股票智能分析系统：基于AI的A股港股美股自动化投资决策看板

daily-stock-analysis by aradotso/trending-skills

320 周安装量

10 GitHub Stars

GitHub

安装命令

npx skills add https://github.com/aradotso/trending-skills --skill daily-stock-analysis

AI/机器学习自动化金融科技

🇨🇳中文介绍

Daily Stock Analysis (股票智能分析系统)

Skill by ara.so — Daily 2026 Skills collection.

基于大语言模型的A股、港股和美股分析系统。自动获取行情、新闻和基本面数据，生成包含买卖目标的AI决策看板，并通过 GitHub Actions 按计划将结果推送到微信/飞书/Telegram/Discord/邮箱——零服务器成本。

功能概述

AI决策看板 : 一行核心结论 + 精确的买入/卖出/止损价格 + 每只股票的检查清单
多市场覆盖 : A股（中国大陆）、港股、美股 + 指数（SPX, DJI, IXIC）
数据源 : AkShare、Tushare、YFinance 用于行情；Tavily/SerpAPI/Brave 用于新闻
LLM后端 : 通过 LiteLLM（统一接口）支持 Gemini、OpenAI、Claude、DeepSeek、Qwen
推送渠道 : 企业微信、飞书、Telegram、Discord、钉钉、邮箱、PushPlus
自动化 : GitHub Actions 定时任务，无需服务器
Web界面 : 投资组合管理、历史记录、回测、智能助手问答
智能助手 : 支持多轮对话的策略问答，内置11种策略（均线交叉、艾略特波浪等）

安装

方法一：GitHub Actions（推荐，零成本）

步骤 1：Fork 仓库

https://github.com/ZhuLinsen/daily_stock_analysis

步骤 2：配置 Secrets (Settings → Secrets and variables → Actions)

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方法二：本地 / Docker

git clone https://github.com/ZhuLinsen/daily_stock_analysis
cd daily_stock_analysis
cp .env.example .env
# 编辑 .env 文件，填入你的密钥
pip install -r requirements.txt
python main.py

docker build -t stock-analysis .
docker run --env-file .env stock-analysis

Docker Compose：

docker-compose up -d

`.env` 文件（本地）

# LLM - 选择一个或多个
GEMINI_API_KEY=your_gemini_key
OPENAI_API_KEY=your_openai_key
OPENAI_BASE_URL=https://api.deepseek.com/v1
OPENAI_MODEL=deepseek-chat
AIHUBMIX_KEY=your_aihubmix_key

# 股票列表（逗号分隔）
STOCKS=600519,300750,AAPL,TSLA,00700.HK

# 通知
TELEGRAM_BOT_TOKEN=your_bot_token
TELEGRAM_CHAT_ID=your_chat_id

# 可选设置
REPORT_TYPE=full           # simple | full | brief
ANALYSIS_DELAY=10          # 股票分析间隔秒数（避免限流）
MAX_WORKERS=3              # 并发分析线程数
SINGLE_STOCK_NOTIFY=false  # 每只股票分析完成立即推送
NEWS_MAX_AGE_DAYS=3        # 忽略超过 N 天的旧新闻

多通道 LLM（高级）

LLM_CHANNELS=gemini,deepseek,claude
LLM_GEMINI_PROTOCOL=google
LLM_GEMINI_API_KEY=your_key
LLM_GEMINI_MODELS=gemini-2.0-flash,gemini-1.5-pro
LLM_GEMINI_ENABLED=true

LLM_DEEPSEEK_PROTOCOL=openai
LLM_DEEPSEEK_BASE_URL=https://api.deepseek.com/v1
LLM_DEEPSEEK_API_KEY=your_key
LLM_DEEPSEEK_MODELS=deepseek-chat
LLM_DEEPSEEK_ENABLED=true

股票分组（将不同股票发送到不同邮箱）

STOCK_GROUP_1=600519,300750,000858
EMAIL_GROUP_1=investor1@example.com

STOCK_GROUP_2=AAPL,TSLA,NVDA
EMAIL_GROUP_2=investor2@example.com

MARKET_REVIEW=cn      # cn | us | both
# cn = A股三阶段回顾策略
# us = 美股市场状态策略（risk-on/neutral/risk-off）
# both = 两个市场都分析

