├── .gitignore ├── LICENSE ├── README.md ├── data ├── csi300.pkl ├── daily_stock │ ├── 2022-11-01.csv │ ├── 2022-11-02.csv │ ├── 2022-11-03.csv │ ├── 2022-11-04.csv │ ├── 2022-11-07.csv │ ├── 2022-11-08.csv │ ├── 2022-11-09.csv │ ├── 2022-11-10.csv │ ├── 2022-11-11.csv │ ├── 2022-11-14.csv │ ├── 2022-11-15.csv │ ├── 2022-11-16.csv │ ├── 2022-11-17.csv │ ├── 2022-11-18.csv │ ├── 2022-11-21.csv │ ├── 2022-11-22.csv │ ├── 2022-11-23.csv │ ├── 2022-11-24.csv │ ├── 2022-11-25.csv │ ├── 2022-11-28.csv │ ├── 2022-11-29.csv │ ├── 2022-11-30.csv │ ├── 2022-12-01.csv │ ├── 2022-12-02.csv │ ├── 2022-12-05.csv │ ├── 2022-12-06.csv │ ├── 2022-12-07.csv │ ├── 2022-12-08.csv │ ├── 2022-12-09.csv │ ├── 2022-12-12.csv │ ├── 2022-12-13.csv │ ├── 2022-12-14.csv │ ├── 2022-12-15.csv │ ├── 2022-12-16.csv │ ├── 2022-12-19.csv │ ├── 2022-12-20.csv │ ├── 2022-12-21.csv │ ├── 2022-12-22.csv │ ├── 2022-12-23.csv │ ├── 2022-12-26.csv │ ├── 2022-12-27.csv │ ├── 2022-12-28.csv │ ├── 2022-12-29.csv │ └── 2022-12-30.csv ├── data_train_predict │ ├── 2022-11-01.pkl │ ├── 2022-11-02.pkl │ ├── 2022-11-03.pkl │ ├── 2022-11-04.pkl │ ├── 2022-11-07.pkl │ ├── 2022-11-08.pkl │ ├── 2022-11-09.pkl │ ├── 2022-11-10.pkl │ ├── 2022-11-11.pkl │ ├── 2022-11-14.pkl │ ├── 2022-11-15.pkl │ ├── 2022-11-16.pkl │ ├── 2022-11-17.pkl │ ├── 2022-11-18.pkl │ ├── 2022-11-21.pkl │ ├── 2022-11-22.pkl │ ├── 2022-11-23.pkl │ ├── 2022-11-24.pkl │ ├── 2022-11-25.pkl │ ├── 2022-11-28.pkl │ ├── 2022-11-29.pkl │ ├── 2022-11-30.pkl │ ├── 2022-12-01.pkl │ ├── 2022-12-02.pkl │ ├── 2022-12-05.pkl │ ├── 2022-12-06.pkl │ ├── 2022-12-07.pkl │ ├── 2022-12-08.pkl │ ├── 2022-12-09.pkl │ ├── 2022-12-12.pkl │ ├── 2022-12-13.pkl │ ├── 2022-12-14.pkl │ ├── 2022-12-15.pkl │ ├── 2022-12-16.pkl │ ├── 2022-12-19.pkl │ ├── 2022-12-20.pkl │ ├── 2022-12-21.pkl │ ├── 2022-12-22.pkl │ ├── 2022-12-23.pkl │ ├── 2022-12-26.pkl │ ├── 2022-12-27.pkl │ ├── 2022-12-28.pkl │ ├── 2022-12-29.pkl │ └── 2022-12-30.pkl ├── model_saved │ └── 2022-12-29_epoch_60.dat ├── prediction │ └── pred.csv └── relation │ ├── 2022-11-30.csv │ └── 2022-12-30.csv ├── data_loader.py ├── main.py ├── main.sh ├── model └── Thgnn.py ├── requirements.txt ├── trainer └── trainer.py └── utils ├── generate_data.py └── generate_relation.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. 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But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction (THGNN) 2 | ## 1. Prepare you training data 3 | The input to your model is a pkl file that includes the stock symbol `code`, the time `dt`, and the volume and price features. Then, you need to use `generate_relation.py` to generate daily stock relationships and `generate_data.py` to generate the final input data for the model. You can adjust the features used in building the stock relationship and generating the final input by changing `feature_cols`. The `relation` directory stores the relations between stocks. The `daily_stock` directory contains stocks that are trained each day. The `data_train_predict` directory stores the final inputs fed to the model each day. The `prediction` directory stores the prediction result of the validation set. The `model_saved` directory stores the trained model. 4 | 5 | ## 2. Train you model 6 | * Before training, make sure to change the parameters in class `Args` and function `main`. 7 | 8 | ``` config 9 | adj_threshold = 0.1 # the threshold of the relations between stocks 10 | max_epochs = 60 # the number of training epochs 11 | epochs_eval = 10 # the number of training epochs per evaluation or test interval 12 | epochs_save_by = 60 # the number of training epochs before a model is saved 13 | lr = 0.0002 # learning rate of the model 14 | gamma = 0.3 # gamma 15 | hidden_dim = 128 # hidden_dim 16 | num_heads = 8 # num_heads 17 | out_features = 32 # out_features 18 | model_name = "StockHeteGAT" # The main model name in model.thgnn.py 19 | dropout = 0.1 # dropout 20 | batch_size = 1 # batch_size 21 | loss_fcn = mse_loss # loss function 22 | epochs_save_by = 60 # the number of training epochs of the saved model 23 | data_start = 20 # index of training start date 24 | data_middle = 39 # index of evaluation or test start date/ index of training end date 25 | data_end = data_middle+4 # index of evaluation or test end date 26 | pre_data = '2021-12-29' # save the last date of the training 27 | ``` 28 | 29 | * Install required packages 30 | 31 | ``` shell 32 | pip install -r requirements.txt for specific versions 33 | ``` 34 | 35 | * Training 36 | 37 | ``` shell 38 | sh train.sh 39 | ``` 40 | ## 3. Citing 41 | 42 | * If you find **THGNN** is useful for your research, please consider citing the following papers: 43 | 44 | ``` latex 45 | @inproceedings{Xiang2022Temporal, 46 | author = {Xiang, Sheng and Cheng, Dawei and Shang, Chencheng and Zhang, Ying and Liang, Yuqi}, 47 | title = {Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction}, 48 | year = {2022}, 49 | isbn = {9781450392365}, 50 | publisher = {Association for Computing Machinery}, 51 | address = {New York, NY, USA}, 52 | url = {https://doi.org/10.1145/3511808.3557089}, 53 | doi = {10.1145/3511808.3557089}, 54 | booktitle = {Proceedings of the 31st ACM International Conference on Information & Knowledge Management}, 55 | pages = {3584–3593}, 56 | numpages = {10}, 57 | location = {Atlanta, GA, USA}, 58 | series = {CIKM '22} 59 | } 60 | ``` -------------------------------------------------------------------------------- /data/csi300.