research(alt): sweep 104 strategie alternative su Deribit (153 agenti) + marginal scorer

Ondata di ricerca onesta a largo spettro su BTC/ETH+DVOL certificati: 104 ipotesi distinte (11 famiglie), un agente-finder per ipotesi, verifica avversariale a 3 scettici sui promettenti, sintesi (153 agenti totali). Esito: NIENTE di nuovo regge -> conferma del soffitto strutturale ~1.3 BTC/ETH-direzionale; lo stack TP01+XS01+VRP01 resta imbattuto. - altlib.py: harness condiviso vettoriale leak-free (eval_weights/study_weights, fee-sweep, both-asset + hold-out 2025+). Riproduce i numeri canonici di TP01. - MARGINAL SCORER (study_marginal/marginal_vs_tp01): Sharpe INCREMENTALE vs baseline TP01 (corr, blend uplift OOS, alpha residua) + jackknife OOS (clean-year + drop-best-month). earns_slot = abs!=FAIL & ADDS & robust_oos. Smaschera gli overlay su TSMOM con PASS assoluti fasulli (CMB04, VOL11, ...) e il falso positivo KAMA (ADDS ma muore al jackknife). - runs/*.py (104) script riproducibili per ipotesi; wf_altstrat.js workflow. - Verdetto: 0 candidati deployabili; 2 LEAD fragili (VOL08, STA05_LS) da forward-monitor. - test_marginal_scorer.py blocca baseline + invarianti. Suite: 32 verde. Diario: docs/diary/2026-06-20-alt-strategies-100agent-sweep.md Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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+"""CMB04 — Momentum + Low-Vol Filter
+HYPOTHESIS: TSMOM long-flat taken only when realized vol is below its rolling median
+(avoid high-vol whipsaw). Vol-target the rest.
+
+Grid: 2 vol-filter windows (30d vs 60d rolling median lookback) x 2 TFs (1d, 12h) = 4 cells total.
+Best config chosen by min(BTC,ETH) holdout Sharpe.
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+
+
+def cmb04_target(df, vol_filter_days: int = 30):
+    """
+    TSMOM multi-horizon (1m/3m/6m) long-flat, gated by a low-vol filter:
+    - Compute realized vol (30d) at each bar.
+    - Compute rolling median of that vol over vol_filter_days.
+    - Only take the TSMOM signal when realized_vol < rolling_median (low-vol regime).
+    - In high-vol regime: go flat (0).
+    - Vol-target the resulting direction.
+    """
+    c = df["close"].values.astype(float)
+    bpd = al.bars_per_day(df)
+    bpy = bpd * 365.25
+
+    # --- TSMOM multi-horizon direction (1m, 3m, 6m) ---
+    horizons = (30 * bpd, 90 * bpd, 180 * bpd)
+    direction = np.zeros(len(c))
+    for h in horizons:
+        h = int(h)
+        sig = np.full(len(c), np.nan)
+        if h < len(c):
+            sig[h:] = np.sign(c[h:] / c[:-h] - 1.0)
+        direction += np.nan_to_num(sig, nan=0.0)
+    # Majority vote -> long or flat
+    direction = np.clip(np.sign(direction), 0.0, 1.0)  # long-flat only
+
+    # --- Realized vol (30d causal) ---
+    rv_win = max(2, 30 * bpd)
+    r = al.simple_returns(c)
+    rv = al.realized_vol(r, rv_win, bpy)
+
+    # --- Rolling median of realized vol over vol_filter_days ---
+    med_win = max(2, vol_filter_days * bpd)
+    rv_median = (
+        al._series_if_array(rv).rolling(med_win, min_periods=max(2, med_win // 2)).median().values
+        if hasattr(al, "_series_if_array")
+        else __import__("pandas").Series(rv).rolling(med_win, min_periods=max(2, med_win // 2)).median().values
+    )
+
+    # --- Gate: only enter when rv < median (low-vol regime) ---
+    low_vol_gate = np.where(
+        np.isfinite(rv) & np.isfinite(rv_median) & (rv < rv_median),
+        1.0,
+        0.0
+    )
+    gated_direction = direction * low_vol_gate
+
+    # --- Vol-target the gated direction ---
+    pos = al.vol_target(gated_direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
+    return pos
+
+
+def make_target_fn(vol_filter_days: int):
+    def fn(df):
+        return cmb04_target(df, vol_filter_days=vol_filter_days)
+    return fn
+
+
+if __name__ == "__main__":
+    import pandas as pd
+
+    best_rep = None
+    best_hold = -9.0
+    best_label = ""
+
+    configs = [
+        ("CMB04-vf30", 30),
+        ("CMB04-vf60", 60),
+    ]
+
+    for label, vfd in configs:
+        fn = make_target_fn(vfd)
+        rep = al.study_weights(label, fn, tfs=("1d", "12h"))
+        v = rep["verdict"]
+        h = v.get("best_holdout_sharpe", -9)
+        print(al.fmt(rep))
+        print(f"  [grid] {label}: holdout={h:.3f}")
+        if h > best_hold:
+            best_hold = h
+            best_rep = rep
+            best_label = label
+
+    print("\n=== BEST CONFIG ===", best_label)
+    print(al.fmt(best_rep))
+    print("JSON:", al.as_json(best_rep))