关键命令（CLI）

# 立即运行完整分析
python main.py

# 仅分析特定股票
STOCKS=600519,AAPL python main.py

# 运行 Web 仪表板
python web_app.py
# 访问地址 http://localhost:5000

# 使用 Docker 运行（需 env 文件）
docker run --env-file .env stock-analysis python main.py

# 运行计划模式（等待定时任务，然后运行）
SCHEDULE_RUN_IMMEDIATELY=true python main.py

GitHub Actions 工作流

工作流文件 .github/workflows/stock_analysis.yml 按计划运行：

# 默认计划 - 可在工作流文件中自定义
on:
  schedule:
    - cron: '30 1 * * 1-5'   # 北京时间 9:30 AM (UTC+8) 工作日
  workflow_dispatch:          # 手动触发

修改计划： 编辑 .github/workflows/stock_analysis.yml 中的 cron 表达式。

通过 GitHub CLI 添加 secrets：

gh secret set GEMINI_API_KEY --body "$GEMINI_API_KEY"
gh secret set STOCKS --body "600519,300750,AAPL,TSLA"
gh secret set TELEGRAM_BOT_TOKEN --body "$TG_TOKEN"
gh secret set TELEGRAM_CHAT_ID --body "$TG_CHAT_ID"

编程式分析（Python）

# 以编程方式分析特定股票
import asyncio
from analyzer import StockAnalyzer

async def analyze():
    analyzer = StockAnalyzer()
    
    # 分析单只A股
    result = await analyzer.analyze_stock("600519")  # 茅台
    print(result['conclusion'])
    print(result['buy_price'])
    print(result['stop_loss'])
    print(result['target_price'])

asyncio.run(analyze())

自定义通知集成

from notifier import NotificationManager

notifier = NotificationManager()

# 发送到 Telegram
await notifier.send_telegram(
    token=os.environ['TELEGRAM_BOT_TOKEN'],
    chat_id=os.environ['TELEGRAM_CHAT_ID'],
    message="📈 分析完成\n600519: 建议在 1680 买入，止损: 1620，目标: 1800"
)

# 发送到飞书 webhook
await notifier.send_feishu(
    webhook_url=os.environ['FEISHU_WEBHOOK_URL'],
    content=analysis_report
)

使用智能助手 API

import requests

# 向股票助手询问策略问题
response = requests.post('http://localhost:5000/api/agent/chat', json={
    "message": "600519现在适合买入吗？用均线金叉策略分析",
    "stock_code": "600519",
    "strategy": "ma_crossover"  # ma_crossover, elliott_wave, chan_theory 等
})

print(response.json()['reply'])

回测分析准确性

import requests

# 触发对某只股票的回测
response = requests.post('http://localhost:5000/api/backtest', json={
    "stock_code": "600519",
    "days": 30  # 评估过去30天的AI预测
})

result = response.json()
print(f"方向准确性: {result['direction_accuracy']}%")
print(f"止盈命中率: {result['tp_hit_rate']}%")
print(f"止损命中率: {result['sl_hit_rate']}%")

从图片导入股票（视觉 LLM）

import requests

# 上传股票列表截图供AI提取
with open('watchlist_screenshot.png', 'rb') as f:
    response = requests.post(
        'http://localhost:5000/api/stocks/import/image',
        files={'image': f}
    )

stocks = response.json()['extracted_stocks']
# 返回: [{"code": "600519", "name": "贵州茅台", "confidence": 0.98}, ...]

