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Big Data in High Frequency Finance

Big Data in High Frequency Finance. Edward Tsang, Richard Olsen, Shaimaa Masry Centre for Computational Finance and Economic Agents (CCFEA). Big data in High Frequency Finance. Foreign Exchange Transactions from OANDA Account for >20% of online spot FX trade

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Big Data in High Frequency Finance

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  1. Big Data in High Frequency Finance Edward Tsang, Richard Olsen, Shaimaa Masry Centre for Computational Finance and Economic Agents (CCFEA)

  2. Big data in High Frequency Finance • Foreign Exchange Transactions from OANDA • Account for >20% of online spot FX trade • This is the biggest data source on FX available • Transactions by 45,000+ traders over 2 years: • 1 January 2007 to 5 March 2009 • Over 147 million transactions • 45,845 accounts • 48 currency pairs (38% EUR/USD)

  3. High frequency Data (HFD) Properties • We are used to time series • E.g. daily closing • In HFD, we are dealing with a lot more data • 147 million means 180,000 per day • In HFD, data arrive at irregular time intervals • Lots of fluctuations • A piece of metal may look smooth to us • But they are rough under the microscope

  4. How to handle HFD? • Most tools for time series (with samples from fixed intervals) won’t apply to HFD • Reduce to 10 minutes averages? • Wasteful of useful information • May miss significant events, such as flash crashes • So big HFD posses new challenges! • More than more processing power for more data • Different nature in data demands new methods

  5. Directional Changes for HFD • History is recorded by significant events • Not snapshots at fixed intervals! • For HFD, record Directional Changes of x% • Where x depends on your scale of significance • Perfect for handling data from irregular time • New perspective in price movements • Striking regularities discovered http://youtu.be/WHTBT5eRx6U

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