semvec/pypi-packages huggingface dataset:
brand-alert 1.0.0
Python client library for Brand Alert API.
License: MIT
Requires Python: >=3.6
Keywords: brand,alert,domain,api,whoisxmlapi

.. image:: https://img.shields.io/badge/License-MIT-green.svg
    :alt: brand-alert-py license
    :target: https://opensource.org/licenses/MIT

.. image:: https://img.shields.io/pypi/v/brand-alert.svg
    :alt: brand-alert-py release
    :target: https://pypi.org/project/brand-alert

.. image:: https://github.com/whois-api-llc/brand-alert-py/workflows/Build/badge.svg
    :alt: brand-alert-py build
    :target: https://github.com/whois-api-llc/brand-alert-py/actions

========
Overview
========

The client library for
`Brand Alert API <https://brand-alert.whoisxmlapi.com/>`_
in Python language.

The minimum Python version is 3.6.

Installation
============

.. code-block:: shell

    pip install brand-alert

Examples
========

Full API documentation available `here <https://brand-alert.whoisxmlapi.com/api/documentation/making-requests>`_

Create a new client
-------------------

.. code-block:: python

    from brandalert import *

    client = Client('Your API key')

Make basic requests
-------------------

.. code-block:: python

    # Get the number of domains.
    result = client.preview(['google'])
    print(result.domains_count)

    # Get raw API response
    raw_result = client.raw_data(
        ['google'],
        response_format=Client.XML_FORMAT,
        mode=Client.PREVIEW_MODE)

    # Get list of recently registered/dropped domains (up to 10,000)
    result = client.purchase(['google'])

Advanced usage
-------------------

Extra request parameters

.. code-block:: python

    today = datetime.date.today()
    delta = datetime.timedelta(days=10)
    result = client.purchase(
        ['google'],
        exclude_terms=['blog'],
        since_date=today - delta,
        with_typos=True,
        punycode=False)

    raw_result = client.raw_data(
        ['google'],
        exclude_terms=['blog'],
        since_date=today - delta,
        with_typos=True,
        punycode=False,
        mode=Client.PURCHASE_MODE,
        response_format=Client.JSON_FORMAT)


Changelog
=========

1.0.0 (2021-05-04)
------------------

* First release
Simple-PairsTrading 1.0.1
Find ideal candidates
License: MIT
Requires Python: >=3.6
Keywords: Pairs Trading

Project description
Pairs_trading aim to help the algorithmic trading community to find the potential candidates base on metrics like cointegrating relationship between the candidates, stationarity of the spreads, level of persistent of the series, the frequency of mean reversion and also the half-life of the spreads for pairs trading.

add(syms, start, end)
add symbols and desired time period

bring_in(price_df, start, end, type='candidates')
import dataframe instead of adding a new one

restore():
using the backup dataframe as a new one

remove(syms)
removing symbols

find_candidates()
search candidates for pair trading

plot(index):
visualize the results