启动 Web 应用：

路由	功能
`/`	今日分析仪表板
`/portfolio`	持仓管理，盈亏追踪
`/history`	历史分析报告（支持批量删除）
`/backtest`	AI 预测准确性回测
`/agent`	多轮策略问答
`/settings`	LLM 通道、通知配置
`/import`	从图片/CSV/剪贴板导入股票

支持的股票格式

# A股（6位代码）
600519    # 贵州茅台
300750    # 宁德时代
000858    # 五粮液

# 港股（5位数字 + .HK）
00700.HK  # 腾讯控股
09988.HK  # 阿里巴巴

# 美股（股票代码）
AAPL
TSLA
NVDA

# 美股指数
SPX       # 标普500
DJI       # 道琼斯
IXIC      # 纳斯达克

规则	配置
不追高	`DEVIATION_THRESHOLD=5` (%，强趋势下自动放宽)
趋势交易	要求 MA5 > MA10 > MA20 多头排列
精确目标	每只股票提供买入价、止损价、止盈价
新闻时效性	`NEWS_MAX_AGE_DAYS=3`（跳过过期新闻）
检查清单	每个条件标记：✅ 满足 / ⚠️ 需注意 / ❌ 未满足

分析运行但未收到推送：

# 检查通知配置
python -c "from notifier import test_all_channels; test_all_channels()"

# 验证 secrets 已设置（GitHub Actions）
gh secret list

LLM API 错误 / 限流：

ANALYSIS_DELAY=15        # 增加股票分析间隔
MAX_WORKERS=1            # 减少并发数
LITELLM_FALLBACK_MODELS=gemini-1.5-flash,deepseek-chat  # 添加备用模型

AkShare 数据获取失败（A股）：

pip install akshare --upgrade
# A股数据需要中国网络或代理

YFinance 美股数据问题：

pip install yfinance --upgrade
# 美股为保持一致性，仅使用 YFinance

GitHub Actions 未触发：

检查 Actions 是否启用：Settings → Actions → General → Allow all actions
在 crontab.guru 验证 cron 语法
检查工作流文件是否存在：.github/workflows/stock_analysis.yml

Web 认证问题（管理员密码）：

# 如果认证曾被禁用后又启用，需要当前密码
# 通过环境变量重置
WEB_ADMIN_PASSWORD=new_password

多工作进程部署的认证状态：

# 认证开关仅对当前进程生效
# 必须重启所有工作进程以同步状态
docker-compose restart

REPORT_TYPE=simple   # 简洁版：仅结论 + 关键价格
REPORT_TYPE=full     # 完整版：所有技术面 + 基本面 + 新闻分析
REPORT_TYPE=brief    # 3-5 句话摘要

完整报告包含：

一句话核心结论
技术面分析（均线排列、筹码分布）
基本面（估值、成长性、盈利、机构持仓）
舆情情报（新闻情绪、社交媒体——美股）
精确买卖点位
操作检查清单
板块涨跌榜

Gemini → Anthropic → OpenAI/AIHubMix/兼容接口

推荐使用 AIHubMix，无需 VPN 即可通过单一密钥访问所有主流模型：

AIHUBMIX_KEY=$AIHUBMIX_KEY  # 覆盖 Gemini, GPT, Claude, DeepSeek
# 无需 OPENAI_BASE_URL — 自动配置

🇺🇸English

Daily Stock Analysis (股票智能分析系统)

Skill by ara.so — Daily 2026 Skills collection.

LLM-powered stock analysis system for A-share, Hong Kong, and US markets. Automatically fetches quotes, news, and fundamentals, generates AI decision dashboards with buy/sell targets, and pushes results to WeChat/Feishu/Telegram/Discord/Email on a schedule via GitHub Actions — zero server cost.

What It Does

AI Decision Dashboard : One-line conclusion + precise buy/sell/stop-loss prices + checklist per stock
Multi-market : A-shares (CN), HK stocks, US stocks + indices (SPX, DJI, IXIC)
Data sources : AkShare, Tushare, YFinance for quotes; Tavily/SerpAPI/Brave for news
LLM backends : Gemini, OpenAI, Claude, DeepSeek, Qwen via LiteLLM (unified)
Push channels : WeChat Work, Feishu, Telegram, Discord, DingTalk, Email, PushPlus
Automation : GitHub Actions cron schedule, no server needed
Web UI : Portfolio management, history, backtesting, Agent Q&A
Agent : Multi-turn strategy Q&A with 11 built-in strategies (MA crossover, Elliott Wave, etc.)

