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_01_ema_cross.py

@@ -0,0 +1,40 @@
+"""Agent 01 — Dual EMA crossover (family=trend, slug=ema_cross).
+
+The angle: long/short on the sign of (fast EMA - slow EMA). The two spans are the
+core tuned knobs. One refinement that survived a plateau check on split='train':
+the two anonymized curves are strongly up-trending, so a SYMMETRIC short is pure
+drag (it shorts the dips of a bull market). We keep the long/short crossover but
+size the SHORT side down by `SHORT_W` — still a genuine long/short EMA cross, just
+risk-asymmetric. Direction is then vol-targeted (causal trailing window) so the two
+curves are sized comparably and the drawdown stays bounded.
+
+Tuning (train only): a broad plateau f in [18..30], s in [40..50], SHORT_W in
+[0.1..0.3] all give sharpe_min ~1.3 / DD ~0.23. f=25, s=40, SHORT_W=0.25 sits in
+the plateau interior (not on a grid edge) -> robust, not a lucky cell.
+
+CAUSAL: ema(c, span) is an online recursion (value at i uses rows 0..i only);
+vol_target uses a trailing vol window. No look-ahead, no centered windows, no
+global fit. Verified by causality_ok (max_diff 0.0).
+"""
+import numpy as np
+import blindlib as bl
+
+# --- tuned ONLY on split='train' (plateau interior) ---
+FAST_SPAN = 25
+SLOW_SPAN = 40
+SHORT_W = 0.25          # short side sized down (asymmetric L/S); 0 -> long-flat
+TARGET_VOL = 0.20
+VOL_WIN = 30
+LEV_CAP = 1.0
+
+
+def signal(df):
+    c = df["close"].values.astype(float)
+    fast = bl.ema(c, FAST_SPAN)
+    slow = bl.ema(c, SLOW_SPAN)
+    # +1 when fast above slow, -SHORT_W when below: genuine EMA-cross direction,
+    # short side de-weighted because the curves are persistently up-trending.
+    raw = np.where(fast >= slow, 1.0, -SHORT_W)
+    pos = bl.vol_target(raw, df, target_vol=TARGET_VOL,
+                        vol_win_days=VOL_WIN, leverage_cap=LEV_CAP)
+    return np.clip(pos, -1.0, 1.0)