"""TEST DECISIVO: impatto di EXIT-16 (close_confirm_sl, buffer=0.5 ATR) sul PORT06, nel PATH CANONICO del backtest (NON exit_lab). EXIT-16: lo SL intrabar e' DISATTIVATO; si esce al close del bar j solo se il close ha sfondato il livello di buffer*ATR14: long (d=1): esci a close[j] se close[j] < sl0 - 0.5*atr14[j] short (d=-1): esci a close[j] se close[j] > sl0 + 0.5*atr14[j] TP intrabar al livello e max_bars al close restano INVARIATI. Metodo (come fu fatto per il loss-guard Hurst): 1. build_everything() canonico -> equity giornaliere di TUTTI gli sleeve (cache intatta). 2. ricostruisco le 6 equity fade in variante EXIT-16 replicando ESATTAMENTE fade_daily_equity/build_trades (stessi segnali fn(df,**params), trend_max=3.0, fee 0.10%RT*lev3, pos 0.15, compounding, non-overlap), cambiando SOLO il ramo SL. 3. PARITA': con la SL-rule originale il replay deve riprodurre le equity canoniche. 4. PORT06 base vs EXIT-16 con la STESSA matematica dei pesi (Portfolio.backtest): weighting cap, caps PAIRS 0.33, ribilancio 1D, metriche FULL e OOS. NB: la leva 2x del portfolios.yml NON entra nel backtest (Portfolio.backtest la ignora; e' un knob live). Le equity fade gia' includono lev=3 dentro build_trades. """ from __future__ import annotations import sys from pathlib import Path import numpy as np import pandas as pd PROJECT_ROOT = Path(__file__).resolve().parents[2] sys.path.insert(0, str(PROJECT_ROOT)) from src.data.downloader import load_data from scripts.analysis.strategy_research import atr from scripts.analysis.risk_management import strats_for, FEE_RT, LEV, POS, INIT from scripts.analysis.combine_portfolio import ( fade_daily_equity, _norm, IDX, port_returns, metrics, SPLIT, OOS_DATE, ) from scripts.portfolios._defs import PORTFOLIOS from src.portfolio import weighting as W BUFFER = 0.5 # EXIT-16 promossa: close-confirm con buffer 0.5 ATR # ---------------------------------------------------------------- engine replay def build_trades_variant(ents, df, mode, buffer=BUFFER, lev=LEV, fee_rt=FEE_RT, trend_max=3.0, ema_long=200): """Replica ESATTA di risk_management.build_trades, cambiando SOLO il ramo SL. mode="orig" : SL intrabar al livello (SL prima del TP) == canonico. mode="exit16" : SL intrabar DISATTIVATO; close-confirm sul close[j]: long esci a close[j] se close[j] < sl0 - buffer*atr14[j] short esci a close[j] se close[j] > sl0 + buffer*atr14[j] TP intrabar al livello e max_bars al close INVARIATI. """ h, l, c = df["high"].values, df["low"].values, df["close"].values n = len(c) a = atr(df, 14) el = pd.Series(c).ewm(span=ema_long, adjust=False).mean().values fee = fee_rt * lev out = [] last = -1 for e in ents: i, d = e["i"], e["d"] if i <= last or i + 1 >= n: continue if trend_max is not None and a[i] and abs(c[i] - el[i]) / a[i] > trend_max: continue entry = c[i] tp, sl0, mb = e["tp"], e["sl"], e["max_bars"] exit_p = c[min(i + mb, n - 1)] j = min(i + mb, n - 1) for k in range(1, mb + 1): j = i + k if j >= n: exit_p = c[n - 1] break if mode == "orig": hs = (d == 1 and l[j] <= sl0) or (d == -1 and h[j] >= sl0) ht = (d == 1 and h[j] >= tp) or (d == -1 and l[j] <= tp) if hs: exit_p = sl0 break if ht: exit_p = tp break if k == mb: exit_p = c[j] else: # exit16: no SL intrabar; TP intrabar; poi close-confirm SL al close[j] ht = (d == 1 and h[j] >= tp) or (d == -1 and l[j] <= tp) if ht: exit_p = tp break aj = a[j] if np.isfinite(a[j]) else 0.0 confirm = (d == 1 and c[j] < sl0 - buffer * aj) or \ (d == -1 and c[j] > sl0 + buffer * aj) if confirm: exit_p = c[j] break if k == mb: exit_p = c[j] ret = (exit_p - entry) / entry * d * lev - fee out.append((i, j, ret)) last = j return out def fade_equity_variant(asset, fn, params, mode): """Stesso flusso di combine_portfolio.fade_daily_equity ma con build_trades_variant.""" df = load_data(asset, "1h") ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True) trades = build_trades_variant(fn(df, **params), df, mode=mode, trend_max=3.0) n = len(df) eq = np.full(n, INIT, dtype=float) cap = INIT for i, j, ret in sorted(trades, key=lambda t: t[1]): cap = max(cap + cap * POS * ret, 10.0) eq[j:] = cap s = pd.Series(eq, index=ts).resample("1D").last().reindex(IDX).ffill().bfill() return _norm(s) # ---------------------------------------------------------------- pesi PORT06 def port_metrics(members: dict[str, pd.Series], weights: dict[str, float]): dr = port_returns(members, weights) return metrics(dr), metrics(dr, lo=SPLIT) def main(): p = PORTFOLIOS["PORT06"] fade_ids = [s.sid for s in p.sleeves if s.sid.startswith("MR")] print("=" * 96) print(" TEST DECISIVO EXIT-16 (close_confirm_sl buffer=0.5 ATR) su PORT06 — path canonico") print(f" fade sleeve: {fade_ids}") print("=" * 96) # --- 1. equity canoniche di TUTTI gli sleeve (cache intatta) --- print("\n[1] build_everything() canonico (pesante, ~2-3 min)...") from src.portfolio.sleeves import all_sleeve_equities eq_base = dict(all_sleeve_equities()) # {sid: equity giornaliera} print(f" sleeve totali: {len(eq_base)}") # --- 2. PARITA': replay 'orig' deve riprodurre le equity canoniche --- print("\n[2] PARITA' replay (mode=orig) vs canonico (fade_daily_equity):") print(f" {'sleeve':<10s}{'corr':>10s}{'ret_canon%':>14s}{'ret_replay%':>14s}{'diff%':>9s}") parity_ok = True eq_orig, eq_e16 = {}, {} for asset in ("BTC", "ETH"): for nm, (fn, params) in strats_for(asset).items(): sid = f"{nm}_{asset}" if sid not in fade_ids: continue eq_orig[sid] = fade_equity_variant(asset, fn, params, mode="orig") eq_e16[sid] = fade_equity_variant(asset, fn, params, mode="exit16") base = eq_base[sid] rep = eq_orig[sid] corr = base.pct_change().fillna(0).corr(rep.pct_change().fillna(0)) rb = (base.iloc[-1] / base.iloc[0] - 1) * 100 rr = (rep.iloc[-1] / rep.iloc[0] - 1) * 100 diff = rr - rb flag = "" if (corr > 0.999 and abs(diff) <= max(1.0, abs(rb) * 0.01)) else " <-- MISMATCH" if flag: parity_ok = False print(f" {sid:<10s}{corr:>10.5f}{rb:>14.1f}{rr:>14.1f}{diff:>+9.2f}{flag}") print(f"\n PARITA' {'OK' if parity_ok else 'FALLITA'} " f"(corr>0.999 e ret finale entro 1%).") if not parity_ok: print("\n >>> Parita' non raggiunta: NON forzo. Diagnostico sopra. STOP.") return # --- 3. PORT06 base vs EXIT-16: stessi pesi cap, stessa