AI Stock Analysis System
An LLM-powered multi-market stock analysis system that generates daily decision dashboards and pushes them to your preferred channels.
- Source repo
- ZhuLinsen/daily_stock_analysis
- Stars
- ★ 67k
- Last updated
- 1d ago
- License
- MIT
- Primary language
- Python
- FA score
- 46/100 · Major gaps
At a glance
- How it runs
- Works with
- Universal · cross-platform
- Cost
- Free software; you pay for model usage
- Setup effort
- Medium · a few setup steps
- You'll need
- Typical use
- Retail investors check AI-generated decision reports for their watchlist every morning.
- Not a fit if
- Users unwilling to configure multiple API keys for AI, news, and push
- Those needing stable long-term data from free sources, which are rate-limited
- Source review
- 46/100 · Major gaps 2 safety controls not found
What does this agent do, and when should you use it?
DSA (daily_stock_analysis) is an LLM-powered multi-market stock analysis system supporting A-shares, Hong Kong, US, Japanese, Korean, and Taiwanese stocks. It aggregates multi-source market data, real-time news, and fundamentals, generates AI-driven decision reports, and pushes them to WeCom, Feishu, Telegram, Discord, Slack, and email. The system offers a Web workspace, Agent-based strategy Q&A, backtesting, portfolio management, and supports deployment via GitHub Actions, Docker, or local scheduled tasks. It defaults to free data sources for zero-config operation, but paid sources are recommended for long-term stability.
The system reads the configured watchlist, fetches quotes, candlesticks, and technical indicators from sources like AkShare, Tushare, YFinance, and TickFlow, pulls real-time news and announcements via search APIs, and invokes AI models (Anthropic, OpenAI, DeepSeek, etc.) to generate decision reports including scores, buy/sell points, risk alerts, and catalysts. Reports are pushed to configured channels via webhooks. Users can run one-off analyses via CLI (e.g., python main.py --stocks 600519,hk00700) or interact through the Web UI (python main.py --webui). The system also supports Agent-based strategy Q&A with 15 built-in strategies and can run as a scheduled GitHub Action.
- Retail investors check AI-generated decision reports for their watchlist every morning.
- Quant enthusiasts use Agent strategy Q&A to ask multi-turn questions about specific stocks.
- Enterprises manage multiple watchlists and backtest strategies through the Web workspace.
- Developers integrate stock analysis into CI/CD via GitHub Actions for scheduled, zero-cost runs.
- Analysts use the market review feature to quickly grasp major indices and sector performance.
How do you install or deploy this agent?
Option 1: GitHub Actions. Fork the repo, add required secrets (e.g., ANSPIRE_API_KEYS, GEMINI_API_KEY) in Settings, set STOCK_LIST, enable Actions, and manually trigger the workflow. Option 2: Locally, clone the repo, run pip install -r requirements.txt, copy .env.example to .env and fill in env vars, then run python main.py.
How do you use this agent?
After installation, run one-off analyses with python main.py --stocks 600519,hk00700; add --debug for verbose output. To use the Web UI, run python main.py --webui and visit http://127.0.0.1:8000. For automation, refer to the docs for scheduling; the default schedule is weekdays at 18:00 Beijing time.
What are this agent's strengths and limitations?
- Supports multiple markets: A-shares, HK, US, Japan, Korea, Taiwan
- Rich Web workspace and Agent Q&A feature
- Multiple notification channels (WeCom, Feishu, Telegram, Discord, Slack, email)
- Zero-cost deployment via GitHub Actions without a server
- Free data sources can be unstable; paid sources recommended for heavy use
- Requires multiple API keys, which may incur costs
- Skips non-trading days; only runs on weekdays by default
- Project is evolving rapidly; APIs may change
How does this agent compare with similar options?
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| AI Stock Analysis System This agent | 46 · Major gaps | CLIFree + model costs | ★ 67k | 1d ago | Python | — |
| OpenBB Open Data Platform | 37 · Major gaps | Library / SDKFreemium | ★ 74k | today | Python | — |
| AlphaGBM Skills | 43 · Major gaps | Agent plugin / skillFreemium | ★ 3.6k | 24d ago | Python | Claude Code |
| QuantDinger AI Trading OS | 89 · Good | Self-hosted serviceFree + model costs | ★ 13k | today | Python | Codex · Claude Code · OpenAI API |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence: Workflow uses GitHub Secrets for API keys, following least privilege; but no user confirmation mechanism (e.g., before push), data flow transparency partial (README describes data sources and push channels but not data flow), sensitive data handling partial (API keys via Secrets but no encryption details), dependency security partial (requirements.txt has version ranges but no vulnerability scanning), external effects partial (push notifications and external API calls but no side effects), rollback absent, source attribution clear (README and LICENSE). Deductions: missing user confirmation, rollback, insufficient data flow and sensitive data handling details.
Evidence: README and code structure consistent, features correspond to files; dependencies have version ranges but not pinned, affecting reproducibility; failure messages partial (README mentions logs but no specific error handling examples). Deductions: dependencies not pinned, failure messages evidence insufficient.
