10.16.2014
By Terry Flanagan

Liquidity: Good, Bad and Ugly

Dark pools come and go, but trying to characterize them as having either ‘good’ or ‘toxic’ liquidity can lead to suboptimal trading efficiency, according to Jeff Bacidore, head of algorithmic trading at Investment Technology Group.

“Most of the dark pools are taking on very similar business models, so the different dark pools are starting to look more and more alike,” Bacidore told Markets Media. “While there might be some liquidity in each pool that we don’t want to interact with, there’s probably some good liquidity too. Our goal is to filter out the bad liquidity and get to that good liquidity.”

Bacidore added, “If there is good liquidity to be found, we’ll take it, but doing it in a very careful, thoughtful way to avoid interacting with bad liquidity.”

ITG has launched a dark aggregation algorithm, Posit Marketplace 3.0 algorithm, which is designed to increase fill rates in non-displayed venues while filtering out potentially toxic liquidity.

“It’s actually taking an existing algorithm and giving it a complete overhaul,” said Bacidore. “We are of the opinion that there’s good liquidity in all the dark pools, so instead of just shutting dark pools off, we should take action that allow us to filter out the bad liquidity in each venue, and find the good liquidity.”

Posit Marketplace 3.0 deploys machine learning to allocate algorithms across some 25 different dark pools, according to Bacidore. “As it’s doing that, it’s learning over time on how to split those orders amongst the dark pools,” he said. “As it receives fills from dark pools, it knows to favor that dark pool, and, conversely, if it’s not getting fills from certain dark pools where it thought it would, it’ll start to route less and less to that dark pool.”

The algorithm also employs analytical techniques to set minimums for minimum share orders. “A min share is a parameter that we put on the order that basically says, ‘We do not want fills that are less than this quantity,’” said Bacidore. “We might put in a 100,000-share order, but say we will not take fills of less than 1,000 shares.”

The idea is to guard against predatory high-frequency trading tactics. “If I have a large block of shares and I rest it a midpoint, I could have an HFT pinging that order for a very small quantity, realize there’s probably a big block there, and then front run it,” said Bacidore.

While this order qualifier may help prevent a fill of 100 shares on a 5,000 share order, it may also prevent the order from being executed at all. “The trick is finding that sweet spot between setting the minimum high enough to weed out the ‘pingers’ — the information seekers — but not so high to miss out on the favorable liquidity — the institutional traders that are out there trading at that midpoint,” Bacidore said.

Featured image via shyshka/ Dollar Photo Club

Related articles

  1. Algorithms have become more prevalent in the spot FX market.

  2. Congress Unlikely to Act on HFT

    QB’s Algo Suite for futures market trade execution is also being co-located to HKEX.

  3. Breaking data silos is key to deploying automation beyond 'nuisance' orders.

  4. They can be used on quantum hardware expected to be available in 5 to 10 years.

  5. Regulation and Liquidity Top Concerns in Fixed income

    Streaming blocks change the basis of matching and price discovery so institutions can find new liquidity.