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>
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Adriano Dal Pastro
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"""Agent 18 — Distance-from-MA reversion, trend-gated (family=meanrev, slug=dist_ma).
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THE ANGLE (assigned): position = -tanh(scaled distance of price from its MA). Buy when price
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is stretched BELOW its MA, sell when stretched ABOVE — a reversion-to-the-MA impulse, sized by
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how far price has wandered. Tune the MA window and the tanh scale.
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WHY THE PURE ANGLE LOSES, AND WHAT SURVIVES.
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The naive symmetric form (-tanh(scale * (price/MA - 1)) traded both sides) is CATASTROPHIC on
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these two curves: both trend up ~7x (A) / ~23x (B) over the train window, so shorting every
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stretch ABOVE the MA just fights a relentless uptrend. Measured: the pure symmetric angle
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returns -79%..-95% with sharpe ~ -0.5..-0.9 (it shorts the bull). A conditioning study of
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next-bar return vs the normalized distance-from-MA confirms the asymmetry: the LARGEST
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positive next-bar returns sit at the HIGHEST positive distance (that's momentum continuation,
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NOT reversion — never short it), while the genuine reversion edge lives only on the DOWNSIDE
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— when price is stretched well below its MA, the next bar bounces (+0.27%..+0.35% in the
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deepest dip bin, pooled A&B). So the distance-from-MA reversion that actually exists here is
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the short-horizon PULLBACK inside the prevailing trend, not a fade of the trend itself.
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THE RULE.
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impulse = -tanh(SCALE * z) where z = (price/SMA(MA) - 1) standardized by a trailing rolling
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std (so A and B, with different vol, get comparable stretch units). impulse>0 = price below
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its MA (a dip -> reversion says go long); impulse<0 = price above its MA (a rally -> short).
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A TREND GATE then keeps only the reversion leg that agrees with the regime:
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* UPTREND (price > SMA(SLOW)): take only the LONG impulse (buy the dip that bounces).
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* DOWNTREND (price < SMA(SLOW)): take only the SHORT impulse (fade the dead-cat rally),
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down-weighted by SHORT_W. Tuning drives SHORT_W -> 0: both curves trend up, so the
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downtrend-short reversion only adds drawdown over this sample.
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A causal vol_target sizes the impulse so the two series are risk-comparable and exposure
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shrinks into vol spikes.
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CAUSAL: SMA(MA), SMA(SLOW), the rolling std and vol_target at bar i use only rows <= i. No
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shift(-k), no centered windows, no global fit. Verified by causality_ok (online-consistent).
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TUNING (train only, combined A&B; coarse->fine, plateau not spike). A FAST MA (the distance is
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a short-horizon pullback, not a slow-trend gap) is decisively better than a medium MA:
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ma=3 beats ma=20+ by ~0.2 sharpe at lower DD. The chosen cell is interior on every axis:
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MA 3..5 -> sharpe_min 0.69..0.81 ; SCALE 1.0..2.5 -> 0.72..0.76 (PnL rises, DD ~flat) ;
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NORM_WIN 30..90 -> 0.75..0.80 ; SLOW 110..140 -> sharpe_min 0.74..0.81 (a real plateau).
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SHORT_W 0->0.5 only lowers sharpe (the downtrend short fights the structural uptrend).
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vol_target trades PnL<->DD ~linearly (sharpe flat), so TARGET_VOL is just the risk dial.
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MA=3, NORM_WIN=60, SCALE=1.5, SLOW=130, SHORT_W=0.0, TARGET_VOL=0.30, VOL_WIN=30, LEV_CAP=2.0
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-> train combined: pnl_mean ~0.70, maxdd_worst ~0.115, sharpe_min ~0.80
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(a solid PnL at an ~11-12% drawdown: the reversion-in-trend harvests the pullback bounces
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while sidestepping the deep declines, vs long-only buy&hold's huge PnL at ~70-80% DD.)
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HONEST CAVEAT: the value here is the DROP IN DRAWDOWN (~6x lower than buy&hold), not beating
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buy&hold's raw PnL on a 7x/23x bull run. The PURE assigned angle (symmetric fade) is a
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loser on trending data — it only becomes positive once gated to the dip side of the trend.
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"""
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import numpy as np
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import pandas as pd
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import blindlib as bl
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MA = 3 # fast SMA -> the distance is a SHORT-HORIZON pullback from price
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NORM_WIN = 60 # trailing window standardizing the distance (so A & B are comparable)
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SCALE = 1.5 # tanh scale on the standardized distance -> reversion impulse magnitude
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SLOW = 130 # trend-regime SMA for the agreement gate
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SHORT_W = 0.0 # weight on the (gated) downtrend-short leg; tuning -> 0 (long-flat best)
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TARGET_VOL = 0.30
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VOL_WIN_DAYS = 30
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LEV_CAP = 2.0
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def signal(df):
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c = df["close"].values.astype(float)
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n = len(c)
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# distance of price from its (fast) MA, standardized by a trailing rolling std (causal).
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dist = c / bl.sma(c, MA) - 1.0
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sd = pd.Series(dist).rolling(NORM_WIN).std().values
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zd = np.nan_to_num(dist / np.where(sd > 0, sd, np.nan), nan=0.0)
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# the assigned angle: reversion impulse = -tanh(scaled distance).
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# zd>0 (price above MA) -> impulse<0 (short the stretch)
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# zd<0 (price below MA) -> impulse>0 (long the dip)
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impulse = -np.tanh(SCALE * zd)
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# trend-agreement gate: keep only the reversion leg that agrees with the regime.
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up = c > bl.sma(c, SLOW)
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raw = np.zeros(n)
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long_ok = (impulse > 0) & up # buy the dip inside an uptrend
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short_ok = (impulse < 0) & (~up) # fade the rally inside a downtrend (down-weighted)
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raw[long_ok] = impulse[long_ok]
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raw[short_ok] = impulse[short_ok] * SHORT_W
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pos = bl.vol_target(raw, df, target_vol=TARGET_VOL,
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vol_win_days=VOL_WIN_DAYS, leverage_cap=LEV_CAP)
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return np.clip(np.nan_to_num(pos, nan=0.0), -1.0, 1.0)
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