Skip to content

Official code implementation of AAAI 2024 paper "StockMixer: A Simple yet Strong MLP-based Architecture for Stock Price Forecasting".

Notifications You must be signed in to change notification settings

SJTU-DMTai/StockMixer

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

5 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

StockMixer

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:

Environment

  • Python 3.7
  • torch~=1.10.1
  • numpy~=1.21.5
  • PyYAML, pandas, tqdm, matplotlib

Dataset and Preprocessing

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.

Running the Code

# edit configurations in train.py
python src/train.py

About

Official code implementation of AAAI 2024 paper "StockMixer: A Simple yet Strong MLP-based Architecture for Stock Price Forecasting".

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages