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:

{
  "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