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/TRD01.py

@@ -0,0 +1,67 @@
+"""TRD01 — EMA Cross 20/100 Long-Flat Strategy.
+
+HYPOTHESIS: Long when EMA(fast) > EMA(slow), else flat.
+Grid: (fast, slow) in {(10,50), (20,100), (50,200)}.
+Vol-targeted position (target_vol=20%, leverage cap 2x).
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+
+# Grid: (fast, slow) pairs — 3 param sets, tested on 2 TFs = 6 total backtests max
+GRID = [
+    (10, 50),
+    (20, 100),
+    (50, 200),
+]
+
+def make_target(fast: int, slow: int):
+    """Returns a target_fn for the given EMA fast/slow parameters.
+    Signal is decided with data <= close[i] (causal EMA), vol-targeted.
+    """
+    def target_fn(df):
+        c = df["close"].values.astype(float)
+        e_fast = al.ema(c, fast)
+        e_slow = al.ema(c, slow)
+        # Direction: +1 when fast > slow, else 0 (long-flat only)
+        direction = np.where(e_fast > e_slow, 1.0, 0.0)
+        # Warmup: NaN-out until slow EMA has enough data (approx 3x slow period)
+        warmup = slow * 3
+        direction[:warmup] = 0.0
+        # Vol-target the position
+        tgt = al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
+        return tgt
+    return target_fn
+
+
+def main():
+    best_rep = None
+    best_score = -9999.0
+    best_params = None
+
+    for (fast, slow) in GRID:
+        name = f"TRD01_ema{fast}_{slow}"
+        print(f"\n=== Testing {name} ===")
+        rep = al.study_weights(
+            name,
+            make_target(fast, slow),
+            tfs=("1d", "12h"),
+        )
+        verdict = rep["verdict"]
+        score = verdict.get("best_holdout_sharpe", -9999.0) or -9999.0
+        print(al.fmt(rep))
+        print("JSON:", al.as_json(rep))
+
+        if score > best_score:
+            best_score = score
+            best_rep = rep
+            best_params = (fast, slow)
+
+    print(f"\n\n=== BEST CONFIG: EMA({best_params[0]},{best_params[1]}) ===")
+    print(al.fmt(best_rep))
+    print("JSON:", al.as_json(best_rep))
+
+
+if __name__ == "__main__":
+    main()