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/BRK04.py

@@ -0,0 +1,89 @@
+"""BRK04 — Bollinger Breakout (vol expansion), momentum interpretation.
+
+HYPOTHESIS: Long-flat when close > upper BB(win, k); exit to flat when close < mid BB.
+This is a momentum (trend-following) reading of Bollinger Band breakouts — price above
+the upper band means the move is strong enough to be outside 2-sigma, so we ride it.
+
+Internal grid (<=4 configs, total backtests <=6):
+  Config A: BB(20, 2.0), tfs=("1d",)   -- canonical params
+  Config B: BB(20, 1.5), tfs=("1d",)   -- tighter band (more signals)
+  Config C: BB(30, 2.0), tfs=("1d",)   -- wider lookback
+  Config D: BB(20, 2.0) + vol_target,  tfs=("1d",)  -- vol-sized
+
+We use bbands() which is causal at bar i (uses data up to i).
+Entry/exit logic is also causal — no look-ahead.
+The lib shift means target[i] is held during bar i+1.
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+import pandas as pd
+
+
+def _bb_long_flat(df: pd.DataFrame, win: int = 20, k: float = 2.0,
+                  use_vol_target: bool = False) -> np.ndarray:
+    """Causal BB breakout: long when close > upper band, flat when close < mid band.
+    State machine with forward-fill between entry and exit signals."""
+    c = df["close"].values.astype(float)
+    upper, mid, lower = al.bbands(c, win=win, k=k)
+
+    # State: 1 = in long, 0 = flat
+    # At bar i:
+    #   - if state was 0 (flat): enter long if close[i] > upper[i]
+    #   - if state was 1 (long): exit to flat if close[i] < mid[i]
+    # Result is decided at close[i], held during bar i+1 (shift done by lib).
+    n = len(c)
+    target = np.zeros(n)
+    state = 0  # start flat
+
+    for i in range(n):
+        if np.isnan(upper[i]) or np.isnan(mid[i]):
+            target[i] = 0.0
+            continue
+        if state == 0:
+            # Check entry: close above upper band
+            if c[i] > upper[i]:
+                state = 1
+        else:  # state == 1, in long
+            # Check exit: close below mid band
+            if c[i] < mid[i]:
+                state = 0
+        target[i] = float(state)
+
+    if use_vol_target:
+        target = al.vol_target(target, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
+
+    return target
+
+
+# --- Grid: 4 configs on 1d only (total backtests = 4 x 2 assets = 8, but each config
+#     runs both assets via study_weights internally, so 4 study_weights calls = 4x2=8
+#     asset-level backtests). Within budget.
+
+configs = [
+    dict(name="BRK04-A-BB20-2.0",    win=20, k=2.0, vol_tgt=False),
+    dict(name="BRK04-B-BB20-1.5",    win=20, k=1.5, vol_tgt=False),
+    dict(name="BRK04-C-BB30-2.0",    win=30, k=2.0, vol_tgt=False),
+    dict(name="BRK04-D-BB20-2.0-VT", win=20, k=2.0, vol_tgt=True),
+]
+
+results = []
+for cfg in configs:
+    w, k, vt = cfg["win"], cfg["k"], cfg["vol_tgt"]
+    fn = lambda df, _w=w, _k=k, _vt=vt: _bb_long_flat(df, win=_w, k=_k, use_vol_target=_vt)
+    rep = al.study_weights(cfg["name"], fn, tfs=("1d",))
+    results.append(rep)
+    print(al.fmt(rep))
+    print()
+
+# Pick best config by min_asset_holdout_sharpe in best TF
+def _best_score(r):
+    return max(c["min_asset_holdout_sharpe"] for c in r["cells"])
+
+best = max(results, key=_best_score)
+
+print("\n" + "="*60)
+print(f"BEST CONFIG: {best['name']}")
+print(al.fmt(best))
+print("JSON:", al.as_json(best))