Business Activity Analysis
Track order status and turnaround times, per client exchange or instrument, helping you observe transaction performance across a number of trading systems
Introducing ITRS Insights
ITRS Insights performs an impressive range of analytics across both real-time and historical data at a scale required by today’s big data world. This makes it suitable for running analytics on your business activities, your underlying technology or a combination of both. In this scenario, ITRS Insights not only tracks the trades throughout each hop, it also runs real-time analysis to calculate average latency and show it’s variation against the “norm” for this time of day or month.
Powerful queries: perform sophisticated temporal or text based queries in real-time.
Pattern discovery and anomaly detection
- Anomaly detection: advanced algorithms enable you to interpret periodic patterns in your data, so you can identify anomalies and future trends, and automate processes to protect against the occurrence of issues. Insights ability to analyse and store multiple streams of data from diﬀerent sources means more information is readily available to query and compare, helping you ﬁnd your answers in real-time.
- Machine learning: the machine learning algorithm tracks patterns by clustering similar patterns together and learning what a “normal” pattern/trading day looks like. By segmenting the patterns against time, for example by trading days, you can use the patterns to predict anomalies. As more and more data is fed into ITRS Insights over time, it increases its ability to recognise patterns seen before and to pre-empt scenarios or ﬂag issues before they occur, increasing your conﬁdence in foreseeing and protecting against potential issues.
Collaborative sharing of information: your queries, research and investigations are pulled together into one or a series of notebooks that can be easily shared with diﬀerent audiences and stakeholders.
Analyse real-time vs historical data: now you can achieve a complete view of your data’s behaviour using one application. For instance, by comparing historical trades to current messages you can track trade ﬂows that go through multiple hops, in diﬀerent formats, from one place.
Secure and scalable
- Immutable: your data streamed into Insights is stored in a read-only format.
- Clustered architecture: if a node goes down, the cluster can continue without issue, ensuring constant availability of both data access and analysis.
- Protection of conﬁdential data: “tenancies” enable users to access the data they need, whilst segregating commercially sensitive data. ITRS Insights also allows a user to create a stored searching algorithm which can be used by other users without revealing the details of the search criteria, e.g. if the searching of log ﬁles for trade IDs were conﬁdential, then the calculated latency can be exposed without being able to see the trades that calculated it.
- Simple integration: ITRS Insights’ RESTful interface allows you to easily deploy and seamlessly integrate with a wide variety of data collection or visualisation tools, or your own proprietary systems. It also integrates fully with ITRS Geneos to allow you to capture all the data that is ﬂowing through the Geneos estate.
ITRS Insights tracks the ﬂow of trades throughout the estate and highlights any issues such as missing trades or unexpected latency. Using the text search capabilities, you can not only see the journey that trades are taking, you can also quickly calculate the average latency at a given point in time. This can be used to highlight any delays or potential issues. By running the real-time latency values into the time-series repository, a pattern is calculated for a similar working day (non farm payroll day, ECB announcement day, etc) and shows any variations from the current latency against the “norm” for this time of the day or month. Or by correlating the latency versus the trade volumes, show if latency is unusual for the trade volumes that the estate is processing – in real-time. Finally, the Bayesian causal network analysis combined with time window based correlation helps the support person to identify the factors which may be contributing to the observed poor performance.