diff --git a/scripts/nexquant_daily_strategies.py b/scripts/nexquant_daily_strategies.py
new file mode 100644
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+++ b/scripts/nexquant_daily_strategies.py
@@ -0,0 +1,269 @@
+#!/usr/bin/env python
+"""
+NexQuant Daily Strategy Generator — systematisch, kein LLM.
+
+Grid-search für SMA/EMA/RSI/MACD/Momentum/Mean-Reversion auf Tagesdaten.
+Speichert Top-Strategien als JSON für den Live-Trading-Workflow.
+
+Usage:
+    python scripts/nexquant_daily_strategies.py
+    python scripts/nexquant_daily_strategies.py --top 10 --cost 2.14
+"""
+
+from __future__ import annotations
+
+import json, sys, time
+from datetime import datetime
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+
+from rdagent.components.backtesting.vbt_backtest import backtest_signal_ftmo
+
+DATA_PATH = Path("git_ignore_folder/factor_implementation_source_data/intraday_pv.h5")
+OUT_DIR = Path("results/strategies_daily")
+OUT_DIR.mkdir(parents=True, exist_ok=True)
+
+TXN_COST_BPS = 2.14
+MIN_TRADES_OOS = 5
+
+
+def load_daily_data():
+    close = pd.read_hdf(DATA_PATH, key="data")["$close"]
+    if isinstance(close.index, pd.MultiIndex):
+        close = close.droplevel(-1)
+    return close.sort_index().dropna().resample("1D").last().dropna()
+
+
+def backtest(signal: pd.Series, close: pd.Series) -> dict:
+    if signal is None or len(signal) < 10:
+        return {}
+    sig = signal.fillna(0).replace([np.inf, -np.inf], 0)
+    r = backtest_signal_ftmo(close, sig, txn_cost_bps=TXN_COST_BPS, wf_rolling=True)
+    return {
+        "is_sharpe": r.get("is_sharpe", None),
+        "is_monthly_pct": r.get("is_monthly_return_pct", None),
+        "is_trades": r.get("is_n_trades", 0),
+        "oos_sharpe": r.get("oos_sharpe", None),
+        "oos_monthly_pct": r.get("oos_monthly_return_pct", None),
+        "oos_max_dd": r.get("oos_max_drawdown", None),
+        "oos_win_rate": r.get("oos_win_rate", None),
+        "oos_trades": r.get("oos_n_trades", 0),
+        "wf_sharpe": r.get("wf_oos_sharpe_mean", None),
+        "wf_monthly_pct": r.get("wf_oos_monthly_return_mean", None),
+        "wf_consistency": r.get("wf_oos_consistency", None),
+        "mc_pvalue": r.get("mc_pvalue", None),
+        "full_metrics": r,
+    }
+
+
+def make_sma_signal(close, fast, slow):
+    f = close.rolling(fast).mean()
+    s = close.rolling(slow).mean()
+    sig = pd.Series(0.0, index=close.index)
+    sig[f > s] = 1
+    sig[f < s] = -1
+    return sig
+
+
+def make_ema_signal(close, fast, slow):
+    f = close.ewm(span=fast).mean()
+    s = close.ewm(span=slow).mean()
+    sig = pd.Series(0.0, index=close.index)
+    sig[f > s] = 1
+    sig[f < s] = -1
+    return sig
+
+
+def make_rsi_signal(close, period, oversold, overbought):
+    delta = close.diff()
+    gain = delta.clip(lower=0)
+    loss = -delta.clip(upper=0)
+    rsi = 100 - (100 / (1 + gain.rolling(period).mean() / (loss.rolling(period).mean() + 1e-8)))
+    sig = pd.Series(0.0, index=close.index)
+    sig[rsi < oversold] = 1
+    sig[rsi > overbought] = -1
+    return sig
+
+
+def make_macd_signal(close, fast, slow, signal_period):
+    ema_fast = close.ewm(span=fast).mean()
+    ema_slow = close.ewm(span=slow).mean()
+    macd = ema_fast - ema_slow
+    sig_line = macd.ewm(span=signal_period).mean()
