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Industry","数据容器和结构","Numerical Libraries","By Language","数据库"],"sub_categories":["Scientific Computing","Time Series","TimeSeries Analysis","Other","Books","Python"],"readme":"[![Documentation Status](https://readthedocs.org/projects/arctic/badge/?version=latest)](https://arctic.readthedocs.io/en/latest/?badge=latest)\n[![CircleCI](https://circleci.com/gh/man-group/arctic/tree/master.svg?style=shield)](https://app.circleci.com/pipelines/github/man-group/arctic?branch=master)\n[![PyPI](https://img.shields.io/pypi/v/arctic)](https://pypi.org/project/arctic)\n[![Python](https://img.shields.io/badge/Python-3.6|3.7|3.8-green.svg)](https://github.com/man-group/arctic)\n\n## Quickstart\n\n### Install Arctic\n\n``\npip install git+https://github.com/man-group/arctic.git\n``\n\n### Run a MongoDB\n\n``\nmongod --dbpath \u003cpath/to/db_directory\u003e\n``\n\n## Using VersionStore\n``\nfrom arctic import Arctic\nimport quandl\n``\n### Connect to Local MONGODB\n``\nstore = Arctic('localhost')\n``\n\n### Create the library - defaults to VersionStore\n``\nstore.initialize_library('NASDAQ')\n``\n\n### Access the library\n``\nlibrary = store['NASDAQ']\n``\n\n### Load some data - maybe from Quandl\n``\naapl = quandl.get(\"WIKI/AAPL\", authtoken=\"your token here\")\n``\n\n### Store the data in the library\n``\nlibrary.write('AAPL', aapl, metadata={'source': 'Quandl'})\n``\n\n### Reading the data\n``\nitem = library.read('AAPL')\naapl = item.data\nmetadata = item.metadata\n``\n\nVersionStore supports much more: [See the HowTo](howtos/how_to_use_arctic.py)!\n\n\n### Adding your own storage engine\n\nPlugging a custom class in as a library type is straightforward. [This example\nshows how.](howtos/how_to_custom_arctic_library.py)\n\n## Documentation\n\nYou can find complete documentation at [Arctic docs](https://arctic.readthedocs.io/en/latest/)\n\n## Concepts\n\n### Libraries\n\nArctic provides namespaced *libraries* of data.  These libraries allow\nbucketing data by *source*, *user* or some other metric (for example frequency:\nEnd-Of-Day; Minute Bars; etc.).\n\nArctic supports multiple data libraries per user.  A user (or namespace)\nmaps to a MongoDB database (the granularity of mongo authentication).  The library\nitself is composed of a number of collections within the database. Libraries look like:\n\n  * user.EOD\n  * user.ONEMINUTE\n\nA library is mapped to a Python class.  All library databases in MongoDB are prefixed with 'arctic\\_'\n\n### Storage Engines\n\nArctic includes three storage engines:\n\n  * [VersionStore](arctic/store/version_store.py): a key-value versioned TimeSeries store. It supports:\n      * Pandas data types (other Python types pickled)\n      * Multiple versions of each data item. Can easily read previous versions.\n      * Create point-in-time snapshots across symbols in a library\n      * Soft quota support\n      * Hooks for persisting other data types\n      * Audited writes: API for saving metadata and data before and after a write.\n      * a wide range of TimeSeries data frequencies: End-Of-Day to Minute bars\n      * [See the HowTo](howtos/how_to_use_arctic.py)\n      * [Documentation](docs/versionstore.md)\n  * [TickStore](arctic/tickstore/tickstore.py): Column oriented tick database.  Supports\n    dynamic fields, chunks aren't versioned. Designed for large continuously ticking data.\n  * [Chunkstore](https://github.com/man-group/arctic/wiki/Chunkstore): A storage type that allows data to be stored in customizable chunk sizes. Chunks\n    aren't versioned, and can be appended to and updated in place. \n    * [Documentation](docs/chunkstore.md)\n\nArctic storage implementations are **pluggable**.  VersionStore is the default.\n\n\n## Requirements\n\nArctic currently works with:\n\n * python 3.6, 3.7, 3.8\n * pymongo \u003e= 3.6.0 \u003c= 3.11.0\n * pandas \u003e= 0.22.0 \u003c 2\n * MongoDB \u003e= 2.4.x \u003c= 4.4.18\n\n\nOperating Systems:\n * Linux\n * macOS\n * Windows 10\n\n## Acknowledgements\n\nArctic has been under active development at [Man Group](https://www.man.com/) since 2012.\n\nIt wouldn't be possible without the work of the Man Data Engineering Team including:\n\n * [Richard Bounds](https://github.com/richardbounds)\n * [James Blackburn](https://github.com/jamesblackburn)\n * [Vlad Mereuta](https://github.com/vmereuta)\n * [Tom Taylor](https://github.com/TomTaylorLondon)\n * Tope Olukemi\n * [Drake Siard](https://github.com/drakesiard)\n * [Slavi Marinov](https://github.com/slavi)\n * [Wilfred Hughes](https://github.com/wilfred)\n * [Edward Easton](https://github.com/eeaston)\n * [Bryant Moscon](https://github.com/bmoscon)\n * [Dimosthenis Pediaditakis](https://github.com/dimosped)\n * [Shashank Khare](https://github.com/shashank88)\n * [Duncan Kerr](https://github.com/dunckerr)\n * ... and many others ...\n\nContributions welcome!\n\n## License\n\nArctic is licensed under the GNU LGPL v2.1.  A copy of which is included in [LICENSE](LICENSE)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fman-group%2Farctic","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fman-group%2Farctic","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fman-group%2Farctic/lists"}