wangshub/RL-Stock resource
📈 如何用深度强化学习自动炒股 observed · 2026-08-28
Health v2 · maintenance only
32/100
- Activity 0
- Release rhythm 35
- Longevity 100
Flags: no_releases
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 2353
- days_rel: n/a
- days_push: 1380
- n_releases_24m: 0
Adoption not part of the score
3738 stars · 800 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
A tutorial-style project demonstrating how to use deep reinforcement learning (PPO via stable-baselines) to simulate automated stock trading on Chinese A-share market data. It provides an OpenAI Gym trading environment, data fetching via baostock, and Jupyter Notebook experiments.
Use cases
- learn reinforcement learning with a stock trading example
- simulate automated stock trading with deep RL
- build a custom OpenAI Gym trading environment
- experiment with PPO on financial time series
- fetch Chinese stock market data with baostock
- backtest an RL trading agent on historical data
When to choose
- you want a hands-on educational introduction to RL-based trading
- you need a simple Gym environment for stock trading experiments
- you are working with Chinese A-share market data from baostock
When to avoid
- you need production-grade, reliable automated trading software
- you expect guaranteed profits or real-money trading support
- you need actively maintained code or recent market data pipelines
Facets
learning-resource · maturity maintenance
reinforcement-learning machine-learning data-science trading machine-learning fintech data-science tutorials python stock-trading openai-gym ppo quantitative-finance jupyter-notebook baostock china-stock-market
1 source
- readme: https://github.com/wangshub/RL-Stock · fetched 2026-08-28 · 82618513e6c0
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| wangshub/RL-Stock | main | 32 |
For agents
markdown · JSON · MCP: product_card(name="wangshub/RL-Stock")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem