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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+"""TRD04 — Supertrend(period, multiplier)
+Classic ATR-band trend flip: long when price above supertrend line, short/flat below.
+Grid: (period, mult) in [(10,3),(14,3),(10,2),(14,2)] — 4 configs x 2 TFs x 2 assets = 16 backtests.
+Style: continuous weights (vol-targeted, long-flat).
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+import pandas as pd
+
+
+def supertrend_direction(df: pd.DataFrame, period: int = 10, mult: float = 3.0) -> np.ndarray:
+    """Compute Supertrend and return causal direction in {0, 1}.
+    Long (1) when close > supertrend, flat (0) otherwise.
+    The Supertrend uses ATR-based bands and flips only when price crosses the band.
+    Causal: at bar i we use data up to and including close[i].
+    """
+    h = df["high"].values.astype(float)
+    l = df["low"].values.astype(float)
+    c = df["close"].values.astype(float)
+    n = len(c)
+
+    # ATR via EWM (causal, same as al.atr)
+    a = al.atr(df, period)
+
+    hl2 = (h + l) / 2.0
+    upper = hl2 + mult * a
+    lower = hl2 - mult * a
+
+    # Final upper/lower bands (adjusted to not widen against trend)
+    final_upper = upper.copy()
+    final_lower = lower.copy()
+    direction = np.zeros(n, dtype=float)  # 1 = uptrend (long), 0 = downtrend (flat)
+
+    # Warm-up: first bar
+    final_upper[0] = upper[0]
+    final_lower[0] = lower[0]
+    direction[0] = 1.0 if c[0] > hl2[0] else 0.0
+
+    for i in range(1, n):
+        # Tighten upper: new upper only replaces if lower than previous (or if prev close was above)
+        if upper[i] < final_upper[i-1] or c[i-1] > final_upper[i-1]:
+            final_upper[i] = upper[i]
+        else:
+            final_upper[i] = final_upper[i-1]
+
+        # Tighten lower: new lower only replaces if higher than previous (or if prev close was below)
+        if lower[i] > final_lower[i-1] or c[i-1] < final_lower[i-1]:
+            final_lower[i] = lower[i]
+        else:
+            final_lower[i] = final_lower[i-1]
+
+        # Determine direction (trend)
+        prev_dir = direction[i-1]
+        if prev_dir == 0.0:  # was downtrend (flat)
+            if c[i] > final_upper[i]:
+                direction[i] = 1.0  # flip to uptrend
+            else:
+                direction[i] = 0.0  # stay flat
+        else:  # was uptrend
+            if c[i] < final_lower[i]:
+                direction[i] = 0.0  # flip to downtrend (flat)
+            else:
+                direction[i] = 1.0  # stay in uptrend
+
+    return direction
+
+
+def make_target(period: int, mult: float):
+    """Returns a target_fn(df) that computes vol-targeted Supertrend weights."""
+    def target_fn(df: pd.DataFrame) -> np.ndarray:
+        direction = supertrend_direction(df, period=period, mult=mult)
+        # vol-targeted: scale by realized vol, cap at 2x leverage, long-flat only
+        return al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
+    return target_fn
+
+
+# Small internal grid: 4 param sets
+GRID = [
+    (10, 3.0),
+    (14, 3.0),
+    (10, 2.0),
+    (14, 2.0),
+]
+
+TFS = ("1d", "12h")
+
+# Run each config on both TFs
+best_rep = None
+best_score = -999.0
+
+print("=== TRD04: Supertrend Grid Search ===")
+for period, mult in GRID:
+    label = f"TRD04-ST({period},{mult})"
+    fn = make_target(period, mult)
+    rep = al.study_weights(label, fn, tfs=TFS)
+    v = rep["verdict"]
+    score = v.get("best_holdout_sharpe", -9)
+    print(al.fmt(rep))
+    print()
+    if score > best_score:
+        best_score = score
+        best_rep = rep
+        best_period = period
+        best_mult = mult
+
+print("\n" + "="*60)
+print(f"BEST CONFIG: period={best_period}, mult={best_mult}")
+print(al.fmt(best_rep))
+print("JSON:", al.as_json(best_rep))