What is pluto?

pluto is a compute cloud for exploratory financial data analysis.

Why?

Financial data is expensive and dirty

• Multiple vendors, different formats, orthogonal assumptions
• Upfront investment in licensing, database setup and cleaning
• Time-stamped meta-data is non-existent

Analysis is incomplete

• Almost all analysis is done by practitioners who may not have a background in statistics
• Non-replicable
• Only positive backtests are shared
• Impossible to share research and solicit feedback

What are the unsolved problems in this space?

Bringing data to code is hard (Quandl, IEX)

• High bandwidth requirement – piping different data to different endpoints
• Usage is not predictable – should a dataset be hot/warm/tepid/cold
• Data needs to be metered

Bringing code to data is harder (Quantopian)

• Prevent malicious code
• Prevent data leakage
• Compute needs to be metered

Defining the user is the hardest

Who are we building for? Academic? Trader? Investor? Student?

Hard Problems + Business Model = Product Design

pluto is primarily built for the academic in you.

What are pluto's design goals and constraints?

• Data leak-proof
• Metered database load
• Metered compute
• Facilitate collaboration
• Familiar interface
• Documented datasets

How does it work?

Jupyterlab is setup on the cloud where users can login with their github account, start a python or R notebook and get started. The homepage, pluto.studio, has recepies and links to working notebooks. If you run into issues, either raise an issue here or post it on slack

• python data-set package: plutoPy, samples
• R data-set package: plutoR, samples

Read to give it a whirl? Explore on pluto.studio