A Pipelined Market Data Processing Architecture to Overcome Financial Data Dependency

被引:0
|
作者
Tang, Qiu [1 ]
Jiang, Lei [1 ]
Su, Majing [1 ]
Dai, Qiong [1 ]
机构
[1] Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
D O I
暂无
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
The ability of ultra-low latency to process market data feed is the premise and foundation for a today's trading system to grab the instant trading profits. The market data feed containing up-to-date information on market changes is multicasted real-timely from financial exchanges to market participants, usually in the form of financial information exchange (FIX) Adapted for STreaming (FAST) protocol. FAST is a differential compression protocol which significantly reduces the bandwidth requirement to transmit market data. However, it also increases the complexity and latency of market data processing. This paper describes a customized architecture for ultra-low latency of market-data processing. Firstly, we propose a bus-based architecture of market-data decoding on Field Programmable Gate Array (FPGA). Our design is a loose-coupled and scalable architecture which is easy to adapt to different FAST templates by connecting different decoders to the main bus. Then we further exploit a dedicated pipelined design to improve the architecture. The pipelined architecture decompresses multiple messages in parallel, overcoming the challenge of data dependency between consecutive differential encoded (FAST) messages. Finally, we implement two prototypes in RTL code and evaluate them on a Xilinx Kintex-7 FPGA. Real test results show that 1) the pipelined processor gains 180% speedup compared with the non-pipelined processor; 2) it achieves an ultra-low decoding latency of 307 ns per message, which is 2 orders of magnitude faster than the software solution.
引用
收藏
页数:8
相关论文
共 50 条
  • [31] Cross correlations in an emerging market financial data
    Cukur, Sadikk
    Eryigit, Mehmet
    Eryigit, Resul
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2007, 376 : 555 - 564
  • [32] DATA DISCRETIZATION FOR THE TRANSFER ENTROPY IN FINANCIAL MARKET
    Wang, Xuejiao
    Shang, Pengjian
    Huang, Jingjing
    Feng, Guochen
    FLUCTUATION AND NOISE LETTERS, 2013, 12 (04):
  • [33] Data Modifications in Blockchain Architecture for Big-Data Processing
    Tulkinbekov, Khikmatullo
    Kim, Deok-Hwan
    SENSORS, 2023, 23 (21)
  • [34] Application of Signal Processing to the Analysis of Financial Data
    Drakakis, Konstantinos
    IEEE SIGNAL PROCESSING MAGAZINE, 2009, 26 (05) : 160 - +
  • [35] Data processing approach to overcome insufficient signal strength in spectroscopy
    Srivastava, Madhur
    Freed, Jack
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2019, 257
  • [36] Data and Tool Integration with Linked Data for Industrial Data Processing Semantic Web-Technologies to Overcome Heterogeneities
    Graube, M.
    Pfeffer, J.
    Ziegler, J.
    Urbas, L.
    AUTOMATION 2012, 2012, 2171 : 89 - 93
  • [37] Aggregating Financial Services Data without Assumptions A Semantic Data Reference Architecture
    Gollapudi, Sunila
    2015 IEEE 9TH INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC), 2015, : 312 - 315
  • [38] Financial Market Liquidity and the Financial Crisis: An Assessment Using UK Data
    Martin, Christopher
    Milas, Costas
    INTERNATIONAL FINANCE, 2010, 13 (03) : 443 - 459
  • [39] A HIGH-PERFORMANCE OVERLAY ARCHITECTURE FOR PIPELINED EXECUTION OF DATA FLOW GRAPHS
    Capalija, Davor
    Abdelrahman, Tarek S.
    2013 23RD INTERNATIONAL CONFERENCE ON FIELD PROGRAMMABLE LOGIC AND APPLICATIONS (FPL 2013) PROCEEDINGS, 2013,
  • [40] Reducing data hazards on multi-pipelined DSP architecture with loop scheduling
    Tongsima, S
    Chantrapornchai, C
    Sha, EHM
    Passos, NL
    JOURNAL OF VLSI SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY, 1998, 18 (02): : 111 - 123