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/TongjiFinLab/THGNN/eefbfd3f38fdc110deeaed8cdf39402ea7ba9c9d/data/csi300.pkl -------------------------------------------------------------------------------- /data/daily_stock/2022-11-01.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | -------------------------------------------------------------------------------- /data/daily_stock/2022-11-02.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | -------------------------------------------------------------------------------- /data/daily_stock/2022-11-03.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | -------------------------------------------------------------------------------- /data/daily_stock/2022-11-04.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | -------------------------------------------------------------------------------- /data/daily_stock/2022-11-07.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | -------------------------------------------------------------------------------- /data/daily_stock/2022-11-08.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | -------------------------------------------------------------------------------- /data/daily_stock/2022-11-09.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | -------------------------------------------------------------------------------- /data/daily_stock/2022-11-10.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | -------------------------------------------------------------------------------- /data/daily_stock/2022-11-11.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | -------------------------------------------------------------------------------- /data/daily_stock/2022-11-14.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | -------------------------------------------------------------------------------- /data/daily_stock/2022-11-15.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | -------------------------------------------------------------------------------- /data/daily_stock/2022-11-16.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | -------------------------------------------------------------------------------- /data/daily_stock/2022-11-17.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | -------------------------------------------------------------------------------- /data/daily_stock/2022-11-18.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | -------------------------------------------------------------------------------- /data/daily_stock/2022-11-21.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | -------------------------------------------------------------------------------- /data/daily_stock/2022-11-22.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | -------------------------------------------------------------------------------- /data/daily_stock/2022-11-23.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | -------------------------------------------------------------------------------- /data/daily_stock/2022-11-24.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | -------------------------------------------------------------------------------- /data/daily_stock/2022-11-25.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | -------------------------------------------------------------------------------- /data/daily_stock/2022-11-28.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | 000001.SZ,2022-11-28 3 | 000002.SZ,2022-11-28 4 | 000063.SZ,2022-11-28 5 | 000069.SZ,2022-11-28 6 | -------------------------------------------------------------------------------- /data/daily_stock/2022-11-29.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | 000001.SZ,2022-11-29 3 | 000002.SZ,2022-11-29 4 | 000063.SZ,2022-11-29 5 | 000069.SZ,2022-11-29 6 | -------------------------------------------------------------------------------- /data/daily_stock/2022-11-30.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | 000001.SZ,2022-11-30 3 | 000002.SZ,2022-11-30 4 | 000063.SZ,2022-11-30 5 | 000069.SZ,2022-11-30 6 | -------------------------------------------------------------------------------- /data/daily_stock/2022-12-01.