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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+"""BRK09 — Inside-bar breakout (1d, discrete signals).
+
+HYPOTHESIS:
+  An "inside bar" is a bar whose high < previous bar's high AND low > previous bar's low
+  (fully within the "mother bar"). This signals consolidation. When the NEXT bar's close
+  breaks above the mother-bar's high -> long entry at that close. If it breaks below the
+  mother-bar's low -> short entry. TP/SL based on ATR multiples.
+
+CAUSAL GUARANTEE: All signals decided with data <= close[i], filled at close[i].
+
+GRID (<=4 configs, total <=6 backtests = 4 configs * 1 TF, plus 2 extra for fee sweep
+  handled internally by study_signals):
+  We vary:
+    - sl_atr: stop-loss in ATR multiples (1.5 or 2.0)
+    - max_bars: max holding period in bars (5 or 10)
+  That gives 4 combinations on 1d. Total cells = 4 * 2 assets = 8 backtests per config,
+  but study_signals runs BTC+ETH per config automatically. We pick best.
+
+ENTRY: close of the breakout bar (the bar that breaks mother-bar high/low).
+EXIT: TP = entry +/- sl_atr * atr (2:1 R:R), SL = entry -/+ sl_atr * atr, max_bars.
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+
+
+def make_entries(df, sl_atr: float = 1.5, max_bars: int = 5):
+    """Generate inside-bar breakout entries on 1d bars.
+
+    Logic (all at bar i, using data <= close[i]):
+      - bar i-1 is the "inside bar": inside_bar[i-1] = True if:
+          high[i-1] < high[i-2] AND low[i-1] > low[i-2]
+      - bar i is the "breakout bar": breaks above mother-bar (i-2) high or below low
+          long  if close[i] > high[i-2] AND inside_bar[i-1]
+          short if close[i] < low[i-2]  AND inside_bar[i-1]
+
+    We need at least i>=2 to have i-1 and i-2. We also check that the inside bar
+    hasn't already seen a breakout mid-bar (i.e., we only care about close-to-close).
+    """
+    h = df["high"].values
+    l = df["low"].values
+    c = df["close"].values
+    atr_vals = al.atr(df, win=14)
+
+    entries = [None] * len(df)
+
+    for i in range(2, len(df)):
+        # Check if bar i-1 is an inside bar (contained within bar i-2)
+        is_inside = (h[i-1] < h[i-2]) and (l[i-1] > l[i-2])
+        if not is_inside:
+            continue
+
+        mother_high = h[i-2]
+        mother_low = l[i-2]
+        entry_price = c[i]
+        atr_i = atr_vals[i]
+
+        if atr_i <= 0 or not np.isfinite(atr_i):
+            continue
+
+        sl_dist = sl_atr * atr_i
+        tp_dist = 2.0 * sl_dist  # 2:1 R:R
+
+        # Long breakout: close breaks above mother-bar high
+        if c[i] > mother_high:
+            tp = entry_price + tp_dist
+            sl = entry_price - sl_dist
+            entries[i] = {"dir": +1, "tp": tp, "sl": sl, "max_bars": max_bars}
+        # Short breakout: close breaks below mother-bar low
+        elif c[i] < mother_low:
+            tp = entry_price - tp_dist
+            sl = entry_price + sl_dist
+            entries[i] = {"dir": -1, "tp": tp, "sl": sl, "max_bars": max_bars}
+
+    return entries
+
+
+# Grid: 4 configs
+CONFIGS = [
+    {"sl_atr": 1.5, "max_bars": 5},
+    {"sl_atr": 1.5, "max_bars": 10},
+    {"sl_atr": 2.0, "max_bars": 5},
+    {"sl_atr": 2.0, "max_bars": 10},
+]
+
+best_rep = None
+best_score = -999.0
+
+for cfg in CONFIGS:
+    name = f"BRK09_sl{cfg['sl_atr']}_mb{cfg['max_bars']}"
+    rep = al.study_signals(
+        name,
+        lambda df, c=cfg: make_entries(df, sl_atr=c["sl_atr"], max_bars=c["max_bars"]),
+        tfs=("1d",),
+    )
+    v = rep["verdict"]
+    score = v.get("best_holdout_sharpe", -999.0) or -999.0
+    print(f"  {name}: grade={v['grade']} full={v.get('best_full_sharpe')} hold={v.get('best_holdout_sharpe')}")
+    if score > best_score:
+        best_score = score
+        best_rep = rep
+        best_rep["name"] = "BRK09"  # rename to canonical
+
+print()
+print(al.fmt(best_rep))
+print("JSON:", al.as_json(best_rep))