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/STA05.py

@@ -0,0 +1,101 @@
+"""STA05 — EWMA-cross ensemble vote.
+
+IDEA: Vote across many EMA crossovers (fast/slow pairs drawn from {5..200}).
+      position = net_vote / n_pairs (continuous, in [-1,+1]).
+      Apply vol-targeting on top. Diversified trend signal.
+
+Grids tested (<=4 configs, <=6 total backtests):
+  Config A: wide pairs (5 fast × 4 slow), log-spaced fast {5,10,20,40},
+            slow {40,80,120,200} — only fast < slow. Position = sum(sign) / n.
+            Vol-target 20% cap 2x. TFs: 1d, 12h  (2 cells × 2 assets = 4 runs, total 4)
+  Config B: same pairs but LONG-ONLY (clip to [0,1]) — long-flat like TP01.
+            TFs: 1d only (2 more runs = 6 total)
+
+Both configs evaluated in the same pass by running study_weights twice on 1d/12h
+for A (4 runs) and once on 1d for B (2 runs). Total = 6.
+"""
+
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+
+
+# ---------------------------------------------------------------------------
+# EMA PAIR POOL
+# ---------------------------------------------------------------------------
+FAST_SPANS = [5, 10, 20, 40]
+SLOW_SPANS = [40, 80, 120, 200]
+
+# all valid (fast, slow) pairs where fast < slow
+PAIRS = [(f, s) for f in FAST_SPANS for s in SLOW_SPANS if f < s]
+# e.g. (5,40),(5,80),...,(40,80),(40,120),(40,200) = 13 pairs
+
+
+def _ewma_vote(df, long_only: bool = False) -> np.ndarray:
+    """Ensemble vote across EMA crossover pairs.
+    For each pair (fast, slow): signal = sign(ema_fast - ema_slow).
+    Position = mean(signals) across pairs, clipped to [-1,1] (or [0,1] if long_only).
+    Apply vol-targeting.
+    """
+    c = df["close"].values.astype(float)
+    n = len(c)
+    votes = np.zeros(n)
+
+    for fast_span, slow_span in PAIRS:
+        ema_fast = al.ema(c, fast_span)
+        ema_slow = al.ema(c, slow_span)
+        # sign: +1 if fast > slow (uptrend), -1 if below
+        sig = np.sign(ema_fast - ema_slow)
+        votes += sig
+
+    # net vote normalized to [-1, 1]
+    direction = votes / len(PAIRS)
+
+    if long_only:
+        direction = np.clip(direction, 0.0, 1.0)
+
+    # vol-target: scale to 20% annualized vol, cap 2x
+    pos = al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
+    return pos
+
+
+# Config A: long-short ensemble
+def target_ls(df):
+    return _ewma_vote(df, long_only=False)
+
+# Config B: long-only ensemble (long-flat)
+def target_lo(df):
+    return _ewma_vote(df, long_only=True)
+
+
+# ---------------------------------------------------------------------------
+# RUN — 4 runs for Config A (1d+12h), 2 for Config B (1d) = 6 total
+# ---------------------------------------------------------------------------
+print(f"EMA pairs: {PAIRS} ({len(PAIRS)} total)")
+print("Running Config A (long-short) on 1d + 12h ...")
+rep_a = al.study_weights("STA05-A-LS", target_ls, tfs=("1d", "12h"))
+print(al.fmt(rep_a))
+print("JSON:", al.as_json(rep_a))
+
+print("\nRunning Config B (long-only) on 1d ...")
+rep_b = al.study_weights("STA05-B-LO", target_lo, tfs=("1d",))
+print(al.fmt(rep_b))
+print("JSON:", al.as_json(rep_b))
+
+# ---------------------------------------------------------------------------
+# PICK BEST CONFIG
+# ---------------------------------------------------------------------------
+best_a = rep_a["verdict"].get("best_holdout_sharpe", -9)
+best_b = rep_b["verdict"].get("best_holdout_sharpe", -9)
+
+if best_a >= best_b:
+    rep_best = rep_a
+    print("\n>>> BEST: Config A (long-short)")
+else:
+    rep_best = rep_b
+    print("\n>>> BEST: Config B (long-only)")
+
+print("\n=== FINAL BEST ===")
+print(al.fmt(rep_best))
+print("JSON:", al.as_json(rep_best))