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | 000001.SZ,2022-12-01 3 | 000002.SZ,2022-12-01 4 | 000063.SZ,2022-12-01 5 | 000069.SZ,2022-12-01 6 | -------------------------------------------------------------------------------- /data/daily_stock/2022-12-02.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | 000001.SZ,2022-12-02 3 | 000002.SZ,2022-12-02 4 | 000063.SZ,2022-12-02 5 | 000069.SZ,2022-12-02 6 | -------------------------------------------------------------------------------- /data/daily_stock/2022-12-05.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | 000001.SZ,2022-12-05 3 | 000002.SZ,2022-12-05 4 | 000063.SZ,2022-12-05 5 | 000069.SZ,2022-12-05 6 | -------------------------------------------------------------------------------- /data/daily_stock/2022-12-06.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | 000001.SZ,2022-12-06 3 | 000002.SZ,2022-12-06 4 | 000063.SZ,2022-12-06 5 | 000069.SZ,2022-12-06 6 | -------------------------------------------------------------------------------- /data/daily_stock/2022-12-07.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | 000001.SZ,2022-12-07 3 | 000002.SZ,2022-12-07 4 | 000063.SZ,2022-12-07 5 | 000069.SZ,2022-12-07 6 | -------------------------------------------------------------------------------- /data/daily_stock/2022-12-08.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | 000001.SZ,2022-12-08 3 | 000002.SZ,2022-12-08 4 | 000063.SZ,2022-12-08 5 | 000069.SZ,2022-12-08 6 | -------------------------------------------------------------------------------- /data/daily_stock/2022-12-09.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | 000001.SZ,2022-12-09 3 | 000002.SZ,2022-12-09 4 | 000063.SZ,2022-12-09 5 | 000069.SZ,2022-12-09 6 | -------------------------------------------------------------------------------- /data/daily_stock/2022-12-12.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | 000001.SZ,2022-12-12 3 | 000002.SZ,2022-12-12 4 | 000063.SZ,2022-12-12 5 | 000069.SZ,2022-12-12 6 | -------------------------------------------------------------------------------- /data/daily_stock/2022-12-13.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | 000001.SZ,2022-12-13 3 | 000002.SZ,2022-12-13 4 | 000063.SZ,2022-12-13 5 | 000069.SZ,2022-12-13 6 | -------------------------------------------------------------------------------- /data/daily_stock/2022-12-14.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | 000001.SZ,2022-12-14 3 | 000002.SZ,2022-12-14 4 | 000063.SZ,2022-12-14 5 | 000069.SZ,2022-12-14 6 | -------------------------------------------------------------------------------- /data/daily_stock/2022-12-15.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | 000001.SZ,2022-12-15 3 | 000002.SZ,2022-12-15 4 | 000063.SZ,2022-12-15 5 | 000069.SZ,2022-12-15 6 | -------------------------------------------------------------------------------- /data/daily_stock/2022-12-16.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | 000001.SZ,2022-12-16 3 | 000002.SZ,2022-12-16 4 | 000063.SZ,2022-12-16 5 | 000069.SZ,2022-12-16 6 | -------------------------------------------------------------------------------- /data/daily_stock/2022-12-19.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | 000001.SZ,2022-12-19 3 | 000002.SZ,2022-12-19 4 | 000063.SZ,2022-12-19 5 | 000069.SZ,2022-12-19 6 | -------------------------------------------------------------------------------- /data/daily_stock/2022-12-20.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | 000001.SZ,2022-12-20 3 | 000002.SZ,2022-12-20 4 | 000063.SZ,2022-12-20 5 | 000069.SZ,2022-12-20 6 | -------------------------------------------------------------------------------- /data/daily_stock/2022-12-21.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | 000001.SZ,2022-12-21 3 | 000002.SZ,2022-12-21 4 | 000063.SZ,2022-12-21 5 | 000069.SZ,2022-12-21 6 | -------------------------------------------------------------------------------- /data/daily_stock/2022-12-22.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | 000001.SZ,2022-12-22 3 | 000002.SZ,2022-12-22 4 | 000063.SZ,2022-12-22 5 | 000069.SZ,2022-12-22 6 | -------------------------------------------------------------------------------- /data/daily_stock/2022-12-23.csv: -------------------------------------------------------------------------------- 1 | code,dt 2 | 000001.SZ,2022-12-23 3 | 000002.SZ,2022-12-23 4 | 000063.SZ,2022-12-23 5 | 000069.SZ,2022-12-23 6 | 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-------------------------------------------------------------------------------- /data/prediction/pred.csv: -------------------------------------------------------------------------------- 1 | ,code,dt,score 2 | 0,000001.SZ,2022-12-26,0.26168692111968994 3 | 1,000002.SZ,2022-12-26,0.25601014494895935 4 | 2,000063.SZ,2022-12-26,0.6623930335044861 5 | 3,000069.SZ,2022-12-26,0.26233309507369995 6 | 0,000001.SZ,2022-12-27,0.2617263197898865 7 | 1,000002.SZ,2022-12-27,0.2553652226924896 8 | 2,000063.SZ,2022-12-27,0.6622966527938843 9 | 3,000069.SZ,2022-12-27,0.2624991834163666 10 | 0,000001.SZ,2022-12-28,0.2617460787296295 11 | 1,000002.SZ,2022-12-28,0.6396672129631042 12 | 2,000063.SZ,2022-12-28,0.6622033715248108 13 | 3,000069.SZ,2022-12-28,0.26258984208106995 14 | 0,000001.SZ,2022-12-29,0.26160314679145813 15 | 1,000002.SZ,2022-12-29,0.648451030254364 16 | 2,000063.SZ,2022-12-29,0.6622130870819092 17 | 3,000069.SZ,2022-12-29,0.2627296447753906 18 | -------------------------------------------------------------------------------- /data/relation/2022-11-30.csv: -------------------------------------------------------------------------------- 1 | ,000001.SZ,000002.SZ,000063.SZ,000069.SZ 2 | 000001.SZ,1.0,0.9183336353345618,0.489495780528676,0.8544688680937519 3 | 000002.SZ,0.9183336353345618,1.0,0.3911211266892482,0.8806050758165241 4 | 000063.SZ,0.489495780528676,0.3911211266892482,1.0,0.27706243583541806 5 | 000069.SZ,0.8544688680937519,0.8806050758165241,0.27706243583541806,1.0 6 | -------------------------------------------------------------------------------- /data/relation/2022-12-30.csv: -------------------------------------------------------------------------------- 1 | ,000001.SZ,000002.SZ,000063.SZ,000069.SZ 2 | 000001.SZ,1.0,0.70799906311983,0.7967784444477405,0.7205848661984243 3 | 000002.SZ,0.70799906311983,1.0,0.5748544637846457,0.8091412898515844 4 | 000063.SZ,0.7967784444477405,0.5748544637846457,1.0,0.6498760383308898 5 | 000069.SZ,0.7205848661984243,0.8091412898515844,0.6498760383308898,1.0 6 | -------------------------------------------------------------------------------- /data_loader.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | from torch.utils import data 4 | import pickle 5 | 6 | class AllGraphDataSampler(data.Dataset): 7 | def __init__(self, base_dir, gname_list=None, data_start=None, data_middle=None, data_end=None, mode="train"): 8 | self.data_dir = os.path.join(base_dir) 9 | self.mode = mode 10 | self.data_start = data_start 11 | self.data_middle = data_middle 12 | self.data_end = data_end 13 | if gname_list is None: 14 | self.gnames_all = os.listdir(self.data_dir) 15 | self.gnames_all.sort() 16 | if mode == "train": 17 | self.gnames_all = self.gnames_all[self.data_start:self.data_middle] 18 | elif mode == "val": 19 | self.gnames_all = self.gnames_all[self.data_middle:self.data_end] 20 | self.data_all = self.load_state() 21 | 22 | def __len__(self): 23 | return len(self.data_all) 24 | 25 | def load_state(self): 26 | data_all = [] 27 | length = len(self.gnames_all) 28 | for i in range(length): 29 | sys.stdout.flush() 30 | sys.stdout.write('{} data loading: {:.2f}%{}'.format(self.mode, i*100/length, '\r')) 31 | data_all.append(pickle.load(open(os.path.join(self.data_dir, self.gnames_all[i]), "rb"))) 32 | print('{} data loaded!'.format(self.mode)) 33 | return data_all 34 | 35 | def __getitem__(self, idx): 36 | return self.data_all[idx] -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | from trainer.trainer import * 2 | from data_loader import * 3 | from model.Thgnn import * 4 | import warnings 5 | import torch 6 | import os 7 | from torch.utils.data import DataLoader 8 | import torch.optim as optim 9 | from torch.optim.lr_scheduler import StepLR 10 | import pandas as pd 11 | from pandas.core.frame import DataFrame 12 | from tqdm import tqdm 13 | 14 | warnings.filterwarnings("ignore") 15 | t_float = torch.float64 16 | torch.multiprocessing.set_sharing_strategy('file_system') 17 | 18 | class Args: 19 | def __init__(self, gpu=0, subtask="regression"): 20 | # device 21 | self.gpu = str(gpu) 22 | self.device = 'cpu' 23 | # data settings 24 | adj_threshold = 0.1 25 | self.adj_str = str(int(100*adj_threshold)) 26 | self.pos_adj_dir = "pos_adj_" + self.adj_str 27 | self.neg_adj_dir = "neg_adj_" + self.adj_str 28 | self.feat_dir = "features" 29 | self.label_dir = "label" 30 | self.mask_dir = "mask" 31 | self.data_start = data_start 32 | self.data_middle = data_middle 33 | self.data_end = data_end 34 | self.pre_data = pre_data 35 | # epoch settings 36 | self.max_epochs = 60 37 | self.epochs_eval = 10 38 | # learning rate settings 39 | self.lr = 0.0002 40 | self.gamma = 0.3 41 | # model settings 42 | self.hidden_dim = 128 43 | self.num_heads = 8 44 | self.out_features = 32 45 | self.model_name = "StockHeteGAT" 46 | self.batch_size = 1 47 | self.loss_fcn = mse_loss 48 | # save model settings 49 | self.save_path = os.path.join(os.path.abspath('.'), "/home/THGNN-main/data/model_saved/") 50 | self.load_path = self.save_path 51 | self.save_name = self.model_name + "_hidden_" + str(self.hidden_dim) + "_head_" + str(self.num_heads) + \ 52 | "_outfeat_" + str(self.out_features) + "_batchsize_" + str(self.batch_size) + "_adjth_" + \ 53 | str(self.adj_str) 54 | self.epochs_save_by = 60 55 | self.sub_task = subtask 56 | eval("self.