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The company claims to be one of the fastest-growing companies that deliver access to trade to the client at any level, increasing its trading volumes daily and currently closing around 32M trades per month. The github profiles of the bulge bracket investment banks are anaemic compared to those of comparably sized companies in different industries. First, most implementations are in-memory only and sit sparsely there, which makes it a pain in the bum to work even with medium sized data sets; a problem Python programs very commonly run into in practice. A lot is talked about high-frequency traders but the majority of financiers are not looking at tick level or frankly even intra-day level data. 3. There are many, many programs which start with an MnTable, apply some list of operations to it and then forward the resulting table somewhere else. This is an open list. Over time the divergence between Bank Python and Open Source Python grows.
MnTable gets used everywhere in Bank Python. I haven’t covered everything that’s in a typical Bank Python implementation. Programmers have an unfortunate tendency to gravitate towards hash tables – particularly in Python and Javascript where they are used to such extent that it is hard to find anything which is not made out of hash tables. Clubbing it all together removes a lot of accidental complexity: you already have a language runtime (and the version in prod is the same as on your computer), a basic database and a place for your code to run before you even start. When everything is in the same repo and all code is just an import away, software packaging just does not not come up. Any new software solution is going to be compared with MS Excel and if the result is unfavourable people will often just use continue to use Excel instead. They can use b-tree indices to allow efficient access by any route; so you never end up having to invert your dictionary in the middle of your program just so that you can access by something other than the key.
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