Adriano Dal Pastro
1afb1014c9
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>
91 lines
3.7 KiB
Python
91 lines
3.7 KiB
Python
"""agent_47_trail_mom — momentum entry with ACTIVE TRAILING-STOP position management.
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Angle [family=mix, slug=trail_mom]:
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* Enter LONG/SHORT on multi-horizon momentum (the "trend is your friend" entry).
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* Then actively MANAGE the position with a trailing stop measured in ATR units from
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the best favourable price seen since the trade opened:
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- adverse excursion (price pulls back toward the trail) -> REDUCE exposure,
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- follow-through (new favourable extreme) -> ADD exposure back, up to full size.
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* Vol-target the whole thing so DD stays bounded.
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CAUSAL: every value at bar i uses only rows 0..i. The trailing state machine is a pure
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forward loop (no future peek). The evaluator shifts the position, so position[i] is the
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weight held during bar i+1 — decided from data up to close[i].
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"""
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import numpy as np
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import blindlib as bl
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def _mom_dir(c):
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"""Multi-horizon momentum direction in [-1,1] (causal). Equal-weight 20/50/100."""
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d = np.zeros(len(c))
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for w, wt in ((20, 0.34), (50, 0.33), (100, 0.33)):
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m = c / bl.sma(c, w) - 1.0
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d += wt * np.tanh(8.0 * m)
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return np.clip(d, -1.0, 1.0)
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def signal(df):
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c = df["close"].values.astype(float)
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h = df["high"].values.astype(float)
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l = df["low"].values.astype(float)
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n = len(c)
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direction = _mom_dir(c) # desired sign + conviction
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a = bl.atr(df, 14) # causal ATR (vol unit for trail)
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a = np.where(np.isfinite(a) & (a > 0), a, np.nan)
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# ---- trailing-stop state machine (pure causal forward loop) -------------
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TRAIL_K = 4.0 # trail distance in ATR from the favourable extreme
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REDUCE_K = 0.8 # adverse excursion (ATR) at which we start shrinking
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sized = np.zeros(n) # managed exposure scalar in [0,1]
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cur_sign = 0.0
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best = np.nan # best favourable price since entry (max if long, min if short)
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expo = 0.0 # current exposure fraction in [0,1]
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for i in range(n):
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d = direction[i]
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sgn = np.sign(d) if abs(d) > 0.20 else 0.0 # dead-zone: avoid chop flip
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ai = a[i]
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if not np.isfinite(ai):
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sized[i] = 0.0
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continue
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# entry / flip: reset trailing state, start at conviction-scaled exposure
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if sgn != 0.0 and sgn != cur_sign:
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cur_sign = sgn
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best = c[i]
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expo = min(1.0, abs(d))
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elif sgn == 0.0:
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cur_sign = 0.0
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expo = 0.0
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best = np.nan
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if cur_sign != 0.0 and np.isfinite(best):
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# update favourable extreme
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if cur_sign > 0:
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best = max(best, h[i])
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adverse = (best - c[i]) / ai # how far pulled back (ATR units)
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else:
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best = min(best, l[i])
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adverse = (c[i] - best) / ai
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# trailing management:
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if adverse >= TRAIL_K:
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expo = 0.0 # stopped out
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elif adverse >= REDUCE_K:
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# linearly reduce between REDUCE_K and TRAIL_K
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frac = 1.0 - (adverse - REDUCE_K) / (TRAIL_K - REDUCE_K)
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target = min(1.0, abs(d)) * max(0.0, frac)
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expo = min(expo, target) # reduce only on adverse
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else:
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# follow-through region -> add back toward full conviction
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target = min(1.0, abs(d))
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expo = expo + 0.34 * (target - expo) # ease back up
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sized[i] = cur_sign * expo
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else:
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sized[i] = 0.0
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# ---- vol-target the managed directional series --------------------------
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pos = bl.vol_target(sized, df, target_vol=0.20, vol_win_days=30, leverage_cap=1.0)
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return np.clip(pos, -1.0, 1.0)
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