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Source channel @githubtrending · Post #14739 · May 23

#c_lang#ctp#ctpapi#futures#options#quant#simnow#stock#tora#trader#tts#xtp openctp is a powerful open-source trading platform compatible with many Chinese securities and futures trading systems, offering both real and simulated trading environments for futures, options, stocks, funds, and bonds across domestic and global markets like A-shares, Hong Kong, and US stocks. It provides easy access to CTPAPI through Python and other programming languages, plus user-friendly trading clients with graphical and command-line interfaces. You can register free simulation accounts instantly via WeChat, enabling you to practice and test trading strategies in real-time or 24/7 environments. It also offers training, development support, and a monitoring platform for multiple trading systems, helping you learn, develop, and trade efficiently with low costs and broad market access. This benefits you by giving a flexible, comprehensive, and cost-effective way to develop, test, and execute trading strategies across many markets with strong community and technical support. https://github.com/openctp/openctp

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djangoproject

@djangoproject · Post #423 · 08/26/2017, 08:39 AM

http://scitools.org.uk/iris/docs/latest/userguide/index.html Iris seeks to provide a powerful, easy to use, and community-driven Python library for analysing and visualising #meteorological and #oceanographic data sets. With Iris you can: Use a single #API to work on your data, irrespective of its original format. Read and write (CF-)netCDF, GRIB, and PP files. Easily produce graphs and maps via integration with #matplotlib and #cartopy.

djangoproject

@djangoproject · Post #424 · 08/26/2017, 08:43 AM

http://scitools.org.uk/cartopy/docs/latest/index.html Cartopy is a Python package designed to make drawing maps for data analysis and visualisation as easy as possible. #Cartopy makes use of the powerful #PROJ.4, #numpy and #shapely libraries and has a simple and intuitive drawing interface to #matplotlib for creating publication quality maps. Some of the key features of cartopy are: object oriented projection definitions point, line, vector, polygon and image transformations between projections integration to expose advanced mapping in matplotlib with a simple and intuitive interface powerful vector data handling by integrating shapefile reading with Shapely capabilities