Python 3.11 to be Twice as Fast as its Predecessors

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cation binary interface) or machine code generation to boost Python’s performance even more.The modifications may help users of Python-based products, according to Van Rossum. Banks like JPMorgan and Bank of America, which are heavy users of Python in their risk pricing systems — though JPMorgan has been slow to move away from Python 2 — and finance firms that utilize the language for data analysis may theoretically be included.Van Rossum isn’t saying anything about how the modifications will affect Python’s finance users. However, given that C++ is over 100 times quicker than Python, Python’s adoption in trading systems is unlikely to happen anytime soon.Because of Python’s “performance penalty,” according to Jeffrey Ryan, an ex-foundational quant analyst at Citadel who now works as a “quant freelancer,” it’s generally utilized in instances when speed isn’t as important as ease of creating code.According to Ryan, a 2x boost in Python’s speed won’t make much of a difference in finance. “Most compute-intensive Python stuff is already written in C (or C++/Fortran) internally — BLAS/LAPACK/NumPy/TensorFlow, for example,” he notes. “If absolute performance is important, you’ll probably code it in C/++ and wrap it in Python, as these libraries do.”Even if Python becomes significantly quicker in 2022 and beyond, Ryan believes banks and other financial institutions will be sluggish to adopt the new version. “The transition from 2.X to 3.X was far too painful and fresh for most people to go through again,” he says. “If anything, I think this will cause many to reconsider

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using Python altogether and turn to other languages that make more sense, like Julia or Golang.”However, if we think beyond big banks, a 2 to 3X speed improvement might mean a lot for data scientists and others who generate scripts that take a while to complete, assuming that the limitation is not input/output. However, in those cases, speed increases are possible when working with input files of different types CSV files are much faster to read than Excel files, and parquet or pickle files are even faster. So in short, who wants to wait, I will take any speed improvements that I can get but if were a large bank I might be slow to adapt considering billions of dollars would be at stake.I hope you enjoyed reading this. If you’d like to support me as a writer, consider signing up to <a href="https://medium.com/@dennisyd/membership">become a Medium member</a>. It’s just $5 a month and you get unlimited access to Medium.Free Cash: Sign up for this Capital One card and Earn a one-time$ 200 cash bonus once you spend $500 on purchases within 3 months from account opening: <a href="https://capital.one/3DRIBSG">https://capital.one/3DRIBSG</a>More content at <a href="https://plainenglish.io/">PlainEnglish.io</a>. Sign up for our <a href="http://newsletter.plainenglish.io/">free weekly newsletter</a>. Follow us on <a href="https://twitter.com/inPlainEngHQ">Twitter</a> and <a href="https://www.linkedin.com/company/inplainenglish/">LinkedIn</a>. Join our <a href="https://discord.gg/GtDtUAvyhW">community Discord</a>.</article></body>

The upcoming version of Python is set to be twice as fast as the previous versions.

While Python is one of the most widely used computer languages in finance, it is more commonly utilized for specific activities such as data analysis than for actual market-making software. This is due to Python’s higher level of abstraction, which makes it slower as an interpreted language than alternatives like C++ or Java.

Guido Van Rossum, the author of Python, wants to change that. Van Rossum stated at the last Python Language Summit that when version 3.11 is published in October 2022, he plans to quadruple Python’s speed. His overall goal over the next four years is to boost Python’s speed by a factor of five.

Van Rossum’s presentation, which is available on Github details how he plans to do this, including an adaptive bytecode interpreter, frame stack optimization, and ‘zero overhead’ exception handling. If these changes double Python’s speed, Van Rossum believes that future changes may include a solid ABI (application binary interface) or machine code generation to boost Python’s performance even more.

The modifications may help users of Python-based products, according to Van Rossum. Banks like JPMorgan and Bank of America, which are heavy users of Python in their risk pricing systems — though JPMorgan has been slow to move away from Python 2 — and finance firms that utilize the language for data analysis may theoretically be included.

Van Rossum isn’t saying anything about how the modifications will affect Python’s finance users. However, given that C++ is over 100 times quicker than Python, Python’s adoption in trading systems is unlikely to happen anytime soon.

Because of Python’s “performance penalty,” according to Jeffrey Ryan, an ex-foundational quant analyst at Citadel who now works as a “quant freelancer,” it’s generally utilized in instances when speed isn’t as important as ease of creating code.

According to Ryan, a 2x boost in Python’s speed won’t make much of a difference in finance. “Most compute-intensive Python stuff is already written in C (or C++/Fortran) internally — BLAS/LAPACK/NumPy/TensorFlow, for example,” he notes. “If absolute performance is important, you’ll probably code it in C/++ and wrap it in Python, as these libraries do.”

Even if Python becomes significantly quicker in 2022 and beyond, Ryan believes banks and other financial institutions will be sluggish to adopt the new version. “The transition from 2.X to 3.X was far too painful and fresh for most people to go through again,” he says. “If anything, I think this will cause many to reconsider using Python altogether and turn to other languages that make more sense, like Julia or Golang.”

However, if we think beyond big banks, a 2 to 3X speed improvement might mean a lot for data scientists and others who generate scripts that take a while to complete, assuming that the limitation is not input/output. However, in those cases, speed increases are possible when working with input files of different types CSV files are much faster to read than Excel files, and parquet or pickle files are even faster. So in short, who wants to wait, I will take any speed improvements that I can get but if were a large bank I might be slow to adapt considering billions of dollars would be at stake.

I hope you enjoyed reading this. If you’d like to support me as a writer, consider signing up to become a Medium member. It’s just $5 a month and you get unlimited access to Medium.

Free Cash: Sign up for this Capital One card and Earn a one-time $200 cash bonus once you spend $500 on purchases within 3 months from account opening: https://capital.one/3DRIBSG