Python for Finance: Analyze Big Financial Data

register for the platform at http://quant-platform.com. It features, among others, the

following basic components:

File manager

A tool to manage file up/downloads and more via a web GUI.

Linux terminal

A Linux terminal to work with the server (for example, a virtual server instance in the

cloud or a dedicated server run on-premise by a company); you can use Vim, Nano,

etc. for code editing and work with Git repositories for version control.

Anaconda

An Anaconda installation that provides all the functionality discussed previously; by

default you can choose between Python 2.7 and Python 3.4.

Python shell

The standard Python shell.

IPython Shell

An enhanced IPython shell.

IPython Notebook

The browser version of IPython. You will generally use this as the central tool.

Chat room/forum

To collaborate, exchange ideas, and to up/download, for example, research

documents.

Advanced analytics

In addition to the Linux server and Python environments, the platform provides

analytical capabilities for, e.g., portfolio, risk, and derivatives analytics as well as for

backtesting trading strategies (in particular, DX analytics; see Part III for a simplified

but fully functional version of the library); there is also an R stack available to call,

for example, R functions from within IPython Notebook.

Standard APIs

Standard Python-based APIs for data delivery services of leading financial data

providers.

When it comes to collaboration, the Python Quant Platform also allows one to define —

under a “company” — certain “user groups” with certain rights for different Python

projects (i.e., directories and files). The platform is easily scalable and is deployed via

Docker containers. Figure 2-1 shows a screenshot of the main screen of the Python Quant

Platform.