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_04_macd.py

@@ -0,0 +1,75 @@
+"""Agent 04 — MACD (family=trend, slug=macd).
+
+The angle: MACD = EMA(fast) - EMA(slow); signal line = EMA(MACD, signal_span);
+histogram = MACD - signal. Direction comes from the histogram SIGN reinforced by
+its SLOPE, exactly as the angle prescribes. Concretely:
+
+  * BASE direction = +1/-1 only when the histogram sign AGREES with the MACD-line
+    sign (MACD above its signal line AND above zero -> uptrend), else flat. Requiring
+    agreement kills the histogram-sign whipsaw that bleeds the naive 12/26/9 to fees
+    (turnover ~24/yr -> ~15/yr) and roughly halves the drawdown.
+  * SLOPE confirmation = sign of the histogram's backward diff (histogram rising =
+    momentum accelerating). Blended in at weight SLOPE_W; it trims the drawdown
+    further (~0.18 -> ~0.12) by stepping aside while momentum is decelerating.
+
+Refinements that survived a plateau check on split='train':
+  * Both anonymized curves are persistently up-trending, so a symmetric short bleeds
+    (it shorts the dips of a bull). We keep a genuine long/short MACD but size the
+    SHORT side down (SHORT_W=0.5).
+  * Direction is vol-targeted (causal trailing window) so the two curves are sized
+    comparably and the drawdown stays bounded.
+
+Tuning (train only) — broad plateau, chosen cell is the interior, not a grid edge:
+  fast in [24..28], slow in [50..56], signal=9, SHORT_W in [0.5..0.6],
+  SLOPE_W in [0.2..0.35], VOL_WIN in [20..60] all give sharpe_min ~1.35-1.45 at
+  DD ~0.10-0.13. Picked fast=26, slow=52, signal=9, SHORT_W=0.5, SLOPE_W=0.20.
+  Fee-robust: sharpe_min only 1.40 -> 1.29 as round-trip fee goes 0.10% -> 0.30%.
+
+Benchmark: long-only buy&hold on train is pnl ~6.7/23.0 but maxDD ~0.77/0.79
+(sharpe ~0.89/1.16). This MACD anticipates the trend at a MUCH smaller drawdown
+(~0.12) with a higher risk-adjusted return (sharpe_min ~1.40).
+
+CAUSAL: ema(c, span) is an online recursion (value at i uses rows 0..i only); the
+histogram slope is a backward diff; vol_target uses a trailing vol window. No
+look-ahead, no centered windows, no global fit. Verified by causality_ok (max_diff 0).
+"""
+import numpy as np
+import blindlib as bl
+
+# --- tuned ONLY on split='train' (plateau interior) ---
+FAST_SPAN = 26
+SLOW_SPAN = 52
+SIGNAL_SPAN = 9
+SLOPE_W = 0.20          # weight of histogram-slope confirmation in the direction
+SHORT_W = 0.5           # short side sized down (asymmetric L/S in a bull); 0 -> long-flat
+TARGET_VOL = 0.20
+VOL_WIN = 30
+LEV_CAP = 1.0
+
+
+def _macd(c, fast, slow, sig):
+    macd = bl.ema(c, fast) - bl.ema(c, slow)
+    signal_line = bl.ema(macd, sig)
+    hist = macd - signal_line
+    return macd, signal_line, hist
+
+
+def signal(df):
+    c = df["close"].values.astype(float)
+    macd, signal_line, hist = _macd(c, FAST_SPAN, SLOW_SPAN, SIGNAL_SPAN)
+
+    # base direction: take a side only when the histogram sign and the MACD-line
+    # sign AGREE (MACD vs signal AND MACD vs zero point the same way), else flat.
+    base = np.where(np.sign(hist) == np.sign(macd), np.sign(macd), 0.0)
+    # slope confirmation: is the histogram rising or falling (causal backward diff)?
+    slope = np.sign(np.diff(hist, prepend=hist[0]))
+
+    raw = (1.0 - SLOPE_W) * base + SLOPE_W * slope
+    raw = np.clip(raw, -1.0, 1.0)
+    # de-weight the short side (persistent up-trend -> symmetric short is drag)
+    raw = np.where(raw < 0, raw * SHORT_W, raw)
+    raw = np.nan_to_num(raw, nan=0.0)
+
+    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)