This is the official code and supplementary materials for our AAAI-2024 paper: MASTER: Market-Guided Stock Transformer for Stock Price Forecasting. [Paper] [ArXiv preprint]
MASTER is a stock transformer for stock price forecasting, which models the momentary and cross-time stock correlation and guides feature selection with market information.
Our original experiments were conducted in a complex business codebase developed based on Qlib. The original code is confidential and exhaustive. In order to enable anyone to quickly use MASTER and reproduce the paper's results, here we publish our well-processed data and core code.
Install dependencies.
Install Qlib. We have minimized the reliance on Qlib, and you can simply install it by
pip install pyqlib
Download data from one of the following links (the data files are the same) and unpack it into data/
Run main.py.
We provide two trained models: model/csi300master_0.pkl, model/csi800master_0.pkl
The downloaded data is split into training, validation, and test sets, with two stock universes. Note the csi300 data is a subset of the csi800 data. You can use the following code to investigate the datetime, instrument, and feature formulation.
with open(f'data/csi300/csi300_dl_train.pkl', 'rb') as f:
dl_train = pickle.load(f)
dl_train.data # a Pandas dataframe
In our code, the data will be gathered chronically and then grouped by prediction dates. the data
iterated by the data loader is of shape (N, T, F), where:
For convenient reference, we extract and organize market information from the published data into data/csi_market_information.csv
.
You can check the datetime and feature formulation in the file. Note that m is shared by all stocks.
The market data is generated by the following pseudo-code.
m = []
for S in csi300, csi500, csi800:
m += [market_index(S,-1)]
for d in [5, 10, 20, 30, 60]:
m += [historical_market_index_mean(S, d), historical_market_index_std(S, d)]
m += [historical_amount_mean(S, d), historical_amount_std(S, d)]
The published data went through the following necessary preprocessing.
We are happy to hear that MASTER has been integrated into the open-sourced Qlib framework at this repo. We thank LIU, Qiaoan and ZHAO, Lifan for their contributions and please also give credits to the new repo if you use it.
As a brief introduction to the new version, with the Qlib framework, you can
In the meantime, please note that
If you use the data or the code, please cite our work! :smile:
@inproceedings{li2024master,
title={Master: Market-guided stock transformer for stock price forecasting},
author={Li, Tong and Liu, Zhaoyang and Shen, Yanyan and Wang, Xue and Chen, Haokun and Huang, Sen},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={38},
number={1},
pages={162--170},
year={2024}
}