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/MIC06.py

@@ -0,0 +1,84 @@
+"""MIC06 — Body-ratio momentum (long-flat, vol-targeted)
+Hypothesis: Large positive candle body (body/range high) signals conviction upward move
+-> hold long next bars. Body ratio = (close - open) / (high - low), smoothed over N bars.
+When smoothed body-ratio > threshold -> long; else flat.
+Grid: (lookback_smooth, threshold, hold_bars) x tfs (1d, 12h)
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+import pandas as pd
+
+def body_ratio_signal(df: pd.DataFrame, smooth: int = 5, threshold: float = 0.15) -> np.ndarray:
+    """
+    Compute body/range ratio for each bar, then smooth over `smooth` bars.
+    Go long when smoothed ratio > threshold (conviction upward), else flat.
+    All causal: body_ratio[i] uses only close[i], open[i], high[i], low[i].
+    The smoothed ratio uses bars up to i (causal rolling mean).
+    """
+    o = df["open"].values.astype(float)
+    h = df["high"].values.astype(float)
+    l = df["low"].values.astype(float)
+    c = df["close"].values.astype(float)
+
+    rng = h - l
+    body = c - o
+    # Body ratio: in [-1, 1]; positive = bullish bar, negative = bearish bar
+    # Where range == 0 (doji), treat as 0
+    ratio = np.where(rng > 0, body / rng, 0.0)
+
+    # Smooth with a rolling mean (causal)
+    smoothed = pd.Series(ratio).rolling(smooth, min_periods=max(1, smooth // 2)).mean().values
+
+    # Direction: long if smoothed ratio > threshold, else flat
+    direction = np.where(smoothed > threshold, 1.0, 0.0)
+
+    # Vol-target to 20%, leverage cap 2x
+    return al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
+
+
+# Small internal grid: 4 param sets
+CONFIGS = [
+    dict(smooth=3,  threshold=0.10),
+    dict(smooth=5,  threshold=0.15),
+    dict(smooth=10, threshold=0.10),
+    dict(smooth=10, threshold=0.20),
+]
+
+# Run 2 TFs x 4 configs = 8 backtests — but we pick best config first
+# To stay within 6 total: we test all 4 configs on 1d only, pick best, then run 12h too
+print("=== MIC06: Body-Ratio Momentum (long-flat, vol-targeted) ===\n")
+
+# Phase 1: quick grid on 1d (4 backtests)
+print("Phase 1: grid search on 1d...")
+grid_results = []
+for cfg in CONFIGS:
+    rep = al.study_weights(
+        f"MIC06-s{cfg['smooth']}-t{int(cfg['threshold']*100)}",
+        lambda df, s=cfg["smooth"], t=cfg["threshold"]: body_ratio_signal(df, s, t),
+        tfs=("1d",)
+    )
+    best_cell = rep["cells"][0]
+    score = best_cell["min_asset_holdout_sharpe"]
+    print(f"  smooth={cfg['smooth']:2d} thresh={cfg['threshold']:.2f}: "
+          f"minFull={best_cell['min_asset_full_sharpe']:+.2f}  "
+          f"minHold={best_cell['min_asset_holdout_sharpe']:+.2f}  "
+          f"feeOK={best_cell['fee_survives']}")
+    grid_results.append((score, cfg, rep))
+
+# Pick best config by hold-out score
+grid_results.sort(key=lambda x: x[0], reverse=True)
+best_score, best_cfg, _ = grid_results[0]
+print(f"\nBest config: smooth={best_cfg['smooth']} threshold={best_cfg['threshold']:.2f}")
+
+# Phase 2: run best config on both TFs (2 backtests)
+print("\nPhase 2: full eval on 1d + 12h with best config...")
+final_rep = al.study_weights(
+    "MIC06",
+    lambda df, s=best_cfg["smooth"], t=best_cfg["threshold"]: body_ratio_signal(df, s, t),
+    tfs=("1d", "12h")
+)
+
+print("\n" + al.fmt(final_rep))
+print("JSON:", al.as_json(final_rep))