Forked from ZhenyaoJiang/PGPortfolio \
./run_docker.sh -b -g
Use option -b
to build docker image and -g
to use GPU.
Reference: see this page for NVIDIA docker installation on Ubuntu.
Note from original repository: \
This is the implementation of our paper, A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem (arXiv:1706.10059), together with a toolkit of portfolio management research.
- The policy optimization method we described in the paper is designed specifically for portfolio management problem.
- Differing from the general-purpose reinforcement learning algorithms, it has similar efficiency and robustness to supervized learning.
- This is because we formulate the problem as an immediate reward optimization problem regularised by transaction cost, which does not require a monte-carlo or bootstrapped estimation of the gradients.
- One can configurate the topology, training method or input data in a separate json file. The training process will be recorded and user can visualize the training using tensorboard. Result summary and parallel training are allowed for better hyper-parameters optimization.
- The financial-model-based portfolio management algorithms are also embedded in this library for comparision purpose, whose implementation is based on Li and Hoi's toolkit OLPS.
Note that this library is a part of our main project, and it is several versions ahead of the article.
- In this version, some technical bugs are fixed and improvements in hyper-parameter tuning and engineering are made.
- The most important bug in the arxiv v2 article is that the test time-span mentioned is about 30% shorter than the actual experiment. Thus the volumn-observation interval (for asset selection) overlapped with the backtest data in the paper.
- With new hyper-parameters, users can train the models with smaller time durations.(less than 30 mins)
- All updates will be incorporated into future versions of the paper.
- Original versioning history, and internal discussions, including some in-code comments, are removed in this open-sourced edition. These contains our unimplemented ideas, some of which will very likely become the foundations of our future publications
Please check out User Guide