In electronic trading, quality of market data is very critical. There has been too many instances where trading firms’ algorithmic engines trading on bad market data has caused huge losses for the trading firm. There are compliance rules that require brokers to ensure the correctness of an order by applying pre-market limit checks. It is important that these checks are applied with correct market data. In this blog we are going to consider a realistic but a hypothetical scenario where bad market data could cause huge losses even when it prevents a legitimate trade from happening.
Scenario: Trading systems subscribe to real time market data which has got bad market data for delta, gamma and other greeks of an option for one of its symbols. Delta is 20x the usual delta for an option with a similar strike and contract expiry month. The limit checker checks the “Delta Adjusted Notional Value” of a client’s option order and rejects the order as it computed a 20x a typical value of the delta adjusted notional value. Because of the limit breach, client is not able to send any orders to the exchange through the broker. It takes a while for the support team to figure out what is happening while the client is restless to place the orders. When knowing about the trading glitch, client is upset with the broker. Broker makes the client whole by agreeing to pay more than $100,000 for the trading loss suffered by the client during the 30 minute period it took to resolve.
It is obvious that a very high quality market data is critical to any trading activity at an institutional scale. When algorithms make trading decisions, it is even more important to have high quality market data. Despite the efforts of the team that supports the market data infrastructure, erroneous data does slip in. EagleAi can be an effective engine to aid in flagging when bad market data creeps in the stream.
EagleAi Market Data Risk module can process streaming data as an input. As it processes this data, it would self-learn about the characteristics of each symbol such as the typical price increase, the typical bid-ask spread at different times of the day, typical volume, typical values for the greeks if it were an option symbol etc. When EagleAi detects a huge deviation from the symbol’s patterns such as the one in this scenario, it can set a flag on the market data stream so that algos can choose to ignore this market data. EagleAi Market Data risk uses many different AI (Artificial Intelligence) techniques to detect these anomalies to compute a proprietary score named “E-score”. False positives are significantly reduced by flagging it only when EagleAi score is above a high threshold. EagleAi Market Data risk module can prevent trading decisions based on bad market data to prevent financial losses to the Client and maximize profits.
EagleAi is a collection of enterprise scale AI engines, which will watch your business’s back and prevent major losses Trading Risk, Fraud Risk, Compliance Risk, position exposures (TradeWatch Position Risk), and Market Data Risk.
- Introduction to EagleAi TradeWatch
- EagleAi Use Case Series: Fraud Risk – Undetected spoofing practice at a trading firm brings disrepute to the firm along with hefty fines
- EagleAi Use Case Series: Compliance Risk – A junior developer accidentally removes critical compliance checks which goes unnoticed for a very long time
- EagleAi Use Case Series: Trading Risk – A single order with incompatible instructions from a client caused $100k+ loss in revenues for a major broker