# wangshub/RL-Stock

📈 如何用深度强化学习自动炒股

Repository: https://github.com/wangshub/RL-Stock
Canonical: https://ross.abutalabs.com/products/rl-stock
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2022-11-22T05:26:28+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2353, "days_push": 1380, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3738, forks 800 (observed 2026-08-28T04:08:15.890677+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: maintenance
- function: reinforcement-learning, machine-learning, data-science, trading
- domain: machine-learning, fintech, data-science, tutorials
- platform: python
- tags: stock-trading, openai-gym, ppo, quantitative-finance, jupyter-notebook, baostock, china-stock-market

## Member repositories
- wangshub/RL-Stock (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:15.890677+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:29:09.081326+00:00, confidence not recorded.
  - readme: https://github.com/wangshub/RL-Stock (fetched 2026-08-28T04:08:15.890677+00:00, sha 82618513e6c0)
- Data as of 2026-08-30T08:39:29.467469+00:00.