Adriano Dal Pastro
ad65a0b344
23 famiglie esplorate (harness condiviso exit_lab, train/OOS embargo nov-2023, tutto lo storico 1h 2018-2026) + 10 verifiche avversariali + test PORT06. 'Cavalcare il prezzo' non esiste (4a conferma: oltre il TP=media non c'e' coda). Scoperta: lo SL intrabar fisso e' il distruttore di valore n.1 delle fade (stop da wick = falsi negativi). Forma robusta: SL solo su CHIUSURA oltre sl0±0.5·ATR14 — PORT06 FULL Sharpe 6.47→7.84 DD 4.10→2.60, OOS 8.82→10.06. Collaterali: bias gap-through dell'engine sugli stop stretti; ramo -2% del worker morto con sl=0. Diario: docs/diary/2026-06-04-exit-lab.md Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
237 lines
11 KiB
Python
237 lines
11 KiB
Python
"""TEST DECISIVO: impatto di EXIT-16 (close_confirm_sl, buffer=0.5 ATR) sul PORT06,
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nel PATH CANONICO del backtest (NON exit_lab).
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EXIT-16: lo SL intrabar e' DISATTIVATO; si esce al close del bar j solo se il close
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ha sfondato il livello di buffer*ATR14:
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long (d=1): esci a close[j] se close[j] < sl0 - 0.5*atr14[j]
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short (d=-1): esci a close[j] se close[j] > sl0 + 0.5*atr14[j]
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TP intrabar al livello e max_bars al close restano INVARIATI.
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Metodo (come fu fatto per il loss-guard Hurst):
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1. build_everything() canonico -> equity giornaliere di TUTTI gli sleeve (cache intatta).
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2. ricostruisco le 6 equity fade in variante EXIT-16 replicando ESATTAMENTE
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fade_daily_equity/build_trades (stessi segnali fn(df,**params), trend_max=3.0,
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fee 0.10%RT*lev3, pos 0.15, compounding, non-overlap), cambiando SOLO il ramo SL.
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3. PARITA': con la SL-rule originale il replay deve riprodurre le equity canoniche.
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4. PORT06 base vs EXIT-16 con la STESSA matematica dei pesi (Portfolio.backtest):
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weighting cap, caps PAIRS 0.33, ribilancio 1D, metriche FULL e OOS.
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NB: la leva 2x del portfolios.yml NON entra nel backtest (Portfolio.backtest la ignora;
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e' un knob live). Le equity fade gia' includono lev=3 dentro build_trades.
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"""
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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 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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fade_daily_equity, _norm, IDX, port_returns, metrics, SPLIT, OOS_DATE,
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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 promossa: close-confirm con buffer 0.5 ATR
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# ---------------------------------------------------------------- engine replay
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def build_trades_variant(ents, df, mode, buffer=BUFFER,
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lev=LEV, fee_rt=FEE_RT, trend_max=3.0, ema_long=200):
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"""Replica ESATTA di risk_management.build_trades, cambiando SOLO il ramo SL.
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mode="orig" : SL intrabar al livello (SL prima del TP) == canonico.
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mode="exit16" : SL intrabar DISATTIVATO; close-confirm sul close[j]:
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long esci a close[j] se close[j] < sl0 - buffer*atr14[j]
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short esci a close[j] se close[j] > sl0 + buffer*atr14[j]
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TP intrabar al livello e max_bars al close INVARIATI.
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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 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: no SL intrabar; TP intrabar; poi close-confirm SL al close[j]
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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 fade_equity_variant(asset, fn, params, mode):
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"""Stesso flusso di combine_portfolio.fade_daily_equity ma con build_trades_variant."""
