The Python Quants


Web-based Financial Analytics and Rapid Financial Engineering with Python

All a Python Quant needs in a single place.


*Open Source for Quantitative Finance*

Open source software (like Python, R and Julia) is revolutionizing how quantitative financial workflows and applications are developed and deployed. Not only that times-to-insights and IT costs are significantly decreased, productivity of quants increases substantially as well. The right use of open source software therefore represents a measurable competitive advantage.

The Python Quants (http://tpq.io) focus on making the best of open source for quantitative finance. To this end, we have developed the *Quant Platform* (http://pqp.io) which allows for the most efficient, scalable and flexible deployment of modern analytics technologies across whole organizations and across regions. The platform makes collaboration on projects within teams easy, facilitates code sharing and also instant financial application roll-outs. It can be used on any kind of infrastructure, from the smallest cloud instance to the biggest servers.

With our open source library *DX Analytics* (http://dx-analytics) we bring global valuation and modern risk management approaches to the front office and illustrate the power of modern analytics technologies. Value and risk manage in near-time option portfolios with 1,000+ positions and 100+ underlyings on a single standard compute node while maintaining scalability to even much larger derivatives books.

You might also benefit from our *books* "Python for Finance" (O'Reilly, http://oreil.ly/1kAwNp5 ) and "Derivatives Analytics with Python" (Wiley Finance, http://bit.ly/1HzDGSG).

In addition to our technology and books, we also provide *consulting, development and support services* as well as standard and customized *trainings*.


Why Python for Finance and Quants?

10 years ago, Python was considered exotic in Finance – at best. Today, Python has become a major force in Finance due to a number of characteristics:

  • Syntax: Python syntax is pretty close to the symbolic language used in mathematical finance (also: symbolic Python with SymPy)

  • Multi-purpose: prototyping, development, production, sytems administration – Python is one for all

  • Libraries: there is a library for almost any task or problem you face

  • Efficiency: Python speeds up all IT development tasks financial institutions typically face and reduces maintenance costs


Further benefits of using Python in a Quant context ares:

  • Performance: Python has evolved from a scripting language to a 'meta' language with bridges to all high performance environments (e.g. LLVM, multi-core CPUs, GPUs, clusters)

  • Interoperability: Python seamlessly integrates with almost any other language and technology

  • Interactivity: Python allows domain experts to get closer to their business and financial data pools and to do interactive real-time analytics

  • Collaboration: tools like Python Quant Platform (http://quant-platform.com) allow developers and financial data scientists to collaborate across companies and business departments, to share code, data and their analytics insights easily


PYTHON QUANT PLATFORM

The Python Quant Platform is targeted towards quantitative analysts and
researchers ("quants") and combines proprietary analytics solutions with the IPython Notebook in a scalable, Web-based environment:

  • DEXISION – GUI-based financial engineering
  • DX Analytics – Python-based financial analytics library
  • IPython Notebook – Browser-based interactive analytics
  • Collaboration – across teams within a company

"Single, integrated solution for interactive, collaborative financial analytics with Python."


IPython Notebook

Using IPython Notebook as the central interactivity tool.

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Python Quant Platform

 


DEXISION

GUI- and Web-based graphical analyses.

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DEXISION Graphics

DEXISION is a Python- and Web-based financial analytics suite, allowing for the GUI-based modeling, valuation and hedging of complex multi-risk, multi-derivatives portfolios/trades.

  • General risk-neutral valuation ("Global Valuation")
  • Monte Carlo simulation for valuation
  • non-redundant modelling of risk factors
  • single & multi risk derivatives
  • European and American exercise
  • Complex cross-asset portfolios of derivatives
  • Anlytical core implemented in Python
  • Flexible, GUI-based graphical analyses

See all the worked out Use Cases for DEXISION.

 


DX Analytics

Just import the Python library and benefit from powerful analytical capabilities.

import dx
  # import of library

python dx_example.py
  # generate example objects
  # European call on the maximum of 2 assets
  # 1 asset geometric Brownian motion
  # 1 asset jump diffusion

Visualize present values of exotic derivatives in 3d.

DX Analytics

Visualize the Vega (and other Greeks) of exotic derivatives in 3d.

DX Analytics

DX Analytics is a Python-based financial analytics library, allowing for the modeling, valuation and hedging of complex multi-risk, multi-derivatives portfolios/trades.

  • General risk-neutral valuation ("Global Valuation")
  • Monte Carlo simulation for valuation, Greeks
  • Fourier-based formulae for calibration
  • arbitrary models for stochastic processes
  • single & multi risk derivatives
  • European and American exercise
  • Completely implemented in Python
  • Hardware not a limiting factor

 


The Python Quants GmbH


The Python Quants GmbH, Germany, provides Python-based financial and derivatives analytics software as well as consulting services and training related to Python and Finance.Dr. Yves J. Hilpisch is the founder and managing director of the company. He is author of the books "Derivatives Analytics with Python" (forthcoming at Wiley Finance, 2015,http://bit.ly/1iiogmm) as well as "Python for Finance - Analyze Big Financial Data" (O'Reilly,http://oreil.ly/1kAwNp5). Visit also his Website http://hilpisch.com and follow him on Twitterhttp://twitter.com/dyjh.

Yves


Contact:


The Python Quants:
Email: sales@pythonquants.com

UK Office: +44 (0) 1273 201364


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