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  3. Investbrain
Investbrain logo
Health: ActiveRecent health check succeeded.Last checked 9/22/2026, 1:02:10 AM

Investbrain

User RatingsBe the first to rate and review this MCP server! Enrichment pendingWe haven’t run our AI enrichment pass on this listing yet, so the overview, use cases, and FAQ below may be sparse or missing. We work through the catalog over time — check back soon.
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Investment discipline MCP server with RAG, behavior detection, journaling, and market data.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to CursorAdd to VS Code
Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "investbrain": {
      "command": "uvx",
      "args": [
        "pyqlib"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing Alternatives💰 More in Finance & Fintech

Documentation Overview

InvestBrain — 投资第二大脑

有经验投资者的纪律自主工具,解决"知行合一"问题 不是投顾,不给建议,而是"镜子" + "纪律锚点"

License: AGPL v3 Python 3.10+ MCP Desktop

English README llms.txt 行为模式挖掘

想法记录 × 投资 RAG × 记忆系统 × 提醒触发 × 行为模式挖掘


5 分钟快速体验

两种使用方式:桌面客户端(推荐) 或 MCP Server。

方式 A:桌面客户端(Windows,推荐)

  1. 下载并安装 InvestBrain_0.1.2_x64_en-US.msi(见 Releases)
  2. 打开桌面应用,即见「大脑已加载」状态条与试聊引导
  3. 想对话时在设置页填入自己的 LLM Key,即可走真实对话链路

桌面端数据存储在 %APPDATA%\InvestBrain\data,设置页可直接查看数据位置。

方式 B:MCP Server(任意 MCP 客户端)

bash
# 1. 安装 MCP Server 依赖
cd src/mcp_server && pip install -r requirements.txt

# 2. 配置你自己的 LLM API Key(见下文「你的 Key,自己填」)
cp .env.example .env
# 编辑 .env,把 DEEPSEEK_API_KEY 填成你从 DEEPSEEK 后台拿到的 key

# 3. 启动 MCP Server
python server.py

首次启动提示:向量库(Chroma + ONNX Embedding)首次会下载约 80MB 模型文件。若希望跳过向量库、纯工具调用体验,可保持 server.py 中相关注释不变;如需完整 RAG 体验,下载后取消 server.py 内的注释行。

启动后 Server 会以 stdio 方式接受 MCP 客户端调用。配置 Claude Desktop 见下方 Claude Desktop 接入。


你的 Key,自己填

重要:本仓库不预置任何用户的 API key。data/config/llm.json 中 api_key 默认为 null(不是真的 key 占位);.env.example 只是模板;webhook.json 是空 schema。

你需要从下面这些官方后台自己申请 / rotate:

服务用途后台地址
DeepSeek API主力 LLM(必需)https://platform.deepseek.com → API Keys
Tushare ProA 股财务/历史数据(可选)https://tushare.pro/register
DashScope阿里通义备选 LLM(可选)https://dashscope.console.aliyun.com
Against Finance另类数据(可选)https://www.against.com
飞书机器人通知(可选)https://open.feishu.cn → 机器人 → Webhook

4 个 key 都写在哪里(都不会进 git)

  • MCP 启动必须:DEEPSEEK_API_KEY 环境变量(推荐 User 级系统环境变量,或 CLAUDE.md / .env)
  • 可选:TUSHARE_TOKEN / DASHSCOPE_API_KEY / AGAINST_API_KEY(环境变量)
  • 飞书 webhook:data/config/webhook.json 里的 feishu 字段(已被 .gitignore 保护)

安全规则

  • 你的 key 是你的——不要 share、不要 commit、不要发到任何对话里
  • key 进 git 历史(如被 commit 过的老版本)即使后来 rotate 仍可能被人滥用 → 发现泄露立即去对应平台 rotate
  • 仓库已有 .gitignore 保护 data/config/*.json(含 llm/webhook 配置)+ data/memory/*.db(用户记忆库,按 CLAUDE.md 决策"用户数据本地存储,不上传云端"),不要 ! 取消忽略

备注:仓库历史曾 commit 过密钥占位(commit 22e6c72),如已 push 远端,老 key 在 clone 历史里仍可被读——清历史成本高、通常接受 + rotate 即可


快速开始:推送配置

从零到收到第一条微信推送,约 3 分钟。无需阅读任何文档。

方式一:一键脚本(推荐,Windows)

在仓库根目录打开 PowerShell,运行:

powershell
powershell -ExecutionPolicy Bypass -File scripts/configure_pushplus.ps1

脚本会交互式引导你完成全部步骤:显示 PushPlus 获取 token 的操作步骤 → 粘贴 token(自动校验格式)→ 写入 data/config/webhook.json 并启用 pushplus 渠道 → 自动调用 send_notification 发送测试消息 → 打印成功/失败原因。

方式二:对话内配置(MCP 客户端内)

直接在任意 MCP 客户端中让 AI 帮你配置:

