PythagorasGoal/Old/scripts/analysis/dip01_exit16_impact.py

"""GATE DIP01 + PORT06: estendere EXIT-16 (close-confirm SL) a DIP01 (sweep punto 9).

DIP01 e' l'unico sleeve BTC con esecuzione REALE round-trip, e gira ancora col
branch SL intrabar wick-sensitive. EXIT-16 e' stato validato SULLE FADE: estenderlo
a una strategia honest richiede la validazione sul grid proprio di DIP01, con
engine GAP-AWARE (lezione exit-lab: l'engine canonico filla gli stop "al livello"
anche su gap-through -> bias PRO stop intrabar stretti; il confronto onesto filla
lo SL a worse(livello, open)).

Protocollo:
  [1] parita': replay engine 'orig' (fill al livello) == equity canonica DIP01_BTC
  [2] grid 3x3x2 (z_in x sl_atr x max_bars) su BTC (deployato) ed ETH (robustezza):
      orig GAP-AWARE vs EXIT-16(buf 0.5), ret/DD/Sharpe train (pre-OOS) e OOS
  [3] plateau buffer {0.4, 0.5, 0.75, 1.0} sulla cella canonica
  [4] gate PORT06: DIP01_BTC exit16 innestato nel canonico, pesi cap
      -> PROMOSSO se OOS Sharpe non peggiora E FULL/DD non degradano materialmente.

NB hurst_max NON valutato: il gate trendmax (2026-06-07) ha mostrato che il
loss-guard Hurst e' ridondante-dannoso POST-EXIT-16 (stesso regime target).

    uv run python scripts/analysis/dip01_exit16_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 scripts.analysis.strategy_research import atr
from scripts.analysis.combine_portfolio import _norm, IDX, metrics, SPLIT, OOS_DATE
from scripts.analysis._port06_gate_common import port_metrics
from scripts.portfolios._defs import PORTFOLIOS

FEE_RT, LEV, POS, INIT = 0.001, 3.0, 0.15, 1000.0
BUFFER = 0.5
GRID_Z = (2.0, 2.5, 3.0)
GRID_SL = (2.0, 2.5, 3.0)
GRID_MB = (24, 48)
CANON = dict(n=50, z_in=2.5, sl_atr=2.5, max_bars=24)


def dip_entries(df, n=50, z_in=2.5, sl_atr=2.5, max_bars=24):
    """Entries DIP01 == honest_improve2.dip_market_gated (market_n=0): crossing
    di z sotto -z_in. Ritorna [{i, tp, sl, mb}] (long-only)."""
    c = df["close"].values
    ma = pd.Series(c).rolling(n).mean().values
    sd = pd.Series(c).rolling(n).std().values
    a = atr(df, 14)
    z = (c - ma) / np.where(sd == 0, np.nan, sd)
    out = []
    for i in range(n + 14, len(c)):
        if np.isnan(z[i]) or np.isnan(a[i]):
            continue
        if z[i] <= -z_in and z[i - 1] > -z_in:
            out.append({"i": i, "tp": ma[i], "sl": c[i] - sl_atr * a[i],
                        "mb": max_bars})
    return out


def dip_trades(ents, df, mode, buffer=BUFFER):
    """Engine exit DIP01 (long-only), non-overlap come il canonico.