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df = load_data(asset, "1h")
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ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
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trades = build_trades_variant(fn(df, **params), df, mode=mode, trend_max=3.0)
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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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# ---------------------------------------------------------------- pesi PORT06
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def port_metrics(members: dict[str, pd.Series], weights: dict[str, float]):
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dr = port_returns(members, weights)
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return metrics(dr), metrics(dr, lo=SPLIT)
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def main():
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p = PORTFOLIOS["PORT06"]
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fade_ids = [s.sid for s in p.sleeves if s.sid.startswith("MR")]
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print("=" * 96)
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print(" TEST DECISIVO EXIT-16 (close_confirm_sl buffer=0.5 ATR) su PORT06 — path canonico")
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print(f" fade sleeve: {fade_ids}")
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print("=" * 96)
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# --- 1. equity canoniche di TUTTI gli sleeve (cache intatta) ---
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print("\n[1] build_everything() canonico (pesante, ~2-3 min)...")
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from src.portfolio.sleeves import all_sleeve_equities
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eq_base = dict(all_sleeve_equities()) # {sid: equity giornaliera}
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print(f" sleeve totali: {len(eq_base)}")
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# --- 2. PARITA': replay 'orig' deve riprodurre le equity canoniche ---
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print("\n[2] PARITA' replay (mode=orig) vs canonico (fade_daily_equity):")
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print(f" {'sleeve':<10s}{'corr':>10s}{'ret_canon%':>14s}{'ret_replay%':>14s}{'diff%':>9s}")
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parity_ok = True
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eq_orig, eq_e16 = {}, {}
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for asset in ("BTC", "ETH"):
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for nm, (fn, params) in strats_for(asset).items():
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sid = f"{nm}_{asset}"
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if sid not in fade_ids:
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continue
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eq_orig[sid] = fade_equity_variant(asset, fn, params, mode="orig")
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eq_e16[sid] = fade_equity_variant(asset, fn, params, mode="exit16")
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base = eq_base[sid]
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rep = eq_orig[sid]
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corr = base.pct_change().fillna(0).corr(rep.pct_change().fillna(0))
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rb = (base.iloc[-1] / base.iloc[0] - 1) * 100
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rr = (rep.iloc[-1] / rep.iloc[0] - 1) * 100
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diff = rr - rb
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flag = "" if (corr > 0.999 and abs(diff) <= max(1.0, abs(rb) * 0.01)) else " <-- MISMATCH"
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if flag:
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parity_ok = False
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print(f" {sid:<10s}{corr:>10.5f}{rb:>14.1f}{rr:>14.1f}{diff:>+9.2f}{flag}")
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print(f"\n PARITA' {'OK' if parity_ok else 'FALLITA'} "
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f"(corr>0.999 e ret finale entro 1%).")
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if not parity_ok:
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print("\n >>> Parita' non raggiunta: NON forzo. Diagnostico sopra. STOP.")
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return
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# --- 3. PORT06 base vs EXIT-16: stessi pesi cap, stessa matematica ---
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members_base = dict(eq_base)
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members_e16 = dict(eq_base)
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for sid in fade_ids:
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members_e16[sid] = eq_e16[sid] # sostituisco SOLO le 6 colonne fade
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ids = p.sleeve_ids
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# pesi cap canonici (gli stessi che usa Portfolio.backtest)
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dr_base = pd.DataFrame({i: members_base[i].pct_change().fillna(0.0) for i in ids})
