A self-hosted dashboard that gauges stock-market mood. It aggregates financial news from public RSS feeds and Yahoo Finance (no API keys required), asks an LLM — a local Ollama model or a cloud provider — to summarize sentiment and produce a custom Fear & Greed Index (0–100), tracks the VIX with a multi-source fallback chain, stores history in PostgreSQL, and sends email alerts when the VIX crosses a subscriber's threshold.

• News aggregation from 10 RSS feeds (Google News, Yahoo Finance, Investing.com, MarketWatch, CNBC, ...) plus Yahoo Finance ticker news — deduplicated, cleaned, capped at 300 articles per run.
• AI sentiment analysis via a configurable provider:
  • Local: Ollama (default, e.g. deepseek-r1:1.5b)
  • Cloud: Azure AI Inference or any OpenAI-compatible endpoint
  • Providers and keys are managed at runtime from the in-app /settings page.
• VIX retrieval with fallbacks: yfinance history → yfinance fast_info → CNBC quote API → Stooq.
• Interactive dashboard: Fear & Greed gauge, VIX card, historical charts (Chart.js), date-range filtering, recent-history table, dark/light mode, Markdown-rendered AI summary, CSV and PDF export.
• VIX email alerts: per-user thresholds, HTML emails over SMTP, 6-hour cooldown per subscriber (configurable).
• Automation: a scheduler container runs the scrape → analyze pipeline every 25 minutes and the alert check every 5 minutes (both configurable); the dashboard also has manual Scraper / Analyzer / Refresh All buttons.
• Operations-ready: /healthz endpoint, container healthchecks, structured logging in every component, non-root Docker image, CI with lint + tests + Docker build.

 ┌─────────────────────────── scheduler container ───────────────────────────┐
 │ scheduler_main.py │
 │ ├─ every 25 min: webScrape.py ──► financial_news_agg.json │
 │ │ analyze_news.py ──► PostgreSQL + latest_indices.json │
 │ └─ every 5 min: alert_monitor.py ──► SMTP email alerts │
 └────────────────────────────────────────────────────────────────────────────┘
 │ │
 ▼ ▼
 ┌──────────────┐ ┌──────────────────┐
 │ PostgreSQL │ ◄──────────────── │ web container │
 │ (db) │ │ Flask + gunicorn│──► Dashboard :5000
 └──────────────┘ └──────────────────┘

Requirements: Docker with the Compose plugin.

git clone https://github.com/dbogdanm/MarketSentiment
cd MarketSentiment
# 1. Configure secrets
cp .env.example .env
# Edit .env — FLASK_SECRET_KEY is required; generate one with:
# python -c "import secrets; print(secrets.token_hex(32))"
# 2. Launch (db + web + scheduler)
docker compose up -d --build

The dashboard is now at http://localhost:5000 . The database schema is created automatically on first boot, and the scheduler immediately runs a first scrape so the dashboard has data within a minute or two.

Note: the AI summary needs a model. Either run Ollama on the host (default endpoint http://localhost:11434) or open Settings in the app and point it at a cloud provider. Without a model, the pipeline still collects news and VIX data gracefully.

# 1. Python environment
python -m venv .venv
# Windows: .\.venv\Scripts\activate | macOS/Linux: source .venv/bin/activate
pip install -r requirements.txt
# 2. Build the Tailwind stylesheet
npm install
npm run build:css
# 3. Configuration
cp .env.example .env # set DB credentials, FLASK_SECRET_KEY, SMTP, ...
 # DB_HOST=localhost for a local PostgreSQL
# 4. Database — start PostgreSQL and create the database; the schema is
# created automatically when the web app starts (or run db/init.sql once).
# 5. Run
python website/appFlask.py # dashboard at http://127.0.0.1:5000
python scheduler_main.py # pipeline + alerts (separate terminal)

All settings come from environment variables (.env, loaded automatically — see .env.example):

AI provider settings (Ollama endpoint/model, cloud endpoint/key/model) live in website/data_files/ai_config.json and are edited from the /settings page; the API key is write-only in the UI and never rendered back.

The manual trigger and settings endpoints are unauthenticated by design (single-user tool). If you expose the app publicly, put it behind a reverse proxy with authentication.

pip install -r requirements-dev.txt
pytest # test suite
ruff check website tests scheduler_main.py # lint

CI (.github/workflows/ci.yml) runs ruff + pytest on Python 3.11 and 3.13, builds the Docker image, and validates the compose file on every push and pull request.

├── website/
│ ├── appFlask.py # Flask app
│ ├── crucialPys/ # webScrape / analyze_news / alert_monitor
│ ├── templates/ # Jinja2 (Tailwind CSS)
│ ├── static/ # JS, images, built style.css (generated)
│ └── data_files/ # runtime JSON cache + AI config (gitignored)
├── scheduler_main.py # periodic job runner
├── db/init.sql # database schema
├── tests/ # pytest suite
├── Dockerfile # multi-stage: Node (Tailwind) → Python
└── docker-compose.yml # db + web + scheduler

MIT — Copyright (c) 2026 Dinu Bogdan. See LICENSE.

Maintainer: Dinu Bogdan-Marius · bogdandinu625@gmail.com · GitHub @dbogdanm

For issues or feature requests, please open a GitHub issue.