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fund-ai-assistant基金 AI 助理

Agent Skill

fund-ai-assistant 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

3,569

周安装

143

GitHub Stars

1

下载量

1,155
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:fund-ai-assistant(基金 AI 助理)
来源仓库:https://github.com/tempest-01/fund-ai-assistant
安装命令:
openclaw skills install fund-ai-assistant
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install fund-ai-assistant

简介

基金投资组合跟踪器,具有人工智能分析、多代理辩论、技术指标(VaR/Sortino/Calmar)、宏观监控和再平衡警报。

SKILL.md

name
fund-ai-assistant
display_name
Fund AI Assistant
description
Fund portfolio tracker with AI analysis, multi-agent debate, technical indicators (VaR/Sortino/Calmar), macro monitoring, and rebalancing alerts.

Fund AI Assistant

Version: 3.0 | License: MIT-0 | LLM: Any OpenAI-compatible provider


Overview

A local-first fund investment assistant running entirely on your machine. Fetches data from East Money, analyzes with any LLM provider, and supports scheduled tasks via OpenClaw or crontab.

⚠️ Security Notice: This skill requires LLM_MODEL + LLM_API_KEY. Use a dedicated API key. See Section 7 for all security considerations.

1. Features

FeatureDescription
📊 Technical AnalysisMACD / KDJ / RSI / Bollinger Bands / MA + VaR / Sortino / Calmar
🤖 AI Quantitative AnalysisAny LLM (OpenAI-compatible), outputs buy/sell/hold with price targets
⚖️ Multi-Agent Debate6 roles → game-theory judge verdict
📋 Portfolio RebalancingDetects allocation drift, outputs precise rebalancing instructions
🎯 Entry TimingRSI + Bollinger + trend composite score
🌡️ Correlation HeatmapPairwise correlation matrix for diversification
🌍 Macro Event MonitorCSI300 / USD-CNY / FOMC / LPR alerts

2. Environment Variables

Required

VariableDescription
LLM_MODELModel name, e.g. gpt-4o-mini
LLM_API_KEYYour LLM API key

Optional

VariableDescription
LLM_API_BASE⚠️ Custom API URL — if untrusted, your key and data go there. Use endpoints you control. Default: https://api.openai.com/v1
TAVILY_API_KEYTavily API for real-time macro search
FUND_SCENE_DIRDirectory with optional .md scene templates (default: ./scenes/)
PUSH_WEBHOOK_URLGeneric HTTP POST webhook (WeCom / Feishu / Slack)
BARK_PUSH_URLiOS Bark notification URL
PUSH_EMAILTarget email for SMTP push
SMTP_HOST/PORT/USER/PASSSMTP configuration (used with PUSH_EMAIL)
QQ_WEBHOOK_URLQQ bot HTTP interface (go-cqhttp / Lagrange)

Setup Example

export LLM_MODEL="gpt-4o-mini"
export LLM_API_KEY="sk-xxx"

# Optional
export TAVILY_API_KEY="tvly-xxx"
export PUSH_WEBHOOK_URL="https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=YOUR_KEY"

3. Installation

# 1. Clone
git clone https://github.com/tempest-01/fund-ai-assistant.git
cd fund-ai-assistant

# 2. Install optional dependencies (charts)
pip install -r requirements.txt

# 3. Configure
cp config.example.json config.json
cp positions.example.json positions.json

# 4. Set environment variables (see Section 2)
export LLM_MODEL="your_model"
export LLM_API_KEY="your_key"

# 5. Verify
python3 analyzer.py list
python3 analyzer.py analyze

4. Script Reference

ScriptFunctionUsage
analyzer.pyMain entry, tracking + analysispython3 analyzer.py list
ai_analysis.pyAI quantitative analysispython3 ai_analysis.py
debate_analyzer.pyMulti-agent debatepython3 debate_analyzer.py <code>
rebalance.pyPortfolio drift detectionpython3 rebalance.py
recommend.pyEntry timing suggestionspython3 recommend.py
event_monitor.pyMacro event monitorpython3 event_monitor.py
correlation_v2.pyCorrelation heatmappython3 correlation_v2.py
fund_api.pyEast Money data APIpython3 fund_api.py
technical.pyTechnical indicatorspython3 technical.py
chart_generator.pyPIL chart generationpython3 chart_generator.py
positions.pyPosition record managementpython3 positions.py
macro_fetcher.pyMacro data fetcherpython3 macro_fetcher.py
strip_color.pyANSI strip for cron`cmd \python3 strip_color.py`
llm.pyUnified LLM interfacefrom llm import get_llm_config, call_llm

