Historically there was one market for an instrument in an asset class and trading was easier. And then the market fragmented. And now there is no single point of normalization or consolidation; time, currency and rules are skewed. The market is fragmented, distributed and de-normalized with phases, codes, flags and conditions.
How do you recreate the whole consolidated, normalized picture so that you can act faster? What are the considerations, criteria and architecture?
Order books apply to many asset classes, and creating one requires normalization. Normalization rules vary depending on strategies and therefore must be customizable. In addition to liquidity discovery, order books are critical to all trading applications, including quant research, strategy and trade execution, pre- and post-trade analytics, and compliance.
The architecture for consolidating fragmented liquidity in trading applications requires low latency, and reducing latency in the latency chain depends on tight integration. With high frequency and the mass of data, it is essential that data analytics and data manipulation be located in the same memory space. Data cannot move to the analytics; analytics must move to the data.
Consolidating fragmented markets creates a trading advantage through greater visibility to liquidity. De-normalization can be solved through customizable rules and built-in facilities for market data normalization. Solving the problems created by fragmented markets will generate more opportunity to find best execution, and inevitably the fastest technology will win.