+    sig = pd.Series(0.0, index=close.index)
+    sig[macd > sig_line] = 1
+    sig[macd < sig_line] = -1
+    return sig
+
+
+def make_momentum_signal(close, n):
+    mom = close.pct_change(n)
+    return pd.Series(np.sign(mom).fillna(0), index=close.index)
+
+
+def make_meanrev_signal(close, n):
+    ret = close.pct_change(n)
+    return pd.Series(-np.sign(ret).fillna(0), index=close.index)
+
+
+def make_bollinger_signal(close, period, std_dev):
+    ma = close.rolling(period).mean()
+    std = close.rolling(period).std()
+    sig = pd.Series(0.0, index=close.index)
+    sig[close < ma - std_dev * std] = 1
+    sig[close > ma + std_dev * std] = -1
+    return sig
+
+
+def main(top_n=15, cost_bps=2.14):
+    global TXN_COST_BPS
+    TXN_COST_BPS = cost_bps
+
+    print(f"\n{'='*60}")
+    print(f"  NexQuant Daily Strategy Generator")
+    print(f"  Cost: {cost_bps} bps | Saving top {top_n}")
+    print(f"{'='*60}")
+
+    close = load_daily_data()
+    print(f"Data: {len(close):,} daily bars ({close.index[0].date()} - {close.index[-1].date()})\n")
+
+    results = []
+
+    # SMA Crossovers
+    print("SMA crossovers...")
+    for fast in [5, 10, 15, 20, 30]:
+        for slow in [fast * 2, fast * 3, fast * 4, fast * 5]:
+            if slow > 250: continue
+            sig = make_sma_signal(close, fast, slow)
+            bt = backtest(sig, close)
+            if bt.get("oos_trades", 0) >= MIN_TRADES_OOS:
+                score = bt.get("oos_sharpe") or -999
+                results.append(("SMA", f"SMA{fast}/{slow}", fast, slow, score, bt))
+
+    # EMA Crossovers
+    print("EMA crossovers...")
+    for fast in [5, 10, 15, 20, 30]:
+        for slow in [fast * 2, fast * 3, fast * 4, fast * 5]:
+            if slow > 250: continue
+            sig = make_ema_signal(close, fast, slow)
+            bt = backtest(sig, close)
+            if bt.get("oos_trades", 0) >= MIN_TRADES_OOS:
+                score = bt.get("oos_sharpe") or -999
+                results.append(("EMA", f"EMA{fast}/{slow}", fast, slow, score, bt))
+
+    # RSI
+    print("RSI strategies...")
+    for period in [7, 10, 14, 21]:
+        for oversold, overbought in [(20, 80), (25, 75), (30, 70), (35, 65)]:
+            sig = make_rsi_signal(close, period, oversold, overbought)
+            bt = backtest(sig, close)
+            if bt.get("oos_trades", 0) >= MIN_TRADES_OOS:
+                score = bt.get("oos_sharpe") or -999
+                results.append(("RSI", f"RSI{period}({oversold}/{overbought})", period, 0, score, bt))
+
+    # MACD
+    print("MACD...")
+    for fast, slow, sig_p in [(8, 17, 9), (12, 26, 9), (5, 35, 5), (10, 20, 7)]:
+        s = make_macd_signal(close, fast, slow, sig_p)
+        bt = backtest(s, close)
+        if bt.get("oos_trades", 0) >= MIN_TRADES_OOS:
+            score = bt.get("oos_sharpe") or -999
+            results.append(("MACD", f"MACD{fast}/{slow}/{sig_p}", fast, slow, score, bt))
+
+    # Momentum
+    print("Momentum...")
+    for n in [5, 10, 20, 30, 50, 60, 90, 100, 120, 150, 200]:
+        sig = make_momentum_signal(close, n)
+        bt = backtest(sig, close)
+        if bt.get("oos_trades", 0) >= MIN_TRADES_OOS:
+            score = bt.get("oos_sharpe") or -999
+            results.append(("Mom", f"Mom{n}d", n, 0, score, bt))
+
+    # Mean Reversion
+    print("Mean reversion...")