{}".format(self.sub_task))() 57 | 58 | def regression(self): 59 | self.save_name = self.save_name + "_reg_rank_" 60 | self.loss_fcn = mse_loss 61 | self.label_dir = self.label_dir + "_regression" 62 | self.mask_dir = self.mask_dir + "_regression" 63 | 64 | def regression_binary(self): 65 | self.save_name = self.save_name + "_reg_binary_" 66 | self.loss_fcn = mse_loss 67 | self.label_dir = self.label_dir + "_twoclass" 68 | self.mask_dir = self.mask_dir + "_twoclass" 69 | 70 | def classification_binary(self): 71 | self.save_name = self.save_name + "_clas_binary_" 72 | self.loss_fcn = bce_loss 73 | self.label_dir = self.label_dir + "_twoclass" 74 | self.mask_dir = self.mask_dir + "_twoclass" 75 | 76 | def classification_tertiary(self): 77 | self.save_name = self.save_name + "_clas_tertiary_" 78 | self.loss_fcn = bce_loss 79 | self.label_dir = self.label_dir + "_threeclass" 80 | self.mask_dir = self.mask_dir + "_threeclass" 81 | 82 | 83 | def fun_train_predict(data_start, data_middle, data_end, pre_data): 84 | args = Args() 85 | os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu) 86 | dataset = AllGraphDataSampler(base_dir="/home/THGNN-main/data/data_train_predict/", data_start=data_start, 87 | data_middle=data_middle, data_end=data_end) 88 | val_dataset = AllGraphDataSampler(base_dir="/home/THGNN-main/data/data_train_predict/", mode="val", data_start=data_start, 89 | data_middle=data_middle, data_end=data_end) 90 | dataset_loader = DataLoader(dataset, batch_size=args.batch_size, pin_memory=True, collate_fn=lambda x: x) 91 | val_dataset_loader = DataLoader(val_dataset, batch_size=1, pin_memory=True) 92 | model = eval(args.model_name)(hidden_dim=args.hidden_dim, num_heads=args.num_heads, 93 | out_features=args.out_features).to(args.device) 94 | 95 | # train 96 | optimizer = optim.Adam(model.parameters(), lr=args.lr) 97 | cold_scheduler = StepLR(optimizer=optimizer, step_size=5000, gamma=0.9, last_epoch=-1) 98 | default_scheduler = cold_scheduler 99 | print('start training') 100 | for epoch in range(args.max_epochs): 101 | train_loss = train_epoch(epoch=epoch, args=args, model=model, dataset_train=dataset_loader, 102 | optimizer=optimizer, scheduler=default_scheduler, loss_fcn=mse_loss) 103 | if epoch % args.epochs_eval == 0: 104 | eval_loss, _ = eval_epoch(args=args, model=model, dataset_eval=val_dataset_loader, loss_fcn=mse_loss) 105 | print('Epoch: {}/{}, train loss: {:.6f}, val loss: {:.6f}'.format(epoch + 1, args.max_epochs, train_loss, 106 | eval_loss)) 107 | else: 108 | print('Epoch: {}/{}, train loss: {:.6f}'.format(epoch + 1, args.max_epochs, train_loss)) 109 | if (epoch + 1) % args.epochs_save_by == 0: 110 | print("save model!") 111 | state = {'model': model.state_dict(), 'optimizer': optimizer.state_dict(), 'epoch': epoch + 1} 112 | torch.save(state, os.path.join(args.save_path, pre_data + "_epoch_" + str(epoch + 1) + ".dat")) 113 | 114 | # predict 115 | checkpoint = torch.load(os.path.join(args.load_path, pre_data + "_epoch_" + str(epoch + 1) + ".dat")) 116 | model.load_state_dict(checkpoint['model']) 117 | data_code = os.listdir('/home/THGNN-main/data/daily_stock') 118 | data_code = sorted(data_code) 119 | data_code_last = data_code[data_middle:data_end] 120 | df_score=pd.DataFrame() 121 | for i in tqdm(range(len(val_dataset))): 122 | df = pd.read_csv('/home/THGNN-main/data/daily_stock/' + data_code_last[i], dtype=object) 123 | tmp_data = val_dataset[i] 124 | pos_adj, neg_adj, features, labels, mask = extract_data(tmp_data, args.device) 125 | model.train() 126 | logits = model(features, pos_adj, neg_adj) 127 | result = logits.data.cpu().numpy().tolist() 128 | result_new = [] 129 | for j in range(len(result)): 130 | result_new.append(result[j][0]) 131 | res = {"score": result_new} 132 | res = DataFrame(res) 133 | df['score'] = res 134 | df_score=pd.concat([df_score,df]) 135 | 136 | #df.to_csv('prediction/' + data_code_last[i], encoding='utf-8-sig', index=False) 137 | df_score.to_csv('/home/THGNN-main/data/prediction/pred.csv') 138 | print(df_score) 139 | 140 | if __name__ == "__main__": 141 | data_start = 20 142 | data_middle = 39 143 | data_end = data_middle+4 144 | pre_data = '2022-12-29' 145 | fun_train_predict(data_start, data_middle, data_end, pre_data) -------------------------------------------------------------------------------- /main.sh: -------------------------------------------------------------------------------- 1 | python main.py > train.log 2>&1 -------------------------------------------------------------------------------- /model/Thgnn.py: -------------------------------------------------------------------------------- 1 | from torch.nn.parameter import Parameter 2 | from torch.nn.modules.module import Module 3 | import torch.nn as nn 4 | import torch 5 | import math 6 | 7 | class GraphAttnMultiHead(Module): 8 | def __init__(self, in_features, out_features, negative_slope=0.2, num_heads=4, bias=True, residual=True): 9 | super(GraphAttnMultiHead, self).