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>
69 lines
3.4 KiB
Python
69 lines
3.4 KiB
Python
"""agent_27_dpo — Detrended Price Oscillator (cycle phase around a LAGGED MA).
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ANGLE [family=osc, slug=dpo]: detrend price by subtracting a moving average that we
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DELAY (lag) so the oscillator measures where price sits in its cycle relative to a
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recent trend baseline. Trade the cycle phase — causal only.
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Classic DPO is price[i] - SMA(n)[i - (n/2 + 1)]. The textbook centers that lag; here we
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keep the displacement STRICTLY BACKWARD (the MA value comes from ~n/2 bars ago, fully in
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the past), so the oscillator is causal/online and deployable.
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What the train data says (tuned on split='train' only):
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dpo = (price - lagged_baseline) / vol(gap) is a z-like CYCLE PHASE around zero.
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Bucketing dpo vs the NEXT-bar return showed a clean MONOTONIC relationship: the higher
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the detrended oscillator (price above its lagged baseline = cycle UP-phase), the higher
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the next return; deep-negative dpo (cycle down-phase) precedes flat/negative returns.
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So on these series the cycle is CONTINUATION, not reversion -> we FOLLOW the phase
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(long the up-phase, flat/short the down-phase), confirmed by a slow trend gate, and
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size with vol-targeting. Result on train: positive PnL at ~19% worst DD vs buy&hold's
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~78% DD — anticipating the move means staying out of (or short) the down-phase.
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Config tuned on train (period=30 / trendwin=200 / scale=1.5 / wc=0.6 / ema=2 / tv=0.18):
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plateau-robust across period 30, trend 150-200, scale 1.5-2.0, cycle weight 0.5-0.8.
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"""
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import numpy as np
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import blindlib as bl
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# --- tuned on split='train' only ------------------------------------------
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PERIOD = 30 # DPO moving-average period
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LAG = PERIOD // 2 + 1 # textbook DPO displacement, kept strictly backward (causal)
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TREND_WIN = 200 # slow-trend confirmation window
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SCALE = 1.5 # tanh softness of the cycle phase
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W_CYCLE = 0.6 # blend weight: cycle phase vs slow-trend confirmation
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EMA_SMOOTH = 2 # position smoothing (cuts turnover/fees)
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TARGET_VOL = 0.18 # annualized vol target
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VOL_WIN = 30
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LEV_CAP = 1.0
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def _dpo_phase(c: np.ndarray) -> np.ndarray:
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"""Detrended price oscillator z-phase: (price - LAGGED SMA) / rolling std of gap.
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The baseline SMA is delayed by LAG bars, so every value uses only past data."""
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n = len(c)
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base = bl.sma(c, PERIOD) # causal SMA
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base_lag = np.full(n, np.nan)
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base_lag[LAG:] = base[:-LAG] # baseline from LAG bars ago (past only)
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gap = c - base_lag
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gap_vol = bl.rolling_std(gap, PERIOD)
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gap_vol = np.where((gap_vol > 0) & np.isfinite(gap_vol), gap_vol, np.nan)
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return gap / gap_vol # z-like cycle phase (NaN during warmup)
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def signal(df):
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c = df["close"].values.astype(float)
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# detrended cycle phase (DPO core) — empirically CONTINUATION on these series
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z = np.nan_to_num(_dpo_phase(c), nan=0.0)
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cycle = np.tanh(z / SCALE) # +1 up-phase, -1 down-phase
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# slow-trend confirmation (don't ride the cycle against a strong regime)
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trend = c / bl.sma(c, TREND_WIN) - 1.0
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follow = np.tanh(np.nan_to_num(trend, nan=0.0) * 6.0)
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raw = np.clip(W_CYCLE * cycle + (1.0 - W_CYCLE) * follow, -1.0, 1.0)
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raw = bl.ema(raw, EMA_SMOOTH) # smooth -> fewer fee-bleeding flips
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pos = bl.vol_target(raw, df, target_vol=TARGET_VOL,
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vol_win_days=VOL_WIN, 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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