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/TRD13.py

@@ -0,0 +1,94 @@
+"""TRD13 — SMA200 regime + vol-target (long-flat).
+
+HYPOTHESIS: Long when close > SMA200, flat otherwise.
+Position sized by vol_target(20%, 30d). Pure regime-trend.
+
+Small grid: SMA window {150, 200} x vol_target window {20, 30} days.
+Only 2 param sets tested (4 total cells with BTC/ETH) to stay within budget.
+Best config selected by min(BTC, ETH) full Sharpe.
+"""
+
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+
+# --------------------------------------------------------------------------
+# Signal factory
+# --------------------------------------------------------------------------
+def make_target(sma_win_bars: int, vol_win_days: int):
+    """Returns a function df -> target_array using SMA regime + vol_target."""
+    def target_fn(df):
+        c = df["close"].values
+        bpd = al.bars_per_day(df)
+
+        # SMA computed causally (sma already uses rolling with min_periods=win)
+        s200 = al.sma(c, sma_win_bars)
+
+        # Direction: +1 when close > SMA, else 0 (long-flat)
+        direction = np.where(c > s200, 1.0, 0.0)
+
+        # Vol-targeted position
+        vol_win = int(round(vol_win_days * bpd))
+        pos = al.vol_target(direction, df, target_vol=0.20,
+                            vol_win_days=vol_win_days, leverage_cap=2.0)
+
+        # Mask NaN (during SMA warmup) -> flat
+        pos = np.where(np.isnan(s200), 0.0, pos)
+        return pos
+    return target_fn
+
+
+# --------------------------------------------------------------------------
+# Grid: 2 configs × 2 TFs (1d, 12h)
+# --------------------------------------------------------------------------
+CONFIGS = [
+    {"label": "SMA150_v20", "sma_days": 150, "vol_win": 20},
+    {"label": "SMA200_v30", "sma_days": 200, "vol_win": 30},
+]
+
+TFS = ("1d", "12h")
+
+reports = []
+for cfg in CONFIGS:
+    sma_days = cfg["sma_days"]
+    vol_win = cfg["vol_win"]
+
+    def make_fn(sd=sma_days, vw=vol_win):
+        def target_fn(df):
+            bpd = al.bars_per_day(df)
+            sma_bars = int(round(sd * bpd))
+            c = df["close"].values
+            s = al.sma(c, sma_bars)
+            direction = np.where(c > s, 1.0, 0.0)
+            pos = al.vol_target(direction, df, target_vol=0.20,
+                                vol_win_days=vw, leverage_cap=2.0)
+            pos = np.where(np.isnan(s), 0.0, pos)
+            return pos
+        return target_fn
+
+    name = f"TRD13_{cfg['label']}"
+    rep = al.study_weights(name, make_fn(), tfs=TFS)
+    reports.append((rep, cfg))
+
+# --------------------------------------------------------------------------
+# Pick best config by min(BTC_full_sharpe, ETH_full_sharpe) on best TF
+# --------------------------------------------------------------------------
+def best_score(rep):
+    v = rep["verdict"]
+    best_tf = v["best_tf"]
+    # find the cell for best_tf
+    for cell in rep["cells"]:
+        if cell["tf"] == best_tf:
+            btc_sh = cell["per_asset"]["BTC"]["full"]["sharpe"]
+            eth_sh = cell["per_asset"]["ETH"]["full"]["sharpe"]
+            return min(btc_sh, eth_sh)
+    return -999.0
+
+best_rep, best_cfg = max(reports, key=lambda x: best_score(x[0]))
+
+print("\n" + "=" * 70)
+print(f"BEST CONFIG: {best_cfg}")
+print("=" * 70)
+print(al.fmt(best_rep))
+print("JSON:", al.as_json(best_rep))