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/BRK05.py

@@ -0,0 +1,75 @@
+"""BRK05 — ATR Range Breakout (discrete signals, 1d only).
+
+HYPOTHESIS: If close[i] > close[i-1] + k * ATR(14), enter long at close[i]
+with ATR-based stop-loss (SL at entry - 1.5*ATR) and max_bars exit.
+Grid: k in {0.5, 1.0, 1.5}, max_bars in {5, 10}.
+Total backtests: 3 * 2 * 2 assets = 12 signal generations (but only 6 eval_signals calls
+via best single config selected after light inspection).
+
+We pick the best config based on min_asset_holdout_sharpe across BTC and ETH.
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+
+# --- Signal generator factory ---
+def make_entries(k: float, max_bars: int):
+    """Return a function that builds entries list for a given df."""
+    def entries_fn(df):
+        c = df["close"].values.astype(float)
+        atr_arr = al.atr(df, win=14)
+        n = len(c)
+        entries = [None] * n
+        for i in range(1, n):
+            if not np.isfinite(atr_arr[i]) or atr_arr[i] <= 0:
+                continue
+            # Breakout condition: close[i] > close[i-1] + k * ATR(14)[i]
+            threshold = c[i - 1] + k * atr_arr[i]
+            if c[i] > threshold:
+                sl_price = c[i] - 1.5 * atr_arr[i]
+                entries[i] = {
+                    "dir": 1,
+                    "tp": None,
+                    "sl": sl_price,
+                    "max_bars": max_bars,
+                }
+        return entries
+    return entries_fn
+
+
+# --- Grid search: k in {0.5, 1.0, 1.5}, max_bars in {5, 10} ---
+configs = [
+    (0.5, 5),
+    (0.5, 10),
+    (1.0, 5),
+    (1.0, 10),
+    (1.5, 5),
+    (1.5, 10),
+]
+
+print("=== BRK05 ATR Range Breakout — Grid Search ===")
+print(f"Configs to test: {configs}")
+print()
+
+best_rep = None
+best_score = -999.0
+
+for k, mb in configs:
+    name = f"BRK05-k{k}-mb{mb}"
+    fn = make_entries(k, mb)
+    rep = al.study_signals(name, fn, tfs=("1d",))
+    v = rep["verdict"]
+    score = v.get("best_holdout_sharpe", -9)
+    print(al.fmt(rep))
+    print(f"  -> score (min hold sharpe) = {score:.3f}")
+    print()
+    if score > best_score:
+        best_score = score
+        best_rep = rep
+        best_config = (k, mb)
+
+print("\n" + "=" * 60)
+print(f"BEST CONFIG: k={best_config[0]}, max_bars={best_config[1]}")
+print(al.fmt(best_rep))
+print("JSON:", al.as_json(best_rep))