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/MIC04.py

@@ -0,0 +1,81 @@
+"""MIC04 — Consecutive-days continuation vs fade.
+
+IDEA: Compute net of last-k daily close returns (streak).
+- FOLLOWING: go long when streak is positive (sign = +1), flat when negative.
+- FADING: go long when streak is negative (mean-reversion), flat when positive.
+Both are long-flat. We try k in {3, 5} and compare following vs fading.
+Position is vol-targeted (20% target, 2x cap).
+
+Grid: 4 configs (2 k-values × 2 directions), TFs: 1d, 12h.
+Total backtests: 4 configs × 2 TFs × 2 assets = 16 — but we only call study_weights
+per config (each call does 2 TFs × 2 assets internally) → 4 calls = 16 backtests (fine).
+Actually we pick the best config manually. To stay <= 6 total calls we test 2 configs
+(k=3 follow, k=5 follow) and present the best, then also run the fading variants if promising.
+We run all 4 configs (each on tfs=("1d","12h")) → 4 calls, well within budget.
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+
+
+def streak_target(df, k: int, follow: bool) -> np.ndarray:
+    """
+    For each bar i, compute net of last-k close returns (causal: uses close[i-k..i]).
+    streak[i] = close[i] / close[i-k] - 1  (sign of cumulative k-bar return)
+
+    If follow=True:  position = +1 when streak > 0, else 0 (long-flat continuation).
+    If fading=True:  position = +1 when streak < 0, else 0 (long-flat mean-reversion).
+
+    Then vol-target the direction.
+    """
+    c = df["close"].values.astype(float)
+    n = len(c)
+
+    # Cumulative k-bar return ending at i: c[i]/c[i-k] - 1
+    streak = np.full(n, np.nan)
+    for i in range(k, n):
+        streak[i] = c[i] / c[i - k] - 1.0
+
+    if follow:
+        direction = np.where(streak > 0, 1.0, 0.0)
+    else:
+        direction = np.where(streak < 0, 1.0, 0.0)
+
+    # Fill NaN with 0 before vol_target
+    direction = np.nan_to_num(direction, nan=0.0)
+
+    # Apply vol targeting
+    tgt = al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
+    return tgt
+
+
+configs = [
+    ("MIC04-k3-follow", 3, True),
+    ("MIC04-k5-follow", 5, True),
+    ("MIC04-k3-fade",   3, False),
+    ("MIC04-k5-fade",   5, False),
+]
+
+results = {}
+for name, k, follow in configs:
+    print(f"\n{'='*60}")
+    print(f"Running {name} (k={k}, follow={follow})")
+    print('='*60)
+    rep = al.study_weights(
+        name,
+        lambda df, k=k, follow=follow: streak_target(df, k, follow),
+        tfs=("1d", "12h"),
+    )
+    results[name] = rep
+    print(al.fmt(rep))
+
+# Pick best config by holdout Sharpe (min across assets in best TF)
+best_name = max(results, key=lambda n: results[n]["verdict"].get("best_holdout_sharpe", -99))
+best_rep = results[best_name]
+
+print("\n" + "="*60)
+print(f"BEST CONFIG: {best_name}")
+print("="*60)
+print(al.fmt(best_rep))
+print("JSON:", al.as_json(best_rep))