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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+"""TRD08 — Hull MA slope strategy.
+
+HYPOTHESIS: HMA(n); long when HMA rising (slope > 0), flat when falling.
+Grid: n in {20, 50, 100}.
+
+Hull Moving Average (causal):
+  WMA(n) = weighted moving average with linear weights
+  HMA(n) = WMA(sqrt(n), 2*WMA(n//2) - WMA(n))
+
+Position sizing: vol-targeted (20% target, 2x cap), long-flat only.
+"""
+
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+from numpy.lib.stride_tricks import as_strided
+
+
+def wma_vectorized(x: np.ndarray, win: int) -> np.ndarray:
+    """Causal weighted moving average — vectorized via cumsum trick."""
+    n = len(x)
+    # Use pandas for clean rolling WMA: sum(w_i * x_i) / sum(w_i)
+    # weights = 1, 2, ..., win
+    # We can compute via cumsum: WMA = (sum(i * x[t-i]) for i=1..win) / (win*(win+1)/2)
+    # Use a numerator via weighted cumsum
+    weights = np.arange(1, win + 1, dtype=float)
+    total_w = weights.sum()
+
+    result = np.full(n, np.nan)
+
+    # Efficient: build a 2D sliding window using stride tricks, then dot with weights
+    if n < win:
+        return result
+
+    # pad at start for alignment
+    # shape: (n - win + 1, win)
+    shape = (n - win + 1, win)
+    strides = (x.strides[0], x.strides[0])
+    windows = as_strided(x, shape=shape, strides=strides)
+    result[win - 1:] = windows @ weights / total_w
+    return result
+
+
+def hma(x: np.ndarray, n: int) -> np.ndarray:
+    """Causal Hull Moving Average."""
+    half_n = max(2, n // 2)
+    sqrt_n = max(2, int(round(np.sqrt(n))))
+
+    wma_full = wma_vectorized(x, n)
+    wma_half = wma_vectorized(x, half_n)
+
+    # 2 * WMA(n//2) - WMA(n)
+    raw = 2.0 * wma_half - wma_full
+
+    # Apply WMA(sqrt(n)) to the raw series
+    return wma_vectorized(raw, sqrt_n)
+
+
+def make_target(n: int):
+    """Return a lambda that computes vol-targeted HMA slope signal."""
+    def target(df):
+        c = df["close"].values.astype(float)
+        h = hma(c, n)
+        # slope: hma[i] > hma[i-1] => rising => long
+        slope = np.zeros(len(h))
+        slope[1:] = np.where(h[1:] > h[:-1], 1.0, 0.0)
+        # NaN protection: flat when HMA not yet valid or slope undefined
+        nan_mask = np.isnan(h) | np.isnan(np.concatenate([[np.nan], h[:-1]]))
+        slope[nan_mask] = 0.0
+        # Vol-target
+        return al.vol_target(slope, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
+    return target
+
+
+# Grid: n in {20, 50, 100} across timeframes {1d, 12h}
+# 3 param sets × 2 TFs = 6 total backtests (within limit)
+tfs = ("1d", "12h")
+grid_n = [20, 50, 100]
+
+best_rep = None
+best_score = -999.0
+best_n = grid_n[0]
+
+for n in grid_n:
+    name = f"TRD08-HMA{n}"
+    rep = al.study_weights(name, make_target(n), tfs=tfs)
+    print(al.fmt(rep))
+    print("JSON:", al.as_json(rep))
+
+    # Score by best_holdout_sharpe
+    score = rep["verdict"].get("best_holdout_sharpe", rep["verdict"].get("min_asset_holdout_sharpe", -999))
+    if score > best_score:
+        best_score = score
+        best_rep = rep
+        best_n = n
+
+print("\n" + "="*60)
+print(f"BEST CONFIG: n={best_n}")
+print(al.fmt(best_rep))
+print("JSON:", al.as_json(best_rep))