Here is the pre-released code for the FTGCN-based quantile and mean models in our paper "Big portfolio selection by graph-based conditional moments method", [paper].

Environment

Main settings: Python 3.9 & Pytorch 1.11.0

Minor settings: To complete.

Data

The price and volume Data of each stock, sector-industry relation data, and wiki relation data, could be downloaded from the official repositiy of Feng (2019); see [stock data].

In the meanwhile, the daily Fama French five factors could be downloaded from the homepage of Kenneth R. French; see [factor data].

data_pipe

Script	Usage
compute_factor_loading.py	To calculate factor loadings from raw End-of-day data and factor data
construct_feature.py	Generate the network input (including lagged values) for each day
construct_label.py	Generate the label for each day

network

Script	Usage
model.py	The model specification of network
my_dataset.py	The dataset specification based on Pytorch
load_data.py	Load the relation data
(F)TGCN.py	The agent used for training (F)TGCN
train_(F)TGCN.py	Train a model of (F)TGCN-based quantile (mean) model
hypothesis_test.py	The Kupiec and Christofer tests
QCM.py	The QCM learning from conditional quantiles
inference_(F)TGCN.py	Obtain four moments from the trained models

Reproduce the results for NASDAQ-wikidata with FTGCN

# Please make sure you have changed the log directory in each file.

# Construct features and labels
python compute_factor_loading.py
python construct_feature.py
python construct_label.py

# Train models
# mean model 
python train_FTGCN.py --tau 0.0 --mse-loss --lam 0.1 --save_folder ... 
# quantile models
python train_FTGCN.py --tau 0.005 --lam 0.1 --save_folder ...
python train_FTGCN.py --tau 0.01 --lam 0.1 --save_folder ...  
...
python train_FTGCN.py --tau 0.99 --lam 0.1 --save_folder ... 
python train_FTGCN.py --tau 0.995 --lam 0.1 --save_folder ... 

# Inference and QCM learning
python inference_FTGCN.py

Cite

If you feel this code helps, please kindly cite the following paper:

@article{zhu2023big,
  title={Big portfolio selection by graph-based conditional moments method},
  author={Zhu, Zhoufan and Zhang, Ningning and Zhu, Ke},
  journal={arXiv preprint arXiv:2301.11697},
  year={2023}
}