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>

scripts/research/alt/runs/BRK06.py

@@ -0,0 +1,68 @@
+"""BRK06 — Opening-Range Breakout (daily).
+
+HYPOTHESIS: On 1d bars, go LONG when today's close > prior-day high (expansion/gap breakout).
+SL = prior-day low. max_bars = configurable (3 or 5). No short side (breakdowns symmetric but
+crypto skew is upward; testing long-only first). Entry at close[i] once close[i] > prior high[i-1].
+Exit at SL=prior_low[i-1] or max_bars (time stop), whichever first.
+
+Grid: max_bars in {3, 5} -> 2 configs × 1 TF × 2 assets = 4 backtests.
+
+Honesty rules:
+- decision uses close[i] vs high[i-1]: CAUSAL (prior-bar high is known by close of bar i).
+- SL = low[i-1]: known causal.
+- entry = close[i] (not high/low extreme of bar i).
+- fee = 0.10% RT (Deribit taker).
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+
+def make_entries(df, max_bars: int):
+    """Long when close[i] > high[i-1]. SL = low[i-1]. Exit at max_bars or SL."""
+    c = df["close"].values
+    h = df["high"].values
+    lo = df["low"].values
+    n = len(c)
+    entries = [None] * n
+    for i in range(1, n):
+        prior_high = h[i - 1]
+        prior_low = lo[i - 1]
+        if c[i] > prior_high:
+            # Long breakout: entry at close[i], SL below prior-day low
+            # TP = None (let the time-stop manage exit)
+            entries[i] = {
+                "dir": 1,
+                "tp": None,
+                "sl": prior_low,
+                "max_bars": max_bars,
+            }
+    return entries
+
+
+configs = [
+    {"max_bars": 3},
+    {"max_bars": 5},
+]
+
+best_rep = None
+best_score = -9999
+
+for cfg in configs:
+    name = f"BRK06-mb{cfg['max_bars']}"
+    rep = al.study_signals(
+        name,
+        lambda df, mb=cfg["max_bars"]: make_entries(df, mb),
+        tfs=("1d",),
+    )
+    print(al.fmt(rep))
+    score = rep["verdict"].get("best_holdout_sharpe", -9999)
+    if score is None:
+        score = -9999
+    if score > best_score:
+        best_score = score
+        best_rep = rep
+
+print("\n=== BEST CONFIG ===")
+print(al.fmt(best_rep))
+print("JSON:", al.as_json(best_rep))