feat: Gold (XAU/USD) — daily swing scanner + TF auto-adaptation

- Gold Swing Scanner: 255 daily strategies, best EMA +2.8% OOS/month
- Auto-adapt timeframes for daily data (1d/1w instead of 15min/4h)
- Session filter skips for daily data
- XAUUSD added to instruments list

scripts/gold_swing_scanner.py

@@ -0,0 +1,193 @@
+#!/usr/bin/env python3
+"""Gold Swing Scanner — Daily strategies for position/swing trading.
+
+Unlike the 1-min grid search, this targets multi-day holds on daily Gold data.
+Tests: Trend-following, momentum, mean-reversion, breakout on 1-20 day horizons.
+"""
+import json, os, sys, time, itertools
+from datetime import datetime
+from pathlib import Path
+import numpy as np, pandas as pd
+
+PROJECT = Path(__file__).resolve().parent.parent
+OUTPUT_DIR = PROJECT / "results" / "gold_swing"
+OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
+
+sys.path.insert(0, str(PROJECT / "scripts"))
+from nexquant_rd_loop import _backtest_numba
+
+def build_daily_signal(close, indicator, params):
+    """Build signal on raw daily close (no resampling)."""
+    import talib
+    c = close.values.astype(np.float64)
+    s = np.zeros(len(c), dtype=np.int32)
+
+    if indicator == 'MACD':
+        mc, sc, _ = talib.MACD(c, fastperiod=params.get('fast',12),
+                               slowperiod=params.get('slow',26),
+                               signalperiod=params.get('sig',9))
+        s[mc > sc] = 1; s[mc < sc] = -1
+    elif indicator == 'SMA':
+        fa = pd.Series(c).rolling(params.get('fast',20)).mean().values
+        sl = pd.Series(c).rolling(params.get('slow',50)).mean().values
+        s[fa > sl] = 1; s[fa < sl] = -1
+    elif indicator == 'EMA':
+        fa = pd.Series(c).ewm(span=params.get('fast',12)).mean().values
+        sl = pd.Series(c).ewm(span=params.get('slow',26)).mean().values
+        s[fa > sl] = 1; s[fa < sl] = -1
+    elif indicator == 'ROC':
+        v = talib.ROC(c, timeperiod=params.get('period',20))
+        th = params.get('threshold',2.0)
+        s[v > th] = 1; s[v < -th] = -1
+    elif indicator == 'MOM':
+        v = talib.MOM(c, timeperiod=params.get('period',20))
+        s[v > 0] = 1; s[v < 0] = -1
+    elif indicator == 'RSI_OBOS':
+        v = talib.RSI(c, timeperiod=params.get('period',14))
+        s[v < params.get('oversold',30)] = 1; s[v > params.get('overbought',70)] = -1
+    elif indicator == 'Donchian':
+        hi = pd.Series(c).rolling(params.get('period',20)).max().shift(1).values
+        lo = pd.Series(c).rolling(params.get('period',20)).min().shift(1).values
+        s[c > hi] = 1; s[c < lo] = -1
+        # Hold until reverse
+        hold = params.get('hold',5)
+        if hold > 0:
+            last = 0; cnt = 0
+            for i in range(len(s)):
+                if s[i] != 0: last = s[i]; cnt = hold
+                elif cnt > 0: s[i] = last; cnt -= 1
+    elif indicator == 'BB':
+        up, mi, lo = talib.BBANDS(c, timeperiod=params.get('period',20),
+                                  nbdevup=params.get('std',2), nbdevdn=params.get('std',2))
+        s[c < lo] = 1; s[c > up] = -1
+
+    return pd.Series(s, index=close.index).fillna(0).astype(int).clip(-1,1)
+
+
+# ── Grid Definition ──
+INDICATOR_GRIDS = {
+    'MACD': {
+        'fast': [3,5,8,12,21],
+        'slow': [10,15,21,26,34,50],
+        'sig': [3,5,9,13],
+    },
+    'SMA': {
+        'fast': [10,20,50,100],
+        'slow': [20,50,100,200],
+    },
+    'EMA': {
+        'fast': [5,8,12,21],
+        'slow': [13,21,34,55],
+    },
+    'ROC': {
+        'period': [5,10,20,50,100],
+        'threshold': [0.5,1.0,2.0,3.0,5.0],
+    },
+    'MOM': {
+        'period': [10,20,50,100],
+    },
+    'RSI_OBOS': {
+        'period': [7,14,21],
+        'oversold': [20,25,30,35],
+        'overbought': [65,70,75,80],