    mode="orig"     : SL intrabar fill AL LIVELLO (== canonico, per la parita')
    mode="orig_gap" : SL intrabar fill a worse(livello, open[j]) — gap-aware
    mode="exit16"   : SL intrabar OFF; TP intrabar al livello (priorita' nel bar);
                      stop solo se close[j] < sl - buffer*ATR14[j], fill a close[j]
    """
    h, l, c, o = df["high"].values, df["low"].values, df["close"].values, df["open"].values
    n = len(c)
    a = atr(df, 14)
    fee = FEE_RT * LEV
    out = []
    last = -1
    for e in ents:
        i = e["i"]
        if i <= last or i + 1 >= n:
            continue
        tp, sl, mb = e["tp"], e["sl"], e["mb"]
        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:
                j = n - 1
                exit_p = c[j]
                break
            if mode in ("orig", "orig_gap"):
                if l[j] <= sl:
                    exit_p = sl if mode == "orig" else min(sl, o[j])
                    break
                if h[j] >= tp:
                    exit_p = tp
                    break
                if k == mb:
                    exit_p = c[j]
            else:  # exit16
                if h[j] >= tp:
                    exit_p = tp
                    break
                aj = a[j] if np.isfinite(a[j]) else 0.0
                if c[j] < sl - buffer * aj:
                    exit_p = c[j]
                    break
                if k == mb:
                    exit_p = c[j]
        ret = (exit_p - c[i]) / c[i] * LEV - fee
        out.append((i, j, ret))
        last = j
    return out


def daily_equity(df, trades):
    """Equity giornaliera con la convenzione CANONICA honest (_daily_equity su punti
    trade-exit). NB: la serie a punti-trade reindexata su IDX ancora il primo valore
    al PRIMO trade dentro IDX (bfill), non al capitale portato avanti da prima —
    convenzione discutibile ma e' quella di build_everything: per la parita' (e il
    confronto col PORT06 canonico) va replicata esattamente."""
    from scripts.analysis.honest_improve2 import _daily_equity
    ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
    cap = INIT
    eq_ts, eq_v = [], []
    for i, j, ret in sorted(trades, key=lambda t: t[1]):
        cap = max(cap + cap * POS * ret, 10.0)
        eq_ts.append(ts.iloc[j])
        eq_v.append(cap)
    return _norm(_daily_equity(eq_ts, eq_v, IDX))


def cell_metrics(eq):
    dr = eq.pct_change().fillna(0.0)
    return metrics(dr), metrics(dr, lo=SPLIT)


def main():
    p = PORTFOLIOS["PORT06"]
    print("=" * 104)
    print("  GATE DIP01 EXIT-16 (close-confirm 0.5 ATR) — grid gap-aware + PORT06")
    print(f"  OOS da {OOS_DATE} | fee {FEE_RT*100:.2f}%RT x lev{LEV:.0f} | pos {POS}")
    print("=" * 104)

    print("\n[1] build_everything() canonico (cache)...")
    from src.portfolio.sleeves import all_sleeve_equities
    eq_base = dict(all_sleeve_equities())

    dfs = {a: load_data(a, "1h") for a in ("BTC", "ETH")}

    # --- parita' ---
    ents = dip_entries(dfs["BTC"], **CANON)
    rep = daily_equity(dfs["BTC"], dip_trades(ents, dfs["BTC"], "orig"))
    base = eq_base["DIP01_BTC"]
    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
    print(f"\n[1] PARITA' orig vs canonico: corr={corr:.5f} ret {rb:+.0f}% vs {rr:+.0f}%")
    if not (corr > 0.999 and abs(rr - rb) <= max(1.0, abs(rb) * 0.01)):
        print("    >>> PARITA' FALLITA: STOP.")
        return

