PythagorasGoal/scripts/analysis/trendmax_port06_impact.py

"""GATE PORT06: trend_max=3.0/ema_long=200 sulle 6 fade LIVE (improvement-sweep punto 7).

Contesto. Il backtest canonico (build_everything) applica il filtro trend alle fade,
ma in produzione le SleeveSpec di _defs.py NON lo passano -> le fade live fadano anche
i trend/crolli estesi (es. MR01/MR02_ETH long ripetuti nel crash ETH del 2026-06-05).
Anche il test PORT06 del loss-guard Hurst (fade_lossguard_port_test) fu misurato su
entries GIA' trend-filtrate -> la config live attuale (hurst SENZA trend) non e' mai
stata gateata. hurst e trend si sovrappongono (entrambi tagliano il regime trending):
il delta marginale va misurato sul path live, non assunto dai numeri canonici.

Confronto, a livello PORT06 (stessa matematica pesi cap di Portfolio.backtest):
  LIVE      = fade con hurst_max=0.55 + EXIT-16 (sl_confirm 0.5 ATR), trend OFF  [prod oggi]
  CANDIDATO = LIVE + trend_max=3.0 / ema_long=200                                 [proposta]
  TREND-ONLY= EXIT-16 + trend, senza hurst                                         [diagnostica overlap]

Parita' preliminare: il replay (mode=orig, trend ON, no hurst, no EXIT-16) deve
riprodurre le equity fade canoniche (come exit16_port06_impact).

La maschera Hurst e' quella ESATTA del live: fade_base.hurst_skip_mask (close-only,
window=100, step=6) — non la cache di regime_lab.

GATE (sweep): PROMOSSO se OOS Sharpe non peggiora (>= base-0.02) E il DD scende,
e in FULL non degrada.

    uv run python scripts/analysis/trendmax_port06_impact.py
"""
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 src.strategies.fade_base import hurst_skip_mask
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 (
    _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 close-confirm (come in produzione)
HURST_MAX = 0.55    # loss-guard live
TREND_MAX = 3.0
EMA_LONG = 200


def build_trades_variant(ents, df, mode, trend_max, hurst_mask=None,
                         buffer=BUFFER, lev=LEV, fee_rt=FEE_RT, ema_long=EMA_LONG):
    """Engine di exit16_port06_impact.build_trades_variant + skip Hurst opzionale.

    mode="orig"   : SL intrabar al livello (SL prima del TP) == canonico.
    mode="exit16" : SL intrabar OFF; TP intrabar al livello (priorita' nel bar);
                    SL solo se il CLOSE sfonda sl0 -/+ buffer*ATR14[j], fill a close[j].
    trend_max     : None = filtro OFF (live attuale); 3.0 = candidato.
    hurst_mask    : bool[i]=True -> salta l'ingresso (loss-guard live).
    """
    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 hurst_mask is not None and hurst_mask[i]:
            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
                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 equity_from_trades(df, trades):
    ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
    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)


def port_metrics(members: dict[str, pd.Series], p):
    ids = p.sleeve_ids
    dr = pd.DataFrame({i: members[i].pct_change().fillna(0.0) for i in ids})
    w = W.weight_vector(p.weighting, ids, dr, weights=p.weights,
                        caps=p.caps, clusters=p.clusters, lookback=p.vol_lookback)
    drp = port_returns({i: members[i] for i in ids}, w)
    return metrics(drp), metrics(drp, lo=SPLIT)


def _dd(s):
    pk = s.cummax()
    return float(((pk - s) / pk).max() * 100)


def main():
    p = PORTFOLIOS["PORT06"]
    fade_ids = [s.sid for s in p.sleeves if s.sid.startswith("MR")]
    print("=" * 100)
    print("  GATE PORT06 — trend_max=3.0/ema200 sulle fade LIVE (hurst 0.55 + EXIT-16 gia' attivi)")
    print(f"  fade sleeve: {fade_ids} | caps={p.caps}")
    print("=" * 100)

    print("\n[1] build_everything() canonico (cache)...")
    from src.portfolio.sleeves import all_sleeve_equities
    eq_base = dict(all_sleeve_equities())
    print(f"    sleeve totali: {len(eq_base)}")

    # dati + entries + maschera hurst (identica al live: close-only, w=100, step=6)
    dfs, masks, entries = {}, {}, {}
    for asset in ("BTC", "ETH"):
        dfs[asset] = load_data(asset, "1h")
        masks[asset] = hurst_skip_mask(dfs[asset], HURST_MAX)
        print(f"    {asset}: {len(dfs[asset])} barre, hurst-skip {masks[asset].mean()*100:.1f}% delle barre")
        for nm, (fn, params) in strats_for(asset).items():
            sid = f"{nm}_{asset}"
            if sid in fade_ids:
                entries[sid] = (asset, fn(dfs[asset], **params))

    # --- [2] PARITA': mode=orig, trend ON, no hurst, no exit16 == canonico ---
    print("\n[2] PARITA' replay (orig, trend ON, no hurst) vs canonico:")
    print(f"    {'sleeve':<10s}{'corr':>10s}{'ret_canon%':>14s}{'ret_replay%':>14s}{'diff%':>9s}")
    parity_ok = True
    for sid in fade_ids:
        asset, ents = entries[sid]
        rep = equity_from_trades(dfs[asset], build_trades_variant(
            ents, dfs[asset], mode="orig", trend_max=TREND_MAX))
        base = eq_base[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
        flag = "" if (corr > 0.999 and abs(rr - rb) <= 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}{rr-rb:>+9.2f}{flag}")
    print(f"\n    PARITA' {'OK' if parity_ok else 'FALLITA'}.")
    if not parity_ok:
        print("    >>> STOP: non procedo col gate su un engine non in parita'.")
        return