Code
1. 查看当前配置状态:notify_get_notifier_config()
2. 配置 PushPlus 并发送测试:notify_configure_notifier(channel="pushplus", token="你的token", enabled=true, send_test=true)

PushPlus token 获取步骤

  1. 微信扫码关注公众号:https://www.pushplus.plus
  2. 进入「个人中心」→ 复制页面上的 token
  3. 填入上述任一方式

验证成功:收到测试消息,或 notify_get_notifier_config() 返回 "enabled_channels": ["pushplus"]。未配置渠道时,提醒触发路径(price_checker / scheduler)返回中会携带 setup_guide 引导信息,而不会静默失败。

安全说明:data/config/webhook.json 已被 .gitignore 保护,token 不会进入 git;请勿把 token 发到任何对话或提交到仓库。


它解决什么问题

核心问题:投资者被叙事带着走,操作没纪律,事后后悔。

常见痛点InvestBrain 的应对
看到某只股票大涨就追高强制记录买入理由 + 关联历史相似决策
跌了恐慌割肉触发 RAG 检索大师对"恐慌"的看法
同一类错误反复犯行为模式挖掘自动识别"追涨杀跌"模式
重要时间/价格点位忘记价格/时间/条件监控 + 飞书推送
想不起自己过去的判断记忆系统 + 决策卡片回溯

核心能力

功能说明
想法记录用户说一句话,AI 解析 + 关联历史 + 生成卡片
投资 RAG大师思想检索问答(16 位投资大师 × 5 个核心概念)
记忆系统用户画像、历史决策、行为模式挖掘
提醒触发价格/时间/条件监控,支持飞书/钉钉/Bark 多通道推送
行为模式挖掘自动发现投资者的偏差模式

示例对话

下面是三个核心价值场景的真实对话流(任意 MCP 客户端均可使用):

场景 1:RAG 问答 — 大师思想碰撞

用户:段永平怎么看"快钱"和"慢钱"?

调用:rag_ask_investment(question="段永平怎么看快钱和慢钱")

AI 返回: 段永平的核心观点是"快钱"看似机会多但风险敞口大,"慢钱"依靠复利与确定性。 在「段永平」语料中相关论述 12 条,节选:

"投资赚钱的速度不应该快过学习的速度。" "敢重仓的就是你能看懂的生意,剩下的不是机会。" 关联概念:能力圈、安全边际、复利。

场景 2:行为模式分析 — 找出反复犯的错

用户:我最近的交易有什么问题?

调用:memory_get_behavior_patterns()

AI 返回: 检测到你的两个行为模式(过去 30 天,11 笔交易):

  1. 追涨模式(置信度 0.83) 触发样本:买入 NVDA/TSLA 在单日涨幅 > 5% 后的 D+1/D+2,胜率 25%,平均收益 -3.2% 建议:买入前强制查看 rag_ask_investment(question="追高的代价")

  2. 恐慌割肉模式(置信度 0.71) 触发样本:持仓亏损 > 15% 后的 5 日内卖出,后续 30 日反弹 67% 建议:触发时自动推送飞书提醒 + 大师原话引用

场景 3:智能提醒 — 纪律锚点

用户:NVDA 到 950 美元时提醒我重新评估。

调用:reminder_set_reminder(condition={ticker:"NVDA", operator:"<=", threshold:950, action:"review"})

AI 返回: 提醒已建立:NVDA <= 950 USD 时触发"复盘评估"。 触发后将自动拉取:当前价格、你对此票的历史决策卡片、相关大师语录。 提醒已同步通知通道(如已配置)。


Claude Desktop 接入

将 InvestBrain 接入 Claude Desktop(macOS / Windows 通用):

配置文件位置:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Windows: %APPDATA%\Claude\claude_desktop_config.json

配置内容(路径按你 clone 的实际位置修改):

config.json
{
  "mcpServers": {
    "investbrain": {
      "command": "python",
      "args": [
        "D:/claudework/invest-brain/src/mcp_server/server.py"
      ],
      "env": {
        "DEEPSEEK_API_KEY": "sk-your-key-here"
      }
    }
  }
}

配置完成后重启 Claude Desktop,会在工具栏出现"投资助手"图标(39 个工具,与 server.json 注册的工具清单一致)。


开源协议与路线图

本项目采用 GNU Affero General Public License v3.0 (AGPLv3) 授权。简言之:你可以自由使用、修改和分发,但通过网络提供服务的衍生作品也必须以相同协议开源。详见 LICENSE。

层级功能状态
免费层本地 MCP Server✅ 已上线
16 位大师 RAG 问答✅ 已上线
行为模式检测✅ 已上线
提醒系统✅ 已上线
无限记忆存储✅ 已上线
个人版云端周报/月报(AI 生成)计划中(¥39/月)
跨设备数据同步计划中
团队版团队协作与共享知识库计划中(¥199/月)

