poem1209 / MTDNN

Multi-scale Two-way Deep Neural Network

MTDNN

This is implementation of Multi-scale Two-way Deep Neural Network paper

Benchmark dataset CSI-2016

CSI-2016 is our collected dataset from three one-minute stock index data, including the Shanghai Stock Exchange (SSE) Composite Index SH000001, Shenzhen Stock Exchange Small & Medium Enterprises (SME Boards) Price Index SZ399005 and ChiNext Price Index SZ399006. It has over 170, 000 samples spanning a year from January 1st, 2016, to December 30th, 2016. Each sample is a one minute data of 6 dimensions which are high, low, open, close, volume and amount, respectively.R

Requirements

For load CSI-2016

PyWavelets
Numpy
path.py

TODO

• [] code for xgboost
• [] code for CNN-based model
• [] code for MTDNN

Results on CSI-2016

Citation

Please cite this paper if you use CSI-2016.

@inproceedings{DBLP:conf/ijcai/LiuMSHGLSLW20,
  author    = {Guang Liu an的Yuzhao Mao and Qi Sun and Hailong Huang and Weiguo Gao and Xuan Li and
    Jianping Shen and Ruifan Li and Xiaojie Wang},
  editor    = {Christian Bessiere},
  title     = {Multi-scale Two-way Deep Neural Network for Stock Trend Prediction},
  booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
               Artificial Intelligence, {IJCAI} 2020},
  pages     = {4555--4561},
  publisher = {ijcai.org},
  year      = {2020},
  url       = {https://doi.org/10.24963/ijcai.2020/628},
  doi       = {10.24963/ijcai.2020/628},
  timestamp = {Mon, 20 Jul 2020 12:38:52 +0200},
  biburl    = {https://dblp.org/rec/conf/ijcai/LiuMSHGLSLW20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}