State Frequency Memory recurrent network for stock price prediction
Author: Liheng Zhang, Date: 08/03/2017
This is the project for the following paper:
Liheng Zhang, Charu Aggarwal, Guo-Jun Qi, Stock Price Prediction via Discovering Multi-Frequency Trading Patterns,
in Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2017), Halifax, Nova Scotia,
Canada, August 13-17, 2017.
Questions about the source codes can be directed to Liheng Zhang at [email protected].
For more applications with SFM, please refer to:
Hao Hu, Guo-Jun Qi. State-Frequency Memory Recurrent Neural Networks, in Proceedings of International Conference
on Machine Learning (ICML 2017), Sydney, Australia, August 6-11, 2017.
Requirements
- Python == 2.7
- Keras == 1.0.1
- Theano == 0.9
Prepare the data
cd dataset; python build_data.py
Test with pretrained model
cd test
python test.py --step=1
The model for n-step prediction is specified with --step. Models for 1-step, 3-step and 5-step prediction are provided.
To visualize the predicted results:
python test --step=1 --visualization=true
Training
cd train
python train.py --step=3 --hidden_dim=50 --freq_dim=10 --niter=4000 --learning_rate=0.01
Note
The codes are expired for Keras >= 2.0.0. Codes for the latest version of Keras will be released.
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