+    for n in [3, 5, 7, 10, 15, 20, 30, 50]:
+        sig = make_meanrev_signal(close, n)
+        bt = backtest(sig, close)
+        if bt.get("oos_trades", 0) >= MIN_TRADES_OOS:
+            score = bt.get("oos_sharpe") or -999
+            results.append(("MR", f"MR{n}d", n, 0, score, bt))
+
+    # Bollinger Bands
+    print("Bollinger...")
+    for period in [10, 20, 50]:
+        for std_dev in [1.5, 2.0, 2.5]:
+            sig = make_bollinger_signal(close, period, std_dev)
+            bt = backtest(sig, close)
+            if bt.get("oos_trades", 0) >= MIN_TRADES_OOS:
+                score = bt.get("oos_sharpe") or -999
+                results.append(("BB", f"BB{period}/{std_dev}", period, std_dev, score, bt))
+
+    # Sort by OOS Sharpe
+    results.sort(key=lambda x: x[4] if x[4] is not None else -999, reverse=True)
+
+    print(f"\n{'='*70}")
+    print(f"  TOP {top_n} DAILY STRATEGIES (Cost: {cost_bps} bps)")
+    print(f"{'='*70}")
+    print(f"  {'#':<3} {'Type':<6} {'Name':<22} {'OOS S':>8} {'Mon%':>7} {'DD%':>6} {'WF S':>8} {'Trades':>6}")
+    print(f"  {'-'*68}")
+
+    saved = []
+    for i, (stype, name, p1, p2, score, bt) in enumerate(results[:top_n]):
+        oos_m = (bt.get("oos_monthly_pct") or 0)
+        oos_dd = (bt.get("oos_max_dd") or 0) * 100
+        wf_s = bt.get("wf_sharpe") or 0
+        trades = bt.get("oos_trades", 0)
+        status = "✅" if score > 0 else "  "
+        print(f"  {i+1:<3} {stype:<6} {name:<22} {score:>+8.2f} {oos_m:>+6.2f}% {oos_dd:>+5.1f}% {wf_s:>+8.2f} {trades:>6}  {status}")
+
+        entry = {
+            "strategy_name": name,
+            "type": stype,
+            "param1": p1,
+            "param2": p2,
+            "cost_bps": cost_bps,
+            "frequency": "daily",
+            "generated_at": datetime.now().isoformat(),
+            "metrics": {k: v for k, v in bt.items() if k != "full_metrics"},
+        }
+        saved.append(entry)
+
+        # Save individual strategy
+        safe_name = name.replace("(", "").replace(")", "").replace("/", "-")
+        fname = OUT_DIR / f"daily_{safe_name}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
+        with open(fname, "w") as f:
+            json.dump(entry, f, indent=2)
+
+    # Save summary
+    summary = {
+        "generated_at": datetime.now().isoformat(),
+        "cost_bps": cost_bps,
+        "frequency": "daily",
+        "n_bars": len(close),
+        "date_range": [str(close.index[0].date()), str(close.index[-1].date())],
+        "top_strategies": [
+            {"name": s["strategy_name"], "oos_sharpe": s["metrics"].get("oos_sharpe"),
+             "oos_monthly_pct": s["metrics"].get("oos_monthly_pct")}
+            for s in saved[:10]
+        ],
+    }
+    with open(OUT_DIR / "daily_summary.json", "w") as f:
+        json.dump(summary, f, indent=2)
+
+    profit_count = sum(1 for r in results if r[4] and r[4] > 0)
+    print(f"\n{profit_count}/{len(results)} strategies profitable ({profit_count/len(results)*100:.0f}%)")
+    print(f"Saved to {OUT_DIR}/")
+    return saved
+
+
+if __name__ == "__main__":
+    import argparse
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--top", type=int, default=15)
+    parser.add_argument("--cost", type=float, default=2.14)
+    args = parser.parse_args()
+    main(top_n=args.top, cost_bps=args.cost)