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How Does Stream Processing Help?

     The later you join th trading, the more you are losing. However to see the fast markets in actual real time would need immediate processing of large volumes of data. This computing power has been slow to come to forex, but new technologies are making it a reality. Sophisticated analysis of real time, low frequency data requires speed. Strategies can never keep up unless there is low latency processing of data feeds as well as historical databases. Since forex caters to an over the counter type of market, there are specific logistical hurdles that come with the package, adding to the challenges. The industry's solution is called stream processing, a brand new way of processing data. Even real time streaming data can now be computed and can execute queries like stored data because of stream processing. Programming languages such as C++ & Java have traditionally been used by coders to create real time analytical applications.

High development costs come as the outcome of using custom coding and low level tools. 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. A process called inbound processing is used by Stream Processing Engines.

The growth in forex transaction volumes is continuous Even as the opportunity depth soars due to tremendous liquidity, monotonous working hours, wide variety of contestants and profit potentials in diminishing markets nevertheless the window of opportunity keeps on shrinking because of greater mechanization and rise in algorithmic business tools. Real time applications need to be extremely customizable due to these characteristics and must be adaptable or adjustable on the fly. This is a scenario where the stream processing technology shines.

It is critical for the sell side that the Forex institutions consistently improve the complete price delivery, encompassing the sourcing, setting, publishing and trade processing. Price quality becomes the key differentiator owing to the high market volatility and the buyers increased options. Pricing engine tasks work majorly on data cleaning and price setting, both of which require latency. Sub second latencies are desired for even with manual operations.

Cross market trading and arbitrage has been made possible by the increasing trend of integrated contact to the multiple sell side institutions and liquidity portals. Latency requirements get further smaller by using algorithmic trading. Difference can be made by a few milliseconds In particular, forex based hedge funds are aggressively leveraging the inefficiencies by arbitraging price differences from multiple liquidity providers.

By using stream processing, applications can analyze historical data as well as consult historical trends when accessed during a real time querry. Risk taking models shall perform more fundamental analysis and be capable of responding automatically to the events fed to it from electronic information sources.

Stream processing is turning forex to look like exchange market in the near future. Stream processing engines have basic advantages in terms of flexibility, agility as well as performance, this makes them perfect candidates for being the key component in the next generation forex platform.

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Posted on 2011-02-09, By: *

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