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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](https://arxiv.org/abs/2212.08656)". | ||
|  | ||
| # MTMD | ||
| * Code: [https://github.com/MingjieWang0606/MTMD-Public/tree/main](https://github.com/MingjieWang0606/MTMD-Public/tree/main) | ||
| * Paper: "[MTMD: Multi-Scale Temporal Memory Learning and Efficient Debiasing Framework for Stock Trend Forecasting](https://arxiv.org/abs/2212.08656)". | ||
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| ## Environment | ||
| 1. Install python3.7, 3.8 or 3.9. | ||
| 2. Install the requirements in [requirements.txt](https://github.com/Wentao-Xu/HIST/blob/main/requirements.txt). | ||
| 3. Install the quantitative investment platform [Qlib](https://github.com/microsoft/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 | ||
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| # 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](https://github.com/Wentao-Xu/HIST/tree/main/data), which is provided by [tushare](https://tushare.pro/document/2?doc_id=81). | ||
| 5. Please put the concept data and stock data in the new' data' folder. | ||
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| ## 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 | ||
| ``` | ||
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| ### 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 | ||
| ``` | ||
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| * 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 | ||
| ``` | ||
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| * 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 | ||
| ``` | ||
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| * 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 | ||
| ``` | ||
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| * 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 | ||
| ``` | ||
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| * 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 | ||
| ``` | ||
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| * 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 | ||
| ``` | ||
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| * ALSTM+TRA | ||
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| We reproduce the ALSTM+TRA with its [source code](https://github.com/microsoft/qlib/tree/main/examples/benchmarks/TRA). | ||
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| ### Acknowledgements | ||
| Special thanks to ChenFeng, Zhang Mingze,Tian Junxi and LiTingXin for the their help and discussion! | ||
| Thanks for the clean and efficient [HIST](https://github.com/Wentao-Xu/HIST) code. | ||
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| ## 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} | ||
| } | ||
| ``` | ||
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