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C++ is the new Python

Sarah Butcher, 2021.07.15

Original article:

C++ is making a comeback. It ranked fourth on the Tiobe Index as the most popular coding language this month after being rated top by 8% of people. That doesn’t exactly put it on a par with C or Java or Python at 11-12%, but it does mean that C++ is up there with the favourites – and that it’s continuing a run of increasing popularity that began at the start of 2020.

As we’ve noted before, C++ has historically been used for a particular set of functions in investment banks and financial services firms. By virtue of its low level memory access and therefore speed, it’s often the language of choice for high speed trading systems. This is why JPMorgan, for example, is currently hiring a C++ engineer for its JISU low-latency platform, why hedge fund Citadel wants a C++ engineer for its own market making systems, and why Goldman looks for C++ expertise for its systematic trading team.

As high speed electronic trading systems become an increasingly important differentiator and algorithmic trading takes hold beyond the equities markets, C++ expertise stands to become more sought-after in finance. Paul Bilokon, a former credit quant at Deutsche Bank and founder of AI company Thalesians, has long been an exponent. Bilokon points out that Bjarne Stroustrup, the Danish computer scientist who created C++, described it as a language for defining and using light-weight abstractions, and that this makes it peculiarly appropriate in banks and hedge funds. “Finance is full of abstractions. And there is a lot of demand for their light-weight implementations – in derivatives pricing and, most pertinently, in high-frequency trading, where there are few alternatives to C++,” he says.

As C++ evolves, Bilokon says its use is spreading. Hedge fund Millennium specifies that its quantitative developers have, “substantial modern C++ programming experience,” a designation that it doesn’t define and that can mean different things to different people. “Modern C++ used to mean C++11 and above, but nowadays may be taken to mean C++17 or even C++20 and above,” says Bilokon.

In finance and elsewhere, the more recent iterations of C++ have considerable advantages over their predecessors. There’s less use of the old C-style idioms and the language is both cleaner and more powerful, which can make users more productive. “In the past, I’ve seen many people write their own smart pointers to manage resources. Nowadays there is no need to do this, one can rely on the C++ Standard Library,” says Bilokon. He says the upshot is C++ has caught up with Python by introducing range-based “for” loops and powerful lambda expressions. “C++20 is all about modules, coroutines, concepts, and the ranges library.”

While C++ isn’t exactly taking over from Python in finance (there are currently 2,150 Python roles advertised on eFinancialCareers versus just 785 for C++), this does mean that the language is becoming easier to use and is venturing beyond some of its historic niches. Goldman Sachs, for example, is migrating its SecDB risk and pricing system away from its proprietary language, Slang, and is looking for people who can code in both Java and C++ to help make the transition. C++ is also well-used in analytics systems, site reliability engineering and for strats roles relating to pricing, risk and P&L calculations.

Python has become a necessary language to learn if you want a job in finance. However, while students everywhere are becoming minor Python coders, the fact that C++ is harder to master can be a differentiator when it comes to getting a job. At the same time, more recent versions of C++ are easier to use than those that came before. C++20 has improved support for large-scale dependable software, says Bilokon. “The light-weight abstractions promised by C++ are now easier to attain. These changes are driving a renaissance of C++ on Wall Street and in the City, and in high-frequency trading centres such as Chicago.”


The ex-banking quants bringing machine learning to the finance elite

Sarah Butcher, 2018.09.10

Original article:

It’s earlyish on a Tuesday morning in a room off the “buttery” (read dining hall) at Oxford’s illustrious Christ Church College. Paul Bilokon, a former quant at Citi, Nomura and Deutsche Bank-turned data science and machine learning lecturer at London’s Imperial College, is bustling around in full academic regalia. Matthew Dixon, a former academic in the computer science department at Stanford University and quant at Lehman Brothers and Barclays Capital, is about to deliver a class. The room is packed.

The 30 or so attendees are drawn from a sturdy cross-section of finance society. Sitting elbow to elbow are media-shy bank staff, hedge fund business development professionals, consultants, senior ETF traders, portfolio managers and heads of fixed income technology.

They’ve come from Asia. They’ve come from Europe and they’ve come from the Americas. And they’ve each paid upwards of £2k ($3.6k) to hear Bilokon and Dixon expound upon the application of machine learning in financial services.