__init__() 10 | self.num_heads = num_heads 11 | self.out_features = out_features 12 | self.weight = Parameter(torch.FloatTensor(in_features, num_heads * out_features)) 13 | self.weight_u = Parameter(torch.FloatTensor(num_heads, out_features, 1)) 14 | self.weight_v = Parameter(torch.FloatTensor(num_heads, out_features, 1)) 15 | self.leaky_relu = nn.LeakyReLU(negative_slope=negative_slope) 16 | self.residual = residual 17 | if self.residual: 18 | self.project = nn.Linear(in_features, num_heads*out_features) 19 | else: 20 | self.project = None 21 | if bias: 22 | self.bias = Parameter(torch.FloatTensor(1, num_heads * out_features)) 23 | else: 24 | self.register_parameter('bias', None) 25 | self.reset_parameters() 26 | 27 | def reset_parameters(self): 28 | stdv = 1. / math.sqrt(self.weight.size(-1)) 29 | if self.bias is not None: 30 | self.bias.data.uniform_(-stdv, stdv) 31 | self.weight.data.uniform_(-stdv, stdv) 32 | stdv = 1. / math.sqrt(self.weight_u.size(-1)) 33 | self.weight_u.data.uniform_(-stdv, stdv) 34 | self.weight_v.data.uniform_(-stdv, stdv) 35 | 36 | def forward(self, inputs, adj_mat, requires_weight=False): 37 | support = torch.mm(inputs, self.weight) 38 | support = support.reshape(-1, self.num_heads, self.out_features).permute(dims=(1, 0, 2)) 39 | f_1 = torch.matmul(support, self.weight_u).reshape(self.num_heads, 1, -1) 40 | f_2 = torch.matmul(support, self.weight_v).reshape(self.num_heads, -1, 1) 41 | logits = f_1 + f_2 42 | weight = self.leaky_relu(logits) 43 | masked_weight = torch.mul(weight, adj_mat).to_sparse() 44 | attn_weights = torch.sparse.softmax(masked_weight, dim=2).to_dense() 45 | support = torch.matmul(attn_weights, support) 46 | support = support.permute(dims=(1, 0, 2)).reshape(-1, self.num_heads * self.out_features) 47 | if self.bias is not None: 48 | support = support + self.bias 49 | if self.residual: 50 | support = support + self.project(inputs) 51 | if requires_weight: 52 | return support, attn_weights 53 | else: 54 | return support, None 55 | 56 | 57 | class PairNorm(nn.Module): 58 | def __init__(self, mode='PN', scale=1): 59 | assert mode in ['None', 'PN', 'PN-SI', 'PN-SCS'] 60 | super(PairNorm, self).__init__() 61 | self.mode = mode 62 | self.scale = scale 63 | 64 | def forward(self, x): 65 | if self.mode == 'None': 66 | return x 67 | col_mean = x.mean(dim=0) 68 | if self.mode == 'PN': 69 | x = x - col_mean 70 | rownorm_mean = (1e-6 + x.pow(2).sum(dim=1).mean()).sqrt() 71 | x = self.scale * x / rownorm_mean 72 | if self.mode == 'PN-SI': 73 | x = x - col_mean 74 | rownorm_individual = (1e-6 + x.pow(2).sum(dim=1, keepdim=True)).sqrt() 75 | x = self.scale * x / rownorm_individual 76 | if self.mode == 'PN-SCS': 77 | rownorm_individual = (1e-6 + x.pow(2).sum(dim=1, keepdim=True)).sqrt() 78 | x = self.scale * x / rownorm_individual - col_mean 79 | return x 80 | 81 | 82 | class GraphAttnSemIndividual(Module): 83 | def __init__(self, in_features, hidden_size=128, act=nn.Tanh()): 84 | super(GraphAttnSemIndividual, self).__init__() 85 | self.project = nn.Sequential(nn.Linear(in_features, hidden_size), 86 | act, 87 | nn.Linear(hidden_size, 1, bias=False)) 88 | 89 | def forward(self, inputs, requires_weight=False): 90 | w = self.project(inputs) 91 | beta = torch.softmax(w, dim=1) 92 | if requires_weight: 93 | return (beta * inputs).sum(1), beta 94 | else: 95 | return (beta * inputs).sum(1), None 96 | 97 | 98 | class StockHeteGAT(nn.Module): 99 | def __init__(self, in_features=6, out_features=8, num_heads=8, hidden_dim=64, num_layers=1): 100 | super(StockHeteGAT, self).