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corintick-0.2.0
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مشاهده بیشترتوضیحات
Column-based datastore for historical timeseries
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | corintick-0.2.0 |
| نام | corintick |
| نسخه کتابخانه | 0.2.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Gustavo Bezerra |
| ایمیل نویسنده | gusutabopb@gmail.com |
| آدرس صفحه اصلی | https://github.com/plugaai/corintick |
| آدرس اینترنتی | https://pypi.org/project/corintick/ |
| مجوز | GPL |
corintick
=========
Column-based datastore for historical timeseries data. Corintick is
designed mainly to store `pandas <http://pandas.pydata.org/>`__
DataFrames that represent timeseries.
Instalation
-----------
In order to use Corintick you need MongoDB. See installation
instructions `here <https://docs.mongodb.com/manual/installation/>`__.
Corintick itself can be installed with ``pip``:
.. code:: bash
$ pip install corintick
Quickstart
----------
Initialize Corintick:
.. code:: python
from corintick import Corintick
corin = Corintick()
Now we need a DataFrame to insert into Corintick. For demonstration
purposes, we will get data from `Quandl <https://www.quandl.com/>`__:
.. code:: python
import quandl
df1 = quandl.get('TSE/7203')
Here, ``df1`` looks like this:
.. code:: text
Open High Low Close Volume
Date
2012-08-23 3240.0 3270.0 3220.0 3260.0 4652200.0
2012-08-24 3225.0 3245.0 3210.0 3235.0 3659600.0
2012-08-27 3250.0 3280.0 3215.0 3220.0 3614600.0
2012-08-28 3235.0 3260.0 3150.0 3180.0 6759100.0
2012-08-29 3180.0 3195.0 3160.0 3175.0 2614800.0
2012-08-30 3180.0 3190.0 3160.0 3170.0 3291700.0
2012-08-31 3135.0 3155.0 3095.0 3095.0 5663800.0
...
Writing
^^^^^^^
Inserting ``df1`` into Corintick is simple:
.. code:: python
corin.write('7203.T', df1, source='Quandl', country='Japan')
The first argument passed to ``corintick.write`` is an UID (universal
identifier) and must be unique for each timeseries inserted in a given
collection. The second argument is the dataframe to be inserted. The
remaining keyword arguments are optional metadata tags that can be
attached to the dataframe/document for querying.
Reading
^^^^^^^
Reading from Corintick is also straightforward:
.. code:: python
df2 = corin.read('7203.T')
You can also specify ``start`` and ``end`` as ISO-8601 datetime string...
.. code:: python
df2 = corin.read('7203.T', start='2014-01-01', end='2014-12-31')
.. code:: text
Open High Low Close Volume
2014-01-06 6360.0 6400.0 6280.0 6300.0 12249300.0
2014-01-07 6270.0 6340.0 6260.0 6270.0 7891400.0
2014-01-08 6310.0 6320.0 6260.0 6300.0 7184100.0
2014-01-09 6310.0 6340.0 6260.0 6270.0 8653000.0
2014-01-10 6260.0 6310.0 6250.0 6290.0 7815900.0
...
2014-12-24 7645.0 7687.0 7639.0 7657.0 9287900.0
2014-12-25 7600.0 7655.0 7597.0 7611.0 5362700.0
2014-12-26 7629.0 7700.0 7615.0 7696.0 6069100.0
2014-12-29 7740.0 7746.0 7565.0 7662.0 9942800.0
2014-12-30 7652.0 7674.0 7558.0 7558.0 7821200.0
...and which columns you want retrieved:
.. code:: python
df2 = corin.read('7203.T', columns=['Close', 'Volume'], start='2017-05-10')
.. code:: text
Close Volume
2017-05-10 6081.0 7823700.0
2017-05-11 6123.0 13511900.0
2017-05-12 6047.0 8216600.0
2017-05-15 6009.0 5925200.0
2017-05-16 6093.0 6449300.0
...
Configuration
^^^^^^^^^^^^^
By default, Corintick tries to use a MongoDB instance running at ``localhost:27017``.
This can be changed through the ``host`` and ``port`` arguments of the ``Corintick`` initializer.
Similarly, the database to be used by Corintick defaults to ``corintick`` and can also be changed using
the ``db`` parameter.
All the data in the ``db`` database is assumed to be Corintick data. Avoid having any
other process/application reading/writing data to that database.
In case your MongoDB setup requires authentication, you can use the ``username`` and ``password`` arguments.
See ``Corintick.__init__`` for details.
Collections
-----------
Corintick can use multiple collections to better organize data. A
Corintick collection is the same as a MongoDB collection. In each
collection, only a single dataframe/document can exist for a given UID
for a given time period.
In case you need to store two different types of data for a same UID
over an overlapping time frame (i.e. trade data and order book data for
a given stock), you should separate the two different types of data into
different collections.
By default, data is written to the ``corintick`` collection.
This default collection can be changed by assigning a string to
``Corintick.default_collection``.
.. code:: python
>>> corin.collection = 'another_collection'
Collections can also be specified on a method call basis:
.. code:: python
df = corin.read('7203.T', collection='orderbook')
.. code:: python
corin.write(df, collection='another_collection')
Corintick mechanics
-------------------
During writing, Corintick does the following:
1) Takes the input DataFrame and splits into columns
2) Serializes/compresses each using the LZ4 compression algorithm
3) Generates a MongoDB document containing the binary blobs
corresponding to each column and other metadata
During reading, the opposite takes places:
1) Documents are fetched
2) Data is decompressed and converted back to numpy arrays
3) DataFrame is reconstructed and returned to the user
Background
----------
Corintick was inspired by and aims to be a simplified version of Man
AHL’s `Arctic <https://github.com/manahl/arctic>`__.
Differences from Arctic
^^^^^^^^^^^^^^^^^^^^^^^
Corintick has a single storage engine, which is column-based and not
versioned, similar to Arctic’s TickStore. However, differently from
TickStore, it does support non-numerical ``object`` dtype columns by
parsing them into MessagePack string objects
Naming
^^^^^^
Corintick aimed from the beginning to be a column-based data storage.
"Corintick" is a blend of “Corinthan” (style of Roman columns) and
"tick".
Benchmarks
----------
**TODO**
- **vs InfluxDB**
- **vs vanila MongoDB**
- **vs MySQL**
- **vs KDB+ (32-bit)**
Contributing
------------
| To contribute, fork the repository on GitHub, make your changes and
submit a pull request.
| Corintick is not a mature project yet, so just simply raising issues
is also greatly appreciated :)
نیازمندی
| مقدار | نام |
|---|---|
| >=1.0.0 | lz4 |
| >=0.23 | pandas |
| >=3.6 | pymongo |
| - | numpy |
| - | pytz |
| - | msgpack-python |
| xtr | pytest; |
| xtr | pytest-cov; |
| xtr | flake8; |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl corintick-0.2.0:
pip install corintick-0.2.0.whl
نصب پکیج tar.gz corintick-0.2.0:
pip install corintick-0.2.0.tar.gz