Imperial College London


Faculty of EngineeringDepartment of Computing

Professor of Computer Engineering



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BibTex format

author = {Funie, AI and Guo, L and Niu, X and Luk, W and Salmon, M},
doi = {10.1007/978-3-319-56258-2_14},
pages = {154--167},
publisher = {Springer},
title = {Custom framework for run-time trading strategies},
url = {},
year = {2017}

RIS format (EndNote, RefMan)

AB - 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.
AU - Funie,AI
AU - Guo,L
AU - Niu,X
AU - Luk,W
AU - Salmon,M
DO - 10.1007/978-3-319-56258-2_14
EP - 167
PB - Springer
PY - 2017///
SN - 0302-9743
SP - 154
TI - Custom framework for run-time trading strategies
UR -
UR -
ER -