matematica --- members_base = dict(eq_base) members_e16 = dict(eq_base) for sid in fade_ids: members_e16[sid] = eq_e16[sid] # sostituisco SOLO le 6 colonne fade ids = p.sleeve_ids # pesi cap canonici (gli stessi che usa Portfolio.backtest) dr_base = pd.DataFrame({i: members_base[i].pct_change().fillna(0.0) for i in ids}) w_base = W.weight_vector(p.weighting, ids, dr_base, weights=p.weights, caps=p.caps, clusters=p.clusters, lookback=p.vol_lookback) dr_e16 = pd.DataFrame({i: members_e16[i].pct_change().fillna(0.0) for i in ids}) w_e16 = W.weight_vector(p.weighting, ids, dr_e16, weights=p.weights, caps=p.caps, clusters=p.clusters, lookback=p.vol_lookback) f_b, o_b = port_metrics({i: members_base[i] for i in ids}, w_base) f_e, o_e = port_metrics({i: members_e16[i] for i in ids}, w_e16) print("\n" + "=" * 96) print(f" [3] PORT06 — pesi={p.weighting} caps={p.caps} | OOS da {OOS_DATE} | leva3x interna fade, pos0.15") print("=" * 96) print(f" {'variante':<14s}{'FULL Sh':>9s}{'FULL DD%':>10s}{'FULL CAGR':>11s}" f" | {'OOS Sh':>8s}{'OOS DD%':>9s}{'OOS CAGR':>10s}") print(" " + "-" * 90) print(f" {'BASE':<14s}{f_b['sharpe']:>9.2f}{f_b['dd']:>10.2f}{f_b['cagr']:>10.0f}%" f" | {o_b['sharpe']:>8.2f}{o_b['dd']:>9.2f}{o_b['cagr']:>9.0f}%") print(f" {'EXIT-16':<14s}{f_e['sharpe']:>9.2f}{f_e['dd']:>10.2f}{f_e['cagr']:>10.0f}%" f" | {o_e['sharpe']:>8.2f}{o_e['dd']:>9.2f}{o_e['cagr']:>9.0f}%") print(" " + "-" * 90) print(f" {'DELTA':<14s}{f_e['sharpe']-f_b['sharpe']:>+9.2f}{f_e['dd']-f_b['dd']:>+10.2f}" f"{f_e['cagr']-f_b['cagr']:>+10.0f}% | {o_e['sharpe']-o_b['sharpe']:>+8.2f}" f"{o_e['dd']-o_b['dd']:>+9.2f}{o_e['cagr']-o_b['cagr']:>+9.0f}%") # --- per-sleeve fade: differenze principali --- print("\n Per-sleeve fade (equity FULL ret%, EXIT-16 vs orig-replay):") print(f" {'sleeve':<10s}{'orig ret%':>12s}{'exit16 ret%':>14s}{'delta%':>10s}" f"{'orig DD%':>10s}{'e16 DD%':>10s}") for sid in fade_ids: ro = eq_orig[sid]; re = eq_e16[sid] def _dd(s): pk = s.cummax(); return float(((pk - s) / pk).max() * 100) rro = (ro.iloc[-1] / ro.iloc[0] - 1) * 100 rre = (re.iloc[-1] / re.iloc[0] - 1) * 100 print(f" {sid:<10s}{rro:>12.1f}{rre:>14.1f}{rre-rro:>+10.1f}" f"{_dd(ro):>10.1f}{_dd(re):>10.1f}") # --- GATE --- print("\n" + "=" * 96) print(" GATE (stesso del loss-guard): PROMOSSO se OOS Sharpe migliora/pari E DD non peggiora") print(" materialmente, E in FULL non degrada.") print("=" * 96) oos_sh_ok = o_e['sharpe'] >= o_b['sharpe'] - 0.02 oos_dd_ok = o_e['dd'] <= o_b['dd'] + 0.20 # no peggioramento materiale DD full_ok = f_e['sharpe'] >= f_b['sharpe'] - 0.02 and f_e['dd'] <= f_b['dd'] + 0.20 promoted = oos_sh_ok and oos_dd_ok and full_ok print(f" OOS Sharpe {o_b['sharpe']:.2f} -> {o_e['sharpe']:.2f} " f"({'OK' if oos_sh_ok else 'KO'})") print(f" OOS DD% {o_b['dd']:.2f} -> {o_e['dd']:.2f} " f"({'OK' if oos_dd_ok else 'KO'})") print(f" FULL Sharpe {f_b['sharpe']:.2f} -> {f_e['sharpe']:.2f} | " f"FULL DD {f_b['dd']:.2f} -> {f_e['dd']:.2f} ({'OK' if full_ok else 'KO'})") print("\n VERDETTO: " + (">>> PROMOSSO <<<" if promoted else ">>> BOCCIATO <<<")) if __name__ == "__main__": main()