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_46_vol_div.py

@@ -0,0 +1,72 @@
+"""agent_46_vol_div — Volume/price divergence (family=vol2, slug=vol_div).
+
+ANGLE: fade moves where volume does NOT confirm; ride where it does.
+
+How the angle is expressed (all causal, decided at close[i], held over bar i+1):
+
+  * CONFIRMATION = is volume EXPANDING as the trend develops?  We compare a short
+    volume mean (5) to a longer one (20): `confirm = v5/v20 - 1`.  When volume is
+    rising while price trends, the move is volume-CONFIRMED.
+      -> RIDE leg: take the multi-bar (15-bar) price momentum, but only with weight
+         proportional to the confirmation (clip(confirm * gain, 0, 1)).  No
+         confirmation -> no momentum bet.  This is "ride where volume confirms".
+
+  * DIVERGENCE / EXHAUSTION = a single-bar thrust on a VOLUME SPIKE that is NOT part
+    of a broader volume up-trend (volume not confirming the direction).  Such thrusts
+    tend to mean-revert.
+      -> FADE leg: -sign(last bar) gated by (a vol z-score spike) AND (volume NOT
+         broadly expanding).  This is "fade where volume does not confirm".
+
+  * The two legs are blended (0.7 ride / 0.3 fade) and vol-targeted so the drawdown
+    stays bounded.  On the train view this is comparable PnL to buy&hold at a fraction
+    of the drawdown, and it can go short / flat the unconfirmed declines.
+
+Decomposition note (train): the RIDE leg is the real edge on both overlaid curves
+(volume-confirmed momentum persists); the FADE leg is a small DD-reducing overlay.
+Parameters chosen on a smooth plateau (rw 12-15, cl 15-20), not a knife-edge.
+"""
+import numpy as np
+import pandas as pd
+import blindlib as bl
+
+RIDE_W = 15        # momentum horizon (bars)
+CONF_S = 5         # short volume mean
+CONF_L = 20        # long volume mean
+GAIN = 6.5         # confirmation -> ride-weight gain
+W_FADE = 0.30      # weight of the divergence/fade overlay
+TARGET_VOL = 0.18  # annualized vol target for sizing
+VOL_WIN = 30       # vol-target lookback (days)
+
+
+def _zscore(x, win):
+    s = pd.Series(x)
+    m = s.rolling(win, min_periods=win // 2).mean()
+    sd = s.rolling(win, min_periods=win // 2).std()
+    z = (s - m) / sd.replace(0.0, np.nan)
+    return np.nan_to_num(z.values)
+
+
+def signal(df):
+    c = df["close"].values.astype(float)
+    v = df["volume"].values.astype(float)
+    logc = np.log(c)
+    r = np.concatenate([[0.0], np.diff(logc)])             # causal bar return
+
+    # ---- Volume confirmation: short vol mean vs long vol mean (>0 = expanding) ----
+    vshort = pd.Series(v).rolling(CONF_S, min_periods=2).mean().values
+    vlong = pd.Series(v).rolling(CONF_L, min_periods=10).mean().values
+    confirm = np.nan_to_num(vshort / np.where(vlong > 0, vlong, np.nan), nan=1.0) - 1.0
+
+    # ---- RIDE leg: multi-bar momentum, weighted by how strongly volume confirms ----
+    pm = np.concatenate([np.zeros(RIDE_W), logc[RIDE_W:] - logc[:-RIDE_W]])
+    ride = np.sign(pm) * np.clip(confirm * GAIN, 0.0, 1.0)
+
+    # ---- FADE leg: fade a single-bar thrust on a volume spike w/o broad expansion ----
+    vol_spike = _zscore(v, 20)
+    fade_gate = np.clip(vol_spike - 1.0, 0.0, 2.0) * np.clip(-confirm * 4.0 + 0.5, 0.0, 1.0)
+    fade = -np.sign(r) * np.clip(fade_gate, 0.0, 1.0)
+
+    raw = np.clip((1.0 - W_FADE) * ride + W_FADE * fade, -1.0, 1.0)
+
+    pos = bl.vol_target(raw, df, target_vol=TARGET_VOL, vol_win_days=VOL_WIN, leverage_cap=1.0)
+    return np.clip(np.nan_to_num(pos), -1.0, 1.0)