CompatibL Technologies, a trading and risk software developer, says that its sweet spot for creating software is when it is “combining quant and engineering expertise in one team.”
That combination has enabled CompatibL to embrace many innovations such as market generators, which the vendor describes as “machine learning algorithms for generating realistic samples of market data when historical time series has insufficient length or gaps.”
Alexander Sokol
One of the key components for market generators is machine learning, which is proving to be a transformative technology impacting almost all aspects of securities operations, says Alexander Sokol, founder, executive chairman, and head of quant research at CompatibL. Sokol is featured in an FTF News video interview.
Among other issues he covers, Sokol says that machine learning is hitting its stride.
“Machine learning is a transformative new technology and it has already found its application in many areas of securities operations including trading, risk management, and operations,” Sokol tells FTF News. “Today, it’s widely used practically everywhere — market making, sales and trading, prime brokerage, research, investment management — as well as in other things: fraud detection transaction monitoring and compliance.”
CompatibL was the first vendor to bring a machine learning-based counterparty credit risk model into production, says Sokol, who adds that the company is committed to leading the charge as applying the cutting-edge to securities operations. CompatibL is the winner of the 2021 FTF Award for Best Cloud-Native Computing Initiative.
The video interview also covers:
- Exactly what machine learning is;
- An explanation of how market generators use machine learning algorithms;
- Why firms need to be aware of market generators;
- What “noisy data” is and its significance;
- How machine learning and modeling work in conjunction with market generators;
- And CompatibL’s plans to apply market generators to its offerings;
- And what the company has in store for 2022.
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