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>
88 lines
4.5 KiB
Python
88 lines
4.5 KiB
Python
"""agent_10_keltner — ANGLE: Keltner channel breakout (long / flat).
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Idea (assigned angle): a Keltner channel is an EMA mid-line wrapped by an ATR band,
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upper[i] = EMA_N(close)[i-1] + K * ATR_M[i-1]
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lower[i] = EMA_N(close)[i-1] - K_EXIT * ATR_M[i-1]
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Ride breakouts: go LONG when close[i] pierces the prior-bar UPPER band (an upside
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breakout out of the channel); EXIT to FLAT when close[i] pierces the prior-bar LOWER
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band. Hold the long between those two events (a turtle-style state machine) so we stay
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in persistent trends and keep turnover (fees) low. Tune N, M, K, K_EXIT on train only.
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WHY LONG/FLAT, NOT LONG/SHORT (honest tuning result on split='train'):
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The textbook Keltner breakout is stop-and-reverse (short below the lower band). I
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tuned both. Long/SHORT tops out at sharpe_min ~1.04 (maxdd ~0.39); switching the short
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leg to FLAT lifts sharpe_min to ~1.56 and cuts maxdd to ~0.28. On BOTH series the short
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leg is value-destroying: the pair trends up, so downside breakouts are mostly V-shaped
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bottoms / chop where a short gets whipsawed. So the breakout *exit* is kept (a lower-
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band break flattens us) but we never flip short. The Keltner breakout EVENT still drives
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every entry and exit — the angle is intact.
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Tuned on split='train' (Series A & B, equal weight). Broad plateau: 59/340 nearby cells
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keep sharpe_min > 1.40, so the chosen point is a plateau CENTER, not an isolated peak:
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* N_EMA = 20 (Keltner mid-line EMA span)
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* N_ATR = 30 (ATR window for the band half-width)
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* K = 1.0 (entry band multiplier: close above EMA + 1.0*ATR -> upside breakout)
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* K_EXIT = 0.5 (exit band multiplier: close below EMA - 0.5*ATR -> flatten; tighter
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than entry so we exit a failing trend faster than we re-enter)
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* vol-target the long to 30% ann vol (vol_win=30d, cap 1.0): the long size shrinks into
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vol spikes (every crash is a vol spike) -> caps the drawdown of late/whipsaw entries.
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Sharpe is ~flat (1.55-1.56) across target_vol 0.20-0.40; target_vol only trades PnL
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for DD (0.20 -> pnl 2.7/DD 0.19 ... 0.40 -> pnl 9.2/DD 0.34). 0.30 is the balance.
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Causality: the channel that close[i] is tested against is EMA/ATR evaluated at i-1 (one-
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bar lag via .shift(1)), so it is built from bars STRICTLY before i; a close[i] that
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pierces it is a real, tradeable event at close[i]. The state machine is a forward scan
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(uses only data <= i). The evaluator then holds the position during bar i+1. No future
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rows -> causality_ok = true.
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Train (combined A&B): pnl_mean ~5.55, maxdd_worst ~0.28, sharpe_min ~1.56.
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Honest note: Keltner breakout is pure trend-following, not alpha. Its value here is
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converting a high-PnL / ~77-79%-DD uptrend into comparable PnL at ~28% drawdown (DD cut
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~2.7x). The full long/short Keltner was MUCH worse (sharpe_min ~1.04, DD ~0.39) — the
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edge that matters is the FLAT side, exactly as for the sibling donchian breakout.
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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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N_EMA = 20 # Keltner mid-line EMA span
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N_ATR = 30 # ATR window for the band half-width
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K = 1.0 # entry band multiplier: break of EMA + K*ATR -> long
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K_EXIT = 0.5 # exit band multiplier: break of EMA - K_EXIT*ATR -> flat
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TARGET_VOL = 0.30
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VOL_WIN_DAYS = 30
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LEV_CAP = 1.0
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def _keltner_band(df, n_ema, n_atr, k):
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"""Lagged Keltner upper/lower at multiplier k: EMA[i-1] +/- k*ATR[i-1]."""
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c = df["close"].values.astype(float)
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mid = pd.Series(bl.ema(c, n_ema)).shift(1).values # EMA built <= i-1
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band = pd.Series(bl.atr(df, n_atr)).shift(1).values # ATR built <= i-1
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return mid + k * band, mid - k * band
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def signal(df):
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c = df["close"].values.astype(float)
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upper, _ = _keltner_band(df, N_EMA, N_ATR, K) # entry channel (wider)
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_, lower = _keltner_band(df, N_EMA, N_ATR, K_EXIT) # exit channel (tighter)
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up = c > upper # upside breakout -> enter / stay long (tradeable at close[i])
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dn = c < lower # downside breakout of tighter band -> exit to flat
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# turtle long/flat state machine (forward scan, uses only data <= i).
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n = len(c)
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state = np.zeros(n)
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s = 0.0
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for i in range(n):
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if np.isfinite(upper[i]) and up[i]:
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s = 1.0
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elif np.isfinite(lower[i]) and dn[i]:
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s = 0.0
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state[i] = s
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# size the long with causal vol-targeting (shrinks into vol spikes -> caps DD).
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pos = bl.vol_target(state, 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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