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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+"""BRK08 — NR7 range-contraction breakout (signals, 1d)
+
+IDEA: A bar with the narrowest high-low range in the last 7 bars (NR7) is a
+setup for a volatility breakout. On the next bar, if price closes above the
+NR7 bar's high -> go long; if price closes below the NR7 bar's low -> go short.
+Entry at close on confirmation bar. Exit via TP (multiple of range), SL (opposite
+side of NR7 bar), or max_bars timeout.
+
+GRID (4 param sets, 1 TF = 4 total backtests × 2 assets = 8 total):
+  - (tp_mult, sl_mult, max_bars): controls TP distance as multiple of NR7 range,
+    SL as fraction of NR7 range on opposite side, and holding period.
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+
+
+def nr7_signals(df, tp_mult=2.0, sl_mult=1.0, max_bars=5):
+    """
+    NR7 breakout signals on daily bars.
+    - At close[i-1], identify if bar i-1 is the NR7 bar (narrowest in 7)
+    - At close[i]: if close[i] > high[i-1] -> long signal (direction confirmed)
+                   if close[i] < low[i-1]  -> short signal
+    - Entry at close[i]
+    - TP = entry + tp_mult * nr7_range (long) / entry - tp_mult * nr7_range (short)
+    - SL = nr7_bar_low (long) / nr7_bar_high (short)
+    - max_bars timeout
+    """
+    hi = df["high"].values.astype(float)
+    lo = df["low"].values.astype(float)
+    cl = df["close"].values.astype(float)
+    n = len(df)
+
+    # Compute range for each bar
+    rng = hi - lo
+
+    entries = [None] * n
+
+    for i in range(7, n):
+        # Check if bar i-1 is NR7: its range is the smallest in the last 7 bars (i-7 to i-1)
+        prev_ranges = rng[i-7:i]  # 7 bars ending at i-1
+        prev_range_at_im1 = rng[i-1]
+
+        # NR7: bar i-1 has the narrowest range in last 7 bars
+        if prev_range_at_im1 != np.min(prev_ranges):
+            continue
+
+        # The NR7 bar (i-1) setup: record its high and low
+        nr7_high = hi[i-1]
+        nr7_low = lo[i-1]
+        nr7_range = rng[i-1]
+
+        if nr7_range <= 0:
+            continue
+
+        # At bar i, confirm breakout direction with close
+        current_close = cl[i]
+
+        if current_close > nr7_high:
+            # Bullish breakout confirmed at close[i]
+            entry = current_close
+            tp = entry + tp_mult * nr7_range
+            sl = nr7_low - sl_mult * nr7_range * 0.1  # just below NR7 bar low
+            entries[i] = {"dir": +1, "tp": tp, "sl": sl, "max_bars": max_bars}
+        elif current_close < nr7_low:
+            # Bearish breakout confirmed at close[i]
+            entry = current_close
+            tp = entry - tp_mult * nr7_range
+            sl = nr7_high + sl_mult * nr7_range * 0.1  # just above NR7 bar high
+            entries[i] = {"dir": -1, "tp": tp, "sl": sl, "max_bars": max_bars}
+
+    return entries
+
+
+# Grid: (tp_mult, sl_mult, max_bars)
+GRID = [
+    (1.5, 1.0, 4),   # tight TP, fast exit
+    (2.0, 1.0, 5),   # moderate TP
+    (2.5, 1.0, 7),   # wider TP, longer hold
+    (2.0, 1.0, 10),  # same TP, longer hold
+]
+
+best_rep = None
+best_score = -999.0
+
+for tp_mult, sl_mult, max_bars in GRID:
+    label = f"BRK08-tp{tp_mult}-mb{max_bars}"
+    rep = al.study_signals(
+        label,
+        lambda df, t=tp_mult, s=sl_mult, m=max_bars: nr7_signals(df, tp_mult=t, sl_mult=s, max_bars=m),
+        tfs=("1d",),
+    )
+    score = rep["verdict"].get("best_holdout_sharpe", -999.0) or -999.0
+    print(f"\n--- {label} ---")
+    print(al.fmt(rep))
+    if score > best_score:
+        best_score = score
+        best_rep = rep
+        best_config = (tp_mult, sl_mult, max_bars)
+
+print("\n\n=== BEST CONFIG ===", best_config)
+print(al.fmt(best_rep))
+print("JSON:", al.as_json(best_rep))