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w_base = W.weight_vector(p.weighting, ids, dr_base, weights=p.weights,
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caps=p.caps, clusters=p.clusters, lookback=p.vol_lookback)
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dr_e16 = pd.DataFrame({i: members_e16[i].pct_change().fillna(0.0) for i in ids})
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w_e16 = W.weight_vector(p.weighting, ids, dr_e16, weights=p.weights,
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caps=p.caps, clusters=p.clusters, lookback=p.vol_lookback)
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f_b, o_b = port_metrics({i: members_base[i] for i in ids}, w_base)
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f_e, o_e = port_metrics({i: members_e16[i] for i in ids}, w_e16)
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print("\n" + "=" * 96)
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print(f" [3] PORT06 — pesi={p.weighting} caps={p.caps} | OOS da {OOS_DATE} | leva3x interna fade, pos0.15")
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print("=" * 96)
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print(f" {'variante':<14s}{'FULL Sh':>9s}{'FULL DD%':>10s}{'FULL CAGR':>11s}"
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f" | {'OOS Sh':>8s}{'OOS DD%':>9s}{'OOS CAGR':>10s}")
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print(" " + "-" * 90)
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print(f" {'BASE':<14s}{f_b['sharpe']:>9.2f}{f_b['dd']:>10.2f}{f_b['cagr']:>10.0f}%"
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f" | {o_b['sharpe']:>8.2f}{o_b['dd']:>9.2f}{o_b['cagr']:>9.0f}%")
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print(f" {'EXIT-16':<14s}{f_e['sharpe']:>9.2f}{f_e['dd']:>10.2f}{f_e['cagr']:>10.0f}%"
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f" | {o_e['sharpe']:>8.2f}{o_e['dd']:>9.2f}{o_e['cagr']:>9.0f}%")
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print(" " + "-" * 90)
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print(f" {'DELTA':<14s}{f_e['sharpe']-f_b['sharpe']:>+9.2f}{f_e['dd']-f_b['dd']:>+10.2f}"
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f"{f_e['cagr']-f_b['cagr']:>+10.0f}% | {o_e['sharpe']-o_b['sharpe']:>+8.2f}"
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f"{o_e['dd']-o_b['dd']:>+9.2f}{o_e['cagr']-o_b['cagr']:>+9.0f}%")
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# --- per-sleeve fade: differenze principali ---
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print("\n Per-sleeve fade (equity FULL ret%, EXIT-16 vs orig-replay):")
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print(f" {'sleeve':<10s}{'orig ret%':>12s}{'exit16 ret%':>14s}{'delta%':>10s}"
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f"{'orig DD%':>10s}{'e16 DD%':>10s}")
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for sid in fade_ids:
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ro = eq_orig[sid]; re = eq_e16[sid]
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def _dd(s):
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pk = s.cummax(); return float(((pk - s) / pk).max() * 100)
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rro = (ro.iloc[-1] / ro.iloc[0] - 1) * 100
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rre = (re.iloc[-1] / re.iloc[0] - 1) * 100
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print(f" {sid:<10s}{rro:>12.1f}{rre:>14.1f}{rre-rro:>+10.1f}"
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f"{_dd(ro):>10.1f}{_dd(re):>10.1f}")
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# --- GATE ---
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print("\n" + "=" * 96)
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print(" GATE (stesso del loss-guard): PROMOSSO se OOS Sharpe migliora/pari E DD non peggiora")
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print(" materialmente, E in FULL non degrada.")
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print("=" * 96)
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oos_sh_ok = o_e['sharpe'] >= o_b['sharpe'] - 0.02
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oos_dd_ok = o_e['dd'] <= o_b['dd'] + 0.20 # no peggioramento materiale DD
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full_ok = f_e['sharpe'] >= f_b['sharpe'] - 0.02 and f_e['dd'] <= f_b['dd'] + 0.20
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promoted = oos_sh_ok and oos_dd_ok and full_ok
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print(f" OOS Sharpe {o_b['sharpe']:.2f} -> {o_e['sharpe']:.2f} "
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f"({'OK' if oos_sh_ok else 'KO'})")
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print(f" OOS DD% {o_b['dd']:.2f} -> {o_e['dd']:.2f} "
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f"({'OK' if oos_dd_ok else 'KO'})")
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print(f" FULL Sharpe {f_b['sharpe']:.2f} -> {f_e['sharpe']:.2f} | "
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f"FULL DD {f_b['dd']:.2f} -> {f_e['dd']:.2f} ({'OK' if full_ok else 'KO'})")
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print("\n VERDETTO: " + (">>> PROMOSSO <<<" if promoted else ">>> BOCCIATO <<<"))
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if __name__ == "__main__":
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main()
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