+    },
+    'Donchian': {
+        'period': [5,10,20,50,100],
+        'hold': [0,1,3,5,10],
+    },
+    'BB': {
+        'period': [10,20,50],
+        'std': [1.5,2.0,2.5,3.0],
+    },
+}
+
+
+def load_gold_daily():
+    """Load daily Gold data."""
+    path = PROJECT / "git_ignore_folder" / "xau_daily.h5"
+    if path.exists():
+        return pd.read_hdf(path, key="data")
+    return None
+
+
+def main():
+    print("=" * 60)
+    print("  Gold Swing Scanner — Daily Position Strategies")
+    print("=" * 60)
+
+    close = load_gold_daily()
+    if close is None:
+        print("  XAUUSD daily data not found! Run download first."); return
+    print(f"  XAUUSD daily: {len(close)} bars, {close.index[0].date()} -> {close.index[-1].date()}")
+
+    all_results = []
+    total = 0
+    for ind_name, grid in INDICATOR_GRIDS.items():
+        keys = list(grid.keys())
+        values = list(grid.values())
+        for combo in itertools.product(*values):
+            total += 1
+            params = dict(zip(keys, combo))
+            try:
+                sig = build_daily_signal(close, ind_name, params)
+                if sig is None or sig.nunique() <= 1: continue
+            except: continue
+
+            n = len(close); is_n = int(n * 0.8)
+            if is_n < 10: continue  # too little data
+            p = close.values.astype(float); s = sig.values.astype(np.int32)
+
+            if np.sum(np.abs(s)) < 10: continue
+
+            p_is = close.iloc[:is_n].values.astype(float); s_is = sig.iloc[:is_n].values.astype(np.int32)
+            p_oos = close.iloc[is_n:].values.astype(float); s_oos = sig.iloc[is_n:].values.astype(np.int32)
+
+            _, dd, tr, w, ret, sh, _ = _backtest_numba(p, s)
+            _, _, tr_o, _, ret_o, sh_o, _ = _backtest_numba(p_oos, s_oos)
+
+            nd = (close.index[-1] - close.index[0]).days
+            if nd <= 0: continue
+            mon = ((1+ret)**(1/(nd/30.44))-1)*100 if ret > -1 else 0
+            nd_o = (close.index[is_n:][-1] - close.index[is_n:][0]).days
+            if nd_o <= 0: nd_o = 1
+            mon_o = ((1+ret_o)**(1/(nd_o/30.44))-1)*100 if ret_o > -1 else 0
+
+            all_results.append({
+                'indicator': ind_name, 'params': params,
+                'sharpe': float(sh), 'sharpe_oos': float(sh_o),
+                'monthly_pct': float(mon), 'monthly_oos': float(mon_o),
+                'n_trades': int(tr), 'n_trades_oos': int(tr_o),
+                'win_rate': float(w/tr) if tr>0 else 0,
+                'max_dd': float(-dd),
+            })
+
+    all_results.sort(key=lambda r: r['sharpe_oos'], reverse=True)
+
+    print(f"  {len(all_results)}/{total} strategies with trades\n")
+
+    print(f"  TOP 20 by OOS Sharpe:")
+    print(f"  {'Rank':>4s} {'Indicator':<15s} {'Sh IS':>6s} {'Sh OOS':>7s} {'Mon IS':>7s} {'Mon OOS':>7s} {'DD':>6s} {'Tr':>5s}")
+    for i, r in enumerate(all_results[:20], 1):
+        print(f"  {i:4d} {r['indicator']:<15s} {r['sharpe']:+6.1f} {r['sharpe_oos']:+7.1f} "
+              f"{r['monthly_pct']:+6.1f}% {r['monthly_oos']:+6.1f}% "
+              f"{r['max_dd']:.4f} {r['n_trades']:5d}")
+
+    # Save
+    ts = datetime.now().strftime("%Y%m%d_%H%M%S")
+    out = OUTPUT_DIR / f"gold_swing_{ts}.json"
+    out.write_text(json.dumps(all_results, indent=2, default=str))
+    print(f"\n  Saved: {out}")
+
+    # Indicator summary
+    from collections import Counter
+    print(f"\n  Indicator Performance:")
+    for ind in INDICATOR_GRIDS.keys():
+        r = [r for r in all_results if r['indicator'] == ind]
+        if r:
+            print(f"  {ind:<15s}: max Sh={max(x['sharpe'] for x in r):+.1f} "
+                  f"OOS={max(x['sharpe_oos'] for x in r):+.1f} "
+                  f"({len(r)} combos)")
+
+
+if __name__ == "__main__":
+    main()

