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MTMD: Multi-Scale Temporal Memory Learning and Efficient Debiasing Framework for Stock Trend Forecasting

The official implementation of the paper "MTMD: Multi-Scale Temporal Memory Learning and Efficient Debiasing Framework for Stock Trend Forecasting".

Environment

1. Install python3.7, 3.8 or 3.9.
2. Install the requirements in requirements.txt.
3. Install the quantitative investment platform Qlib and download the data from Qlib:

   # install Qlib from source
   pip install --upgrade  cython
   git clone https://github.com/microsoft/qlib.git && cd qlib
   python setup.py install
   
   # Download the stock features of Alpha360 from Qlib
   python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/cn_data --region cn --version v2
   mkdir data
   

4. Please download the concept matrix, which is provided by tushare.
5. Please put the concept data and stock data in the new' data' folder.

Reproduce the stock trend forecasting results

git clone https://github.com/MingjieWang0606/MTMD-Public.git
cd MTMD-Public
mkdir output

Reproduce our MTMD framework

# CSI 100
python learn_memory.py --model_name HIST --data_set csi100 --hidden_size 128 --num_layers 2 --outdir ./output/csi100_MTMD

# CSI 300
python learn_memory.py --model_name HIST --data_set csi300 --hidden_size 128 --num_layers 2 --outdir ./output/csi300_MTMD

Reproduce our HIST framework

# CSI 100
python learn.py --model_name HIST --data_set csi100 --hidden_size 128 --num_layers 2 --outdir ./output/csi100_HIST

# CSI 300
python learn.py --model_name HIST --data_set csi300 --hidden_size 128 --num_layers 2 --outdir ./output/csi300_HIST

Reproduce the baselines

• MLP

# MLP on CSI 100
python learn.py --model_name MLP --data_set csi100 --hidden_size 512 --num_layers 3 --outdir ./output/csi100_MLP

# MLP on CSI 300
python learn.py --model_name MLP --data_set csi300 --hidden_size 512 --num_layers 3 --outdir ./output/csi300_MLP

• LSTM

# LSTM on CSI 100
python learn.py --model_name LSTM --data_set csi100 --hidden_size 128 --num_layers 2 --outdir ./output/csi100_LSTM

# LSTM on CSI 300
python learn.py --model_name LSTM --data_set csi300 --hidden_size 128 --num_layers 2 --outdir ./output/csi300_LSTM

• GRU

# GRU on CSI 100
python learn.py --model_name GRU --data_set csi100 --hidden_size 128 --num_layers 2 --outdir ./output/csi100_GRU

# GRU on CSI 300
python learn.py --model_name GRU --data_set csi300 --hidden_size 64 --num_layers 2 --outdir ./output/csi300_GRU

• SFM

# SFM on CSI 100
python learn.py --model_name SFM --data_set csi100 --hidden_size 64 --num_layers 2 --outdir ./output/csi100_SFM

# SFM on CSI 300
python learn.py --model_name SFM --data_set csi300 --hidden_size 128 --num_layers 2 --outdir ./output/csi300_SFM

• GATs

# GATs on CSI 100
python learn.py --model_name GATs --data_set csi100 --hidden_size 128 --num_layers 2 --outdir ./output/csi100_GATs

# GATs on CSI 300
python learn.py --model_name GATs --data_set csi300 --hidden_size 64 --num_layers 2 --outdir ./output/csi300_GATs

• ALSTM

# ALSTM on CSI 100
python learn.py --model_name ALSTM --data_set csi100 --hidden_size 64 --num_layers 2 --outdir ./output/csi100_ALSTM

# ALSTM on CSI 300
python learn.py --model_name ALSTM --data_set csi300 --hidden_size 128 --num_layers 2 --outdir ./output/csi300_ALSTM

• Transformer

# Transformer on CSI 100
python learn.py --model_name Transformer --data_set csi100 --hidden_size 32 --num_layers 3 --outdir ./output/csi100_Transformer

# Transformer on CSI 300
python learn.py --model_name Transformer --data_set csi300 --hidden_size 32 --num_layers 3 --outdir ./output/csi300_Transformer

• ALSTM+TRA

We reproduce the ALSTM+TRA with its source code.

Acknowledgements

Special thanks to ChenFeng, Zhang Mingze,Tian Junxi and LiTingXin for the their help and discussion!
Thanks for the clean and efficient HIST code.

Citation

Please cite the following paper if you use this code in your work.

@article{wang2022mtmd,
  title={MTMD: Multi-Scale Temporal Memory Learning and Efficient Debiasing Framework for Stock Trend Forecasting},
  author={Mingjie Wang and Mingze Zhang and Jianxiong Guo and Weijia Jia},
  journal={arXiv preprint arXiv:2212.08656},
  year={2022}
}