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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+"""BRK03 — Keltner Channel Breakout
+HYPOTHESIS: Long when close > EMA20 + k*ATR(20); flat when close < EMA20.
+Try k in {1.5, 2.0, 2.5}. Vol-targeted continuous weights.
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+
+
+def keltner_breakout(df, k: float) -> np.ndarray:
+    """Long (vol-targeted) when close > EMA20 + k*ATR(20); flat when close < EMA20.
+    All values causal: EMA/ATR at i use data <= i. Decision at close[i] -> held during bar i+1.
+    """
+    c = df["close"].values.astype(float)
+    ema20 = al.ema(c, span=20)
+    atr20 = al.atr(df, win=20)
+
+    upper_band = ema20 + k * atr20
+
+    # Direction: +1 if close > upper_band (breakout above), else 0 (flat)
+    # Exit: go flat when close < EMA20 (mean reversion back below center)
+    n = len(c)
+    direction = np.zeros(n, dtype=float)
+
+    # Vectorized: long when above upper band; we then hold until close < EMA20
+    # Implement as a state machine
+    in_trade = False
+    for i in range(n):
+        if np.isnan(ema20[i]) or np.isnan(atr20[i]):
+            direction[i] = 0.0
+            continue
+        if not in_trade:
+            # Enter long on breakout above upper keltner band
+            if c[i] > upper_band[i]:
+                in_trade = True
+                direction[i] = 1.0
+        else:
+            # Exit when price drops back below EMA
+            if c[i] < ema20[i]:
+                in_trade = False
+                direction[i] = 0.0
+            else:
+                direction[i] = 1.0
+
+    # Apply vol-targeting to scale position size
+    pos = al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
+    return pos
+
+
+# Grid: k in {1.5, 2.0, 2.5} — try all 3 param sets; pick best by min_asset_holdout_sharpe
+best_rep = None
+best_score = -999.0
+best_k = None
+
+for k_val in [1.5, 2.0, 2.5]:
+    name = f"BRK03-k{k_val}"
+    print(f"\n--- Running {name} ---")
+    rep = al.study_weights(
+        name,
+        lambda df, k=k_val: keltner_breakout(df, k),
+        tfs=("1d", "12h")
+    )
+    score = rep["verdict"].get("best_holdout_sharpe", -999.0) or -999.0
+    print(al.fmt(rep))
+    if score > best_score:
+        best_score = score
+        best_rep = rep
+        best_k = k_val
+
+print("\n" + "="*60)
+print(f"BEST CONFIG: k={best_k}")
+print("="*60)
+print(al.fmt(best_rep))
+print("JSON:", al.as_json(best_rep))