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EP: 96 Ex-HFT Trader and MIT-Grad Tackles Spoofing

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EP: 96 Ex-HFT Trader and MIT-Grad Tackles Spoofing
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Guest:

David Widerhorn

davidheadshotcrop

CEO at Neurensic

Record Date: 11/08/16

Dear Traders,

Today we talked to a man who has been ahead of the technology curve an entrepreneur since he was 12.  He graduated MIT at the age of 19 and his latest technology is now changing the trading industry.  His name is David Widerhorn, CEO of Neurensic.

David and his company are addressing the inefficiencies within the HFT universe, and recreating the trade surveillance industry using Artificial Intelligence and Machine Learning.

We talk about predatory trading practices such as spoofing and he explains what Neurensic is doing to detect it.

It was fascinating talking with David about the technology they have created at neurensic.  Going forward I believe that Neurensic is a company to keep your eye on for not only the way they are already assisting the industry with current issues, but what they may come up with in the future to further change and disrupt the industry.

Biggest Takeaways:

  1. Addressing the problems and inefficiencies in the HFT universe.
  2. What is Spoofing and why it’s illegal.
  3. Predatory trading and how Neurensic detects it.
  4. The “Integrity Score” and how it affects the industry.
  5. Using AI and machine learning for trader surveillance.

Answers to the 4 rapid fire questions at the end of the show:

  1. Favorite Book About Trading:
    – Flash Boys
  2. Favorite Movie About Trading:
    – Billions
  3. Best Advice Ever Received About Trading:
    – Bulls make money, bears make money, but pigs get slaughtered.
  4. Advice to Give to Others About Trading:
    – Technology drives all innovation in the markets. Be ahead of the curve. The biggest disservice you can do today is not learning computer programming.   

Resources:

Quotes:

  • “Technology drives all innovation in the markets”

Relevant chart pertaining to guest’s comments:

market-ladder-replay
Market Ladder Replay – the Market Ladder Replay is a research tool that provides users with a frame-by-frame replay of the underlying trading activity within a given cluster on a ladder style interface. The ladder is 10-ticks deep and displays all market data including the orders for each relevant participant in the order book. Users can stop, start, and rewind the activity within a given cluster in order to gain a precise understanding of the sequence of events that occurred in the order book and how the trader’s orders impacted the market. Users also have the ability to slow down the activity in order to visualize orders submitted by HFT strategies that occur only milliseconds apart. Because of the Market Ladder Replay feature, regulators and compliance staff have the ability to visualize exactly what the trader was seeing in the order book at the time the activity in question occurred, and can therefore effectively determine whether or not the trader submitted orders with the intent to disrupt or manipulate the market.
heatmap
Heatmap – the Heatmap allows the user to see which trader(s) are generating activity that has a high degree of similarity to known regulatory activity. The bright red indicates high risk clusters. The size of each square indicates the number of clusters. With just a glance, the Heatmap allows the user to efficiently allocate time and resources necessary to review the activity that generates the most risk.
rainbow-chart
Rainbow Chart – the Rainbow Chart shows the breakdown of the clusters that comprise a particular square on the heatmap. Clusters with lower scores have lighter color shades to reflect that these particular clusters are less interesting to the user because they do not have a high degree of similarity to known regulatory activity.
scorecard-1
Scorecard – the Scorecard provides a summary of the vital statistics of the cluster, the order audit trail of the cluster, and a visualization of the pattern that was formed by the order and fill messages that comprised the cluster. The compliance officer can quickly view what happened for the given cluster of order transactions.

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