Liquidnet to imminently roll out derivatives predicted volume curve and sweep price analytics

New analytics make up the second phase of a three-part roll-out by Liquidnet. The first phase included a set of pre-trade analytics covering volume and liquidity information.

Liquidnet is set to roll out the second phase of its derivatives analytics suite in the next few weeks, The TRADE can reveal.

Mike du Plessis (left), Darren Smith (right)

Aimed at supporting buy-side traders with their derivatives flow by creating an ecosystem of information all in one place, the analytics suite is being rolled out in three phases.

Already brought to market, phase one includes volume and liquidity information. Traders can access the user face via GUI or API.

“Liquidity or the quality of conditions can change day-to-day and even intraday depending on what’s happening,” says Darren Smith, head of listed derivatives EQS at Liquidnet.

“By consuming our data from the API, you have a relatively sophisticated metric that can power automation and combine market touch liquidity and volume run rates etc.” 

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The rollouts are aimed at supporting the buy-side as TCA becomes more real time and prevalent in the execution decision, as seen in the equites markets. In particular, the suite can be used when selecting what execution method to use, be it on-exchange, request for quote (RFQ) or using an algo.

“The pre trade analytics are a very useful means by which clients can assess the quality of a block price,” the global head of listed derivatives at Liquidnet, Mike du Plessis, tells The TRADE.   

“Having submitted an RFQ, they can choose not to trade on block and actually aggress the order book directly if that seems to be a better option at the point of trade. You then start think about TCA a little bit differently. It’s not so much that post-trade piece but more of a point of trade process as well.” 

Phase two

Phase two set to be rolled out in the coming weeks will be focused on predicted volume curves and sweep prices, Smith explains.

“One of the most frequent questions we get asked by traders with large orders is “how long is it going to take me to do X thousand lots in Y obscure product,” he says.  

“We were able to calculate where projected liquidity is going throughout the day as it evolves and then present that relatively scientifically. There’s an advantage in putting all that information in one place. It’s very difficult for traders now to get all that information quickly from the number of screens they would have to go to. There are different calculations you would have to do, collate it all and put in one place and by that time the markets moved.” 

Phase three is set to cover hidden liquidity, something that the pair confirm has been at the forefront of their minds for the last few months.

“There can be far more liquidity available than there seems on screen. It is really difficult to measure because by its very nature it’s hidden so we’re working through some ideas of how you proxy for it,” says Smith.

“For example, touch liquidity may look awful but if you aggress the market you get size done. In the immediate aftermath of SVB last spring, touch liquidity just disappeared. There was nothing there but as soon as you were participating in the market you were getting filled and able to get size done.” 

High frequency

The roll outs fit into a much wider ongoing story taking place in the markets. Given the reduced balance sheet offered by banks paired with the proliferation of higher frequency and electronic firms, Smith and du Plessis confirm that the buy-side need better tools to help them to compete and achieve their outcomes.

By offering asset managers and other non-high frequency firms enhanced data capabilities in one place, it allows them to execute in the market more freely without being run over.

Exchanges generally notify firms who have a resting order that has been filled, first. Given the high frequency industry’s preference to face algos that slice orders up into smaller chunks, it’s usually these firms that find out first when an order has been filled. While these may only be small lots, that’s valuable information.

“If you are a super large high frequency trading hedge fund or CTA etc you’ve certainly got access to all this market data but if you are even a relatively large asset manager you don’t necessarily have the resources to build a suite of analytics like that,” says Smith. 

Elsewhere, speaking to The TRADE, du Plessis adds: “It’s really important context to think about the fact that the pendulum has swung so far from the providers of brute force balance sheet liquidity i.e. banks in the 90s and early 2000s towards participants who, by design, use limited balance sheet but with a much higher velocity of hedging. It’s interesting that many of those faster houses lobby in such a way as to promote top-of-book trading (a space that they continuously occupy) in order to support the lower balance sheet model.

“End users necessarily slice up orders in an attempt to minimise market impact – invariably that process comes at a cost to the liquidity seeker of information leakage over time. The low-balance sheet participant is both informed and able to hedge the small pieces of business on a continuous basis rather than deal with the big chunk at the outset. Again, we are trying to help the buy-side confound that predictability and switch seamlessly between execution “modes” which will help ensure they are paying the right amount of spread in a given liquidity state.”