"""Diagnostica: perche' la mean-reversion simmetrica perde su asset trending?
Test: long-only vs short-only, e MR FILTRATA DAL TREND (buy-dip in uptrend,
sell-rip in downtrend) per evitare di fadeare i trend forti.
"""
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_lab import (  # noqa: E402
    atr, ema, get_df, simulate, oos_split, available_assets, FEE_RT,
)


def zscore_entries(df, n=50, z_in=2.5, sl_atr=2.5, max_bars=24,
                   trend_n=0, side="both"):
    """Z-score revert con filtro trend opzionale.
    trend_n>0: EMA di lungo periodo. Long solo se close>EMA (uptrend),
               short solo se close<EMA (downtrend).
    side: 'both' | 'long' | 'short'
    """
    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)
    et = ema(c, trend_n) if trend_n > 0 else None
    start = max(n + 14, trend_n + 1 if trend_n else 0)
    ents = []
    for i in range(start, len(c)):
        if np.isnan(z[i]) or np.isnan(a[i]):
            continue
        long_ok = (et is None or c[i] > et[i]) and side in ("both", "long")
        short_ok = (et is None or c[i] < et[i]) and side in ("both", "short")
        if z[i] <= -z_in and z[i - 1] > -z_in and long_ok:
            ents.append({"i": i, "d": 1, "tp": ma[i], "sl": c[i] - sl_atr * a[i], "max_bars": max_bars})
        elif z[i] >= z_in and z[i - 1] < z_in and short_ok:
            ents.append({"i": i, "d": -1, "tp": ma[i], "sl": c[i] + sl_atr * a[i], "max_bars": max_bars})
    return ents


def row(label, df, ents):
    if len(ents) < 20:
        print(f"  {label:<34s} {'<20 trd':>50s}")
        return None
    full = simulate(ents, df)
    _, oe = oos_split(ents, df)
    oos = simulate(oe, df)
    print(f"  {label:<34s}{full.trades:>6d}{full.win:>7.1f}{full.ret:>+9.0f}"
          f"{oos.ret:>+9.0f}{full.dd:>6.0f}{f'{full.pos_years}/{full.n_years}':>8s}")
    return full, oos


if __name__ == "__main__":
    assets = available_assets()
    print(f"HONEST DIAG — z-score revert, fee {FEE_RT*100:.2f}% RT, leva 3x | OOS 30%")
    for tf in ["1h"]:
        for a in assets:
            df = get_df(a, tf)
            print(f"\n  === {a} {tf} ===   {'Trd':>5s}{'Win%':>7s}{'FULL%':>8s}{'OOS%':>8s}{'DD%':>6s}{'AnniP':>8s}")
            base = dict(n=50, z_in=2.5, sl_atr=2.5, max_bars=24)
            row("both, no filter", df, zscore_entries(df, **base, side="both"))
            row("long-only, no filter", df, zscore_entries(df, **base, side="long"))
            row("short-only, no filter", df, zscore_entries(df, **base, side="short"))
            row("both + trend200 filter", df, zscore_entries(df, **base, trend_n=200, side="both"))
            row("both + trend500 filter", df, zscore_entries(df, **base, trend_n=500, side="both"))
            row("long + trend200 filter", df, zscore_entries(df, **base, trend_n=200, side="long"))