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compute cloud for exploratory financial data analysis
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media v1 Jun 18, 2019 readme! Jun 22, 2019

What is pluto?

pluto is a compute cloud for exploratory financial data analysis.


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?

The public facing website is a customized JupyterHub that allows you to login through your github account and run either python or R code. To access data-sets provided by pluto, just include some standard headers and get going. When you hit "execute", the entire notebook is executed by a local compute instance and the entire result is sent back. This happens through a mongodb queue. And everything is updated on your github account under a repo called plutons. /media has presentations and a screencast of the POC.

Read to give it a whirl? Explore on

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