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Algo Development 2.0 Looks to Open Source, Cloud & Big Data

Traders Magazine Online News, July 11, 2018

Ivy Schmerken

For its part, QuantConnect began to allow hedge funds to subscribe to the algorithms coded by its users. Quants work on their alpha on the platform, and QuantConnect reviews and hosts their signals. “There’s no hiring the six month recruiter. They hook up to the API and search for what they want.” Connecting via the API makes it easy to consume signals and merge them into their portfolios, said Broad. Such signals are insights into where the market is going in the short term, such as the next five minutes, plus or minus 1%.

Some hedge funds may trade on the signal blindly while some will trust it a little and use risk controls, said Broad. Others may take the signal and do the opposite, said Broad in a follow-up interview.

“The process of consuming a signal and how you apply it to your portfolio is also part of the secret sauce of the hedge fund,” said Broad. Each hedge fund has its own unique slant. A signal could be one factor while the hedge fund may have 100 factors in its model.

Algos, Cloud & Big Data

Though algorithms have come far in the past 20 to 30 years, there is more innovation ahead, said Harts. The next evolution of algorithmic trading is going to incorporate big data, machine learning and artificial intelligence.

With the explosion of data sources made available, panelists agreed that quants and traders are able to mine data they never had before. Even the US government is opening up its data, said Broad.

“To build a server farm for US data or a data storage farm just to store US tick-by-tick equities data used to be prohibitively expensive,” said Harts. Today, you can rent it from Amazon, Microsoft or Google,” he said. Similarly, to build a server farm of 32 or 64 core servers would have been unaffordable except for the biggest prop traders or banks.

Today, firms can perform machine learning on 20 years of US equities data with much less effort and significantly less cost. “The cloud has democratized that. You can leave everything up there, pull your signals, pull your results and really optimize the cost,” agreed Maragos.

As for which high-performance trading technology organizations are implementing or planning to implement this year, a poll of attendees showed that 43% are investing in artificial intelligence, 25% said improved APIs and gateways, 12% predictive analytics and 13% said other trading technologies.

Even with technologies such as A.I., big data and machine learning in focus, outsourcing has a role to play, argues Broad.

“Why spend 20 years and a $1 billion building artificial intelligence, when you could use Google and Amazon’s AI and train it?” said Broad.

With the demand for quants and data scientists increasing, firms may find that it’s time for a new paradigm. Recently, the industry has seen several large institutions like Two Sigma, Goldman Sachs and Bloomberg publishing open source work, said Broad. Two Sigma ran a financial model competition for data scientists on Kaggle, an open source site where data science projects are worked on.

A number of quantitative firms are running their own open source projects as a way to recruit the brightest minds who like to solve challenging projects. “Transparency is a marketing strategy,” said Broad.

But algorithmic development will likely always be partially closed, said Broad, referring to financial firms acting in their own self-interest. Although firms are making institutional public contributions to their own open source projects, “there is still generally a tight policy internally about using open source tools in the course of their work,” said Broad. In that sense, “the vast majority of institutions have a long way to go with using open source.”


Ivy Schmerken is Editorial Director aat FlexTrade



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