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Files needed to run the code: factor_data.npy, Y_cl.npy.

factor_data.npy contains the features needed for A share stocks and can be downloaded from https://drive.google.com/file/d/1Jc5o6LdMBkwMMiWSCz8w4MbWjymBM6AE/view?usp=sharing

Y_cl.npy contains the corresponding weekly price and can be downloaded from https://drive.google.com/file/d/11Gsqm_dbCVm5Npehz83mylKkmxJ_2xR8/view?usp=sharing

For test run, put these two files under main directory.

Flowchart

1. We first run first_process.py, which would turn factor_data.npy to features_processed.npz. It will change all True, False to 1, 0 and compress the factor data.

   Estimated time of running: 5 minutes.

2. Then we run process_rolling.py. Now the default training and testing length is 300 16. This code will rolling split the data (features and prices) and put them in a folder.

   Estimated time of running: 5 minutes, also depends on training and testing length.

3. Then we train the model using train_20200111_rolling_torch.py. It will save all the models to a folder. An example run is python train_20200111_rolling_torch.py --pp 21.Here 21 denotes the total number of train-test pairs.

   Estimated time of running: 1 hour, also depends on training and testing length.

4. For back test, an example run is python back_test.py, which will generate a csv file, recording all positive and negative positions and combined return each week.

Arguments

In train.py, we can control training and testing length, batch size, training epochs. Use python train.py -h to see details.

In back_test.py, we can pick different saved models to load and test different strategies. Details see python back_test.py -h.

MSE

We don't have relu for MSE loss, so we use train_mlp.py and back_test_mlp.py, who have slightly different network structures.