This repository is the official PyTorch implementation of the experiments in the following paper:
Junran Wu, Ke Xu, Xueyuan Chen, Shangzhe Li, Jichang Zhao. Price graphs: Utilizing the structural information of financial time series for stock prediction
Install PyTorch following the instuctions on the official website. The code has been tested over PyTorch 1.7.0+cu110 version.
Then install the other dependencies.
pip install networkx pyunicorn
- Unfold data
tar -xvzf data.tar.gz
- Change directory to code
cd code
- Transform stock prices to graphs
python price_graph.py
- Calculate collective influence (CI) for graph nodes
python price_ci.py
- Price graph embedding
python price_embedding.py
- Generate dataset for train and test
python dataset.py
Train:
usage: trainer.py [-h] [-e EPOCH] [-b BATCH] [-ts TIMESTEP] [-hs HIDDENSIZE] [-y YEARS [YEARS ...]] [-sn SEASON] [-dr DROPRATIO] [-s SPLIT] [-i INTERVAL] [-l LRATE] [-l2 L2RATE] [-t]
Train the price graph model on stock
optional arguments:
-h, --help show this help message and exit
-e EPOCH, --epoch EPOCH
the number of epochs
-b BATCH, --batch BATCH
the mini-batch size
-ts TIMESTEP, --timestep TIMESTEP
the length of time_step
-hs HIDDENSIZE, --hiddensize HIDDENSIZE
the length of hidden size
-y YEARS [YEARS ...], --years YEARS [YEARS ...]
an integer for the accumulator
-sn SEASON, --season SEASON
the test season of 2019
-dr DROPRATIO, --dropratio DROPRATIO
the ratio of drop
-s SPLIT, --split SPLIT
the split ratio of validation set
-i INTERVAL, --interval INTERVAL
save models every interval epoch
-l LRATE, --lrate LRATE
learning rate
-l2 L2RATE, --l2rate L2RATE
L2 penalty lambda
-t, --test train or test
An example of training process is as follows:
python trainer.py -e 1000 -l 0.001 -hs 32 -ts 20 -b 256 -dr 0 -i 50 -s 30 -l2 0 -y 2017 2018 -sn 1
An example of test with existing models is as follows:
python trainer.py -e 1000 -l 0.001 -hs 32 -ts 20 -b 256 -dr 0 -i 50 -s 30 -l2 0 -y 2017 2018 -sn 1 -t --test_2019
VG algorithm is adopted from https://github.com/pik-copan/pyunicorn
CI algorithm is adopted from https://github.com/zhfkt/ComplexCi.
Struc2vec implementation is adopted from https://github.com/shenweichen/GraphEmbedding.
DARNN implementation in PyTorch is adopted from https://github.com/ysn2233/attentioned-dual-stage-stock-prediction
If you use this code for you research, please cite our paper.
@article{wu2022price,
title = {Price graphs: Utilizing the structural information of financial time series for stock prediction},
journal = {Information Sciences},
volume = {588},
pages = {405-424},
year = {2022},
issn = {0020-0255},
doi = {https://doi.org/10.1016/j.ins.2021.12.089},
url = {https://www.sciencedirect.com/science/article/pii/S0020025521013104},
author = {Junran Wu and Ke Xu and Xueyuan Chen and Shangzhe Li and Jichang Zhao},
keywords = {Stock prediction, Complex network, Time series graph, Graph embedding, Structure information},
}
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