"""Affina il filtro trend (soglia assoluta ATR) e costruisce il portafoglio combinato.

Due risultati:
  (1) trend filter: salta le fade quando |close-EMA200|/ATR > soglia (non fadare un
      trend estremo). Soglia ASSOLUTA in multipli di ATR -> stessa regola per tutte
      le strategie/asset, basso rischio di overfit. Sweep soglie, FULL e OOS.
  (2) portafoglio: equity curve combinata delle 4 strategie sullo stesso conto
      (rischio diviso fra N posizioni). Curve poco correlate -> DD aggregato << DD
      della singola strategia. Confronto singola vs portafoglio, con/senza filtro.
"""
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 bollinger_fade, atr
from scripts.analysis.strategy_research_v2 import donchian_fade, keltner_fade, return_reversal

FEE_RT, LEV, INIT, OOS_FRAC = 0.001, 3.0, 1000.0, 0.30

STRATS = {
    "MR01": (bollinger_fade, dict(n=50, k=2.5, sl_atr=2.0, max_bars=24)),
    "MR02": (donchian_fade,  dict(n=20, sl_atr=2.0, max_bars=24)),
    "MR03": (keltner_fade,   dict(n=30, k=2.0, sl_atr=2.0, max_bars=24)),
    "MR07": (return_reversal,dict(n=50, k=3.5, tp_atr=2.0, sl_atr=1.5, max_bars=24)),
}
STRATS_ETH = dict(STRATS); STRATS_ETH["MR03"] = (keltner_fade, dict(n=50, k=2.0, sl_atr=2.0, max_bars=24))


def build_trades(ents, df, lev=LEV, fee_rt=FEE_RT, trend_max=None, ema_long=200):
    """Ritorna lista trade non-overlap: (entry_idx, exit_idx, ret_netto). Filtro trend opzionale."""
    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 trend_max is not None and a[i] and abs(c[i] - el[i]) / a[i] > trend_max:
            continue
        entry = c[i]; tp, sl, 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
            hs = (d == 1 and l[j] <= sl) or (d == -1 and h[j] >= sl)
            ht = (d == 1 and h[j] >= tp) or (d == -1 and l[j] <= tp)
            if hs: exit_p = sl; break
            if ht: exit_p = tp; 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 metrics_single(trades, ts, pos=0.15, split=-1):
    cap = peak = INIT; dd = 0.0; trd = wins = 0; rets = []
    for i, j, ret in trades:
        if i < split:
            continue
        cap = max(cap + cap * pos * ret, 10.0)
        peak = max(peak, cap); dd = max(dd, (peak - cap) / peak)
        trd += 1; wins += ret > 0; rets.append(ret * pos)
    sh = float(np.mean(rets) / np.std(rets) * np.sqrt(len(rets))) if len(rets) > 1 and np.std(rets) > 0 else 0.0
    return dict(trades=trd, acc=wins / trd * 100 if trd else 0.0,
                ret=(cap / INIT - 1) * 100, dd=dd * 100, sharpe=sh)


def sleeve_equity(trades, n_bars, pos=0.15, split=-1):
    """Equity curve di uno sleeve su sotto-conto indipendente (capitale INIT, pos fissa).
    Ritorna array lungo n_bars (step aggiornato alla chiusura di ogni trade)."""
    eq = np.full(n_bars, INIT, dtype=float)
    cap = INIT
    for i, j, ret in sorted(trades, key=lambda t: t[1]):
        if i < split:
            continue
        cap = max(cap + cap * pos * ret, 10.0)
        eq[j:] = cap            # da j in poi il sotto-conto vale cap
    return eq


