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Context of financial industry

Increasing regulatory requirements.
Require cost efficiency and short time to market; contradictory.
- Outsourcing, cheaper in short term, has higher end-to-end long term cost.
  - Need better QA, more management.

Used to have everything (OpenVMS, COBOL) for 10-15 years.
Now very hetereogenous (LAMP).
Financial companies keep adding very niche, massive services, and interconnect with interfaces.
- Unwieldy, massive, extremely proprietary.
Can Python become a universal end-to-end tool? Deutsche Börse Group have tried.

What is an exchange now?

Used to be large, spacious buildings full of people
Now large data centres, multiple to offer failover.
Spectacular amount of cooling. Data centre techs like talking about cooling.
Deutsche Börse is unique, integrated vertical chain of many services, unlike LSE or NASDAQ.
- Many integrated services, many direct connections to other countries.
- more than 420 participants, > 8000 traders, > 30 markets
Round trip processing time from 4ms to 200 microseconds. New system Linux, distribution.
- Why does it need to be fast? To ameliorate arbitrage.
  - If you don't execute fast enough then the fastest competitors can effectively execute arbitrage amongst exchanges.

SEC proposed Issuers of Asset Backed Securities, in April 2010, to document algorithms in executable Python
- Caused consternation amongst big compnies; why Python, and can't do open source.
2010-today: Python for analysis and simulation of Eurex pricing, very successful.

Very complex, unmodelable in spreadsheets
- e.g. Market makers get discount for providing volume that allows exchanges to function, risky.
Use Python to run simulations to determine pricing
- Can't use averages, summary statistics. Too stochastic.
- pandas + HDF5 makes short work of massive data sets that serve as basis of simulations.
  - e.g. Monte Carlo
- Can execute 10 million+ row data set simulations in seconds.
- Also very concise, human-readable descriptions of contracts that govern pricing agreements that cannot be expressed in spreadsheets.

Help people new to the VSTOXX index develop their own stochastic models and conduct their own research
pandas / numpy / scipy / HDF5
http://eurexchange.com/vstoxx
JavaScript visualisation of example generated model.
(!!AI I think this is the same tutorial we did the first day)

Currently share SQL data to customers. Interface is phone calls or email (!)
Want to move to an on-demand Python-based interface to exchange data.
Excited about IPython Notebook as primary customer interface.
Also DevOps (configuration management), to improve documentation of system architecture.

Python is increasingly used not to small websites but large deployments, many industries.
Java/C++ decreasing in mind-share, Python is creeping up.
Ohloh statistics: Java/C++ stagnating, Python is steadily increasing in monthy contributors and contributions.
Python is glue to extremely high performance software libraries (LLVM, Numba, Cython, pandas, HDF5, scipy, scikit).

Python is multi-paradigm. Teach once, apply everywhere.
Rapid prototyping, test and fail fast. Reduce time to market.
Avoid vendor lock in, because increasingly vendors lag too far behind industry cutting edge.
Open source: "invest in people, not in licenses".

IT value chain:
- Business -> IT -> IT Factory -> IT Operations
End-to-end Python development framework
Good at last two, not good at first two.
Get everyone in business to use Python, integrate the whole business under one active, supported framework.
But, not looking for a Holy Grail and throw away everything and start from scratch.
Python is a swiss army knife, fun, vibrant, and offers end-to-end integration.

How can you hope to convert people from spreadsheets?
- No Holy Grail. Financial people are very wedded to spreadsheets and VBA.
- People, when they encounter problems difficult to solve in spreadsheets/VBA, will move themselves to Python.
Do you use Python for high performance tasks?
- No, not for low-latency environment. There they use C to achieve sub-millisecond round trip times.
- But not all tasks are low latency.
- For end-to-end IT projects, higher proportion, substantial portion, spent in prototyping / initial feasibility stages.
- Python helps to reduce upfront costs on large IT proejcts.
- And if there are specific latency/speed/data issues, then tweak that small fraction.
- Prototyping in Python 3-4 lines can take massive amounts of time in C++, and you never shed this initial burden or share it easily.
Largest obstacles to Python are human and cultural. What are your experiences?
- No, haven't encountered this.
- Don't force people to use Python, just show them how much easier it is.



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