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Analyzing a Signal and Creating a Contest Algorithm with Self-Serve Data

A few weeks back we announced the release of Self-Serve Data, which provides you the ability to upload your own time-series data to Quantopian and access it in Pipeline.

Refer to this forum post by Chris Myles for the initial release announcement of Self-Serve Data. For another high-level walkthrough of the tool, watch this recently-released video, Upload Your Custom Datasets with Self-Serve Data.

Analyze Your Signal

Seamless integration with the Pipeline API means you can use your dataset as you would any other on the Quantopian platform, which includes compatibility with tools like Alphalens and Pyfolio.

The attached notebook uses Self-Serve Data to load a signal into Pipeline and analyze it using Alphalens.

Template Contest Algorithm

Because Self-Serve Data allows you to live-update your datasets in point-in-time fashion, your data can now be used in algorithms you submit to the daily Quantopian Contest.

We’ve prepared and attached (in the first response to this post) a template algorithm to clone and build off of with your uploaded data. The TO-DOs throughout the template provide guidance as you develop the algorithm for submission to the contest. The template should help you pass most, if not all of the criteria required for the contest.

Feel free to email any questions or issues to [email protected].

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2 responses

Attached is the template algorithm you can start with to build an algorithm with your uploaded data. Follow the TO-DOs throughout the template for guidance.

For those interested, attached is the template algorithm incorporating the campaign contributions dataset used in the notebook above.