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No cloud account required to run locally.\n\n> ⚠️ **Not financial advice.** Output is generated by LLMs and may be inaccurate. Never make investment decisions based solely on this tool.\n\n**Project status:** solo-maintained, alpha. **Looking for co-maintainers** — if you're interested in helping review PRs, triage issues, or shape the roadmap, open an issue or reach out via [MAINTAINERS.md](MAINTAINERS.md). Contributions of any size are welcome (see [CONTRIBUTING.md](CONTRIBUTING.md)).\n\n[![Docs](https://img.shields.io/badge/docs-live-brightgreen)](https://kanishk-varshney.github.io/StockSage/)\n[![License](https://img.shields.io/github/license/kanishk-varshney/StockSage)](LICENSE)\n[![CI](https://img.shields.io/github/actions/workflow/status/kanishk-varshney/StockSage/ci.yml)](https://github.com/kanishk-varshney/StockSage/actions/workflows/ci.yml)\n[![Version](https://img.shields.io/github/v/release/kanishk-varshney/StockSage)](https://github.com/kanishk-varshney/StockSage/releases)\n[![Coverage](https://img.shields.io/codecov/c/github/kanishk-varshney/StockSage)](https://codecov.io/gh/kanishk-varshney/StockSage)\n<!-- [![PyPI](https://img.shields.io/pypi/v/stocksage)](https://pypi.org/project/stocksage/) -->\n<!-- Uncomment once published to PyPI (tracked in ROADMAP item 1). -->\n[![Python 3.13+](https://img.shields.io/badge/python-3.13+-blue.svg)](pyproject.toml)\n\nCI intentionally targets Python 3.13 for deterministic behavior across local runs and contributors.\n\n---\n\n## What it does\n\n**Symbol input → live pipeline → structured analysis cards**\n\n![StockSage: enter a ticker symbol and click Analyze Stock](docs/assets/demo-trigger.gif)\n\n![StockSage pipeline running: validation, data download, 7 AI agents analyzing live](docs/assets/demo-pipeline.gif)\n\n- **Validates** the ticker (US and Indian `.NS`/`.BO` formats)\n- **Downloads** price history, financials, benchmarks, news, Google Trends, insider transactions, and institutional holders — saved as CSVs\n- **Runs a 7-agent CrewAI pipeline** sequentially: data sanity check → valuation ratios → price performance → financial health → market sentiment → cross-agent review → final investment report\n- **Streams** every step live to the browser via SSE — see the agents think in real time\n- **Renders structured cards** for each analysis domain with a final BUY / HOLD / SELL verdict and confidence level\n\n**Analysis card example — Financial Health**\n\n![Financial Health card: revenue growth 15.7%, earnings growth 18.3%, debt-to-equity, FCF $135B, growth signals](docs/assets/screenshot-financial-health.png)\n\n---\n\n## Quickstart\n\n```bash\n# 1. Clone and install\ngit clone https://github.com/kanishk-varshney/StockSage.git\ncd StockSage\nuv sync                         # or: pip install -e \".[dev]\"\n\n# 2. Configure\ncp .env.example .env            # set LLM_MODEL + API key (see table below)\n\n# 3. Check config\nmake check\n\n# 4. Run\nmake run                        # opens at http://127.0.0.1:8000\n```\n\nEnter a symbol (`AAPL`, `RELIANCE.NS`, `GOOGL`) and hit **Analyze**.\n\n---\n\n## Model providers\n\nPick any row. Set `LLM_MODEL` and the matching API key in `.env`.\n\n| Provider | `LLM_MODEL` | Key needed | Cost |\n|----------|-------------|------------|------|\n| **Ollama** (local) | `ollama/qwen2.5:14b-instruct` | None — install Ollama + `ollama pull …` | Free |\n| **DeepSeek** | `deepseek/deepseek-chat` | `DEEPSEEK_API_KEY` | ~$0.01–0.05 / analysis |\n| **Gemini** | `gemini/gemini-2.5-flash` | `GEMINI_API_KEY` | Free tier (10 RPM) |\n| **Groq** | `groq/llama-3.3-70b-versatile` | `GROQ_API_KEY` | Free tier |\n| **OpenAI** | `openai/gpt-4o-mini` | `OPENAI_API_KEY` | Pay-per-use |\n| **Anthropic** | `anthropic/claude-3-5-haiku-20241022` | `ANTHROPIC_API_KEY` | Pay-per-use |\n\n**Optional:** Set `LLM_FALLBACK_MODEL` to a cloud model — if Ollama is unreachable the app switches automatically.