RRepoGEO

REPOGEO REPORT · LITE

wangshub/RL-Stock

Default branch master · commit 22d2cbf8 · scanned 5/22/2026, 11:28:09 AM

GitHub: 3,660 stars · 798 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface wangshub/RL-Stock, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • hightopics#1
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    reinforcement-learning, deep-reinforcement-learning, stock-trading, algorithmic-trading, finance, quantitative-finance, machine-learning, openai-gym, python
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/wangshub/RL-Stock (or a dedicated project page if one exists)
  • lowreadme#3
    Add a concise, direct problem statement to the README's opening

    Why:

    CURRENT
    # 📈 如何用深度强化学习自动炒股

## 💡 初衷

最近一段时间，受到新冠疫情的影响，股市接连下跌，作为一棵小白菜兼小韭菜，竟然产生了抄底的大胆想法，拿出仅存的一点私房钱梭哈了一把.

第二天，暴跌，俺加仓

第三天，又跌，俺加仓

第三天，又跌，俺又加仓...

一番错误操作后，结果惨不忍睹，第一次买股票就被股市一段暴打，受到了媳妇无情的嘲讽。痛定思痛，俺决定换一个思路：**如何用深度强化学习来自动模拟炒股？** 实验验证一下能否获得收益.
    COPY-PASTE FIX
    # 📈 如何用深度强化学习自动炒股

本项目旨在利用深度强化学习（Deep Reinforcement Learning, DRL）技术，构建一个自动化的股票交易代理，以实现智能化的投资决策和收益最大化。通过模拟股票市场环境，DRL代理能够学习并执行买入、卖出、持有等操作，从而探索在复杂金融市场中获取收益的策略。

## 💡 初衷

最近一段时间，受到新冠疫情的影响，股市接连下跌，作为一棵小白菜兼小韭菜，竟然产生了抄底的大胆想法，拿出仅存的一点私房钱梭哈了一把.

第二天，暴跌，俺加仓

第三天，又跌，俺加仓

第三天，又跌，俺又加仓...

一番错误操作后，结果惨不忍睹，第一次买股票就被股市一段暴打，受到了媳妇无情的嘲讽。痛定思痛，俺决定换一个思路：**如何用深度强化学习来自动模拟炒股？** 实验验证一下能否获得收益.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface wangshub/RL-Stock
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
QuantConnect (Lean)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. QuantConnect (Lean) · recommended 1×
  2. Zipline · recommended 1×
  3. Backtrader · recommended 1×
  4. Alpaca-py (Alpaca API) · recommended 1×
  5. TensorFlow · recommended 1×
  • CATEGORY QUERY
    How can I build an AI agent for automated stock market trading decisions?
    you: not recommended
    AI recommended (in order):
    1. QuantConnect (Lean)
    2. Zipline
    3. Backtrader
    4. Alpaca-py (Alpaca API)
    5. TensorFlow
    6. PyTorch
    7. Scikit-learn
    8. Pandas

    AI recommended 8 alternatives but never named wangshub/RL-Stock. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a framework for applying reinforcement learning to optimize financial portfolio management.
    you: not recommended
    AI recommended (in order):
    1. OpenAI Gym (openai/gym)
    2. Stable Baselines3 (DLR-RM/stable-baselines3)
    3. Ray RLlib (ray-project/ray)
    4. FinRL (AI4Finance-Foundation/FinRL)
    5. TF-Agents (tensorflow/agents)
    6. TensorFlow (tensorflow/tensorflow)
    7. PyTorch-Ignite (pytorch/ignite)
    8. PyTorch (pytorch/pytorch)

    AI recommended 8 alternatives but never named wangshub/RL-Stock. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of wangshub/RL-Stock?
    pass
    AI named wangshub/RL-Stock explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts wangshub/RL-Stock in production, what risks or prerequisites should they evaluate first?
    pass
    AI named wangshub/RL-Stock explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo wangshub/RL-Stock solve, and who is the primary audience?
    pass
    AI named wangshub/RL-Stock explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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wangshub/RL-Stock — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite