Official code implementation and supplementary material of AAAI 2024 paper "StockMixer: A Simple yet Strong MLP-based Architecture for Stock Price Forecasting". This work proposes a lightweight and effective MLP-based architecture for stock price forecasting named StockMixer. It consists of indicator mixing, temporal mixing and stock mixing to capture complex correlations in the stock data. The end-to-end training flow of StockMixer is presented as follows:
- Python 3.7
- torch~=1.10.1
- numpy~=1.21.5
- PyYAML, pandas, tqdm, matplotlib
The original datasets(NASDAQ, NYSE and S&P500) are respectively available:
NASDAQ/NYSE: Temporal Relational Ranking for Stock Prediction
S&P500: Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction
In order to improve file reading speed, we process the raw data to generate corresponding .pkl or .npy files. Datasets are provided in the dataset folder. Because StockMixer does not require prior knowledge similar to graphs or hypergraphs, our preprocessed dataset did not provide either. You can find them from the original datasets.
# edit configurations in train.py
python src/train.py