Add institutional indicator stack (VWAP/TWAP/RVOL/MTF RSI/OBV/Advanced MACD) to Pine + Python strategy logic

[Copilot]

This PR upgrades the strategy from basic trend/volume confirmation to an institutional-style multi-signal framework across both TradingView and Python. It adds VWAP, TWAP, RVOL, multi-horizon RSI, OBV, and advanced MACD (with divergence proxies), and aligns enhanced entry/exit logic between implementations.

• Pine strategy (strategies/aapl_daily_ema_rsi_strategy.pine)

  • Added configurable VWAP, session TWAP, RVOL thresholding, RSI(7/14/21), OBV + OBV SMA, and MACD line/signal/histogram.
  • Replaced prior volume gate with RVOL-based confirmation and expanded entry/exit gates:
    • Entry: trend + regime + VWAP alignment + RVOL + OBV accumulation + MACD momentum + RSI divergence guard.
    • Exit: EMA reversal, MACD momentum reversal, VWAP rejection, OBV distribution, plus existing risk exits.
  • Added signal visual/alert hooks for VWAP cross, OBV reversal, and MACD divergence.

• Python indicator module (indicators/institutional_indicators.py)

  • Introduced reusable indicator primitives: vwap, twap, rvol, rsi, obv, obv_sma, macd.
  • Added divergence helpers and non-negative volume guards.
  • Kept session-aware VWAP/TWAP behavior for parity with strategy-level usage.

• Python backtest engine (test_netrade_dashboard.py)

  • Integrated the new indicator stack into feature computation and signal generation.
  • Updated portfolio logic to consume enhanced entry/exit conditions and maintain per-indicator signal attribution counters.
  • Added dashboard plotting for new institutional indicators and expanded metrics output.

• Documentation and reporting

  • Updated README.md with new indicator definitions, strategy logic, and parameterized institutional filters.
  • Added BACKTEST_RESULTS.md to document baseline vs enhanced institutional configuration and archived result framing.

• Focused coverage (tests/test_institutional_strategy.py)

  • Added targeted tests for indicator outputs, expected computed columns/signals, and backtest return structures.

Example (Python signal stack):

data["entry_long"] = (
    (data["ema_fast"] > data["ema_slow"])
    & (data["rsi_14"].between(params.rsi_min, params.rsi_max, inclusive="both"))
    & (data["close"] > data["sma_trend"])
    & (data["close"] > data["vwap"])
    & (data["rvol"] > params.rvol_threshold)
    & (data["obv"] > data["obv_sma"])
    & (data["macd_hist"] > 0)
    & (data["macd_line"] > data["macd_signal"])
    & (~data["rsi_bear_div"].fillna(False))
)