5. Scheduled Tasks (Reference)

# OpenClaw users
openclaw cron add --cron "0 8 * * 1-5" \
  --message "cd /path/to/fund-ai-assistant && python3 event_monitor.py --dry-run"

openclaw cron add --cron "30 9 * * 1-5" \
  --message "cd /path/to/fund-ai-assistant && python3 analyzer.py analyze"

# crontab users
0 8 * * 1-5 cd /path/to/fund-ai-assistant && python3 event_monitor.py >> /var/log/fund.log 2>&1

6. File Structure

fund-ai-assistant/
├── .gitignore
├── _meta.json              # Registry metadata
├── SKILL.md               # This file
├── README.md              # Full bilingual documentation
├── config.example.json     # Tracking list template
├── positions.example.json  # Position record template
├── requirements.txt       # Optional: Pillow / numpy / matplotlib
├── llm.py                # Unified LLM interface
├── analyzer.py            # Main entry
├── ai_analysis.py         # AI quantitative analysis
├── debate_analyzer.py    # Multi-agent debate
├── rebalance.py           # Portfolio drift detection
├── recommend.py          # Entry timing
├── event_monitor.py      # Macro event monitor
├── correlation_v2.py     # Correlation heatmap
├── fund_api.py           # East Money API
├── technical.py          # Technical indicators
├── chart_generator.py    # PIL chart generation
├── macro_fetcher.py      # Macro data
├── positions.py          # Position records
├── strip_color.py        # ANSI color strip
└── assets/             # Chart output (auto-created)

7. Security Notes

Read carefully before installing and running.

Required Credentials

  • LLM_MODEL and LLM_API_KEY are required. Use a dedicated API key, not a high-value production key.
  • Before first run, inspect llm.py and fund_api.py to confirm no credential exfiltration.

Filesystem Access

  • FUND_SCENE_DIR: The skill reads .md template files from the directory you specify.

- Do NOT point it at system directories, home directories, or folders containing secrets. - If unset, defaults to {skill_dir}/scenes/ (which is created empty). - Only .md files in that directory are read.

Network Access

  • East Money APIs: fundgz.1234567.com.cn, api.fund.eastmoney.com — public fund data.
  • Tavily (if TAVILY_API_KEY set): Real-time macro search.
  • Push endpoints (if configured): Analysis summaries are sent to the URLs you provide.

Push Channels

If any of these are set, analysis output will be transmitted externally:

VariableTransmission
PUSH_WEBHOOK_URLHTTP POST to your webhook URL
BARK_PUSH_URLGET request to your Bark URL
PUSH_EMAILSMTP email to your address
QQ_WEBHOOK_URLHTTP POST to your QQ bot
Use endpoints you control. Do not set these with untrusted third-party URLs.

Data Privacy

  • All fund data is fetched from East Money on demand; no persistent storage of market data.
  • Position records (positions.json) are stored locally in the skill directory only.
  • LLM API key is sent only to the configured LLM_API_BASE endpoint.
  • No telemetry, no external analytics, no data sent to third parties without explicit configuration.

Recommended Precautions

  1. Use a dedicated LLM API key — not your main production key.
  2. ⚠️ Inspect LLM_API_BASE — if set to an untrusted endpoint, your API key and fund data will be sent there. Only use https://api.openai.com/v1 or endpoints you control.
  3. Review llm.py and fund_api.py before first run.
  4. Run in an isolated environment (container or VM) on first use.
  5. Do not set FUND_SCENE_DIR to sensitive directories.
  6. Do not share your LLM_API_KEY or push endpoint URLs.

8. Inspiration & Attribution

  • astrbot_plugin_fund_analyzer — Multi-agent debate framework inspiration; adapted from stock to fund analysis with added portfolio management features.
  • OpenClaw — Scheduling and notification infrastructure.
  • East Money (东方财富) — Fund price and history data source.
  • Tencent Finance (腾讯财经) — CSI300 real-time data source.

*This skill was developed with AI assistance.*

适合场景

01

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02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

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能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

84.98%
按下载量换算982

安全审计

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安装前确认

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