Installation

Method 1: GitHub Actions (Recommended, Zero Cost)

Step 1: Fork the repository

https://github.com/ZhuLinsen/daily_stock_analysis

Step 2: Configure Secrets (Settings → Secrets and variables → Actions)

Required — at least one LLM key:

GEMINI_API_KEY        # Google AI Studio (free tier available)
OPENAI_API_KEY        # OpenAI or compatible (DeepSeek, Qwen, etc.)
OPENAI_BASE_URL       # e.g. https://api.deepseek.com/v1
OPENAI_MODEL          # e.g. deepseek-chat, gpt-4o
AIHUBMIX_KEY          # AIHubMix (recommended, covers Gemini+GPT+Claude+DeepSeek)
ANTHROPIC_API_KEY     # Claude

Required — stock list:

STOCKS                # e.g. 600519,300750,AAPL,TSLA,00700.HK

Required — at least one notification channel:

TELEGRAM_BOT_TOKEN
TELEGRAM_CHAT_ID
FEISHU_WEBHOOK_URL
WECHAT_WEBHOOK_URL
EMAIL_SENDER / EMAIL_PASSWORD / EMAIL_RECEIVERS
DISCORD_WEBHOOK_URL

Step 3: Trigger manually or wait for cron

Go to Actions → stock_analysis → Run workflow

Method 2: Local / Docker

git clone https://github.com/ZhuLinsen/daily_stock_analysis
cd daily_stock_analysis
cp .env.example .env
# Edit .env with your keys
pip install -r requirements.txt
python main.py

Docker:

docker build -t stock-analysis .
docker run --env-file .env stock-analysis

Docker Compose:

docker-compose up -d

Configuration

`.env` File (Local)

# LLM - pick one or more
GEMINI_API_KEY=your_gemini_key
OPENAI_API_KEY=your_openai_key
OPENAI_BASE_URL=https://api.deepseek.com/v1
OPENAI_MODEL=deepseek-chat
AIHUBMIX_KEY=your_aihubmix_key

# Stock list (comma-separated)
STOCKS=600519,300750,AAPL,TSLA,00700.HK

# Notification
TELEGRAM_BOT_TOKEN=your_bot_token
TELEGRAM_CHAT_ID=your_chat_id

# Optional settings
REPORT_TYPE=full           # simple | full | brief
ANALYSIS_DELAY=10          # seconds between stocks (avoid rate limiting)
MAX_WORKERS=3              # concurrent analysis threads
SINGLE_STOCK_NOTIFY=false  # push each stock immediately when done
NEWS_MAX_AGE_DAYS=3        # ignore news older than N days

Multi-Channel LLM (Advanced)

LLM_CHANNELS=gemini,deepseek,claude
LLM_GEMINI_PROTOCOL=google
LLM_GEMINI_API_KEY=your_key
LLM_GEMINI_MODELS=gemini-2.0-flash,gemini-1.5-pro
LLM_GEMINI_ENABLED=true

LLM_DEEPSEEK_PROTOCOL=openai
LLM_DEEPSEEK_BASE_URL=https://api.deepseek.com/v1
LLM_DEEPSEEK_API_KEY=your_key
LLM_DEEPSEEK_MODELS=deepseek-chat
LLM_DEEPSEEK_ENABLED=true

Stock Grouping (Send Different Stocks to Different Emails)

STOCK_GROUP_1=600519,300750,000858
EMAIL_GROUP_1=investor1@example.com

STOCK_GROUP_2=AAPL,TSLA,NVDA
EMAIL_GROUP_2=investor2@example.com

Market Review Mode

MARKET_REVIEW=cn      # cn | us | both
# cn = A-share three-phase review strategy
# us = US Regime Strategy (risk-on/neutral/risk-off)
# both = both markets

Key Commands (CLI)

# Run full analysis immediately
python main.py

# Analyze specific stocks only
STOCKS=600519,AAPL python main.py

# Run web dashboard
python web_app.py
# Access at http://localhost:5000

# Run with Docker (env file)
docker run --env-file .env stock-analysis python main.py

# Run schedule mode (waits for cron, then runs)
SCHEDULE_RUN_IMMEDIATELY=true python main.py

GitHub Actions Workflow

The workflow file .github/workflows/stock_analysis.yml runs on schedule:

# Default schedule - customize in the workflow file
on:
  schedule:
    - cron: '30 1 * * 1-5'   # 9:30 AM CST (UTC+8) weekdays
  workflow_dispatch:          # manual trigger

To change schedule: Edit .github/workflows/stock_analysis.yml cron expression.

