TT^® Connect Blog

At Trading Technologies, we don’t wait for the future; we build it. We pioneered electronic trading with MD Trader®, launched the industry’s first enterprise SaaS platform, and embraced cloud and API-first architecture years before they became industry standards. That track record matters now because the next wave is here: Artificial Intelligence.

European power and gas markets are evolving rapidly, as exchanges continue to introduce new products and market structures. One important upcoming change is the European Energy Exchange’s (EEX’s) migration of the EEX Gas Spot market to a new trading engine, with implementation expected in 2027. For many energy trading firms, this could bring disruption, integration costs and operational risk. 

Across the sell side, three key themes are shaping investment decisions in 2026: automation, consolidation and artificial intelligence. These priorities reflect the pressures firms face today—rising costs, tightening margins and increasing trading volumes.

As trading volumes continue to grow, many firms are finding that their legacy infrastructure—often built as separate systems across asset classes—is becoming harder and more expensive to maintain, creating both competitive and operational challenges. 

Across asset classes within capital markets, the demands on trading and analytics platforms are becoming more complex by the day. In today’s markets, traders want broad asset coverage, cleaner data, faster insights and analytics that reflect the complexity of modern market structure. With the rebrand of Abel Noser Solutions to Trading Technologies now complete, our focus is to build technology that scales with how firms actually transact and anticipates where markets are heading.

Whatever the market or asset class, the goal of trade surveillance is always the same: to identify potentially manipulative behavior and ensure regulatory compliance. What differs is how firms achieve that goal. Each firm operates uniquely, with its own risk profiles, data models and resources. As a result, there is no “one-size-fits-all” surveillance approach.