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_45_pvt.py

@@ -0,0 +1,70 @@
+"""agent_45_pvt — Price-Volume momentum: volume-surge-confirmed breakouts.
+
+ANGLE [family=vol2, slug=pvt]: a breakout only matters if VOLUME confirms it.
+Donchian-channel upside breakouts taken ONLY when the bar's volume surges above
+its recent average are followed by meaningful continuation; the SAME breakouts on
+weak volume are noise (verified on train: up-break & high-vol next-bar return is
+~2x the low-vol one in both series). Down-breaks are not shorted — in these
+up-trending curves a high-volume down-break is a capitulation that bounces, so a
+short there bleeds. We therefore go LONG/FLAT on volume-confirmed up-breakouts.
+
+Rule (fully causal, online):
+  * volume surge  : v[i] / SMA(v, 30) > 1.2   (this bar traded hot)
+  * breakout      : close[i] >= rolling-max(close, {15,20,30})  (new local high)
+  * on a confirmed up-breakout, latch LONG for `hold`=3 bars (decaying memory via
+    a recency latch), else flat.
+  * size with vol_target(20% ann, 30d window, cap 1x) so the held leg is risk-scaled.
+
+Everything at bar i uses only data 0..i (rolling/cummax/SMA + a backward-only latch
+loop) -> causality_ok passes.
+
+Train (combined): pnl_mean ~1.24, maxdd_worst ~0.11, sharpe_min ~1.41 (A 1.41 / B 1.48).
+A small drawdown for buy&hold-comparable PnL: the volume gate is what keeps DD low
+(it sits out the unconfirmed chop and most of the down moves).
+"""
+import numpy as np
+import pandas as pd
+import blindlib as bl
+
+# Tuned ONLY on split='train'. Plateau center; robust to don in 10..40, vwin 20..30.
+DONS = (15, 20, 30)   # breakout looks new-high vs several lookbacks (robustness)
+VOL_WIN = 30          # window for the volume average
+VOL_TH = 1.2          # volume must exceed 1.2x its average to confirm a breakout
+HOLD = 3              # bars to stay long after a confirmed breakout
+TARGET_VOL = 0.20
+VOL_WIN_DAYS = 30
+LEV_CAP = 1.0
+
+
+def signal(df):
+    c = df["close"].values.astype(float)
+    v = df["volume"].values.astype(float)
+    n = len(c)
+
+    # --- volume surge (causal): today's volume vs its trailing average ---
+    vma = pd.Series(v).rolling(VOL_WIN, min_periods=5).mean().values
+    vsurge = v / np.where(vma > 0, vma, np.nan)
+    hivol = np.nan_to_num(vsurge, nan=0.0) > VOL_TH
+
+    # --- breakout: new local high vs several donchian windows (causal) ---
+    up_break = np.zeros(n, dtype=bool)
+    for don in DONS:
+        roll_hi = pd.Series(c).rolling(don, min_periods=2).max().values
+        up_break |= (c >= roll_hi)
+
+    # confirmed event = breakout AND volume confirms it
+    event = up_break & hivol
+
+    # --- latch LONG for HOLD bars after a confirmed event (backward-only) ---
+    raw = np.zeros(n)
+    last_event = -10 ** 9
+    for i in range(n):
+        if event[i]:
+            last_event = i
+        if (i - last_event) < HOLD:
+            raw[i] = 1.0  # long/flat only
+
+    # --- risk-scale the held leg ---
+    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)