A Labeling Method for Financial Time Series Prediction Based on Trends

This is a non-official implementation of the trend labeling method proposed in the paper A Labeling Method for Financial Time Series Prediction Based on Trends.

In this method, the trend is labeled based on a certain parameter w. Once the price rises by more than w from the local trough, it is regarded as an uptrend, and the local trough is labeled as the beginning of the uptrend. Meanwhile, when the price falls by more than w from the local peak, this method labels a downtrend starting from the local peak.

Examples

The following three figures show the labeling results for the CSI 300 price time series with w=10%, 15%, and 20%, respectively.

Setup

To run this example you need the following packages:

pip install numpy tqdm akshare matplotlib

Citation

If you find this code useful please cite these following two papers in your work:

@article{wu_labeling_2020,
    title = {A {Labeling} {Method} for {Financial} {Time} {Series} {Prediction} {Based} on {Trends}},
    volume = {22},
    issn = {1099-4300},
    doi = {10.3390/e22101162},
    language = {en},
    number = {10},
    journal = {Entropy},
    author = {Wu, Dingming and Wang, Xiaolong and Su, Jingyong and Tang, Buzhou and Wu, Shaocong},
    month = oct,
    year = {2020},
    pages = {1162}
}
@article{xiu_crash_2021,
    title = {Crash {Diagnosis} and {Price} {Rebound} {Prediction} in {NYSE} {Composite} {Index} {Based} on {Visibility} {Graph} and {Time}-{Evolving} {Stock} {Correlation} {Network}},
    volume = {23},
    issn = {1099-4300},
    url = {https://www.mdpi.com/1099-4300/23/12/1612},
    doi = {10.3390/e23121612},
    language = {en},
    number = {12},
    journal = {Entropy},
    author = {Xiu, Yuxuan and Wang, Guanying and Chan, Wai Kin Victor},
    month = dec,
    year = {2021},
    pages = {1612}
}

Acknowledgement

This research is funded by the Shenzhen Science and Technology Innovation Commission (Grant No. JCYJ20210324135011030, WDZC20200818121348001 and KCXFZ202002011010487), the National Natural Science Foundation of China (Grant No. 71971127 and 72171164), Guangdong Pearl River Plan (2019QN01X890), and the Hylink Digital Solutions Co., Ltd. (120500002).