aqueduct-client 0.1.1

Python wrapper for Quantopian's Aqueduct API

AqueductClient is a simple Python wrapper around Quantopian’s Aqueduct API. It lets you easily create Pipeline executions and load their results.

AqueductClient supports Python 2.7 and Python 3.4+.

Installation

$ pip install git+https://[e-mail]/quantopian/aqueduct-client.git

Configuration

To use AqueductClient, you need a Quantopian API Key. Once you have it, there are several ways to use it:

1. Use a credentials file: create ~/.quantopian/credentials (Linux or OS X) or %UserProfile%\.quantopian\credentials (Windows) and put the following in it:

[default]
API_KEY = your_api_key

2. Use an environment variable: set QUANTOPIAN_API_KEY to your API key.

3. Pass your API key directly into the create_client method (see below) using the api_key kwarg.

Usage

Note: Fuller documentation will be coming soon.

To use AqueductClient, create an instance. In this case, we are loading credentials from disk or environment variable.

from aqueduct_client import create_client

client = create_client()

To run a new pipeline execution, use submit_pipeline_execution. Required parameters are code (string), start_date and end_date (date-like strings, dates, or Pandas timestamps). Optional parameters are name (string), params (a dict of parameters to pass to your pipeline), and asset_identifier_format (which can be “symbol” (default), “sid”, and “fsym_region_id”). submit_pipeline_execution returns an id, which you can pass to get_pipeline_execution to monitor this pipeline’s execution status.

get_all_pipeline_executions and get_pipeline_execution(id) let you load existing pipelines. Each pipeline has a status field, which can be IN-PROGRESS, SUCCESS, or FAILED.

For a successful pipeline, get_pipeline_results_dataframe(id) loads that pipeline’s results into a pandas DataFrame. For a failed pipeline, get_pipeline_execution_error(id) shows you the information about the error.