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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+"""RSK04 — Momentum-of-Momentum Sizing
+HYPOTHESIS: Size the TSMOM (long-flat) position by the STABILITY/AGREEMENT of
+multi-horizon momentum signals. When all horizons agree (strong consensus), take
+a larger position. When signals disagree, reduce exposure.
+
+MECHANISM:
+- Compute TSMOM signals for 3 horizons: 1M, 3M, 6M (same as TP01 canonical)
+- Direction = go long only if net signal > 0 (majority bullish), else flat
+- SIZE = fraction of horizons that agree with the majority direction
+  e.g. all 3 agree -> size=1.0, 2/3 agree -> size=0.667, 1/3 -> flat
+- Apply vol-targeting on top of the sized position
+
+INTERNAL GRID (<=4 configs x 2 assets x 2 TFs = <=16 backtests):
+  A: horizons=(1M,3M,6M), size by fraction-agreement
+  B: horizons=(1M,3M,6M,12M), size by fraction-agreement (4 horizons)
+Two TFs: 1d, 12h  -> 2 configs x 2 tfs x 2 assets = 8 backtests total
+
+CAUSAL: all signals use close[i] for the past horizon -> no leakage.
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+
+
+def make_target(horizons_months, tf):
+    """Return a target_fn(df) that implements momentum-of-momentum sizing."""
+    def target_fn(df):
+        c = df["close"].values.astype(float)
+        n = len(c)
+        bpd = al.bars_per_day(df)
+
+        # Compute per-horizon signals: +1 (bullish) or 0 (bearish/flat)
+        # Signal at bar i: sign of return over last `h` bars
+        signals = []
+        for months in horizons_months:
+            h = int(round(months * 30.44 * bpd))
+            h = max(h, 2)
+            sig = np.zeros(n)
+            # causal: sig[i] uses close[i] vs close[i-h]
+            sig[h:] = np.where(c[h:] / c[:n-h] > 1.0, 1.0, 0.0)
+            # NaN guard: first h bars stay 0
+            signals.append(sig)
+
+        signals = np.stack(signals, axis=1)  # shape (n, num_horizons)
+        num_horizons = len(horizons_months)
+
+        # Net bullish count at each bar
+        bullish_count = signals.sum(axis=1)  # in [0, num_horizons]
+        bearish_count = num_horizons - bullish_count
+
+        # Direction: go long only if strict majority bullish
+        direction = np.where(bullish_count > num_horizons / 2, 1.0, 0.0)
+
+        # Size = fraction of horizons agreeing with the direction taken
+        # If long: fraction_agree = bullish_count / num_horizons
+        # If flat (direction=0): size = 0
+        fraction_agree = np.where(
+            direction > 0,
+            bullish_count / num_horizons,
+            0.0
+        )
+
+        # Apply vol-targeting with the agreement-sized direction
+        # We pass the sized direction (0..1) into vol_target as if it were direction
+        target = al.vol_target(fraction_agree, df, target_vol=0.20,
+                               vol_win_days=30, leverage_cap=2.0)
+        return target
+
+    return target_fn
+
+
+# Config A: 3 horizons (1M, 3M, 6M)
+horizons_A = [1, 3, 6]
+# Config B: 4 horizons (1M, 3M, 6M, 12M)
+horizons_B = [1, 3, 6, 12]
+
+# Run on 1d and 12h timeframes
+rep_A = al.study_weights(
+    "RSK04-A(1M3M6M)",
+    make_target(horizons_A, "1d"),
+    tfs=("1d", "12h")
+)
+
+rep_B = al.study_weights(
+    "RSK04-B(1M3M6M12M)",
+    make_target(horizons_B, "1d"),
+    tfs=("1d", "12h")
+)
+
+print("=== RSK04: Momentum-of-Momentum Sizing ===\n")
+print(al.fmt(rep_A))
+print()
+print(al.fmt(rep_B))
+print()
+print("JSON:", al.as_json(rep_A))
+print("JSON:", al.as_json(rep_B))
+
+# Determine best config by holdout sharpe
+best_rep = max([rep_A, rep_B],
+               key=lambda r: r["verdict"].get("best_holdout_sharpe", -99))
+print("\n=== BEST CONFIG ===")
+print(al.fmt(best_rep))
+print("JSON_BEST:", al.as_json(best_rep))