This document summarizes Jess Stauth's presentation on being open source in quantitative finance. Some key points: - Open source can benefit quant finance through transparency, robustness from many eyes on code, and access to global talent. However, there are also costs and risks around IP leakage. - Many quant firms use a mosaic of open source and proprietary components. Research tools are often open source while backtesting and production systems rely on proprietary models and data. - Quantopian actively maintains several open source projects and contributes to others like Jupyter, Pandas, and PyMC3 to benefit the quant community. Being open can also help hiring.

1. A beginner’s guide to being open
(source) in the traditionally secretive
field of quantitative finance.
PyData NYC October 2018
Jess Stauth, PhD
Portfolio Management and Research , Quantopian

2. Disclaimer
Quantopian provides this presentation to help people write trading algorithms -
it is not intended to provide investment advice. More specifically, the material is
provided for informational purposes only and does not constitute an offer to
sell, a solicitation to buy, or a recommendation or endorsement for any security
or strategy, nor does it constitute an offer to provide investment advisory or
other services by Quantopian. In addition, the content neither constitutes
investment advice nor offers any opinion with respect to the suitability of any
security or any specific investment. Quantopian makes no guarantees as to
accuracy or completeness of the views expressed in the website. The views are
subject to change, and may have become unreliable for various reasons,
including changes in market conditions or economic circumstances.

3. Quants and open source – let’s start with a scary
story.
https://www.vanityfair.com/news/2013/09/michael-lewis-goldman-sachs-programmer
https://en.wikipedia.org/wiki/Sergey_Aleynikov

4. Excerpted from: https://www.vanityfair.com/news/2013/09/michael-lewis-goldman-sachs-programmer
“…A one-way relationship with open source.”

5. What followed was a decade of legal battle…
• Aleynikov was convicted in 2010 of violating the Economic Espionage Act and
the Interstate Transportation of Stolen Property Act.
• In 2012, the U.S. Court of Appeals in New York reversed the conviction.
• Aleynikov was rearrested in August 2012 on state charges.
• In 2015, he was convicted of one count of unlawfully using secret scientific
material, but the judge threw out the verdict and acquitted him.
• The District attorney’s office appealed and the conviction was reinstated by an
intermediate appellate court.
• In May of 2018 the appeals court upheld the conviction but declined to seek
additional jail time. All told Aleynikov served one year in prison.

7. Scared yet?

8. Can open-source be a two-way street, even in
traditionally secretive quant finance land?
Short answer: YES!
Longer answer: Yes, but…
• Effort and some expertise are required to separate source code that is truly
proprietary from that which is effectively commoditized.
• Some companies just don’t see the benefits as large enough to outweigh the costs
and risks.
• The first real example of this in the quant finance space of course was Wes’ open
sourcing of pandas while at AQR, built on Numpy/Travis’ work!

9. Costs and risks – both real and perceived
• Costs:
• Engaging users on mailing lists
• Reviewing pull requests
• Making proper releases
• Documentation
• Navigating interdependencies between open and closed source
• Risks:
• May fail to get engagement if response times are slow
• IP leakage, if you misjudge the line between commodity and proprietary code
• Perception of IP leakage, non-technical stakeholders need to trust that what is
open sourced is not a ‘trade secret’
• Embarrassment “What if everyone thinks my code is crap!?!”

10. Why it’s worth it to be open, and not just with
code.
• Transparency builds trustful relationships
that extend far beyond the walls of your
company headquarters.
• Robustness scales with use.
• Linus’ Law – Given enough eyes, all bugs are
shallow.
• Talent is globally distributed.
• Some of our best hires have come from OSS
community.
• Our asset management business model relies
on contributions from the community in the
form of (closed source) algorithms.
• Life comes at you fast 
h/t Thomas Wiecki’s open source talk

11. Ok so it’s worth it, but there remain costs and
risks – so how does this all come together?

12. The modern quant finance workflow – a mosaic of
open and closed source components
Research
• Ingest both publicly available data such as company financial statements and proprietary data sources
• Open source tools (e.g. Jupyter, Pandas, Numpy, SciPy, Alphalens, Qgrid, Qdb)
• Product is proprietary signals/factors
Back testing
• Open source event simulation tools (e.g. Zipline)
• May exploit vendor models/tools for cost modeling.
• Product is a proprietary alpha model (or trading algo)
Production
• Vendor tools for risk management and portfolio construction (e.g. Axioma, Barra, Northfield), or
Quantopian’s free Risk Model
• Product is a *very* proprietary trade list and possibly an execution strategy

13. Research
Back testing
Production
Risk Model
Individual Quant
Developer / Researcher
Proprietary signals, factors,
insights from data
3rd party vendors
• Factset
• Estimize
• FRED
• ITG Market Impact Model
• Axioma
• Northfield
• Barra
• Executing Brokers
Proprietary logic combining
individual factors into a
predictive model or algo
Proprietary execution strategy
if HFT, else rebalance
frequency choice.
Same mosaic, fewer words…

14. Some (more) Open source projects we maintain
• qdb - A debugger for python that allows users to debug code executing on remote machine.
• empyrical - A python library for computing common financial risk and performance metrics. Used by
zipline and pyfolio.
• warp_prism
• pgcontents
• DockORM
• coal-mine
• PenguinDome
• trading-calendars
• serializable-traitlets

15. We have committers or made significant contributions to:
• jupyter
• PyMC3
• blaze/odo/datashape
Smaller contributions to:
• CPython
• Airflow
• pandas

16. Acknowledgement to our amazing OSS
contributors:
Q Current
Jean Bredeche
Andrew Daniels
John Fawcett
Rich Frank
Kathryn Glowinski
Gus Gordon
Eddie Hebert
George Ho
Joe Jevnik
Jonathan Kamens
Abhijeet Kalyan
Samantha Klonaris
Max Margenot
David Michalowicsz
Vikram Narayan
Jacob Nazarenko
Ernesto Perez
Scott Sanderson
Adrian Seybolt
Tim Shawver
Paul Sutherland
Freddie Vargus
Thomas Wiecki
Rene Zhang
Q Alums
Andrew Campbell
James Christopher
Stewart Douglas
James Kirk
Andrew Liang
Ana Ruelas
Maya Tydykov
… and counting!

17. Contribute $ as well
as code!
https://numfocus.org/blog/quantopian-commits-to-fund-pandas-as-a-
new-numfocus-corporate-partner

18. Thank you and Happy Halloween!
Questions?
Email: jess at quantopian dot com @jstauth

19. Zipline is a Python library for algorithmic backtesting and trading. Zipline is the backtesting
and live-trading engine powering Quantopian – a free, community-centered, hosted
platform for researching quantitative trading strategies. The best strategies are eligible for
performance-based royalty licenses.

20. Pyfolio is a Python library for performance and risk analysis of financial portfolios. It works
well with the Zipline open source backtesting library. At the core of pyfolio is a tearsheet
consisting of individual plots that provide a comprehensive overview of the performance of
a trading algorithm.

21. Alphalens is a Python library for performance analysis of predictive (alpha) stock factors.
Alphalens is compatible with the Zipline open source backtesting library,
and Pyfolio which provides performance and risk analysis of financial portfolios.

22. qgrid - An interactive grid for sorting, filtering, and editing DataFrames in Jupyter
notebooks