env create -f python2.7-environment.yml 4. Activate the environment (which you need to do every time you start a new session to run Catalyst): **Linux or OSX:** .. code-block:: bash source activate catalyst **Windows:** .. code-block:: bash activate catalyst Congratulations! You now have Catalyst installed. Troubleshooting ``conda`` Install ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ If the command ``conda env create -f python2.7-environment.yml`` in step 3 above failed for any reason, you can try setting up the environment manually with the following steps: 1. Create the environment: .. code-block:: bash conda create --name catalyst python=2.7 scipy zlib 2. Activate the environment: **Linux or OSX:** .. code-block:: bash source activate catalyst **Windows:** .. code-block:: bash activate catalyst 3. Install the Catalyst inside the environment: .. code-block:: bash pip install enigma-catalyst matplotlib Getting Help ------------ If after following the instructions above, and going through the *Troubleshooting* sections, you still experience problems installing Catalyst, you can seek additional help through the following channels: - Join our `Discord community `_, and head over the #catalyst_dev channel where many other users (as well as the project developers) hang out, and can assist you with your particular issue. The more descriptive and the more information you can provide, the easiest will be for others to help you out. - Report the problem you are experiencing on our `GitHub repository `_ following the guidelines provided therein. Before you do so, take a moment to browse through all `previous reported issues `_ in the likely case that someone else experienced that same issue before, and you get a hint on how to solve it. .. _`Debian-derived`: https://www.debian.org/misc/children-distros .. _`RHEL-derived`: https://en.wikipedia.org/wiki/Red_Hat_Enterprise_Linux_derivatives .. _`Arch Linux` : https://www.archlinux.org/ .. _`Hitchhiker's Guide to Python` : http://docs.python-guide.org/en/latest/ .. _`Homebrew` : http://brew.sh