"""PORT01 — Portafoglio combinato delle 3 strategie oneste (equal-weight, daily rebal).

Sleeve (meccanismi anti-correlati):
    DIP01     dip-buy reversion su BTC (1h)        regime: reversione
    TR01      EMA 20/100 trend su paniere (4h)     regime: momentum singolo
    ROT02     dual-momentum rotation (1d)          regime: forza relativa + risk-off

La diversificazione e' il vero motore di risk-reduction: il DD del portafoglio
scende SOTTO quello della sleeve meno rischiosa, mantenendo una CAGR alta e
azzerando quasi gli anni negativi (il 2022 bear passa da -30% di ROT a -1%).

Risultato (netto, 2021-2026, leva 3x pos 15% per sleeve):
    DIP01_BTC     +322%  DD 15%  CAGR 31%
    TR01_basket   +591%  DD 27%  CAGR 43%
    ROT02_dualmom +771%  DD 40%  CAGR 49%
    PORTAFOGLIO   +642%  DD 12%  CAGR 45%   <-- DD piu' basso di ogni sleeve
    Per-anno: 2021 +203 · 2022 -1 · 2023 +47 · 2024 +50 · 2025 +14 · 2026 -2
Logica e ricostruzione: scripts/analysis/honest_improve2.py.
"""
from __future__ import annotations

import sys
from pathlib import Path

import pandas as pd

PROJECT_ROOT = Path(__file__).resolve().parents[2]
sys.path.insert(0, str(PROJECT_ROOT))

from scripts.analysis.honest_improve import _dd  # noqa: E402
from scripts.analysis.honest_improve2 import (  # noqa: E402
    dip_market_gated, _daily_equity, _norm, _tr_basket_daily, _rot_daily_equity,
)


def run():
    idx = pd.date_range("2021-01-01", "2026-05-26", freq="1D", tz="UTC")
    d = dip_market_gated("BTC", market_n=0, return_equity=True)
    members = {
        "DIP01_BTC": _norm(_daily_equity(d["eq_ts"], d["eq_v"], idx)),
        "TR01_basket": _norm(_tr_basket_daily(["BNB", "BTC", "DOGE", "SOL", "XRP"], idx)),
        "ROT02_dualmom": _norm(_rot_daily_equity(idx)),
    }
    drets = pd.DataFrame({k: v.pct_change().fillna(0) for k, v in members.items()})
    port_ret = drets.mean(axis=1)
    combo = (1 + port_ret).cumprod()
    yrs = (idx[-1] - idx[0]).days / 365.25

    print("=" * 80)
    print(f"  PORT01 — portafoglio equal-weight (daily rebal) | {idx[0].date()} -> {idx[-1].date()}")
    print("=" * 80)
    print(f"  {'sleeve':<16s}{'ret%':>9s}{'DD%':>7s}{'CAGR%':>8s}")
    for name, s in members.items():
        r = (s.iloc[-1] / s.iloc[0] - 1) * 100
        cagr = ((s.iloc[-1] / s.iloc[0]) ** (1 / yrs) - 1) * 100
        print(f"  {name:<16s}{r:>+9.0f}{_dd(s.values):>7.0f}{cagr:>8.0f}")
    r = (combo.iloc[-1] / combo.iloc[0] - 1) * 100
    cagr = ((combo.iloc[-1] / combo.iloc[0]) ** (1 / yrs) - 1) * 100
    print(f"  {'PORTAFOGLIO':<16s}{r:>+9.0f}{_dd(combo.values):>7.0f}{cagr:>8.0f}")
    pa = port_ret.groupby(port_ret.index.year).apply(lambda x: ((1 + x).prod() - 1) * 100)
    print("  Per-anno: " + "  ".join(f"{y}:{v:+.0f}%" for y, v in pa.items()))


if __name__ == "__main__":
    run()