Initial design is done. It is now time to start laying the basis of the project.\nAt this point, there are a few technical choices we can no longer defer.
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\nInitial design is done. It is now time to start laying the basis of the project.\nAt this point, there are a few technical choices we can no longer defer.
\nAs per our architecture constraints, we must choose either C++ or Python, or a mix thereof.\nI personally tend to favor Python, as a more flexible language, which allows building software\nmuch faster than C++, albeit at the cost of performance.
\nI will stick to this choice here. The initial design will be full python and then, as we\nidentify bottlenecks, we will convert to C++ piece-wise. Especially, as we will have a lot of\ninput/output, python 3.5 asyncio module will be very convenient and allow us to reach an\ninitial working version much faster than C++.
\nThus, we will build the initial version using python, with support for version 3.5 and 3.6.\nAnd when potential benefit arises, we will use C++14, which has a good balance of modern\nfeatures and compiler support.
\nWe have a couple of basic tools we will need to support our project. Without ceremony, here are\nthe essential tools we will use:
\nThose tools come a little from personal preference, and a lot from being industry standards.\nI will not explain the setup here, but the\n“starting a python project” post covers the details.
\nOur newly created repository is on github, and you can check the initial commit.\nIt is now time to look at our directory hierarchy.
\nCryptomate has multiple modules with similar requirements, so to keep its parts consistent,\nwe decide to choose some dependencies at the global level.
\nWe need a single event loop component. Handling tick data from many markets will put a\nlot of stress on our I/O loop. Also, as we plan to move modules from Python to C++ as\nwe go, an event loop readily available in both languages would be great.
We shall use libuv, with the uvloop\npython bindings. It is widely used, and renowned for its performance.
In order not to restrict Python-C++ interactions to integration boundaries, we need a\nstandard way to make them talk.
We shall initially use Cython for this purpose. Though not as featureful\nas Boost.Python or the more recent pybind11, it integrates better within a Python\nworkflow and produces leaner modules. Should we find ourselves moving large parts of the\ncode over to C++, we might reconsider this choice later on.
As per the traditional python model, we will adopt a package-by-module approach.\nEvery module defined in the\nmodules overview\nshall have its own, possibly nested, directory structure, reflecting its own\nsubdivisions in sub-modules and internal layers.
\nTo enforce isolation and encapsulation:
\nOne consequence of this approach is that layer boundaries end up within each module.\nFor instance, the real-time market module will contain all objects related to fetching\nmarket data in real-time:
\nAnother way to view this structure is to see those modules as an outer hexagon, the interfaces\nexposing module functionality being adapters to inner hexagon's ports.
\nThe resulting hierarchy has been added.
\nThus, Cryptomate was born. In the next post, we will start working on the main\ncomponent of the system, that is the business logic and, most importantly, its ports.
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