    # --- [3] varianti live-path delle 6 fade ---
    VARIANTS = {
        "LIVE":       dict(trend_max=None,      hurst=True),    # produzione oggi
        "CANDIDATO":  dict(trend_max=TREND_MAX, hurst=True),    # + trend filter
        "TREND-ONLY": dict(trend_max=TREND_MAX, hurst=False),   # swap hurst->trend
        "NESSUNO":    dict(trend_max=None,      hurst=False),   # solo EXIT-16 (baseline filtri)
        "TREND-2.5":  dict(trend_max=2.5,       hurst=False),   # sensibilita' soglia
        "TREND-3.5":  dict(trend_max=3.5,       hurst=False),
    }
    eqs = {v: {} for v in VARIANTS}
    ntr = {v: {} for v in VARIANTS}
    for sid in fade_ids:
        asset, ents = entries[sid]
        for v, cfg in VARIANTS.items():
            tr = build_trades_variant(ents, dfs[asset], mode="exit16",
                                      trend_max=cfg["trend_max"],
                                      hurst_mask=masks[asset] if cfg["hurst"] else None)
            eqs[v][sid] = equity_from_trades(dfs[asset], tr)
            ntr[v][sid] = len(tr)

    # --- [4] PORT06 per variante ---
    print("\n" + "=" * 100)
    print(f"  [4] PORT06 (pesi cap, OOS da {OOS_DATE}) — fade in path LIVE (exit16+hurst)")
    print("=" * 100)
    print(f"  {'variante':<12s}{'FULL Sh':>9s}{'FULL DD%':>10s}{'FULL CAGR':>11s}"
          f"   | {'OOS Sh':>8s}{'OOS DD%':>9s}{'OOS CAGR':>10s}")
    print("  " + "-" * 94)
    res = {}
    for v in VARIANTS:
        members = dict(eq_base)
        for sid in fade_ids:
            members[sid] = eqs[v][sid]
        f, o = port_metrics(members, p)
        res[v] = (f, o)
        print(f"  {v:<12s}{f['sharpe']:>9.2f}{f['dd']:>10.2f}{f['cagr']:>10.0f}%"
              f"   | {o['sharpe']:>8.2f}{o['dd']:>9.2f}{o['cagr']:>9.0f}%")
    f_l, o_l = res["LIVE"]
    f_c, o_c = res["CANDIDATO"]
    print("  " + "-" * 94)
    print(f"  {'DELTA C-L':<12s}{f_c['sharpe']-f_l['sharpe']:>+9.2f}{f_c['dd']-f_l['dd']:>+10.2f}"
          f"{f_c['cagr']-f_l['cagr']:>+10.0f}%   | {o_c['sharpe']-o_l['sharpe']:>+8.2f}"
          f"{o_c['dd']-o_l['dd']:>+9.2f}{o_c['cagr']-o_l['cagr']:>+9.0f}%")

    # --- [5] per-sleeve ---
    print("\n  Per-sleeve (FULL): ret% | DD% | n trade   [LIVE -> CANDIDATO]")
    print(f"    {'sleeve':<10s}{'ret L%':>10s}{'ret C%':>10s}{'DD L%':>8s}{'DD C%':>8s}"
          f"{'ntr L':>7s}{'ntr C':>7s}")
    for sid in fade_ids:
        el_, ec = eqs["LIVE"][sid], eqs["CANDIDATO"][sid]
        rl = (el_.iloc[-1] / el_.iloc[0] - 1) * 100
        rc = (ec.iloc[-1] / ec.iloc[0] - 1) * 100
        print(f"    {sid:<10s}{rl:>10.1f}{rc:>10.1f}{_dd(el_):>8.1f}{_dd(ec):>8.1f}"
              f"{ntr['LIVE'][sid]:>7d}{ntr['CANDIDATO'][sid]:>7d}")

    # --- GATE ---
    print("\n" + "=" * 100)
    print("  GATE (sweep): PROMOSSO se OOS Sharpe >= LIVE-0.02 E OOS DD scende, e FULL non degrada")
    print("=" * 100)
    oos_sh_ok = o_c["sharpe"] >= o_l["sharpe"] - 0.02
    oos_dd_ok = o_c["dd"] < o_l["dd"]
    full_ok = f_c["sharpe"] >= f_l["sharpe"] - 0.02 and f_c["dd"] <= f_l["dd"] + 0.20
    promoted = oos_sh_ok and oos_dd_ok and full_ok
    print(f"  OOS Sharpe {o_l['sharpe']:.2f} -> {o_c['sharpe']:.2f} ({'OK' if oos_sh_ok else 'KO'})")
    print(f"  OOS DD%    {o_l['dd']:.2f} -> {o_c['dd']:.2f} ({'OK' if oos_dd_ok else 'KO'})")
    print(f"  FULL Sharpe {f_l['sharpe']:.2f} -> {f_c['sharpe']:.2f} | "
          f"FULL DD {f_l['dd']:.2f} -> {f_c['dd']:.2f} ({'OK' if full_ok else 'KO'})")
    print("\n  VERDETTO: " + (">>> PROMOSSO <<<" if promoted else ">>> BOCCIATO <<<"))


if __name__ == "__main__":
    main()