From 53e6e078296a53686d32bf7b279097a376c7940a Mon Sep 17 00:00:00 2001
From: Harwinder <hssekhon93@hotmail.com>
Date: Thu, 7 May 2026 17:40:39 -0400
Subject: [PATCH] Wire regime filter into live mode

--regime-filter previously affected only backtests. Live mode now also
checks higher-TF ADX after analysis: signals are downgraded to NEUTRAL
with a warning when ADX < threshold, and confirming when above.

This brings live behavior into parity with what the backtester measures.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
---
 signal_aggregator.py | 74 +++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 73 insertions(+), 1 deletion(-)

diff --git a/signal_aggregator.py b/signal_aggregator.py
index f5179c7..a7e53cf 100644
--- a/signal_aggregator.py
+++ b/signal_aggregator.py
@@ -109,6 +109,10 @@ class SignalAggregator:
             self.config.confirmation_timeframes
         )
 
+        bt = self.config.backtest
+        if bt.regime_filter and bt.regime_filter_timeframe not in timeframes:
+            timeframes.append(bt.regime_filter_timeframe)
+
         dfs: Dict[Timeframe, pd.DataFrame] = {}
         for tf in timeframes:
             try:
@@ -116,13 +120,81 @@ class SignalAggregator:
             except Exception as e:
                 logger.warning(f"Fetch failed for {symbol} {tf.value}: {e}")
 
-        return self.analyze_from_dataframes(
+        report = self.analyze_from_dataframes(
             symbol=symbol,
             dfs=dfs,
             context=context,
             use_news=True,
         )
 
+        if bt.regime_filter:
+            self._apply_regime_filter_live(report, dfs)
+
+        return report
+
+    def _apply_regime_filter_live(
+        self, report: SignalReport, dfs: Dict[Timeframe, pd.DataFrame]
+    ) -> None:
+        """If higher-TF ADX is below threshold, downgrade signal to NEUTRAL."""
+        bt = self.config.backtest
+        regime_tf = bt.regime_filter_timeframe
+        df = dfs.get(regime_tf)
+        if df is None or df.empty:
+            report.warnings.append(
+                f"Regime filter requested but {regime_tf.value} data unavailable"
+            )
+            return
+
+        adx_value = self._latest_adx(df, self.config.technical.adx_period)
+        if adx_value is None:
+            report.warnings.append(
+                f"Regime filter: ADX could not be computed on {regime_tf.value}"
+            )
+            return
+
+        if adx_value < bt.regime_filter_min_adx:
+            report.warnings.append(
+                f"REGIME FILTER: {regime_tf.value} ADX={adx_value:.1f} < "
+                f"{bt.regime_filter_min_adx:.0f} — market choppy, skipping signal"
+            )
+            report.final_signal = SignalStrength.NEUTRAL
+            report.signal_quality = "F"
+            report.action_recommendation = (
+                f"NO TRADE — Regime filter (ADX {adx_value:.1f} on {regime_tf.value})"
+            )
+            report.trade_setup = None
+        else:
+            report.bullish_reasons.append(
+                f"Regime: {regime_tf.value} ADX={adx_value:.1f} confirms trending market"
+            ) if report.final_score > 0 else report.bearish_reasons.append(
+                f"Regime: {regime_tf.value} ADX={adx_value:.1f} confirms trending market"
+            )
+
+    @staticmethod
+    def _latest_adx(df: pd.DataFrame, period: int = 14) -> Optional[float]:
+        """Wilder ADX on a fresh df; returns the last finite value or None."""
+        if df is None or len(df) < period * 3:
+            return None
+        high, low, close = df["High"], df["Low"], df["Close"]
+        tr = pd.concat([
+            high - low,
+            (high - close.shift()).abs(),
+            (low - close.shift()).abs(),
+        ], axis=1).max(axis=1)
+        plus_dm = high.diff()
+        minus_dm = -low.diff()
+        plus_dm = plus_dm.where((plus_dm > minus_dm) & (plus_dm > 0), 0.0)
+        minus_dm = minus_dm.where((minus_dm > plus_dm) & (minus_dm > 0), 0.0)
+        atr_s = tr.ewm(alpha=1/period, min_periods=period).mean()
+        plus_di = 100 * (plus_dm.ewm(alpha=1/period, min_periods=period).mean() / atr_s)
+        minus_di = 100 * (minus_dm.ewm(alpha=1/period, min_periods=period).mean() / atr_s)
+        denom = (plus_di + minus_di).replace(0, float("nan"))
+        dx = 100 * (plus_di - minus_di).abs() / denom
+        adx = dx.ewm(alpha=1/period, min_periods=period).mean().dropna()
+        if adx.empty:
+            return None
+        return float(adx.iloc[-1])
+
     # ── Public: bar-replayable entry point ─────────────────────────
 
     def analyze_from_dataframes(