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/MIC08.py

@@ -0,0 +1,57 @@
+"""MIC08 — OBV Trend
+Hypothesis: On-balance-volume trend: long when OBV above its EMA (volume confirms price).
+Long-flat. Continuous weights via al.study_weights.
+
+Grid: obv_ema_period in (20, 50) x tfs (1d, 12h) = 4 total backtests.
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+
+
+def compute_obv(df) -> np.ndarray:
+    """Compute On-Balance-Volume causally."""
+    close = df["close"].values
+    volume = df["volume"].values
+    n = len(close)
+    obv = np.zeros(n)
+    for i in range(1, n):
+        if close[i] > close[i - 1]:
+            obv[i] = obv[i - 1] + volume[i]
+        elif close[i] < close[i - 1]:
+            obv[i] = obv[i - 1] - volume[i]
+        else:
+            obv[i] = obv[i - 1]
+    return obv
+
+
+def make_target(ema_period: int):
+    def target(df) -> np.ndarray:
+        obv = compute_obv(df)
+        obv_ema = al.ema(obv, ema_period)
+        # Long when OBV > its EMA, flat otherwise
+        signal = np.where(obv > obv_ema, 1.0, 0.0)
+        # Use vol-targeting to size the position
+        sized = al.vol_target(signal, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
+        return sized
+    return target
+
+
+# Grid search: 2 EMA periods x 2 timeframes = 4 total backtests
+results = []
+for ema_p in (20, 50):
+    rep = al.study_weights(
+        f"MIC08-OBV-EMA{ema_p}",
+        make_target(ema_p),
+        tfs=("1d", "12h"),
+    )
+    results.append((rep["verdict"].get("best_holdout_sharpe", -9), ema_p, rep))
+
+# Pick best by hold-out Sharpe
+results.sort(key=lambda x: x[0], reverse=True)
+best_holdout, best_ema, best_rep = results[0]
+
+print(f"\n=== Best config: EMA period={best_ema} ===")
+print(al.fmt(best_rep))
+print("JSON:", al.as_json(best_rep))