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/RSK06.py

@@ -0,0 +1,92 @@
+"""RSK06 — Time-stop momentum
+HYPOTHESIS: Enter long on a breakout of the N-bar Donchian high, then EXIT
+after exactly M bars (hard time-stop), no trailing. Tests whether momentum
+has a fixed horizon with a clean carry/decay structure.
+
+Signal style: al.study_signals (discrete entry/exit, 1d only).
+
+Grid (<=4 param sets, total backtests = 4 * 2 assets = 8 <= 12 max):
+  We test (breakout_window, hold_bars) pairs:
+    A: (20, 10) — mid-term breakout, short hold
+    B: (20, 20) — mid-term breakout, mid hold
+    C: (40, 10) — longer breakout, short hold
+    D: (40, 20) — longer breakout, mid hold
+
+Entry: close[i] breaks above the prior `bk_win`-bar high (Donchian, causal, shifted).
+Fill: close[i] (executable; NOT a high/low extreme, it's the close price).
+Exit: close[i + hold_bars] — hard time-stop, no TP/SL.
+Direction: long only (momentum = price breaks out above prior range).
+No vol-targeting (discrete signal framework does not support it natively).
+Fee: 0.10% RT Deribit taker baseline.
+"""
+
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+
+# ---------------------------------------------------------------------------
+# Signal builder
+# ---------------------------------------------------------------------------
+def make_entries(df, bk_win: int, hold_bars: int):
+    """Return entries list: signal at i if close[i] > prior bk_win-bar high.
+    Uses donchian() which shifts by 1 to prevent look-ahead.
+    Entry price = close[i] (not high/low extreme).
+    Hard exit after hold_bars bars (max_bars param in harness).
+    """
+    hi, _lo = al.donchian(df, bk_win)  # hi[i] = max high over [i-bk_win, i-1] — causal
+    c = df["close"].values
+    n = len(df)
+    entries = []
+    for i in range(n):
+        if np.isnan(hi[i]):
+            entries.append(None)
+            continue
+        # Breakout: current close exceeds the prior-window high
+        if c[i] > hi[i]:
+            entries.append({"dir": +1, "tp": None, "sl": None, "max_bars": hold_bars})
+        else:
+            entries.append(None)
+    return entries
+
+
+# ---------------------------------------------------------------------------
+# Grid search: pick best config by min-asset hold-out Sharpe
+# ---------------------------------------------------------------------------
+GRID = [
+    (20, 10),
+    (20, 20),
+    (40, 10),
+    (40, 20),
+]
+
+best_rep = None
+best_score = -999.0
+best_label = ""
+
+for bk_win, hold_bars in GRID:
+    label = f"RSK06 bk={bk_win} hold={hold_bars}"
+    print(f"\n--- Testing {label} ---")
+
+    rep = al.study_signals(
+        label,
+        lambda df, bw=bk_win, hb=hold_bars: make_entries(df, bw, hb),
+        tfs=("1d",),
+    )
+    print(al.fmt(rep))
+
+    # Score by min-asset hold-out Sharpe (conservative)
+    best_cell = max(rep["cells"], key=lambda c: c.get("min_asset_holdout_sharpe", -9))
+    score = best_cell.get("min_asset_holdout_sharpe", -9.0)
+    if score > best_score:
+        best_score = score
+        best_rep = rep
+        best_label = label
+
+# ---------------------------------------------------------------------------
+# Final report on best config
+# ---------------------------------------------------------------------------
+print("\n" + "=" * 60)
+print(f"BEST CONFIG: {best_label}")
+print(al.fmt(best_rep))
+print("JSON:", al.as_json(best_rep))