"""Tabella per-anno (PnL% e DD% intra-anno) delle versioni MIGLIORATE:
ROT02 (dual-momentum), le 3 sleeve e il PORTAFOGLIO combinato.
Tutto NETTO. Riusa gli engine di honest_improve / honest_improve2.
"""
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 scripts.analysis.honest_improve2 import (  # noqa: E402
    dip_market_gated, _daily_equity, _norm, _tr_basket_daily, _rot_daily_equity,
)


def _year_dd(eq: pd.Series) -> dict[int, float]:
    out = {}
    for y, g in eq.groupby(eq.index.year):
        peak = g.iloc[0]; dd = 0.0
        for v in g:
            peak = max(peak, v); dd = max(dd, (peak - v) / peak if peak > 0 else 0.0)
        out[int(y)] = dd * 100
    return out


def _year_pnl(eq: pd.Series) -> dict[int, float]:
    out = {}
    for y, g in eq.groupby(eq.index.year):
        out[int(y)] = (g.iloc[-1] / g.iloc[0] - 1) * 100
    return out


def table(name, eq):
    eq = _norm(eq)
    dd = _year_dd(eq); pnl = _year_pnl(eq)
    print(f"\n  {name}")
    print(f"  {'Anno':>6s}{'PnL%':>9s}{'DD%':>7s}")
    print("  " + "-" * 22)
    for y in sorted(pnl):
        print(f"  {y:>6d}{pnl[y]:>+9.0f}{dd[y]:>7.0f}")
    tot = (eq.iloc[-1] / eq.iloc[0] - 1) * 100
    print(f"  {'TOT':>6s}{tot:>+9.0f}{_year_dd(eq) and max(_year_dd(eq).values()):>7.0f}(max anno)")


if __name__ == "__main__":
    print("=" * 60)
    print("  RISULTATI PER ANNO — versioni migliorate (NETTO)")
    print("=" * 60)

    # ROT02 dal 2020 (dati paniere)
    idx_rot = pd.date_range("2020-09-01", "2026-05-26", freq="1D", tz="UTC")
    eq_rot = _rot_daily_equity(idx_rot)
    table("ROT02 — dual-momentum rotation (1d)", eq_rot)

    # sleeve + portafoglio dal 2021
    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)
    eq_dip = _norm(_daily_equity(d["eq_ts"], d["eq_v"], idx))
    eq_tr = _norm(_tr_basket_daily(["BNB", "BTC", "DOGE", "SOL", "XRP"], idx))
    eq_r2 = _norm(_rot_daily_equity(idx))
    table("Sleeve DIP01 — BTC (1h)", eq_dip)
    table("Sleeve TR01 — basket (4h)", eq_tr)
    table("Sleeve ROT02 (1d)", eq_r2)

    drets = pd.DataFrame({"DIP": eq_dip.pct_change().fillna(0),
                          "TR": eq_tr.pct_change().fillna(0),
                          "ROT": eq_r2.pct_change().fillna(0)})
    combo = (1 + drets.mean(axis=1)).cumprod()
    table("PORTAFOGLIO equal-weight (daily rebal)", combo)