research(blind): 52 agenti ciechi su curve anonime BTC/ETH — orchestratore valuta PnL/maxDD, niente di nuovo regge

Flotta di 52 subagenti "esperti di segnali" su storico BTC/ETH ANONIMIZZATO (Series A/B
rebased a 100, calendario sintetico, split 70/30) — non sanno cosa siano. Ognuno scrive un
signal(df)->position causale (script o ML), tunato solo sul train. Orchestratore valuta su
PnL e maxDD nel test held-out.

Harness cieco leak-free (riusabile):
- make_blind.py: export anonimo + overlay; blindlib.py: evaluator con shift della posizione +
  GUARDIA DI CAUSALITA' online (squalifica ogni look-ahead, ML incluso); blind_eval.py CLI;
  score_all.py giudice OOS; verify_top.py (corr-al-trend, fee-stress, jackknife).
- 52/52 passano la guardia (zero leak su tutta la flotta).

Esito OOS (benchmark buy&hold: -7% PnL, 68% DD):
- top = macd (+21%, DD 11%, Sh 0.84), accel, vol_of_vol, regime_switch, rf, obv — tutti
  trend/vol-regime. Sharpe OOS ~0.84 decade dal train ~1.4. Mean-rev e ML in fondo.
- 3 scettici indipendenti: REFUTED. regime-luck (top-5 bar = 67-102% del PnL); trend-redundancy
  (HAC alpha t=+0.9..+1.5, nessuno >1.96 — TSMOM travestito); overfit (accel/vov knife-edge).

Verdetto: ri-conferma CIECA e indipendente del soffitto direzionale ~1.3. macd = classe-TP01,
forward-monitor non deploy. Diario 2026-06-21-blind-signal-fleet.md.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

scripts/research/blind/agents/agent_03_ma_ribbon.py

@@ -0,0 +1,68 @@
+"""Agent 03 — MA ribbon (family=trend, slug=ma_ribbon).
+
+The angle: a quad-EMA "ribbon" (fast -> slow). The position is the FRACTION of the
+ribbon that is in the correct trend order. When the ribbon is perfectly stacked
+bullish (each faster EMA above the next slower one) the trend is clean and aligned
+-> position +1. Perfectly stacked bearish -> -1. A tangled ribbon (MAs crossing,
+no clear order) -> small / flat: we only press the position when the whole trend
+structure agrees. This is a GRADED-conviction trend filter, not a binary cross.
+
+Construction (all causal — value at i uses rows 0..i only):
+  * ribbon = 4 EMAs with spans SPANS (monotone fast->slow), the canonical "quad".
+  * For each adjacent pair (k, k+1) score +1 if ema_k > ema_{k+1} (bullish step),
+    -1 if below. ribbon score = mean of the K-1 step signs  ->  in [-1, +1]:
+    exactly "fraction of MAs in correct order" mapped to a signed conviction
+    (all-bullish -> +1, all-bearish -> -1, tangled half/half -> ~0).
+  * The two anonymized curves are persistently up-trending, so a symmetric short of
+    every partial-ribbon dip is pure drag. We de-weight the short side by SHORT_W
+    (still a genuine ribbon long/short, just risk-asymmetric). SHORT_W>0 helps a
+    little: a small short into a stacked-bearish ribbon trims the drawdown.
+  * Size with causal vol-targeting so Series A & B are risk-comparable and the
+    drawdown stays bounded (long size shrinks into vol spikes = every crash).
+
+Tuning (ONLY split='train', both A & B equal weight). The chosen cell sits in the
+interior of a broad plateau, not on a grid edge:
+  * SPANS base in {5,6,7} x(2 ratio) -> sharpe_min 1.32-1.37 (6 is the interior).
+  * VOL_WIN 20-25 best; 25 interior.    * SHORT_W 0.1-0.25 flat at sharpe_min ~1.37,
+    DD falling 0.26->0.24 as SHORT_W rises; 0.2 interior.
+Train combined: pnl_mean ~3.20, maxdd_worst ~0.241, sharpe_min ~1.37, turnover ~11/yr.
+Fee-robust: sharpe_min 1.39 at 0% RT -> 1.30 at 0.40% RT (low turnover = fee-insensitive).
+
+CAUSAL: ema is an online recursion, vol_target uses a trailing window -> no
+look-ahead, no centered windows, no global fit. Verified by causality_ok (max_diff 0).
+
+Honest note: this is a DEFENSIVE trend filter (value = converting a high-PnL/~50-67%-DD
+uptrend into comparable PnL at ~24% DD), not standalone alpha — like every long-biased
+trend overlay it inherits the bull-market beta of the curves.
+"""
+import numpy as np
+import blindlib as bl
+
+# --- tuned ONLY on split='train' (plateau interior, not a grid edge) ---
+SPANS = (6, 12, 24, 48)   # quad ribbon, fast -> slow (monotone)
+SHORT_W = 0.2             # short side de-weighted (asymmetric L/S); 0 -> long/flat
+TARGET_VOL = 0.25
+VOL_WIN_DAYS = 25
+LEV_CAP = 1.0
+
+
+def _ribbon_score(c: np.ndarray) -> np.ndarray:
+    """Signed fraction of adjacent ribbon steps in bullish order, in [-1, +1]."""
+    emas = [bl.ema(c, s) for s in SPANS]
+    steps = []
+    for k in range(len(emas) - 1):
+        # +1 where the faster EMA is above the next slower one (bullish step)
+        steps.append(np.where(emas[k] > emas[k + 1], 1.0, -1.0))
+    score = np.mean(np.vstack(steps), axis=0)   # mean of K-1 step signs in [-1,1]
+    score[: SPANS[-1]] = 0.0                     # ribbon undefined before slowest span
+    return score
+
+
+def signal(df):
+    c = df["close"].values.astype(float)
+    score = _ribbon_score(c)
+    # graded conviction: keep the full long fraction, de-weight the short fraction
+    raw = np.where(score >= 0.0, score, SHORT_W * score)
+    pos = bl.vol_target(raw, df, target_vol=TARGET_VOL,
+                        vol_win_days=VOL_WIN_DAYS, leverage_cap=LEV_CAP)
+    return np.clip(np.nan_to_num(pos, nan=0.0), -1.0, 1.0)