Portfolio and Risk Analytics in Python with pyfolio - On open source library compatible with Zipline and Quantopian.

1. Portfolio and Risk Analytics in Python with pyfolio
PyData NYC 2015
Jessica Stauth
VP Quant Strategy
Justin Lent, Thomas Weicki PhD, Andrew Campbell
#PyData #PyDataNYC 1

2. Why use Python for Quant Finance?
• Python is a general purpose language
• No hodge-podge of perl, bash, matlab, R, excel
fortran.
• Very easy to learn.
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3. The Quant Finance PyData Stack
• Source: [Jake VanderPlas: State of the Tools]
– (https://www.youtube.com/watch?v=5GlNDD7qbP4)#PyData #PyDataNYC 3

4. Python in Quantitative Finance
• When Quantopian started in 2011, we needed a backtester
– Open-sourced Zipline in 2012
• When we started to build a crowd-source hedge fund, we
needed a better way to evaluate algorithms
– Open-sourced pyfolio in 2015
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5. pyfolio
• State-of-the-art portfolio and risk analytics
http://quantopian.github.io/pyfolio/
• Open source and free: Apache v2 license
• Can be used:
– stand alone
– with Zipline
– on Quantopian in a hosted Research Environment
– with PyThalesians
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6. Using pyfolio stand-alone
• Installation
• Use Anaconda to get a Python system with the full
PyData ecosystem. Then:
• pip install pyfolio
• Import it in your project
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7. Tearsheets analysis package
Visualizations
• Daily returns of a stock, or trading strategy
• Positions
• Transactions
• Periods of market stress
• Bayesian risk analyses
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8. Tearsheet Components
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13. Long/Short Exposure over Time
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14. Sector Exposure over Time
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15. Slippage and Transaction Cost Sensitivity
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16. Zipline + pyfolio, locally or via quantopian.com
• Zipline: open-source backtester by Quantopian
• Powers quantopian.com
– 12 years of stock market data for US Equities (minute-bar
prices, corporate fundamentals, sentiment, events, etc.)
– Various models for transaction costs and slippage.
– Web based IDE for creating and deploying trading algorithms
• Hosted ipython notebook research server
– Ad-hoc data analysis. We provide market data.
– Pull in strategy backtest results from the Web IDE and use
pyfolio
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17. Bayesian analysis in pyfolio
• Sneak-peek into ongoing research.
• Can a backtest (in-sample data) be used to predict the future results
(out of sample data)?
• Sophisticated statistical modeling takes uncertainty into account.
• Uses T-distribution to model returns (instead of normal).
– Addresses ‘fat-tail’ nature of financial returns
• Relies on PyMC3.
– Python module for Bayesian statistical modeling and model fitting which
focuses on advanced Markov chain Monte Carlo fitting algorithms.
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18. Modeling Trading Strategy Uncertainty with Bayesian Analysis
How do I know my trading
strategy is “working” after I’ve
put real $ into it?
How many Out-of-Sample trading
days must be observed for me to
be certain?
Calculate: P(mean > 0)
(Probability of out-of-sample means > 0%)
Re-compute model as new data is sampled.
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19. Modeling Trading Strategy Uncertainty with Bayesian Analysis
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20. Bayesian analysis – real world example
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paper
trading

21. Bayesian analysis – real world example
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22. Bayesian analysis – real world example
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June2015
Nov2015
Backtest – “in-sample”

23. More Info on Bayesian Analysis
Accompanying blog post:
http://blog.quantopian.com/bayesian-cone/
Bayesian Methods for Hackers:
http://camdavidsonpilon.github.io/Probabilistic-Programming-and-
Bayesian-Methods-for-Hackers/
PyMC3: http://pymc-devs.github.io/pymc3
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24. Summary
• Pyfolio bundles various useful analyses and includes
advanced statistical modeling.
• “Using pyfolio” webinar tutorial:
https://www.youtube.com/watch?v=-VmZAlBWUko
• Still young -- please contribute:
https://github.com/quantopian/pyfolio/labels/help%2
0wanted
• Bugs: https://github.com/quantopian/pyfolio/issues
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25. Up next right here: Andrew Campbell - Bootstrapping
Applications and Dashboards with IPython Widgets
Tomorrow 4:25pm Room A: Scott Sandersen –
Developing an Expression Language for Quantitative
Financial Modeling
jstauth@quantopian.com
@jstauth
www.quantopian.com/fund
Thank you.
Questions?
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