Arctic TimeSeries and Tick store

Arctic is a high performance datastore for numeric data. It supports Pandas,
numpy arrays and pickled objects out-of-the-box, with pluggable support for
other data types and optional versioning.

Arctic can query millions of rows per second per client, achieves ~10x compression on network bandwidth,
~10x compression on disk, and scales to hundreds of millions of rows per second per
MongoDB instance.

Arctic has been under active development at Man AHL since 2012.

Quickstart
Install Arctic

pip install git+https://github.com/manahl/arctic.git

Run a MongoDB

mongod --dbpath <path/to/db_directory>

Using VersionStore

```
from arctic import Arctic
import quandl

Connect to Local MONGODB

store = Arctic('localhost')

Create the library - defaults to VersionStore

store.initialize_library('NASDAQ')

Access the library

library = store['NASDAQ']

Load some data - maybe from Quandl

aapl = quandl.get("WIKI/AAPL", authtoken="your token here")

Store the data in the library

library.write('AAPL', aapl, metadata={'source': 'Quandl'})

Reading the data

item = library.read('AAPL')
aapl = item.data
metadata = item.metadata
```

VersionStore supports much more: See the HowTo!

Adding your own storage engine

Plugging a custom class in as a library type is straightforward. This example
shows how.

Documentation

You can find complete documentation at Arctic docs

Concepts
Libraries

Arctic provides namespaced libraries of data. These libraries allow
bucketing data by source, user or some other metric (for example frequency:
End-Of-Day; Minute Bars; etc.).

Arctic supports multiple data libraries per user. A user (or namespace)
maps to a MongoDB database (the granularity of mongo authentication). The library
itself is composed of a number of collections within the database. Libraries look like:

• user.EOD


• user.ONEMINUTE

A library is mapped to a Python class. All library databases in MongoDB are prefixed with 'arctic_'

Storage Engines

Arctic includes three storage engines:

• VersionStore: a key-value versioned TimeSeries store. It supports:
  
  
  • Pandas data types (other Python types pickled)
  
  
  • Multiple versions of each data item. Can easily read previous versions.
  
  
  • Create point-in-time snapshots across symbols in a library
  
  
  • Soft quota support
  
  
  • Hooks for persisting other data types
  
  
  • Audited writes: API for saving metadata and data before and after a write.
  
  
  • a wide range of TimeSeries data frequencies: End-Of-Day to Minute bars
  
  
  • See the HowTo
  
  
  • Documentation
  
  
  
  


• TickStore: Column oriented tick database. Supports
  dynamic fields, chunks aren't versioned. Designed for large continuously ticking data.


• Chunkstore: A storage type that allows data to be stored in customizable chunk sizes. Chunks
  aren't versioned, and can be appended to and updated in place.
  
  
  • Documentation
  
  
  
  

Arctic storage implementations are pluggable. VersionStore is the default.

Requirements

Arctic currently works with:

• Python 2.7, 3.4, 3.5, 3.6


• pymongo >= 3.6


• Pandas


• MongoDB >= 2.4.x

Operating Systems:
* Linux
* macOS
* Windows 10

Acknowledgements

Arctic has been under active development at Man AHL since 2012.

It wouldn't be possible without the work of the AHL Data Engineering Team including:

• Richard Bounds


• James Blackburn


• Vlad Mereuta


• Tom Taylor


• Tope Olukemi


• Drake Siard


• Slavi Marinov


• Wilfred Hughes


• Edward Easton


• Bryant Moscon


• Dimosthenis Pediaditakis


• Shashank Khare


• ... and many others ...

Contributions welcome!

License

Arctic is licensed under the GNU LGPL v2.1. A copy of which is included in LICENSE