Add production-ready AAPL strategy implementation in Pine Script and Python backtesting framework

[Copilot]

Implements a unified, backtestable long strategy for AAPL (daily, 2015–2024 spec) across TradingView and Python, with identical entry/exit/risk rules and required data/metric handling. This PR adds complete strategy logic, portfolio simulation, and documentation for deployment and interpretation.

• Strategy parity across platforms

  • Added Pine v5 strategy: /pine/aapl_ema_rsi_trend_strategy.pine
  • Added Python backtesting module: /test_netrade_dashboard.py
  • Kept signal logic aligned in both implementations:
    • Entry: EMA(20) cross over EMA(50) + RSI(14) in [40,70] + Close > SMA(200) + volume filter + ATR/close filter
    • Exit precedence: 8% trailing stop, EMA cross down, 12% hard stop, 15% profit target
    • Position sizing: 2% equity risk with 12% stop-distance sizing model

• Pine Script deliverable (TradingView-ready)

  • Parameterized inputs for optimization/backtest tuning
  • Entry/exit alerts and chart overlays (EMA20/50, SMA200, signal markers)
  • Performance table (net profit, closed trades, win rate, profit factor, max drawdown, equity)
  • Guardrails for quantity sizing and EMA length ordering

• Python backtesting deliverable (test_netrade_dashboard.py)

  • Data ingestion from Yahoo Finance (auto_adjust=True) plus offline CSV mode
  • Indicator pipeline: EMA, RSI, SMA, ATR, volume SMA, ATR ratio
  • Stateful trade engine: fills, slippage (0.02%), trade log, equity curve, drawdown
  • Metrics: profit factor, win rate, Sharpe, CAGR, total return, trade count
  • Visualization: price/indicator panel, equity curve, drawdown, trade marks
  • CLI surface for symbol/date/capital/plot/optimization options
  • Built-in unit tests for indicator generation, signal schema, and backtest metric outputs

• Documentation updates

  • Expanded README.md with strategy spec, run commands, optimization/offline usage, data assumptions (including business-day approximation), and risk disclaimer.

params = StrategyParams()  # defaults match Pine strategy
data = fetch_ohlcv("AAPL", "2015-01-01", "2024-12-31")
enriched, trades, metrics = backtest(data, params, initial_capital=100_000)
print(metrics["profit_factor"], metrics["max_drawdown"], metrics["trades"])