__init__() 101 | self.encoding = nn.GRU( 102 | input_size=in_features, 103 | hidden_size=hidden_dim, 104 | num_layers=num_layers, 105 | batch_first=True, 106 | bidirectional=False, 107 | dropout=0.1 108 | ) 109 | self.pos_gat = GraphAttnMultiHead( 110 | in_features=hidden_dim, 111 | out_features=out_features, 112 | num_heads=num_heads 113 | ) 114 | self.neg_gat = GraphAttnMultiHead( 115 | in_features=hidden_dim, 116 | out_features=out_features, 117 | num_heads=num_heads 118 | ) 119 | self.mlp_self = nn.Linear(hidden_dim, hidden_dim) 120 | self.mlp_pos = nn.Linear(out_features*num_heads, hidden_dim) 121 | self.mlp_neg = nn.Linear(out_features*num_heads, hidden_dim) 122 | self.pn = PairNorm(mode='PN-SI') 123 | self.sem_gat = GraphAttnSemIndividual(in_features=hidden_dim, 124 | hidden_size=hidden_dim, 125 | act=nn.Tanh()) 126 | self.predictor = nn.Sequential( 127 | nn.Linear(hidden_dim, 1), 128 | nn.Sigmoid() 129 | ) 130 | 131 | for m in self.modules(): 132 | if isinstance(m, nn.Linear): 133 | nn.init.xavier_uniform_(m.weight, gain=0.02) 134 | 135 | def forward(self, inputs, pos_adj, neg_adj, requires_weight=False): 136 | _, support = self.encoding(inputs) 137 | support = support.squeeze() 138 | pos_support, pos_attn_weights = self.pos_gat(support, pos_adj, requires_weight) 139 | neg_support, neg_attn_weights = self.neg_gat(support, neg_adj, requires_weight) 140 | support = self.mlp_self(support) 141 | pos_support = self.mlp_pos(pos_support) 142 | neg_support = self.mlp_neg(neg_support) 143 | all_embedding = torch.stack((support, pos_support, neg_support), dim=1) 144 | all_embedding, sem_attn_weights = self.sem_gat(all_embedding, requires_weight) 145 | all_embedding = self.pn(all_embedding) 146 | if requires_weight: 147 | return self.predictor(all_embedding), (pos_attn_weights, neg_attn_weights, sem_attn_weights) 148 | else: 149 | return self.predictor(all_embedding) 150 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | torch==1.8.0 2 | pandas==1.5.3 3 | tqdm==4.65.0 4 | numpy==1.23.5 5 | networkx==3.1 -------------------------------------------------------------------------------- /trainer/trainer.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | 4 | def mse_loss(logits, targets): 5 | mse = nn.MSELoss() 6 | loss = mse(logits.squeeze(), targets) 7 | return loss 8 | 9 | 10 | def bce_loss(logits, targets): 11 | bce = nn.BCELoss() 12 | loss = bce(logits.squeeze(), targets) 13 | return loss 14 | 15 | 16 | def evaluate(model, features, adj_pos, adj_neg, labels, mask, loss_func=nn.L1Loss()): 17 | model.eval() 18 | with torch.no_grad(): 19 | logits = model(features, adj_pos, adj_neg) 20 | 21 | loss = loss_func(logits,labels) 22 | return loss, logits 23 | 24 | 25 | def extract_data(data_dict, device): 26 | pos_adj = data_dict['pos_adj'].to(device).squeeze() 27 | neg_adj = data_dict['neg_adj'].to(device).squeeze() 28 | features = data_dict['features'].to(device).squeeze() 29 | labels = data_dict['labels'].to(device).squeeze() 30 | mask = data_dict['mask'] 31 | return pos_adj, neg_adj, features, labels, mask 32 | 33 | 34 | def train_epoch(epoch, args, model, dataset_train, optimizer, scheduler, loss_fcn): 35 | model.train() 36 | loss_return = 0 37 | for batch_data in dataset_train: 38 | for batch_idx, data in enumerate(batch_data): 39 | model.zero_grad() 40 | pos_adj, neg_adj, features, labels, mask = extract_data(data, args.device) 41 | logits = model(features, pos_adj, neg_adj) 42 | loss = loss_fcn(logits[mask], labels[mask]) 43 | loss.backward() 44 | optimizer.step() 45 | scheduler.step() 46 | if batch_idx == 0: 47 | loss_return += loss.data 48 | return loss_return/len(dataset_train) 49 | 50 | 51 | def eval_epoch(args, model, dataset_eval, loss_fcn): 52 | loss = 0. 53 | logits = None 54 | for batch_idx, data in enumerate(dataset_eval): 55 | pos_adj, neg_adj, features, labels, mask = extract_data(data, args.device) 56 | loss, logits = evaluate(model, features, pos_adj, neg_adj, labels, mask, loss_func=loss_fcn) 57 | break 58 | return loss, logits 59 | 60 | def train_epoch(epoch, args, model, dataset_train, optimizer, scheduler, loss_fcn): 61 | model.train() 62 | loss_return = 0 63 | for batch_data in dataset_train: 64 | for batch_idx, data in enumerate(batch_data): 65 | model.zero_grad() 66 | pos_adj, neg_adj, features, labels, mask = extract_data(data, args.device) 67 | logits = model(features, pos_adj, neg_adj) 68 | loss = loss_fcn(logits[mask], labels[mask]) 69 | loss.backward() 70 | optimizer.step() 71 | scheduler.step() 72 | if batch_idx == 0: 73 | loss_return += loss.data 74 | return loss_return/len(dataset_train) 75 | 76 | 77 | def eval_epoch(args, model, dataset_eval, loss_fcn): 78 | loss = 0. 79 | logits = None 80 | for batch_idx, data in enumerate(dataset_eval): 81 | pos_adj, neg_adj, features, labels, mask = extract_data(data, args.device) 82 | loss, logits = evaluate(model, features, pos_adj, neg_adj, labels, mask, loss_func=loss_fcn) 83 | break 84 | return loss, logits -------------------------------------------------------------------------------- /utils/generate_data.py: -------------------------------------------------------------------------------- 1 | import os 2 | import torch 3 | import pickle 4 | import numpy as np 5 | from tqdm import tqdm 6 | import networkx as nx 7 | import pandas as pd 8 | from torch.autograd import Variable 9 | 10 | feature_cols = ['open','high','low','close','to','vol'] 11 | 12 | path1 = "/home/THGNN-main/data/csi300.pkl" 13 | df1 = pickle.load(open(path1, 'rb'), encoding='utf-8') 14 | relation = os.listdir('/home/THGNN-main/data/relation/') 15 | relation = sorted(relation) 16 | date_unique=df1['dt'].unique() 17 | stock_trade_data=date_unique.tolist() 18 | stock_trade_data.sort() 19 | 20 | df1['dt']=df1['dt'].astype('datetime64') 21 | 22 | def fun(relation_dt, start_dt_month, end_dt_month,df1): 23 | prev_date_num = 20 24 | adj_all = pd.read_csv('/home/THGNN-main/data/relation/'+relation_dt+'.csv', index_col=0) 25 | adj_stock_set = list(adj_all.index) 26 | pos_g = nx.Graph(adj_all > 0.1) 27 | pos_adj = nx.adjacency_matrix(pos_g).toarray() 28 | pos_adj = pos_adj - np.diag(np.diag(pos_adj)) 29 | pos_adj = torch.from_numpy(pos_adj).type(torch.float32) 30 | neg_g = nx.Graph(adj_all < -0.1) 31 | neg_adj = nx.adjacency_matrix(neg_g) 32 | neg_adj.data = np.ones(neg_adj.data.shape) 33 | neg_adj = neg_adj.toarray() 34 | neg_adj = neg_adj - np.diag(np.diag(neg_adj)) 35 | neg_adj = torch.from_numpy(neg_adj).type(torch.float32) 36 | print('neg_adj over') 37 | print(neg_adj.shape) 38 | dts = stock_trade_data[stock_trade_data.index(start_dt_month):stock_trade_data.index(end_dt_month)+1] 39 | print(dts) 40 | for i in tqdm(range(len(dts))): 41 | end_data=dts[i] 42 | start_data = stock_trade_data[stock_trade_data.index(end_data)-(prev_date_num - 1)] 43 | df2 = df1.loc[df1['dt'] <= end_data] 44 | df2 = df2.loc[df2['dt'] >= start_data] 45 | code = adj_stock_set 46 | feature_all = [] 47 | mask = [] 48 | labels = [] 49 | day_last_code = [] 50 | for j in range(len(code)): 51 | df3 = df2.loc[df2['code'] == code[j]] 52 | y = df3[feature_cols].values 53 | if y.T.shape[1] == prev_date_num: 54 | one = [] 55 | feature_all.append(y) 56 | mask.append(True) 57 | label = df3.loc[df3['dt'] == end_data]['label'].values 58 | labels.append(label[0]) 59 | one.append(code[j]) 60 | one.append(end_data) 61 | day_last_code.append(one) 62 | feature_all = np.array(feature_all) 63 | features = torch.from_numpy(feature_all).type(torch.float32) 64 | mask = [True]*len(labels) 65 | labels = torch.tensor(labels, dtype=torch.float32) 66 | result = {'pos_adj': Variable(pos_adj), 'neg_adj': Variable(neg_adj), 'features': Variable(features), 67 | 'labels': Variable(labels), 'mask': mask} 68 | with open('/home/THGNN-main/data/data_train_predict/'+end_data+'.pkl', 'wb') as f: 69 | pickle.dump(result, f) 70 | df = pd.DataFrame(columns=['code', 'dt'], data=day_last_code) 71 | df.to_csv('/home/THGNN-main/data/daily_stock/'+end_data+'.csv', header=True, index=False, encoding='utf_8_sig') 72 | 73 | #The first parameter and third parameters indicate the last trading day of each month, and the second parameter indicates the first trading day of each month. 74 | # for i in ['2020','2021','2022']: 75 | # for j in ['01','02','03','04','05','06','07','08','09','10','11','12']: 76 | # stock_m=[k for k in stock_trade_data if k>i+'-'+j and k 1: 32 | pool = mp.Pool(processes=processes) 33 | args_all = [(ref_dict[code], ref_dict, n) for code in codes] 34 | results = [pool.apply_async(calculate_pccs, args=args) for args in args_all] 35 | output = [o.get() for o in results] 36 | data = np.stack(output) 37 | return pd.DataFrame(data=data, index=codes, columns=codes) 38 | data = np.zeros([len(codes), len(codes)]) 39 | for i in tqdm(range(len(codes))): 40 | data[i, :] = calculate_pccs(ref_dict[codes[i]], ref_dict, n) 41 | return pd.DataFrame(data=data, index=codes, columns=codes) 42 | 43 | path1 = "/home/THGNN-main/data/csi300.pkl" 44 | df1 = pickle.load(open(path1, 'rb'), encoding='utf-8') 45 | #prev_date_num Indicates the number of days in which stock correlation is calculated 46 | prev_date_num = 20 47 | date_unique=df1['dt'].unique() 48 | stock_trade_data=date_unique.tolist() 49 | stock_trade_data.sort() 50 | stock_num=df1.code.unique().shape[0] 51 | #dt is the last trading day of each month 52 | dt=['2022-11-30','2022-12-30'] 53 | # for i in ['2020','2021','2022']: 54 | # for j in ['01','02','03','04','05','06','07','08','09','10','11','12']: 55 | # stock_m=[k for k in stock_trade_data if k>i+'-'+j and k= start_data] 65 | code = sorted(list(set(df2['code'].values.tolist()))) 66 | test_tmp = {} 67 | for j in tqdm(range(len(code))): 68 | df3 = df2.loc[df2['code'] == code[j]] 69 | y = df3[feature_cols].values 70 | if y.T.shape[1] == prev_date_num: 71 | test_tmp[code[j]] = y.T 72 | t1 = time.time() 73 | result = stock_cor_matrix(test_tmp, list(test_tmp.keys()), prev_date_num, processes=1) 74 | result=result.fillna(0) 75 | for i in range(0,stock_num): 76 | result.iloc[i,i]=1 77 | t2 = time.time() 78 | print('time cost', t2 - t1, 's') 79 | result.to_csv("/home/THGNN-main/data/relation/"+str(end_data)+".csv") 80 | --------------------------------------------------------------------------------