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Today, banks and other financial institutions cannot afford to ignore the tremendous potential that trends like open source, open data or open communities have to offer—but there are often no other big counterpart institutions to interact with. The Python Quants plays that role when it comes to Python introduction and deployment, training the people working in banks and providing ongoing support and services.
Python and open source technologies are empowering organizations and individuals to do financial and data analytics in real-time and on a highly customized basis as well as to rapidly develop new financial applications and deploy them based on weekly or even daily cycles. “We support financial institutions in introducing, training and deploying Python and a major building block in this regard is our Quant Platform,” adds Hilpisch. “Our training offerings are based on more than 10 years of experience with Python for Finance and provide a hands-on learning experience, making heavy use, for instance, also of our Quant Platform.”
The company’s Quant Platform makes central, standardized Python deployment an easy and efficient affair while mitigating risks and reducing maintenance costs considerably during deployment.
Our major focus has always been on the use of Python and open source technologies for financial data science, computational finance and algorithmic trading
In an instance, Eurex, one of the leading derivatives exchanges, wanted to support investors, traders, market makers and quants in the understanding and trading of their listed volatility and variance products. Eurex decided to use Python for this project and The Python Quants were tasked to create the content and in particular the Python codes accompanying it. While the content itself became part of the Eurex website, all Python codes were provided to Eurex partners and other interested parties on a Eurex-labeled version of the Quant Platform for easy code access and execution. “Deploying open source technologies, like Python, is often a tedious and sometimes even a risky process, with our services and products we help our clients to make this process more efficient and mitigate risks,” adds Hilpisch.
Another product of The Python Quants Group assisting organizations to model, price and risk manage complex portfolios of (multi-risk) derivatives with potentially complex correlation structures is DX Analytics. Being an open source derivatives, portfolio and risk analytics library written exclusively in Python— it makes heavy use of the capabilities of Python and the capabilities of its numerical and data analytics libraries.
As the Python ecosystem sees tremendous momentum, The Python Quants Group’s near-term focus will be on machine and deep learning techniques, technologies emerging in algorithmic trading as well as on cryptocurrencies and blockchain. “We will improve our value proposition in particular for hedge funds and other buy side players for the days to come,” concludes Hilpisch.