Evidence: README clearly defines target users (individual investors) and scenarios (daily analysis, push); capability boundaries partial (README lists supported markets and data sources but not limitations); trigger precision partial (GitHub Actions scheduled and manual triggers but no details); environment fit partial (supports GitHub Actions, Docker, local but no system requirements). Deductions: capability boundaries and trigger precision details insufficient.
Evidence: README well-structured with TOC and doc links; install notes detailed (quick start, Secrets configuration); naming stability partial (project name and main commands stable but no API stability); examples and FAQ partial (README has push effect examples but no FAQ); known limitations partial (README mentions free source instability but not comprehensive); license clear (MIT); versioning and changelog partial (auto-tag workflow but no CHANGELOG); maintenance responsibility clear (contact email and Issue link). Deductions: missing FAQ, CHANGELOG, known limitations not comprehensive.
Evidence: Output usability (README shows decision dashboard and push effects, clear format); marginal value (multi-market analysis, multiple push channels but no comparison with existing tools); cost benefit (README emphasizes zero-cost but no actual cost analysis). Deductions: marginal value and cost benefit evidence insufficient.
Evidence: Claim traceability (README describes features but no implementation details); cross-source verification (no data source cross-validation mechanism); fact-inference separation (AI analysis may mix facts and inferences but not clearly separated). Deductions: lack of implementation details and verification mechanisms.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Dependencies are not pinned to exact versions, which may affect reproducibility and security.
- Lack of user confirmation mechanism; push notifications may be sent without user consent.
- Insufficient details on data flow and sensitive data handling; further review needed.
- No rollback mechanism; configuration errors may be difficult to recover.
- AI analysis may mix facts and inferences; handle with caution.
Recent releases
Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.
-
Release notes excerpt
v3.33.0 发布亮点 - feat: 问股讨论对象由服务端保存，刷新、重开及 Bot/Web 交替使用可恢复；修复“长电科技”等完整股票名称被内部短别名误判的问题。 - feat: 新增无需 LLM 的规则化 ETF 双动量轮动，提供最新调仓信号、扣成本回测、年度对比、参数平原检验及数据质量告警。 - feat: Web 新增只读数据中心、持仓风险与暴露看板，以及自选股“变化 / 状态 / 下一步”提示；设置页保护未保存草稿并改善首次配置引导。 - improve: 选股补齐带来源与质量状态的入选理由及新闻事件证据，修复日线新鲜度、交易日期、缓存降级和检索时间一致性。 - feat: 简化 Modelflare 单 Key 配置，补充 Requesty 渠道、妙想筹码与资金流数据、FXMacroData 宏观工具，以及单票决策复盘记忆和 Multi-Agent 阶段评估。 - fix: 修复基本面和资金流解析、科创板成交量单位、重复定时派发及桌面 MiniRacer 打包；调度超时后默认推送已完成的部分结果，可通过 DSA_TIMEOUT_PARTIAL_NOTIFY=false 关闭。 What's Changed …View on GitHub ↗ -
Release notes excerpt
v3.32.0 Highlights - 已登记 A 股指数贯通 Web/API、Bot、CLI 与 GitHub Actions 分析入口，注册表扩展至 33 项，并统一指数身份、任务去重、历史与 Chat 上下文。 - 新增个股研究聚合 API、ResearchArtifact 结构化研究产物，以及数据源能力与数据集质量只读接口。 - 完善 Futu OpenD 港股数据源接入，新增 Web Chat 分词基础模块与 Agent 轨迹评估入口。 - 自建 SearXNG 搜索超时可配置，Tavily 改用 basic 搜索以降低 credit 消耗。 - 定时分析采用独立进程与硬超时保护，选股结果支持历史恢复，桌面端增加全局更新入口，并修复 Linux/Docker 分享图中韩文字体缺失。 - 收敛 LiteLLM 兼容版本窗口，更新 Web/桌面依赖，并修复美股数据源优先级与股票名称归一问题。 Contributors @Gach-Coder, @SPEC, @ZhuLinsen, @anupamme, @ariesy, @daladada666-a11y, @elvisw, @subaoyan16, @sunkai174634, @tzlwn1, @wvyan, @zhanghao56666-svg, @zhulinsen …View on GitHub ↗ -
Release notes excerpt
v3.31.0 发布亮点 - feat: Agent 工具调用新增按类别和单工具配置的超时契约，并补齐防重试、协作取消、并发预算、线程安全与热重载一致性。 - feat: 股票名称解析、StockDaily.canonical_id 双写和 A 股指数多数据源 fallback 路由共同完善股票身份与行情降级链路。 - improve: 新闻检索为空时在报告中如实披露证据边界；Anspire 默认覆盖全球区域，公共 SearXNG 实例改为显式启用。 - fix: 修复服务重启后的定时任务恢复、分析无报告时的失败反馈、钉钉通知识别，以及大盘复盘历史摘要和实际生成后端诊断。 - security: 桌面端升级 builder-util-runtime 至 9.7.0，修复 CVE-2026-54673 涉及的 HTTP 重定向凭据头信息泄露风险。 - docs: 增加 xAI Grok 的 LiteLLM 配置示例、Grok Bot 集成说明与可复用异步分析 Skill，并更新 AIHubMix 中国大陆直连入口。 What's Changed …View on GitHub ↗