Inbound Processing And Other Techniques
If you’re late in trading, you’re losing. However to see the fast markets in actual real time would need immediate processing of large volumes of data. It was hard to get such computing power in the past, however latest technologies are making it a slow reality. If one has the necessary speed, he can analyse low frequency data in a sophisticated fashion in real time. Keeping up would be impossible for strategies if there were no historical databases supporting the low latency processing of data feeds. This is a particular challenge in forex, which depends on an over the counter type market and the logistical hurdles that come with it. The new way to process data is known as stream processing, an answer to the industry’s worries. Just like stored data’s ability to execute queries and compute requests, stream processing has made the same possible for real time streaming data. Programmers traditionally code using Java & C++ to create real time analytical applications on high volume event streams.
However by using custom coding or low level tools, one can end up with high development costs. Stream processing on the other hand takes data differently, allowing for faster performance, simpler coding and integrated access to both real time as well as historical data. Stream processing engines or SPEs use what’s called inbound processing.
There is a regular and consistent growth in the forex transaction volumes. Wide variety of contestants, potential gains in falling markets, long trading hours even though excessive liquidity is leading to increased depth of opportunity, the opportunity window now shrinking due to mechanization and up and coming algorithmic trading tools. Customizable, adjustable and adaptable real time applications are the need of the hour due to these characteristics. The case here is apt for the stream processing technology.
On the sell side, it is critical for forex institutions to continually optimize the overall price delivery from price sourcing, setting and publishing to trade processing. The key differentiator is price quality because of its dependence on high market volatility, increased choices to the customer and being a function of speed. Latency is a very important consideration for both price setting and data cleaning, the two basic pricing engine tasks. One would desire sub-second latencies even with manual operations.
Liquidity portals and sell side institutions contribute to the increasing trend of arbitrage and cross market trading by integrated access. Algorithmic trading leads to drastic cuts in the latency requirements. A few milliseconds is all it takes to make a difference In specific, forex based hedge funds are belligerently leveraging the inefficiencies by arbitraging price differences from various liquidity providers.
Using the power of stream processing, applications can analyze historical data and trends. The increased automation seen now shall make it possible for fundamental analysis and risk taking models to generate automated responses to events from sources such as news feeds.
Forex is walking on the lines of the exchange market where stream processing is already extensively used. The next generation forex platforms shall utilize stream processing engines as a key component because of their uncompared performance, ease of workability advantages.
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