chore(analysis): dedup engine gate PORT06 + drift monitor giornaliero + impact bfill
- _port06_gate_common.py: build_trades_variant/equity_from_trades/port_metrics/dd fattorizzati dai 3 gate exit16/trendmax/dip01 (-214 righe duplicate). Nessun copy-drift trovato; versione promossa = trendmax (superset con hurst_mask). Output dei 3 gate verificato BYTE-IDENTICO prima/dopo. dip_trades resta nel suo script (sibling deliberato long-only/orig_gap, non una copia). - drift_monitor.py: rolling-return per famiglia vs distribuzione storica propria (warn sotto p5; oggi: FADE 120g al p2). In crontab host giornaliero 07:15 UTC con report Telegram. Osservabilita', non filtro di trading. - daily_equity_bfill_impact.py: bug bfill _daily_equity QUANTIFICATO -> non materiale (OOS invariato per costruzione, FULL DD 3.46->3.67 col fix, nessun verdetto gate a rischio). Lasciato documentato in TODO, niente fix. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Adriano Dal Pastro
@@ -29,116 +29,20 @@ from __future__ import annotations
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import sys
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from pathlib import Path
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import numpy as np
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import pandas as pd
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PROJECT_ROOT = Path(__file__).resolve().parents[2]
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sys.path.insert(0, str(PROJECT_ROOT))
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from src.data.downloader import load_data
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from src.strategies.fade_base import hurst_skip_mask
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from scripts.analysis.strategy_research import atr
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from scripts.analysis.risk_management import strats_for, FEE_RT, LEV, POS, INIT
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from scripts.analysis.combine_portfolio import (
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_norm, IDX, port_returns, metrics, SPLIT, OOS_DATE,
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from scripts.analysis.risk_management import strats_for
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from scripts.analysis.combine_portfolio import OOS_DATE
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from scripts.analysis._port06_gate_common import (
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build_trades_variant, equity_from_trades, port_metrics, dd as _dd,
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)
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from scripts.portfolios._defs import PORTFOLIOS
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from src.portfolio import weighting as W
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BUFFER = 0.5 # EXIT-16 close-confirm (come in produzione)
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HURST_MAX = 0.55 # loss-guard live
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TREND_MAX = 3.0
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EMA_LONG = 200
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def build_trades_variant(ents, df, mode, trend_max, hurst_mask=None,
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buffer=BUFFER, lev=LEV, fee_rt=FEE_RT, ema_long=EMA_LONG):
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"""Engine di exit16_port06_impact.build_trades_variant + skip Hurst opzionale.
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mode="orig" : SL intrabar al livello (SL prima del TP) == canonico.
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mode="exit16" : SL intrabar OFF; TP intrabar al livello (priorita' nel bar);
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SL solo se il CLOSE sfonda sl0 -/+ buffer*ATR14[j], fill a close[j].
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trend_max : None = filtro OFF (live attuale); 3.0 = candidato.
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hurst_mask : bool[i]=True -> salta l'ingresso (loss-guard live).
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"""
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h, l, c = df["high"].values, df["low"].values, df["close"].values
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n = len(c)
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a = atr(df, 14)
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el = pd.Series(c).ewm(span=ema_long, adjust=False).mean().values
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fee = fee_rt * lev
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out = []
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last = -1
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for e in ents:
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i, d = e["i"], e["d"]
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if i <= last or i + 1 >= n:
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continue
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if hurst_mask is not None and hurst_mask[i]:
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continue
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if trend_max is not None and a[i] and abs(c[i] - el[i]) / a[i] > trend_max:
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continue
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entry = c[i]
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tp, sl0, mb = e["tp"], e["sl"], e["max_bars"]
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exit_p = c[min(i + mb, n - 1)]
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j = min(i + mb, n - 1)
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for k in range(1, mb + 1):
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j = i + k
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if j >= n:
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exit_p = c[n - 1]
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break
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if mode == "orig":
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hs = (d == 1 and l[j] <= sl0) or (d == -1 and h[j] >= sl0)
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ht = (d == 1 and h[j] >= tp) or (d == -1 and l[j] <= tp)
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if hs:
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exit_p = sl0
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break
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if ht:
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exit_p = tp
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break
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if k == mb:
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exit_p = c[j]
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else: # exit16
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ht = (d == 1 and h[j] >= tp) or (d == -1 and l[j] <= tp)
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if ht:
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exit_p = tp
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break
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aj = a[j] if np.isfinite(a[j]) else 0.0
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confirm = (d == 1 and c[j] < sl0 - buffer * aj) or \
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(d == -1 and c[j] > sl0 + buffer * aj)
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if confirm:
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exit_p = c[j]
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break
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if k == mb:
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exit_p = c[j]
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ret = (exit_p - entry) / entry * d * lev - fee
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out.append((i, j, ret))
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last = j
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return out
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def equity_from_trades(df, trades):
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ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
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n = len(df)
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eq = np.full(n, INIT, dtype=float)
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cap = INIT
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for i, j, ret in sorted(trades, key=lambda t: t[1]):
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cap = max(cap + cap * POS * ret, 10.0)
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eq[j:] = cap
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s = pd.Series(eq, index=ts).resample("1D").last().reindex(IDX).ffill().bfill()
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return _norm(s)
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def port_metrics(members: dict[str, pd.Series], p):
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ids = p.sleeve_ids
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dr = pd.DataFrame({i: members[i].pct_change().fillna(0.0) for i in ids})
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w = W.weight_vector(p.weighting, ids, dr, weights=p.weights,
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caps=p.caps, clusters=p.clusters, lookback=p.vol_lookback)
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drp = port_returns({i: members[i] for i in ids}, w)
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return metrics(drp), metrics(drp, lo=SPLIT)
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def _dd(s):
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pk = s.cummax()
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return float(((pk - s) / pk).max() * 100)
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def main():
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