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# 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