Custom Framework for Run-Time Trading Strategies

  • Andreea-Ingrid FunieEmail author
  • Liucheng Guo
  • Xinyu Niu
  • Wayne Luk
  • Mark Salmon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10216)


A trading strategy is generally optimised for a given market regime. If it takes too long to switch from one trading strategy to another, then a sub-optimal trading strategy may be adopted. This paper proposes the first FPGA-based framework which supports multiple trend-following trading strategies to obtain accurate market characterisation for various financial market regimes. The framework contains a trading strategy kernel library covering a number of well-known trend-following strategies, such as “triple moving average”. Three types of design are targeted: a static reconfiguration trading strategy (SRTS), a full reconfiguration trading strategy (FRTS), and a partial reconfiguration trading strategy (PRTS). Our approach is evaluated using both synthetic and historical market data. Compared to a fully optimised CPU implementation, the SRTS design achieves 11 times speedup, the FRTS design achieves 2 times speedup, while the PRTS design achieves 7 times speedup. The FRTS and PRTS designs also reduce the amount of resources used on chip by 29% and 15% respectively, when compared to the SRTS design.


Trading Strategy Market Data Closing Price Memory Controller Market Regime 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The support of UK EPSRC (EP/I012036/1, EP/L00058X/1, EP/L016796/1 and EP/N031768/1), the European Union Horizon 2020 Research and Innovation Programme under grant agreement number 671653, the China Scholarship Council, the Maxeler University Programme, Altera, Intel and Xilinx is gratefully acknowledged.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Andreea-Ingrid Funie
    • 1
    Email author
  • Liucheng Guo
    • 1
  • Xinyu Niu
    • 1
  • Wayne Luk
    • 1
  • Mark Salmon
    • 1
  1. 1.Imperial College LondonLondonUK

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