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>
2026-06-21 07:05:04 +00:00
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+"""Agent 16 — Z-score reversion to SMA, trend-gated (family=meanrev, slug=zrev).
+
+THE ANGLE (assigned): reversion of price to its SMA via a CAUSAL rolling z-score —
+short positive extremes / long negative extremes — WITH A TREND-AGREEMENT GATE.
+
+Why the gate is the whole story here. Naive z-reversion (short every z>+thr, long every
+z<-thr against a price-vs-SMA z-score) LOSES on these two curves: both trend up ~8x/24x
+over the sample, so a positive z-extreme above a medium SMA is usually momentum that keeps
+going (study: z>1.5 -> next-bar +0.005/+0.008, NOT a reversal), and shorting it just fights
+the trend. The reversion that actually exists is the SHORT-HORIZON pullback inside the
+prevailing trend:
+
+  * In an UPTREND (price > slow SMA), a negative z-extreme (a dip below the FAST SMA) is a
+    pullback that bounces -> go LONG. (study: UP & z<-1 -> next-bar +0.003 .. +0.012.)
+  * In a DOWNTREND (price < slow SMA), a positive z-extreme (a rally above the FAST SMA) is
+    a dead-cat that fades -> go SHORT. (study: DOWN & z>+1 -> next-bar ~0 .. -0.004.)
+  * A z-extreme that DISAGREES with the trend (rally in an uptrend / dip in a downtrend) is
+    momentum/continuation, not reversion -> stay FLAT (those bins are where naive z-reversion
+    bleeds: UP & z>1 -> +0.003 continuation; you must NOT short it).
+
+So the position is the reversion impulse (-z, clipped to extremes) FILTERED by trend
+agreement: keep only longs in uptrends and shorts in downtrends. A causal vol-target then
+sizes it so A and B are risk-comparable and exposure shrinks into vol spikes.
+
+CAUSAL: zscore(c, FAST) and sma(c, SLOW) at i use only rows <= i; the trend gate and
+vol_target are trailing. No shift(-k), no centered windows, no global fit. Verified by
+causality_ok.
+
+Tuning (train only, combined A&B; coarse->fine sweep). A CONTINUOUS reversion impulse
+(-z, saturating) gated by the trend beats sparse extreme-only entries (more of the dips are
+captured while the gate keeps the trend on your side). The chosen cell is interior on every
+axis and is a plateau, not a spike: FAST 2..3, SLOW 100..150, Z_SAT 1.5..2.0 all stay in
+sharpe_min ~0.6..0.8 at DD ~0.06..0.12; SHORT_W 0->0.5 only lowers sharpe_min (the downtrend
+short reversion fights the structural uptrend). vol_target scales PnL<->DD linearly (sharpe
+flat), so TARGET_VOL just sets the risk dial.
+  FAST=2, SLOW=120, Z_SAT=1.75, SHORT_W=0.0, TARGET_VOL=0.30, VOL_WIN_DAYS=30, LEV_CAP=2.0
+  -> train combined: pnl_mean ~0.31, maxdd_worst ~0.11, sharpe_min ~0.78
+     (a modest PnL at a ~10% drawdown — the reversion-in-trend captures the bounces while
+      sidestepping the big declines, vs long-only buy&hold's huge PnL at ~70-80% DD).
+"""
+import numpy as np
+import blindlib as bl
+
+FAST = 2            # short SMA for the reversion z-score (the "stretch from SMA" detector)
+SLOW = 120          # slow SMA defining the trend regime for the agreement gate
+Z_SAT = 1.75        # z magnitude that saturates the reversion impulse to +-1
+SHORT_W = 0.0       # weight on the (gated) short leg; tuning -> 0 (long-flat best on train)
+TARGET_VOL = 0.30
+VOL_WIN_DAYS = 30
+LEV_CAP = 2.0
+
+
+def signal(df):
+    c = df["close"].values.astype(float)
+    n = len(c)
+
+    z = np.nan_to_num(bl.zscore(c, FAST), nan=0.0)      # price-vs-fast-SMA, standardized (causal)
+    slow = bl.sma(c, SLOW)                               # trend regime line (causal)
+    uptrend = c > slow                                   # boolean trend gate
+
+    # reversion impulse = -z: long when price is stretched BELOW its SMA (dip, z<0),
+    # short when stretched ABOVE (rally, z>0). Proportional, saturating at +-Z_SAT.
+    impulse = np.clip(-z / Z_SAT, -1.0, 1.0)             # -z direction = reversion to the SMA
+
+    # TREND-AGREEMENT GATE: keep ONLY longs in an uptrend and shorts in a downtrend.
+    # A z-extreme that DISAGREES with the trend (rally in an uptrend / dip in a downtrend)
+    # is momentum/continuation, not reversion -> stay FLAT. The short leg is gated AND
+    # down-weighted by SHORT_W (tuning drives it to 0: both curves trend up, so the
+    # downtrend-short reversion only adds drawdown here).
+    raw = np.zeros(n)
+    long_ok = (impulse > 0) & uptrend                    # buy the dip inside an uptrend
+    short_ok = (impulse < 0) & (~uptrend)                # fade the rally inside a downtrend
+    raw[long_ok] = impulse[long_ok]
+    raw[short_ok] = impulse[short_ok] * SHORT_W
+
+    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)