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Posts Tagged: nyse

Anti-Gaming Logic

Anti-gaming logic that is dynamic and embedded

Anti-gaming logic is at the core of what we do. We deploy both dynamic strategies and embedded intelligence to ensure best execution you can trust.

Dynamic Strategies

Deep Value uses advanced visualization tools and statistical analysis packages to challenge gaming risk. We invest approximately 4,000 hours annually reviewing our NYSE trading algorithms. Our algorithms emit their internal state at the rate of approximately 39 emissions per algorithm minute. These emissions are the detailed internal driving variables of the algorithm as it moves through its various internal states through time, including market data. Our research and automated validation teams analyze these emissions to measure how our algorithms are performing, including potential market impact and leakage.

Embedded Intelligence

Deep Value embeds complex logic into all of its algorithms as a means of eliminating gaming risk at the core. Our algorithms deploy a host of sophisticated techniques designed to reveal as little information as possible, while still executing the trader intention expressed in the choice of algorithms and its settings. Rather than focusing on detection, Deep Value algorithms are shielded by (a) intelligent algorithmic logic that reduces information leakage and (b) constant tuning of algorithms for improving performance measurably that, apart from delivering better prices, also introduces variations in automated behavior and resulting signatures in the market that make the detection of their presence harder.

These techniques include the following:

  • Deep Value algorithms are volume-sensitive and do not overreact to out sized volume, or volumes that print too far away from the inside market; rather they exclude these volumes from consideration, so that our algorithms don’t mindlessly “catch up” and reveal their hands when large volumes print.
  • Deep Value algorithms display sizes that are sensitive to the ambient liquidity at the inside market, measured real-time, so that our participation is, to the extent allowed by the algorithmic intent, statistically “like” the liquidity already visible to market participants.
  • Deep Value algorithms are rebate and price performance sensitive, and as a rule try and fill as much of an order passively as possible. This has two benefits: first, economic benefit of making Deep Value a leader in terms of price performance and rebate performance; and second, protecting the customer from impacting the stock price mechanically and paying too much crossing wide spreads.
  • Deep Value algorithms don’t overreact by chasing adverse price moves (unless validated broadly in the market).
  • When serving aggressive orders, Deep Value algorithms typically don’t just reach across to take volume to the limits allowed, but rather wait to give contras an opportunity to replenish sizes at better prices after takes (as algorithmic orders typically do) before resuming aggressive actions.

NOTE: Supporting performance analysis with regard to arrival price is contained within NYSE Executive Summary POV Performance Report (August 2010 – June 2011).

January 30 2012 · , , , · Filed in: Technology