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/TRD12.py

@@ -0,0 +1,59 @@
+"""TRD12 — Triple-MA alignment (SMA10 > SMA50 > SMA200).
+Long only when all three SMAs are in full bullish alignment; flat otherwise.
+No look-ahead: SMA values at i use close[0..i], position 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
+
+
+def triple_ma_weights(df, short=10, mid=50, long=200, use_vol_target=True):
+    """Return position array: +1 when SMA_short > SMA_mid > SMA_long, else 0."""
+    c = df["close"].values
+    s = al.sma(c, short)
+    m = al.sma(c, mid)
+    l = al.sma(c, long)
+
+    # Bullish alignment: short > mid > long
+    bullish = (s > m) & (m > l)
+
+    # Direction: +1 or 0 (long-only)
+    direction = np.where(bullish, 1.0, 0.0)
+
+    # Replace NaN regions (first `long` bars) with 0
+    direction = np.where(np.isnan(s) | np.isnan(m) | np.isnan(l), 0.0, direction)
+
+    if use_vol_target:
+        return al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
+    else:
+        return direction
+
+
+# Run study on 1d and 12h timeframes (Triple-MA needs long history, so >=12h)
+# We try two configurations: with and without vol-targeting
+# That's 2 configs x 2 TFs = 4 total backtests (within the <=6 limit)
+
+print("=" * 60)
+print("TRD12 — Triple-MA alignment (SMA10 > SMA50 > SMA200)")
+print("=" * 60)
+
+# Config 1: with vol-targeting
+rep_vt = al.study_weights(
+    "TRD12-VT",
+    lambda df: triple_ma_weights(df, use_vol_target=True),
+    tfs=("1d", "12h"),
+)
+print("\n--- Vol-targeted ---")
+print(al.fmt(rep_vt))
+print("JSON:", al.as_json(rep_vt))
+
+# Config 2: raw (no vol-targeting, simple long/flat)
+rep_raw = al.study_weights(
+    "TRD12-RAW",
+    lambda df: triple_ma_weights(df, use_vol_target=False),
+    tfs=("1d", "12h"),
+)
+print("\n--- Raw (no vol-target) ---")
+print(al.fmt(rep_raw))
+print("JSON:", al.as_json(rep_raw))