Ashare Analyzer

An intelligent A-share stock analysis system powered by LLMs with multi-agent architecture. Automatically analyzes your watchlist and delivers a "Decision Dashboard" to Discord, Telegram, or Email.

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Features

• Multi-Agent Architecture: Specialized AI agents collaborate to provide comprehensive analysis

  • Technical Analysis Agent - Chart patterns, indicators, trend analysis
  • Fundamental Analysis Agent - Financial statements, valuation metrics
  • News Sentiment Agent - News aggregation and sentiment scoring
  • Chip Distribution Agent - Institutional/retail fund flow analysis
  • Risk Management Agent - Position sizing, risk assessment
  • Portfolio Manager Agent - Final decision synthesis

• Multi-Source Data: Aggregates data from Akshare, Baostock, Tushare, eFinance, etc.

• Flexible Notifications: Push results to Discord, Telegram, or Email

• Multiple AI Providers: Supports 100+ LLM providers via LiteLLM (DeepSeek, OpenAI, Gemini, Claude, etc.)

• Scheduled Execution: Run daily analysis automatically

Quick Start

Prerequisites

• Python 3.13+

Installation

# Install with uv (recommended)
uv tool install ashare-analyzer

# Or run directly with uvx (no installation needed)
uvx ashare-analyzer

# Or install with pip
pip install ashare-analyzer

Configuration

Create a .env file in your working directory (or use environment variables):

# Copy example config (if you cloned the repo)
cp .env.example .env

# Stock watchlist (comma-separated)
STOCK_LIST=600519,300750,002594

# AI Model (LiteLLM format: provider/model-name)
LLM_MODEL=deepseek/deepseek-reasoner
LLM_API_KEY=your_api_key_here

See Configuration for all options.

Usage

# Run analysis
ashare-analyzer

# Debug mode (verbose logging)
ashare-analyzer --debug

# Analyze specific stocks
ashare-analyzer --stocks 600519,300750

# Override AI model settings via CLI
ashare-analyzer --model openai/gpt-5 --api-key your_key --base-url https://api.openai.com/v1

# Scheduled mode (runs daily at configured time)
ashare-analyzer --schedule

# Dry run (fetch data only, no AI analysis)
ashare-analyzer --dry-run

# Skip notifications
ashare-analyzer --no-notify

# Show help
ashare-analyzer --help

Configuration Details

AI Model Configuration

Supports 100+ providers via LiteLLM format:

Provider	Model Example	API Key Source
DeepSeek	deepseek/deepseek-reasoner	platform.deepseek.com
OpenAI	openai/gpt-5.2	platform.openai.com
Gemini	gemini/gemini-3.1-pro-preview	aistudio.google.com
Claude	anthropic/claude-sonnet-4-6	console.anthropic.com

Full provider list: LiteLLM Providers

Notification Channels

Configure one or more notification channels:

# Discord
DISCORD_WEBHOOK_URL=https://discord.com/api/webhooks/...

# Telegram
TELEGRAM_BOT_TOKEN=your_bot_token
TELEGRAM_CHAT_ID=your_chat_id

# Email (SMTP)
EMAIL_SMTP_HOST=smtp.gmail.com
EMAIL_SMTP_PORT=587
EMAIL_SMTP_USER=your_email@gmail.com
EMAIL_SMTP_PASSWORD=your_app_password
EMAIL_RECIPIENTS=recipient@example.com

Search Engines (for news)

# Tavily (recommended)
TAVILY_API_KEY=your_tavily_key

# SerpAPI (alternative)
SERPAPI_API_KEY=your_serpapi_key

News Filter Configuration

The news filter uses AI to filter out low-relevance and stale news results.

Environment Variable	Description	Default
NEWS_FILTER_ENABLED	Enable/disable news filter	true
NEWS_FILTER_MIN_RESULTS	Minimum results after filtering	3
NEWS_FILTER_MODEL	LLM model for filtering (optional, falls back to LLM_MODEL)	-

Example:

NEWS_FILTER_ENABLED=true
NEWS_FILTER_MIN_RESULTS=3
NEWS_FILTER_MODEL=deepseek/deepseek-chat

Development

# Format code
ruff format

# Lint code
ruff check --fix

# Type check
ty check .

# Run tests
uv run pytest

# Run tests with coverage
uv run pytest --cov=ashare_analyzer

Docker

# Build image
docker build -t ashare-analyzer -f docker/Dockerfile .

# Run container
docker run -it --env-file .env ashare-analyzer

# Using docker-compose
cd docker && docker-compose up -d

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• ZhuLinsen/daily_stock_analysis - Original project this was forked from
• virattt/ai-hedge-fund - Multi-agent architecture inspiration
• All the open-source libraries that made this possible

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