To add secrets via GitHub CLI:

gh secret set GEMINI_API_KEY --body "$GEMINI_API_KEY"
gh secret set STOCKS --body "600519,300750,AAPL,TSLA"
gh secret set TELEGRAM_BOT_TOKEN --body "$TG_TOKEN"
gh secret set TELEGRAM_CHAT_ID --body "$TG_CHAT_ID"

Code Examples

Programmatic Analysis (Python)

# Run analysis for specific stocks programmatically
import asyncio
from analyzer import StockAnalyzer

async def analyze():
    analyzer = StockAnalyzer()
    
    # Analyze a single A-share stock
    result = await analyzer.analyze_stock("600519")  # Moutai
    print(result['conclusion'])
    print(result['buy_price'])
    print(result['stop_loss'])
    print(result['target_price'])

asyncio.run(analyze())

Custom Notification Integration

from notifier import NotificationManager

notifier = NotificationManager()

# Send to Telegram
await notifier.send_telegram(
    token=os.environ['TELEGRAM_BOT_TOKEN'],
    chat_id=os.environ['TELEGRAM_CHAT_ID'],
    message="📈 Analysis complete\n600519: BUY at 1680, SL: 1620, TP: 1800"
)

# Send to Feishu webhook
await notifier.send_feishu(
    webhook_url=os.environ['FEISHU_WEBHOOK_URL'],
    content=analysis_report
)

Using the Agent API

import requests

# Ask the stock agent a strategy question
response = requests.post('http://localhost:5000/api/agent/chat', json={
    "message": "600519现在适合买入吗？用均线金叉策略分析",
    "stock_code": "600519",
    "strategy": "ma_crossover"  # ma_crossover, elliott_wave, chan_theory, etc.
})

print(response.json()['reply'])

Backtest Analysis Accuracy

import requests

# Trigger backtest for a stock
response = requests.post('http://localhost:5000/api/backtest', json={
    "stock_code": "600519",
    "days": 30  # evaluate last 30 days of AI predictions
})

result = response.json()
print(f"Direction accuracy: {result['direction_accuracy']}%")
print(f"Take-profit hit rate: {result['tp_hit_rate']}%")
print(f"Stop-loss hit rate: {result['sl_hit_rate']}%")

Import Stocks from Image (Vision LLM)

import requests

# Upload screenshot of stock list for AI extraction
with open('watchlist_screenshot.png', 'rb') as f:
    response = requests.post(
        'http://localhost:5000/api/stocks/import/image',
        files={'image': f}
    )

stocks = response.json()['extracted_stocks']
# Returns: [{"code": "600519", "name": "贵州茅台", "confidence": 0.98}, ...]

Web Dashboard Features

Start the web app:

python web_app.py

Route	Feature
`/`	Today's analysis dashboard
`/portfolio`	Holdings management, P&L tracking
`/history`	Past analysis reports (bulk delete supported)
`/backtest`	AI prediction accuracy backtest
`/agent`	Multi-turn strategy Q&A
`/settings`	LLM channels, notification config

Supported Stock Formats

# A-shares (6-digit code)
600519    # 贵州茅台
300750    # 宁德时代
000858    # 五粮液

# HK stocks (5-digit + .HK)
00700.HK  # 腾讯控股
09988.HK  # 阿里巴巴

# US stocks (ticker)
AAPL
TSLA
NVDA

# US indices
SPX       # S&P 500
DJI       # Dow Jones
IXIC      # NASDAQ

Built-in Trading Rules

Rule	Config
No chasing highs	`DEVIATION_THRESHOLD=5` (%, auto-relaxed for strong trend)
Trend trading	MA5 > MA10 > MA20 bullish alignment required
Precise targets	Buy price, stop-loss, take-profit per stock
News freshness	`NEWS_MAX_AGE_DAYS=3` (skip stale news)
Checklist	Each condition marked: ✅ Satisfied / ⚠️ Caution / ❌ Not Met