架构

Code
src/mcp_server/            # MCP Server(39 个工具)
├── server.py               # 主入口
├── tools/                  # 工具集
│   ├── thought_tools.py    # 想法记录
│   ├── rag_tools.py        # 投资 RAG
│   ├── memory_tools.py     # 记忆系统
│   ├── reminder_tools.py   # 提醒系统
│   ├── pattern_tools.py    # 行为模式
│   ├── report_tools.py     # 周报/月报
│   ├── roundtable_tools.py # 大师圆桌
│   └── notifier_tools.py   # 多通道通知
├── datasources/            # 数据源
│   ├── akshare_datasource.py
│   └── tushare_datasource.py
├── knowledge/              # 知识库
│   ├── vector_store.py     # Chroma 向量存储
│   └── graph_client.py     # 图存储
├── memory/                 # 记忆存储
├── patterns/               # 行为模式挖掘
├── llm/                    # LLM 客户端
│   ├── llm_router.py       # 通用 LLM 路由
│   ├── providers.py        # Provider 配置
│   └── deepseek_client.py
└── api_server.py           # REST API (LLM 配置)

data/
├── graph/                  # 知识图谱(16 位大师 + 概念)
├── knowledge/
│   └── vectors/            # 向量索引(Chroma)
├── memory/                 # 用户记忆
├── cards/                  # 想法卡片
├── reminders/              # 提醒条件
└── config/                 # 配置文件

MCP Tools(40 个)

json
// 想法记录(3)
"thought_record_thought(text)",
"thought_search_memories(query)",
"thought_get_thought_cards(ticker)",

// 投资 RAG(4)
"rag_ask_investment(question)",
"rag_get_master_view(master, topic)",
"rag_search_knowledge(query)",
"rag_search_reports(query, top_k)",

// 记忆系统(3)
"memory_get_user_profile()",
"memory_record_decision(data)",
"memory_get_behavior_patterns()",

// 提醒系统(3)
"reminder_set_reminder(condition)",
"reminder_get_reminders()",
"reminder_delete_reminder(id)",

// 行为模式(3)
"pattern_run_pattern_detection()",
"pattern_get_pattern_summary()",
"pattern_get_pattern_report(id)",

// 周报/月报(1)
"report_run_scheduled_report(range)",

// 大师圆桌(1)
"invest_roundtable(question)",

// 通知配置(2)
"notify_configure_notifier(channel, webhook_url)",
"notify_get_notifier_config()",

// 行情数据 — AKShare(5)
"market_get_stock_quote(ticker)",
"market_get_stock_history(ticker, period)",
"market_get_index_components(index_code)",
"market_get_valuation(ticker)",
"market_get_market_sentiment()",

// 行情数据 — Tushare(15)
"tushare_get_daily_price(ts_code)",
"tushare_get_weekly_price(ts_code)",
"tushare_get_realtime_quote(ts_code)",
"tushare_get_index_daily(index_code)",
"tushare_get_financial_indicator(ts_code)",
"tushare_get_income_statement(ts_code)",
"tushare_get_balance_sheet(ts_code)",
"tushare_get_cash_flow(ts_code)",
"tushare_get_index_components(index_code)",
"tushare_get_industry_classification(ts_code)",
"tushare_get_valuation_multi(ts_code)",
"tushare_get_market_top_movers()",
"tushare_get_stock_pledge_status(ts_code)",
"tushare_convert_ticker(ticker)",
"tushare_check_token_status()"

完整工具列表见 src/mcp_server/tools/。


知识库

16 位投资大师: 巴菲特、芒格、段永平、霍华德·马克斯、李录、格雷厄姆、达莫达兰、阿克曼、凯西·伍德、迈克尔·伯里、帕伯莱、塔勒布、林奇、费雪、德鲁肯米勒

5 个核心概念: 护城河、安全边际、能力圈、第二层思维、非对称风险

行业知识: 28 个申万行业深度研究框架(向量库内检索,不公开原文,保护核心方法论 IP)


数据源

数据源用途状态
AKShare实时行情✅
Tushare财务/历史数据✅
Chroma向量索引✅
SQLite本地记忆✅

开发

bash
# 启动 REST API Server(用于 LLM 配置)
python src/mcp_server/api_server.py
# 访问 http://localhost:8000/api/llm/config

# 启动前端(Next.js 落地页 + 设置面板)
npm install
npm run dev
# 访问 http://localhost:3000

测试:

bash
pytest tests/

相关项目

项目用途
invest-buddy-pet入门陪伴产品(人格测试)
mangoview超级系统站(专业分析)

Read the full README →View source on GitHub →

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Frequently Asked Questions about Investbrain

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "investbrain": { "command": "uvx", "args": ["pyqlib"] } }

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Technical Specs & Signals

Category💰Finance & Fintech
More technical detailsExpand ▾
TransportSTDIO
RuntimePython
Last updatedSep 10, 2026
5/5 checks healthy over the last 42d
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Last commit14d ago
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Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

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Scanned 2d ago via OSV.dev · pyqlib (PyPI)

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