Even if you have the fastest links to exchanges and dark pools, precious time is not taken in data delivery, but in data analytics. Of critical importance to strategy decision time is immediacy of pricing analytics whether from single or multiple sources.
In the trading world, understanding data is a game changer. Firms are awash in data across a market structure fragmented into dozens of sources and data types. In the Equities markets there are 13 major exchanges in the U.S. alone and 20 across Europe and Asia providing trades and book depth. True price discovery… volume and trading patterns can only be achieved by analysis across markets. That old saying, the whole is greater than the sum of the parts fits well in this case. And of course the importance is to find trading opportunities. That is in the immediacy of pricing data… time is of the essence.
By dramatically reducing the time to understanding, we provide a time advantage to spread trading, pairs, reversion, smart routing and risk analysis. Increasing algo sophistication implies more calculations and more analysis – in realtime. This should not be diametrically opposed to latency goals.
Analysis is not just about the here and now and the blending of multiple venues but also understanding the past. History is how we got to now, time is just a continuum. Data analysis reveals unique observations and patterns and the possibility for predicting future values. Only by comparing current values to past activity can it determine unusual behaviour, or market abnormalities. Monitoring raw market data may show us the prices, history gives us benchmarks.
So given that basic problem scope, you can divide the solutions into 3 domains; managing scale for data capture and storage; analytics and visualization. Of course they are not mutually exclusive but highly intertwined.
For high precision market analytics to derive real business value it requires enterprise an data management platform able to deliver capture and query performance… and data quality – to ensure prices reflect cancels, corrections, splits and dividends and symbologies across markets. And the data architecture must be able to easily conflate order books across markets – this is critical to the discovery process.
The third is visualization… fashioning analytical metrics into a human readable format. Visual display across deep time allows users to see things they were not aware of. It can simplify comprehension of data and promote understanding.
The terabyte volumes of market data and order activity can be easily consumed and processed by high-speed data management and analytics. However, the single easiest way for our brains to interpret large amounts of information, communicate trends and identify anomalies is to create visualizations against the distilled, filtered and smoothed content.
This webinar recording demonstrates that by looking at the U.S. Equity Markets across all major venues, calculating metrics based on market behavior, and visually analyzing trading abnormalities such as order book imbalance.
The right analytical content at right time to the right people creates the competitive advantage everyone seeks.
Once again thanks for reading.
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