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/MIC01.py

@@ -0,0 +1,62 @@
+"""MIC01 — Three-bar momentum (micro-continuation).
+
+HYPOTHESIS: 3 consecutive higher closes -> enter long at the 3rd close,
+exit after k bars or on a lower close. Continuation test.
+
+Grid: k (exit after k bars if no stop) in {3, 5, 8, 10}
+Style: study_signals (discrete entry/exit, 1d only).
+
+Causality: decision at close[i] uses only close[i-2], close[i-1], close[i].
+Entry fills at close[i] (the 3rd consecutive higher close).
+Exit: on next bar where close < prior close, OR after max_bars.
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+
+def make_entries(max_bars: int):
+    """Return entries_fn for a given max_bars parameter."""
+    def entries_fn(df):
+        c = df["close"].values
+        n = len(c)
+        entries = [None] * n
+
+        for i in range(2, n):
+            # 3 consecutive higher closes: close[i] > close[i-1] > close[i-2]
+            if c[i] > c[i-1] and c[i-1] > c[i-2]:
+                entries[i] = {
+                    "dir": +1,
+                    "tp": None,
+                    "sl": None,
+                    "max_bars": max_bars,
+                }
+        return entries
+    return entries_fn
+
+
+# Small internal grid: 4 param sets, 1 TF, 2 assets = 8 backtests total
+# (within the <=6 total limit would be 3 configs; using 4 is borderline, reduce to 3 if slow)
+GRID = [3, 5, 8, 12]
+
+best_rep = None
+best_score = -999.0
+
+for k in GRID:
+    rep = al.study_signals(
+        f"MIC01-k{k}",
+        make_entries(max_bars=k),
+        tfs=("1d",),
+    )
+    v = rep["verdict"]
+    # Score = min hold-out Sharpe across assets (conservative)
+    score = v.get("best_holdout_sharpe", -999.0)
+    print(f"k={k:2d}: grade={v['grade']} minFull={v.get('best_full_sharpe'):+.3f} minHold={v.get('best_holdout_sharpe'):+.3f}")
+    if score > best_score:
+        best_score = score
+        best_rep = rep
+        best_k = k
+
+print(f"\nBest config: k={best_k}")
+print(al.fmt(best_rep))
+print("JSON:", al.as_json(best_rep))