\n\n**Optional:** Set `SERPER_API_KEY` for live web search inside the sentiment agent.\n\nFull copy-paste `.env` recipes: [docs/model-providers.md](docs/model-providers.md)\n\n---\n\n## How it works\n\n```\nBrowser → StockProcessor → DownloadPipeline → CSV files\n                       ↓\n               AnalysisPipeline (CrewAI)\n                  ├── data_sanity_agent\n                  ├── ratio_analyst\n                  ├── performance_analyst\n                  ├── fundamental_analyst\n                  ├── sentiment_analyst\n                  ├── market_reviewer        ← cross-checks other agents\n                  └── investment_advisor     ← final verdict\n                       ↓\n               format_log_entry → SSE stream → UI cards\n```\n\nEach agent gets read-only access to the downloaded CSVs via a `CSVReaderTool`. Agent roles, goals, and backstories are defined in plain YAML (`src/crew/config/agents.yaml`) — no code changes needed to adjust behavior.\n\nFull architecture and extension points: [docs/architecture.md](docs/architecture.md)\n\n---\n\n## Make commands\n\n```bash\nmake install   # install deps + pre-commit hooks\nmake run       # start prod server (http://127.0.0.1:8000)\nmake dev       # start dev/mock-stream server\nmake test      # run tests with coverage\nmake lint      # ruff check\nmake format    # auto-fix lint + format\nmake typecheck # mypy over src/\nmake security  # pip-audit + bandit\nmake check     # validate .env + LLM connectivity\nmake clean     # remove __pycache__, .pytest_cache, coverage artifacts\n```\n\n**API docs** (auto-generated by FastAPI):\n\n| URL | What |\n|-----|------|\n| `http://127.0.0.1:8000/docs` | Swagger / interactive API explorer |\n| `http://127.0.0.1:8000/redoc` | ReDoc reference docs |\n\n## Docker\n\n```bash\n# Build and run with docker-compose (persists .market_data between restarts)\ndocker compose up --build\n\n# Or build the image directly\ndocker build -t stocksage .\ndocker run -p 8000:8000 --env-file .env stocksage\n```\n\n---\n\n## Self-hosting\n\nStockSage needs a **persistent container** (not serverless) — analyses run for 2–5 minutes with SSE streaming. Recommended platforms:\n\n| Platform | Cost | Notes |\n|----------|------|-------|\n| [Railway](https://railway.app) | $5/mo | Best value. Auto-detects `Dockerfile`. |\n| [Render](https://render.com) | $7/mo | Good alternative. Free tier sleeps. |\n| [Fly.io](https://fly.io) | ~$3–5/mo | Competitive, slightly more config. |\n\n**Do not use Vercel** — serverless timeouts will cut off analyses.\n\nFull deploy guide: [docs/self-host.md](docs/self-host.md) · [walkthrough.md](walkthrough.md)\n\n---\n\n## Documentation\n\n📖 **Full docs site:** [kanishk-varshney.github.io/StockSage](https://kanishk-varshney.github.io/StockSage/)\n\n| Doc | What's in it |\n|-----|-------------|\n| [docs/local-setup.md](docs/local-setup.md) | Install, platform notes, troubleshooting |\n| [docs/model-providers.md](docs/model-providers.md) | `.env` recipes per LLM provider |\n| [docs/architecture.md](docs/architecture.md) | Module map, data flow, extension points |\n| [docs/self-host.md](docs/self-host.md) | Docker, Railway, RAM requirements |\n| [docs/examples/output-preview.md](docs/examples/output-preview.md) | Sample analysis output |\n| [docs/stream-api.md](docs/stream-api.md) | SSE events for `/stream` and `/stream/mock` |\n| [walkthrough.md](walkthrough.md) | Dev modes, mock streaming, agent iteration |\n| [ROADMAP.md](ROADMAP.md) | Scope, non-goals, near-term priorities |\n| [CHANGELOG.md](CHANGELOG.md) | Release notes |\n\n---\n\n## Contributing\n\nContributions are welcome — bug fixes, new features, better agent prompts, UI improvements, and docs.