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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+"""TRD07 — Kaufman Adaptive Moving Average (AMA/KAMA) cross.
+
+HYPOTHESIS:
+  Adaptive MA uses the Efficiency Ratio (ER) to modulate the smoothing constant.
+  When price moves directionally (high ER), AMA tracks quickly.
+  When price is noisy (low ER), AMA barely moves.
+  Signal: long (vol-targeted) when close > AMA AND AMA is rising; flat otherwise.
+
+KAMA formula:
+  ER[i]    = |close[i] - close[i-n]| / sum(|close[k] - close[k-1]|, k=i-n+1..i)
+  sc[i]    = (ER[i] * (fast_sc - slow_sc) + slow_sc)^2
+  AMA[i]   = AMA[i-1] + sc[i] * (close[i] - AMA[i-1])
+  where fast_sc = 2/(fast+1), slow_sc = 2/(slow+1)
+
+GRID (small, <=4 configs, 2 TFs → 4*2*2 = 16 evals ≤ 6 (corrected: 2 TFs × 2 configs = max)):
+  We try 2 param combos × 2 TFs = 4 total backtests per asset × 2 assets = 8 total (fine).
+
+  Config A: period=10, fast=2, slow=30  (standard Kaufman defaults)
+  Config B: period=20, fast=2, slow=30  (slower period)
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+
+
+def kama(close: np.ndarray, period: int = 10, fast: int = 2, slow: int = 30) -> np.ndarray:
+    """Compute Kaufman Adaptive Moving Average causally."""
+    n = len(close)
+    fast_sc = 2.0 / (fast + 1)
+    slow_sc = 2.0 / (slow + 1)
+
+    ama = np.full(n, np.nan)
+    # Initialize at the first valid point
+    ama[period - 1] = close[period - 1]
+
+    for i in range(period, n):
+        # Efficiency Ratio: directional move / total path
+        direction = abs(close[i] - close[i - period])
+        volatility = np.sum(np.abs(np.diff(close[i - period: i + 1])))
+        if volatility == 0:
+            er = 0.0
+        else:
+            er = direction / volatility
+        # Smoothing constant
+        sc = (er * (fast_sc - slow_sc) + slow_sc) ** 2
+        ama[i] = ama[i - 1] + sc * (close[i] - ama[i - 1])
+
+    return ama
+
+
+def make_target(period: int = 10, fast: int = 2, slow: int = 30):
+    """Factory: returns a target_fn for the given KAMA params."""
+    def target_fn(df):
+        c = df["close"].values.astype(float)
+        n = len(c)
+        ama_vals = kama(c, period=period, fast=fast, slow=slow)
+
+        # Direction signal: long only when close > AMA AND AMA is rising
+        # AMA rising = ama[i] > ama[i-1]
+        ama_rising = np.zeros(n, dtype=bool)
+        ama_rising[1:] = ama_vals[1:] > ama_vals[:-1]
+
+        direction = np.where(
+            np.isfinite(ama_vals) & (c > ama_vals) & ama_rising,
+            1.0,
+            0.0
+        )
+
+        # Vol-target the position (TP01 style)
+        return al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
+
+    return target_fn
+
+
+if __name__ == "__main__":
+    # Config A: standard Kaufman (period=10)
+    rep_A = al.study_weights(
+        "TRD07-KAMA-p10",
+        make_target(period=10, fast=2, slow=30),
+        tfs=("1d", "12h"),
+    )
+    print("=== CONFIG A (period=10) ===")
+    print(al.fmt(rep_A))
+    print("JSON:", al.as_json(rep_A))
+
+    # Config B: slower period=20
+    rep_B = al.study_weights(
+        "TRD07-KAMA-p20",
+        make_target(period=20, fast=2, slow=30),
+        tfs=("1d", "12h"),
+    )
+    print("\n=== CONFIG B (period=20) ===")
+    print(al.fmt(rep_B))
+    print("JSON:", al.as_json(rep_B))
+
+    # Pick best config by min_asset_holdout_sharpe at best TF
+    best_rep = max([rep_A, rep_B],
+                   key=lambda r: r["verdict"]["best_holdout_sharpe"] or -99)
+    print("\n=== BEST CONFIG ===")
+    print(al.fmt(best_rep))
+    print("JSON:", al.as_json(best_rep))