Overview of Quantopian: where we are and where we are headed. 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.

1. Crowd-sourced Systematic Alpha

2. Contents
§ What is Quantopian?
§ How do we source investment algorithms?
§ How do we evaluate investment algorithms?
§ What’s next?

3. Uncorrelated returns streams are the Holy
Grail of investing.

4. Blackrock Investor Day ‘14
Source: http://www.blackrock.com/corporate/en-us/investor-relations Investor day 2014

5. What is Quantopian?
Internet scale producer of uncorrelated return streams.
Diverse people, markets, and data.

6. Crowdsourcing – the process of obtaining needed services, ideas or content by
soliciting contributions from a large group of people, and especially an online
community, rather than from traditional employees or suppliers.
http://en.wikipedia.org/wiki/Crowdsourcing
Crowdsourcing investment algos maximizes diversification.

7. How do we source investment
algorithms?
Huge, active, global community of researchers.
Free access to world-class research/development/trading
technology.
Strong incentives for the community to create algos.

8. The Quantopian Open
launched in January 2015

10. How do we evaluate algorithms?
Combination of automated quality screen, out of sample
validation, and expert manual due diligence.

11. 1. Algorithm is born.
2. Backtest score is assigned
(~1% pass quality screen)
3. Predict out of sample results
4. Validate out of sample results
and tear-sheet (~5% pass)
5. Deploy prop capital,
validate $ results
Algorithm Evaluation Process

12. Your algorithm gets selected for the
fund, what happens now?
§ You license your algorithm to Quantopian for use in our
fund.
§ You get paid a 10% royalty based on the profits earned
by your algorithm.
§ You retain ownership of your intellectual property.
§ Quantopian takes responsibility for operating your
algorithm, allocating capital, and managing risk.

13. What’s next?
§ More Data www.quantopian.com/data
§ More Markets - Futures coming soon!
§ Capital allocations!

14. @fawceisfawce
fawce@quantopian.com
www.quantopian.com
Thank you.
Questions?

15. Tear sheet analysis
quantopian.github.io/pyfolio/

16. Backtest score: Algo Quality Screen
1. Annual returns
2. Annual volatility
3. Sharpe ratio (annual)
4. Max Drawdown
5. Calmar ratio
6. Market Beta
7. Stability of returns
8. Hedged

18. What we look for:
Low Exposure to the Market
We want algorithms that aren't correlated to the rest of the stock
market. We are looking for algorithms with a market beta between -0.3
and +0.3.

19. We want algorithms that show stable profits. If your algorithm is losing
money, or makes money in a volatile way, it isn't very helpful. We are
looking for algorithms that consistently have a Sharpe ratio over 1.0.
What we look for:
Consistent Profitability

20. Your algorithm has to actively trade. We measure that by looking at
how often your portfolio turns over per year. We are looking for
algorithms that turn over their portfolio between 12 and 500 times/year.
What we look for:
Active Trading

21. We look for algorithms with between -30% and +30% average pairwise
correlation with the other algorithms in our portfolio.
What we look for:
Low Correlation to Peers

22. We are are looking for algorithms that are driven by underlying
economic reasoning.
What we look for:
Strategic Intent

23. What we look to avoid…
§ Overfitting
§ Data snooping
§ Spurious correlations
CONSISTENCY
0.85