Financial software solutions provider CQG has developed a big data visualisation desktop application that gathers financial market data and applies it to support intelligent trading decisions. The application uses the swing trading methodology to interpret a variety of charts and graphs that can be used to provide competitive insights for trading.
Swing trading looks at market behaviour for a single factor, for example price variation, and analyses how the market will typically retrace its steps between a certain range. Unless there is significant market upheaval, the range of movement is going to move up and down within a standard deviation, allowing a mathematical analysis and the creation of algorithms that study the behaviour. Looking at how price behaves across different timeframes, the swing trading methodology allows traders to assess at what point the market is likely to turn and, more specifically, where the impact of the price oscillation will start.
Marcus Kwan, vice president of product development and design at CQG, says the app is ideal for filtering out some of the noise that comes with any big data problem, allowing market data to be visualised in new ways, and the creation of back-tested systems to deliver increased value.
The challenge is in how to tackle the big data problem. Kwan notes: “With an application like ours, getting the data is no longer hard because data can be aggregated from many exchanges around the world into a common set of tools. The difficulty is that this is now a ‘thinking man’s game’, as people are looking at the right way to combine and interpret the data. We have all the data at our fingertips, but what’s the magic way to slice, dice and use the data, make intelligent decisions and filter out the noise.”
In terms of applying the data across the enterprise, Kwan suggests there is a lot of potential and says several CQG customers have inquired about application programming interfaces (APIs). CQG is working on a new class of APIs that won’t require customers to have the application running on the desktop to get their market data and allow them to access data across the enterprise or even via mobile devices. In addition, Kwan says the company has begun work on a mobile HTML 5 based version of the application, using WebSocket for near real-time delivery of market data.
Looking ahead to future developments, Kwan explains that CQG is extending the application for greater visualisation, allowing customers to consider more than a handful of charts on a screen and expanding, potentially, to cover 500-plus instruments at a time. Ultimately, development is underway to present signals in a different format through the creating a dashboard of various on/off signals that will allow a filtered view of a complex picture. Long term, Kwan expects the information to be used in a more predictive way, allowing traders to adapt and be ahead of the market, while alleviating fears around algorithm-based systems as they start to make more intelligent trading decisions.
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