    # --- [2] grid gap-aware ---
    for asset in ("BTC", "ETH"):
        df = dfs[asset]
        print(f"\n[2] GRID {asset} — orig GAP-AWARE vs EXIT-16 (train | OOS: ret% e Sharpe)")
        print(f"    {'cella':<16s}{'tr retO':>9s}{'tr retE':>9s} {'oos retO':>9s}{'oos retE':>9s}"
              f"  {'oos ShO':>8s}{'oos ShE':>8s}  {'ddO':>6s}{'ddE':>6s}  esito")
        wins_tr = wins_oos = cells = 0
        for z in GRID_Z:
            for slm in GRID_SL:
                for mb in GRID_MB:
                    ents = dip_entries(df, n=50, z_in=z, sl_atr=slm, max_bars=mb)
                    eo = daily_equity(df, dip_trades(ents, df, "orig_gap"))
                    ee = daily_equity(df, dip_trades(ents, df, "exit16"))
                    fo, oo = cell_metrics(eo)
                    fe, oe = cell_metrics(ee)
                    tr_o = fo["ret"] - oo["ret"]; tr_e = fe["ret"] - oe["ret"]  # ~train (full-oos, approssimato su ret composti: usare segni)
                    # train ret esatto: equity al SPLIT
                    tr_o = (eo.iloc[SPLIT] / eo.iloc[0] - 1) * 100
                    tr_e = (ee.iloc[SPLIT] / ee.iloc[0] - 1) * 100
                    cells += 1
                    w_tr = tr_e >= tr_o
                    w_oos = oe["ret"] >= oo["ret"]
                    wins_tr += w_tr
                    wins_oos += w_oos
                    tag = ("OK" if (w_tr and w_oos) else "tr-" if w_oos else "oos-" if w_tr else "KO")
                    print(f"    z{z} sl{slm} mb{mb:<3d}{tr_o:>9.0f}{tr_e:>9.0f} "
                          f"{oo['ret']:>9.0f}{oe['ret']:>9.0f}  {oo['sharpe']:>8.2f}{oe['sharpe']:>8.2f}"
                          f"  {fo['dd']:>6.1f}{fe['dd']:>6.1f}  {tag}")
        print(f"    -> EXIT-16 >= orig-gap: train {wins_tr}/{cells}, OOS {wins_oos}/{cells}")

    # --- [3] plateau buffer (BTC, cella canonica) ---
    print("\n[3] Plateau buffer EXIT-16 (BTC, cella canonica):")
    ents = dip_entries(dfs["BTC"], **CANON)
    for buf in (0.4, 0.5, 0.75, 1.0):
        ee = daily_equity(dfs["BTC"], dip_trades(ents, dfs["BTC"], "exit16", buffer=buf))
        fe, oe = cell_metrics(ee)
        print(f"    buf {buf:<5}FULL ret {fe['ret']:>+7.0f}% DD {fe['dd']:>5.1f} Sh {fe['sharpe']:>5.2f}"
              f"  | OOS ret {oe['ret']:>+6.0f}% DD {oe['dd']:>5.1f} Sh {oe['sharpe']:>5.2f}")

    # --- [4] gate PORT06 ---
    ee = daily_equity(dfs["BTC"], dip_trades(ents, dfs["BTC"], "exit16"))
    members_b = dict(eq_base)
    members_e = dict(eq_base)
    members_e["DIP01_BTC"] = ee
    f_b, o_b = port_metrics(members_b, p)
    f_e, o_e = port_metrics(members_e, p)
    print("\n" + "=" * 104)
    print(f"  [4] PORT06 (pesi cap {p.caps}) — DIP01_BTC orig vs EXIT-16")
    print("=" * 104)
    print(f"  {'variante':<10s}{'FULL Sh':>9s}{'FULL DD%':>10s}{'CAGR':>6s} | {'OOS Sh':>7s}{'OOS DD%':>8s}{'CAGR':>6s}")
    for nm, (f, o) in (("BASE", (f_b, o_b)), ("EXIT-16", (f_e, o_e))):
        print(f"  {nm:<10s}{f['sharpe']:>9.2f}{f['dd']:>10.2f}{f['cagr']:>5.0f}% | "
              f"{o['sharpe']:>7.2f}{o['dd']:>8.2f}{o['cagr']:>5.0f}%")

    oos_ok = o_e["sharpe"] >= o_b["sharpe"] - 0.02 and o_e["dd"] <= o_b["dd"] + 0.20
    full_ok = f_e["sharpe"] >= f_b["sharpe"] - 0.02 and f_e["dd"] <= f_b["dd"] + 0.20
    promoted = oos_ok and full_ok
    print(f"\n  GATE: OOS {'OK' if oos_ok else 'KO'} | FULL {'OK' if full_ok else 'KO'}")
    print("  VERDETTO: " + (">>> PROMOSSO <<<" if promoted else ">>> BOCCIATO <<<"))


if __name__ == "__main__":
    main()