This is a PyTorch implementation of (Re-)Imag(in)ing Price Trends
- 2022/1/24: Upload pre-train model
- 2022/1/24: Support performance analysis
- 2022/1/23: Support tensorboard
- 2022/1/23: Support multi-GPU training
- 2022/1/23: Support ONNX format export
The net is defined in the folder ./models,
you can just run the notebook train.ipynb in ./notebooks to train and save the model.
After training the model, you can evaluate it by test.ipynb.
Here, you can see the return of the profolio built by your model.
We choose the threshold (for the predict logit) as 0.58.
Here shows the comparison of log return. (same weighted)
The accumulate return of protfolio.
Jiang, Jingwen and Kelly, Bryan T. and Xiu, Dacheng,
(Re-)Imag(in)ing Price Trends (December 1, 2020).
Chicago Booth Research Paper No. 21-01,
http://dx.doi.org/10.2139/ssrn.3756587