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