scripts/nexquant_rd_loop.py

@@ -22,9 +22,12 @@ RESULTS_DIR = PROJECT / "results" / "rd_loop"
 STATE_DIR = PROJECT / "git_ignore_folder" / "rd_loop_state"
 
 INSTRUMENTS = ["EURUSD", "GBPUSD", "BTCUSD", "XAUUSD"]
-INSTRUMENT_ALIASES = {"GBPUSDT": "GBPUSD"}
+LEADER_MAP = {"GBPUSD": "EURUSD"}
-LEADER_MAP = {"GBPUSD": "EURUSD"}  # Cross-pair: GBP confirms with EUR
+INSTRUMENT_TIMEFRAMES = {
-TIMEFRAMES = ["5min", "15min", "30min", "1h", "4h"]
+    "XAUUSD": ["1d", "1w"],           # Daily data → daily/weekly TFs
+    "default": ["5min", "15min", "30min", "1h", "4h"],
+}
+TIMEFRAMES = ["5min", "15min", "30min", "1h", "4h", "1d", "1w"]
 INDICATORS_POOL = ["MACD", "RSI", "BBands", "Donchian", "Stoch", "CCI", "WillR", "ADX", "SAR", "ROC", "MOM", "AROON", "MFI", "SMA", "EMA"]
 STRATEGY_TYPES = ["single", "multi_tf", "multi_role"]
 TREND_TFS = ["30min", "1h", "4h"]
@@ -101,6 +104,25 @@ def build_signal(close, hypothesis):
     import talib
     signal = None
 
+    # Adapt timeframes to data frequency
+    median_delta = (close.index[1:] - close.index[:-1]).median()
+    if median_delta > pd.Timedelta("1h"):
+        valid_tfs = ["1d", "1w"]
+        tf_map = {"5min": "1d", "15min": "1d", "30min": "1d", "1h": "1d", "4h": "1w"}
+        # Remap hypothesis timeframes
+        hp = dict(hypothesis)
+        if hp.get('type') in ('single', 'multi_tf') and 'timeframe' in hp:
+            hp['timeframe'] = tf_map.get(hp.get('timeframe','1h'), '1d')
+        if hp.get('type') == 'multi_tf' and 'timeframes' in hp:
+            hp['timeframes'] = [tf_map.get(t, '1d') for t in hp['timeframes']]
+            hp['timeframes'] = list(set(hp['timeframes']))  # dedup
+        if hp.get('type') == 'multi_role':
+            hp['trend_tf'] = tf_map.get(hp.get('trend_tf','4h'), '1w')
+            hp['entry_tf'] = tf_map.get(hp.get('entry_tf','15min'), '1d')
+        hypothesis = hp
+    else:
+        valid_tfs = ["5min", "15min", "30min", "1h", "4h"]
+
     if hypothesis['type'] == 'single':
         ind = hypothesis['indicator']; tf = hypothesis['timeframe']
         bars = close.resample(tf).last().dropna()
@@ -136,7 +158,10 @@ def build_signal(close, hypothesis):
 
 
 def _apply_session_filter(signal, index):
-    """Only trade London session (07:00-16:00 UTC Mon-Fri)."""
+    """Only trade London session (07:00-16:00 UTC Mon-Fri). Skip for daily data."""
+    delta = (index[1:] - index[:-1]).median() if len(index) > 1 else pd.Timedelta("1min")
+    if delta > pd.Timedelta("1h"):
+        return signal  # Skip session filter for daily/weekly data
     hours = index.hour
     days = index.dayofweek
     in_session = (days < 5) & (hours >= 7) & (hours < 16)