Expert Opinion: Optimising connectivity for algorithmic FX trading

June 2023 in Previous Features

If you’re new to FX algorithmic trading, one of the first technological challenges you will face is connectivity. And it is not a trivial one. Despite what you might think, optimising connectivity is not something to be left only to high frequency trading firms, as in our current times, bad latency means money left on the table for each trade you make. You may already have a solid background in equities algorithmic trading, and you might consequently treat this question as a “déjà vu”, but this would be a mistake.

THE CHALLENGES To start with the good news. FX data complexity is quite low when compared to other asset classes, and the volume of data is also much lower than what you can have on equities. For example, EBS Live, perceived as one of the main and fastest FX data feeds available on the market, “only” publishes updates once every 100ms. But in FX, which is by definition a decentralised market, you will have to deal with much more potential THE CHALLENGES To start with the good news. FX data complexity is quite low when compared to other asset classes, and the volume of data is also much lower than what you can have on equities. For example, EBS Live, perceived as one of the main and fastest FX data feeds available on the market, “only” publishes updates once every 100ms. But in FX, which is by definition a decentralised market, you will have to deal with much more potential

THE CHALLENGES To start with the good news. FX data complexity is quite low when compared to other asset classes, and the volume of data is also much lower than what you can have on equities. For example, EBS Live, perceived as one of the main and fastest FX data feeds available on the market, “only” publishes updates once every 100ms. But in FX, which is by definition a decentralised market, you will have to deal with much more potential

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