feat: Initial Docusaurus setup with Stock Market Professional docs

- Set up Docusaurus with pnpm and TypeScript
- Structured docs for multi-application portfolio
- Added Stock Market Professional documentation:
  - Architecture & system design
  - Data monitoring module
  - Technical analysis engine
  - Signal generation with constraints (max 3/day, 30min hold)
- Added platform architecture overview
- Added getting started guide
- Added Gitea Actions CI/CD pipeline (build + artifact)

docs/applications/stock-market-professional/overview/architecture.md

@@ -0,0 +1,95 @@
+---
+sidebar_position: 1
+---
+
+# Architecture & Design
+
+## System Design
+
+Stock Market Professional follows a **pipeline architecture** with three distinct stages:
+
+### 1. Data Layer (Ingestion)
+
+Responsible for acquiring and normalizing market data from multiple sources.
+
+**Requirements:**
+- Real-time or near-real-time price data (1-minute candles minimum)
+- Support for multiple forex pairs and stock tickers
+- Historical data for backtesting (minimum 30 days)
+- Volume and order book data where available
+
+**Potential Data Sources:**
+- Yahoo Finance API (free tier)
+- Alpha Vantage API
+- Twelve Data API
+- Interactive Brokers TWS API
+- Polygon.io
+
+### 2. Analysis Layer (Processing)
+
+Applies technical indicators, statistical models, and pattern recognition.
+
+**Technical Indicators:**
+- Moving Averages (SMA, EMA, WMA)
+- RSI (Relative Strength Index)
+- MACD (Moving Average Convergence Divergence)
+- Bollinger Bands
+- Fibonacci Retracements
+- Volume-Weighted Average Price (VWAP)
+- Stochastic Oscillator
+
+**Pattern Recognition:**
+- Candlestick patterns (Doji, Hammer, Engulfing, etc.)
+- Chart patterns (Head & Shoulders, Double Top/Bottom, Triangles)
+- Support/Resistance levels
+- Breakout detection
+
+**Statistical Analysis:**
+- Volatility clustering (GARCH models)
+- Mean reversion detection
+- Momentum scoring
+- Correlation analysis between pairs
+
+### 3. Signal Layer (Output)
+
+Generates actionable trading signals with strict constraints.
+
+**Signal Format:**
+```json
+{
+  "date": "2026-07-16",
+  "signals": [
+    {
+      "pair": "EUR/USD",
+      "direction": "BUY",
+      "entry_price": 1.0892,
+      "stop_loss": 1.0875,
+      "take_profit": 1.0920,
+      "confidence": 0.82,
+      "reasoning": "Bullish engulfing + RSI oversold bounce + VWAP support",
+      "time_window": "09:00-09:30 UTC",
+      "max_hold_minutes": 30
+    }
+  ],
+  "max_signals_per_day": 3
+}
+```
+
+## Technology Choices
+
+| Component | Technology | Reason |
+|---|---|---|
+| Language | Python 3.11+ | Rich ecosystem for finance/ML |
+| Data | pandas, numpy | Industry standard for time series |
+| Indicators | ta-lib, pandas-ta | Comprehensive TA libraries |
+| ML/Stats | scikit-learn, statsmodels | Pattern recognition, GARCH |
+| Scheduling | APScheduler / cron | Regular data pulls and analysis |
+| Delivery | Discord webhook / API | Direct consumer notification |
+| Storage | SQLite / TimescaleDB | Time-series optimized |
+
+## Deployment
+
+- Containerized (Docker)
+- Runs on TrueNAS infrastructure
+- CI/CD via Gitea Actions
+- Health monitoring and alerting