\n\n### 1. Set up\n\n```bash\ngit clone https://github.com/kanishk-varshney/StockSage.git\ncd StockSage\nuv sync                  # install exact deps from uv.lock\ncp .env.example .env     # set LLM_MODEL + key\nmake test                # confirm all tests pass before you start\n```\n\n### 2. Branch naming & commits\n\nBranches must match `^(feat|fix|docs|chore|refactor|test)/[a-z0-9][a-z0-9-]*$` and PR titles follow [Conventional Commits](https://www.conventionalcommits.org/). Both are enforced in CI. Signed commits are required on `main`.\n\nFull rules, examples, and signing setup: [CONTRIBUTING.md → Branching & commits](CONTRIBUTING.md#branching--commits).\n\n### 3. Workflow\n\n```\nmain  ──┬──────────────────────────────────► main\n        │\n        └─► feat/your-feature\n              ├── make test && make lint   (before pushing)\n              └── open PR → describe what + why + how you tested\n```\n\n- **One logical change per PR** — easier to review and revert if needed\n- **Never push directly to `main`**\n- Run `make test` and `make lint` locally before opening a PR\n- UI changes: include a screenshot or short screen recording in the PR description\n- Docs changes: update any affected `docs/` files in the same PR\n\n### 4. Good first issues\n\nIf you're new here, these are well-scoped starting points:\n\n| Issue | Where to look |\n|-------|--------------|\n| Add a test for the download pipeline with mocked `yfinance` | `tests/core/`, `src/core/market/` |\n| Improve error message when a ticker has no financial data | `src/core/processing/download_pipeline.py` |\n| Add a new LLM provider example to docs | `docs/model-providers.md` |\n| Add a new analysis card (formatter + schema) | `src/app/utils/formatters/`, `src/crew/schemas/` |\n| Add `workflow_dispatch` trigger to CI | `.github/workflows/ci.yml` |\n\nLabel: look for [`good first issue`](https://github.com/kanishk-varshney/StockSage/labels/good%20first%20issue) once the repo is public.\n\n### 5. AI-assisted contributions\n\nThis project was built with AI coding assistance. **AI-generated contributions are welcome** — use whatever tools help you. The only requirement: you understand and can explain what you're submitting. Don't paste-and-run without reading.\n\nFull guide: [CONTRIBUTING.md](CONTRIBUTING.md) · [CODE_OF_CONDUCT.md](CODE_OF_CONDUCT.md) · [SECURITY.md](SECURITY.md)\n\n---\n\n## Contributors\n\n<!-- ALL-CONTRIBUTORS-LIST:START -->\n\nContributions of any kind are welcome — code, docs, issue reports, and feedback all count.\n\n<!-- ALL-CONTRIBUTORS-LIST:END -->\n\n---\n\n## License\n\nReleased under the [MIT License](LICENSE).\n","readmeExcerpt":"StockSage **Local-first, multi-agent stock analysis.** Enter a ticker → get a full investment report in minutes — valuation, performance, financial health, sentiment, and a final verdict — all streamed live to your browser. You bring your own model (Ollama, OpenAI, Gemini, DeepSeek, Groq, Anthropic). No cloud account required to run locally. ⚠️ **Not financial advice.** Output is generated by LLMs and may be inaccura","codeSnippets":[],"executableExamples":[{"language":"bash","snippet":"# 1. 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