“I’m here to deepen my understanding of neural networks,” says Joachim Tigler, head of exchange traded fund (ETF) trading at ADG Trading and a former Deutsche Bank managing director and head of the fixed income derivatives group at the German bank. Mchine learning can help clean and structure data, optimize execution and pick stocks, Tigler adds, knowledgeably.

The people in the room are just the tip of the iceberg. Bilokon, Dixon and Saeed Amen (now head of Cuemacro, a consulting and research firm focused on systematic trading) are founders of the Thalesians, a self-described “consultancy and think tank” comprised of, around 2,500 “dedicated professionals working in quant finance, economics, mathematics, physics and computer science” This particular machine learning seminar is just one of their events: there have been 15 others this year alone. Most but not all were related to finance: others covered tumour formation, quantum computing, and energy bills.

Machine learning is a proudly academic area. Bilokon himself is fully credentialled: he has a masters in mathematical finance from Oxford University (distinction) and a masters (mathematics and computer science) and PhD (mathematics and computing) from Imperial College. But the Thalesians aren’t just about rarefied academia: a poster at seminar advertises their tagline – “It’s easy for philosophers to be rich if they choose.”

Whether Bilokon et al are “rich” is open to question, but some in the machine learning community certainly are. “Renaissance has been doing this for a long time,” says Dixon at one point during his presentation on the application of neural networks in finance and the frontiers of machine learning. The renaissance he’s referring to is Renaissance Technologies, the quantitative hedge fund whose founder, Jim Simons, is thought to be worth $18.5bn.

Dixon uses part of his session to clarify how machine learning is different to other forms of quantitative finance. For example, he points out that accuracy should not be used as a measure of performance in supervised learning models (models where you know the right answer and teach the machine how to achieve it). If a machine learning model is rewarded for accuracy, it will focus on the 98% of times that it’s right and ignore the 2% of times that it’s wrong. “You will get supervised learning that’s very good at predicting zeroes because it has seen a lot of them, but which won’t know what to do with the 1s [which it’s seen fewer of],” says Dixon. “In finance, the tail events are quite important. It’s easy to brush them under the carpet because they are inconvenient but you need to face them head-on,” he adds.

Dixon also expounds upon unsupervised learning, where models aren’t told what the correct answer is, and on reinforcement learning. The latter requires dynamic programming so that the machine is penalized for making a wrong decision and rewarded for making a good one Q learning is a subset of reinforcement learning where you look at the probability distribution of responses to various actions. This kind of machine learning is distinct from statistics, explains Dixon: “Reinforcement learning comes from optimization and decision science.”

For this reason, the best machine learning practitioners in finance may not be statisticians at all. – Dixon says reinforcement learning is best suited to financial markets. “In supervised learning, you don’t account for the fact that your decision changes the state of the world. You are observing the data and making decisions – nothing about your decision feeds back into the market. This isn’t the case in the markets, so by definition reinforcement learning seems most appropriate.”

Tigler, who knows Bilokon from Deutsche Bank and has been to various Thalesians events, thinks the seminar was very well worth it. Now that artificial intelligence is the new hot thing, he says a lot of events are “superficial”, but that the Thalesians are distinct: “There are people here with significant experience and influence who understand the mathematical foundations of machine learning and its practical applications in finance.

And if you can’t attend a $2.6k seminar to get with machine learning? Dixon recommends a book: Marcos López de Prado, the founder of Guggenheim Partners’ Quantitative Investment Strategies (QIS) business, has just published a new book “Advances in Machine Learning.” “He’s against heavy mathematics and is very practical,” says Dixon. “The book has a lot of wisdom on the practicalities.”


London Startup Thalesians Is Merging Philosophy With Finance

By Newsweek Staff on 2017.03.02 AT 1:25 PM EST

Original article:

University friends Paul Bilokon and Saeed Amen speak with Newsweek senior writer and finance editor Leah McGrath about their startup, Thalesians.

Bilokon and Amen were inspired by the math-savvy Greek philosopher, Thales of Miletus, to merge philosophy with finance in co-founding Thalesians together. Amen even wrote a book about how to apply Thales’s ancient principles to investment management in “Trading Thalesians.”

The pair were speaking at Newsweek’s Artificial Intelligence and Data Science in Capital Markets conference, in London, March 2.