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

Traders Magazine Online News, July 11, 2018

Ivy Schmerken

With the shift from active to passive strategies, performance has become critical for active fund managers seeking to beat passive index funds.

“People are charging a lot and not meeting their benchmarks and that creates a lot of focus on performance,” said Peter Maragos, CEO of Dash Financial, which provides algorithmic trading tools for equities and listed options. 

Citing the battle over active vs. passive management and the problem of underperformance, Maragos said these trends are focusing attention on trading costs and best execution.

Regulatory events like Europe’s MiFID II with the unbundling of research vs. execution, have shined a light on best execution, which is going to impact algo development.

“People want more visibility, more control and more measurement and more analytics to really help them to squeeze all the juice out of the market structure,” said Maragos.

Referring to US equity and options markets – where there are 16 options exchanges, Maragos said, these are complex market structures. Instead of becoming an expert, institutions want to rely on high-performing tools to help them access liquidity and obtain the right price.

In the futures space, algorithms are playing a growing role in executing more efficiently and reducing costs. “We see things around real-time analytics on our futures and best execution, and working on real-time execution on those algos,” said Andrew Keane, Global Head, Listed Derivatives Algo Trading, Futures & OTC Clearing, Citi. The bank is also using trade analytics to determine the choice of algorithms (i.e., TWAP or implementation shortfall) for executing futures.

Transparency is also going to play a role in development of the next generation of algo trading. Visualization tools and animations can reveal what’s happening inside of an algo, where it routed child orders, changes in market data before and after the trade, and what is the latency of the exchange, said Dash Financial’s Maragos.

But the cost of hiring development teams can be a barrier to new entrants, said Broad of QuantConnect. A two-person hedge fund shop with $2 million will find it expensive to pay licensing fees for a black box. QuantConnect is working to open-source the algorithm infrastructure so quants can focus on writing the best algorithms possible, he said. “I see a movement of people focusing on alpha, thinking about what they are trying to achieve, [and] they outsource the engineering to the cloud,” he said.

QuantConnect is one of several upstart platforms providing an open-source, cloud-based platform including access to Python, documentation, data libraries, and educational resources. It provides free data for backtesting algorithms on US equities, FX, futures, CFDs and recently added cryptocurrencies. More than 55,000 users including quants, computer scientists, engineers and professional traders have designed and developed more than 1.2 million strategies on the platform, according to HedgeWeek.

But the lines are blurring between freelance quants and institutional markets. The two best known platforms in the space, Quantopian and Numerai, are running hedge funds.

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