Visually Analyze the Output of CEP Engines
Datawatch can handle direct real-time feeds from all popular CEP engines. With data coming in at more than 1000 events per second and nearly a quarter of stock market trading volume executed by software, Complex Event Processing (CEP) is rapidly becoming a key element in systems that support capital markets activities. CEP is also having an impact in telecoms, energy and utilities which require fast response to quickly changing data from multiple sources.
With Datawatch, you can connect directly to your CEP engines and incorporate CEP data in your analytical dashboards. You can also combine CEP data with information from other internal and third-party real-time streams, and historical time series data stored in relational databases, columnar tick databases, OData sources, or flat files.
Connect directly to popular CEP engines
Datawatch can connect to all popular CEP engines including SAP Sybase Event Stream Processor, StreamBase, OneTick CEP, Oracle CEP and Kx Kdb+Tick.
Capital markets applications
Financial services applications for Datawatch's visual analysis of CEP data include:
- Visualizing the relative success of alternative trading strategies in algorithmic trading systems. Users can program new algorithms into a CEP engine and quickly analyze the results using a Datawatch dashboard.
- Market liquidity analysis to combine order book data from multiple markets to analyze full market liquidity, to determine where, when and how to trade.
- Quantitative trading to monitor the market for pre-defined conditions that represent opportunities, automating the response to capitalize on the opportunity before it disappears.
- Smart order routing using live market data to intelligently route orders for best results and in compliance with best execution policies.
- Best execution compliance to collect data at the time of each trade to monitor and show compliance with best execution policies.
Reduce time to action using low-latency visual analysis
Visual analysis dashboards connected to CEP engines, databases storing historical time series data and real-time streaming sources provide users with the ability to filter, correlate and aggregate real-time event data in an extremely low latency environment. The seamless integration of historical data with real-time events enables much more efficient trading since properly designed data visualizations reduce the time required to make well informed buy and sell decisions. In addition, detecting anomolous or potentially fraudulent behavior and gaming by traders becomes much easier to accomplish is less time than ever before.