ZhengyaoJiang/PGPortfolio
PGPortfolio: Policy Gradient Portfolio, the source code of "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem"(https://arxiv.org/pdf/1706.10059.pdf). observed · 2026-08-28
Health v2 · maintenance only
23/100
- Activity 0
- Release rhythm 8
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 3216
- days_rel: n/a
- days_push: 1789
- n_releases_24m: 0
Adoption not part of the score
1849 stars · 757 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
PGPortfolio is a Python library implementing a deep reinforcement learning framework for financial portfolio management, based on the paper 'A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem'. It includes a configurable training toolkit with TensorBoard visualization, parallel training, and embedded financial-model-based portfolio algorithms for comparison.
Use cases
- train a deep reinforcement learning agent to manage a cryptocurrency portfolio
- backtest portfolio management strategies against financial-model-based baselines
- research policy gradient methods for immediate-reward portfolio optimization
- run hyperparameter optimization experiments for trading policies
- compare RL-based portfolio selection with online portfolio selection algorithms
- visualize and log training runs with tensorboard
When to choose
- you are doing academic research on RL for portfolio management and want a reproducible paper implementation
- you need a configurable framework to experiment with policy network topologies and training data
- you want built-in classical portfolio strategies (from OLPS) as baselines for comparison
When to avoid
- you need a production trading system with live broker integrations and low-latency execution
- you require modern maintained TensorFlow 2.x or PyTorch support, since the code targets older TensorFlow 1.x
- you want plug-and-play stock or options trading out of the box without writing market adapters yourself
Facets
library · maturity maintenance
machine-learning reinforcement-learning trading data-science benchmarking machine-learning fintech data-science windows python portfolio-management deep-reinforcement-learning policy-gradient cryptocurrency-trading backtesting tensorboard research-toolkit cryptocurrency algorithms linux
1 source
- readme: https://github.com/ZhengyaoJiang/PGPortfolio · fetched 2026-08-28 · 26ac0eaf6469
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| ZhengyaoJiang/PGPortfolio | main | 23 |
For agents
markdown · JSON · MCP: product_card(name="ZhengyaoJiang/PGPortfolio")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem