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Optimize your algo trading models: Tick data best practices

As the financial world seeks to optimize proprietary pricing and trading strategies, the huge volume of tick data demands models that can deal with multi-asset class data and incorporate ancillary data for insights and risk profiling in real-time.

We’re excited to join KX, the leading provider of time-series database technology, to discuss different approaches to optimizing your algo trade models using real-time & historical data analytics.

Topics

  • The most valued features and functions of a real-time analytical platform.
  • Technologies available to build a high performance, flexible DataOps environment to derive maximum value out of your tick data.
  • Backloading historical data to validate trading models before deploying them to production using real-time data.
  • The market trends amongst buy-side and sell-side, and the importance of high-quality market data to an alpha-generating model across listed and OTC assets.

Speakers

  • Martin Haines, Managing Director, Asia, KX
  • Leon Liang, Director, Feeds Business Development, ICE Data Services
  • Nick Laurence, Vice President Marketing, KX (Host)

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