🛡️ Aegis Equity Terminal

A multi-factor stock analysis engine & Material Design 3 Dark web application supporting both US (e.g. AAPL, NVDA, TSLA) and International stock equities (e.g. CBA.AX, SHEL.L, SAP.DE).

Synthesizes live market data across 6 analytical pillars into a composite score (0–100), explicit recommendation signal (BUY / SELL / HOLD), confidence level, Plotly gauge visualizers, financial health metrics, technical indicators, news/Reddit sentiment, analyst price targets, and macro/moat modeling.

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🎨 Material Design 3 (M3) Dark Web Interface

• M3 Dark Palette: Deep background surface (#121318), surface containers (#1E1F25), primary tokens (#D0BCFF), and high-contrast text typography.
• Full Font Size & Contrast Enhancement: Enlarged metric values, table cells, pillar progress bars, and header cards for maximum readability.
• HTML Rendering Engine: Custom line-stripping algorithm prevents Markdown parsers from escaping custom M3 HTML cards.
• Clean Ticker Presets: Dropdown selectbox and custom text input pattern without Streamlit session state collisions.
• Interactive Plotly Gauge: Score gauge with M3 dark threshold steps.

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🚀 Option A: Deploy to Streamlit Community Cloud (FREE)

Step 1: Push to GitHub

git add .
git commit -m "feat: upgrade to Aegis Equity Terminal with M3 Dark Theme"
git branch -M main
git remote add origin https://github.com/YOUR_USERNAME/stock-analysis.git
git push -u origin main

Step 2: Deploy on Streamlit Cloud (2 minutes)

1. Go to share.streamlit.io and sign in with GitHub.
2. Click "New app".
3. Fill in the form:
   • Repository: YOUR_USERNAME/stock-analysis
   • Branch: main
   • Main file path: app.py
4. Click "Deploy!".

🎉 Your web terminal will be live at https://YOUR_APP.streamlit.app!

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💻 Option B: Run Locally

1. Install Dependencies

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

2. Launch Streamlit Web App

streamlit run app.py

Open http://localhost:8501 in your browser.

3. Or Run Local CLI Launcher

# Analyze US stock
./analyse.py AAPL

# Analyze Australian stock
./analyse.py CBA.AX

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📊 6-Pillar Analytical Model

Pillar	Weight	Indicators Measured
🏦 Financial Fundamentals	25%	Debt-to-Equity, ROE, Profit Margins, Revenue Growth, Free Cash Flow
🏷️ Valuation Multiples	20%	Trailing P/E, Forward P/E, PEG Ratio, Price-to-Sales, EV/EBITDA
📈 Technical Momentum	15%	14-day RSI, 50/200-day Moving Average Crossovers, MACD, 52-week position
📰 News & Social Sentiment	15%	Real-time news & Reddit headline sentiment polarity indexing
🎯 Analyst Consensus	15%	Wall Street price targets, upside/downside %, rating distribution
🛡️ Macro, Moat & Industry	10%	Economic moat rating, monetary policy sensitivity, market expectation pricing

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📂 Project Structure

├── app.py                          # Streamlit M3 Dark Web Terminal
├── analyse.py                      # Executable CLI Launcher with auto venv self-exec
├── analyze.py                      # Alias wrapper script
├── .streamlit/
│   └── config.toml                 # Streamlit M3 Dark color tokens
├── requirements.txt                # Python package dependencies
├── README.md                       # Documentation & deployment guide
├── .gitignore                      # Git ignore rules
└── stock_bot/                      # Core Python analytical modules
    ├── config.py                   # Exchange currencies & monetary policy metadata
    ├── data_fetcher.py             # Financial ratios & yfinance data extractor
    ├── technical_analysis.py      # RSI, SMA moving averages & MACD engine
    ├── sentiment_analysis.py      # News & Reddit headline sentiment analyzer
    ├── qualitative_analysis.py    # Economic moat & macro expectation modeling
    ├── scoring_engine.py          # 6-Pillar composite score calculator
    └── report_generator.py        # Terminal formatting & HTML/MD report generators

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📜 Disclaimer

This tool is for educational and research purposes only. It does not constitute financial advice.