Troubleshooting

Analysis runs but no push received:

# Check notification config
python -c "from notifier import test_all_channels; test_all_channels()"

# Verify secrets are set (GitHub Actions)
gh secret list

LLM API errors / rate limiting:

ANALYSIS_DELAY=15        # increase delay between stocks
MAX_WORKERS=1            # reduce concurrency
LITELLM_FALLBACK_MODELS=gemini-1.5-flash,deepseek-chat  # add fallbacks

AkShare data fetch fails (A-shares):

pip install akshare --upgrade
# A-share data requires Chinese network or proxy

YFinance US stock data issues:

pip install yfinance --upgrade
# US stocks use YFinance exclusively for consistency

GitHub Actions not triggering:

Check Actions are enabled: Settings → Actions → General → Allow all actions
Verify cron syntax at crontab.guru
Check workflow file exists: .github/workflows/stock_analysis.yml

Web auth issues (admin password):

# If auth was disabled and re-enabled, current password required
# Reset via environment variable
WEB_ADMIN_PASSWORD=new_password

Multi-worker deployment auth state:

# Auth toggle only applies to current process
# Must restart all workers to sync state
docker-compose restart

Report Types

REPORT_TYPE=simple   # Concise: conclusion + key prices only
REPORT_TYPE=full     # Complete: all technical + fundamental + news analysis
REPORT_TYPE=brief    # 3-5 sentence summary

Full report includes:

一句话核心结论 (one-line core conclusion)
技术面分析 (technical: MA alignment, chip distribution)
基本面 (valuation, growth, earnings, institutional holdings)
舆情情报 (news sentiment, social media — US stocks)
精确买卖点位 (precise entry/exit levels)
操作检查清单 (action checklist)
板块涨跌榜 (sector performance boards)

LLM Priority Order

Gemini → Anthropic → OpenAI/AIHubMix/Compatible

AIHubMix is recommended for single-key access to all major models without VPN:

AIHUBMIX_KEY=$AIHUBMIX_KEY  # covers Gemini, GPT, Claude, DeepSeek
# No OPENAI_BASE_URL needed — auto-configured

Weekly Installs

320

Repository

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GitHub Stars

First Seen

7 days ago

Security Audits

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股票智能分析系统：基于AI的A股港股美股自动化投资决策看板

🇨🇳中文介绍

Daily Stock Analysis (股票智能分析系统)

功能概述

安装

方法一：GitHub Actions（推荐，零成本）

相关 Skills

方法二：本地 / Docker

配置

.env 文件（本地）

多通道 LLM（高级）

股票分组（将不同股票发送到不同邮箱）

市场回顾模式

关键命令（CLI）

GitHub Actions 工作流

代码示例

编程式分析（Python）

自定义通知集成

使用智能助手 API

回测分析准确性

从图片导入股票（视觉 LLM）

Web 仪表板功能

支持的股票格式

内置交易规则

故障排除

报告类型

LLM 优先级顺序

🇺🇸English

Daily Stock Analysis (股票智能分析系统)

What It Does

Installation

Method 1: GitHub Actions (Recommended, Zero Cost)

Method 2: Local / Docker

Configuration

.env File (Local)

Multi-Channel LLM (Advanced)

Stock Grouping (Send Different Stocks to Different Emails)

Market Review Mode

Key Commands (CLI)

GitHub Actions Workflow

Code Examples

Programmatic Analysis (Python)

Custom Notification Integration

Using the Agent API

Backtest Analysis Accuracy

Import Stocks from Image (Vision LLM)

Web Dashboard Features

Supported Stock Formats

Built-in Trading Rules

Troubleshooting

Report Types

LLM Priority Order

最新 Skills

`.env` 文件（本地）

`.env` File (Local)