def metrics_portfolio(strat_trades, n_bars, ts, pos=0.15, split=-1):
    """Portafoglio equipesato: capitale diviso in N sotto-conti indipendenti, ciascuno
    con la sua strategia a `pos` fisso. Equity aggregata = media dei sotto-conti (somma
    normalizzata a base INIT). DD misurato sull'equity aggregata. Niente leva sovrapposta."""
    sleeves = [sleeve_equity(tr, n_bars, pos=pos, split=split) for tr in strat_trades.values()]
    agg = np.mean(sleeves, axis=0)            # media -> base INIT, diversificazione reale
    # restringi alla finestra effettiva (da split in poi se OOS)
    lo = max(split, 0)
    agg = agg[lo:]
    peak = np.maximum.accumulate(agg)
    dd = float(np.max((peak - agg) / peak) * 100)
    trd = sum(1 for tr in strat_trades.values() for i, _, _ in tr if i >= split)
    wins = sum(1 for tr in strat_trades.values() for i, _, r in tr if i >= split and r > 0)
    return dict(trades=trd, acc=wins / trd * 100 if trd else 0.0,
                ret=(agg[-1] / INIT - 1) * 100, dd=dd, sharpe=0.0)


def main():
    # ---------- (1) sweep soglia trend ----------
    print("=" * 104)
    print("  (1) FILTRO TREND |close-EMA200|/ATR > soglia -> SALTA | NETTO fee 0.10% RT, leva 3x")
    print("=" * 104)
    print(f"  {'Strat/Asset':<14s}{'soglia':>8s}{'Trd':>6s}{'Acc%':>7s}{'Ret%':>9s}{'DD%':>7s}"
          f"  | {'oAcc':>6s}{'oRet':>9s}{'oDD':>7s}{'oShrp':>7s}")
    print("  " + "-" * 100)
    thresholds = [None, 4.0, 3.0, 2.5, 2.0]
    for asset in ["BTC", "ETH"]:
        df = load_data(asset, "1h"); ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
        split = int(len(df) * (1 - OOS_FRAC))
        table = STRATS_ETH if asset == "ETH" else STRATS
        for nm, (fn, params) in table.items():
            ents = fn(df, **params)
            for thr in thresholds:
                tr = build_trades(ents, df, trend_max=thr)
                f = metrics_single(tr, ts); o = metrics_single(tr, ts, split=split)
                lab = "base" if thr is None else f"{thr}ATR"
                print(f"  {nm+' '+asset:<14s}{lab:>8s}{f['trades']:>6d}{f['acc']:>7.1f}{f['ret']:>+9.0f}{f['dd']:>7.1f}"
                      f"  | {o['acc']:>6.1f}{o['ret']:>+9.0f}{o['dd']:>7.1f}{o['sharpe']:>7.2f}")
            print("  " + "-" * 100)

    # ---------- (2) portafoglio combinato ----------
    print("\n" + "=" * 104)
    print("  (2) PORTAFOGLIO equipesato: capitale diviso in N sotto-conti indipendenti")
    print("      (pos 0.15 ciascuno, filtro trend 3.0 ATR). DD aggregato vs singola strategia.")
    print("=" * 104)
    print(f"  {'Universo':<26s}{'Trd':>6s}{'Acc%':>7s}{'Ret%':>10s}{'DD%':>7s}{'':>7s}"
          f"  | {'oAcc':>6s}{'oRet':>9s}{'oDD':>7s}{'':>7s}")
    print("  " + "-" * 100)
    all_trades = {}
    for asset in ["BTC", "ETH"]:
        df = load_data(asset, "1h"); ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
        split = int(len(df) * (1 - OOS_FRAC)); n = len(df)
        table = STRATS_ETH if asset == "ETH" else STRATS
        st = {f"{nm}_{asset}": build_trades(fn(df, **p), df, trend_max=3.0) for nm, (fn, p) in table.items()}
        all_trades.update(st)
        f = metrics_portfolio(st, n, ts); o = metrics_portfolio(st, n, ts, split=split)
        print(f"  {'Portafoglio '+asset+' (4 strat)':<26s}{f['trades']:>6d}{f['acc']:>7.1f}{f['ret']:>+10.0f}{f['dd']:>7.1f}{f['sharpe']:>7.2f}"
              f"  | {o['acc']:>6.1f}{o['ret']:>+9.0f}{o['dd']:>7.1f}{o['sharpe']:>7.2f}")
    # globale 8 sleeve
    df0 = load_data("BTC", "1h"); ts0 = pd.to_datetime(df0["timestamp"], unit="ms", utc=True)
    split0 = int(len(df0) * (1 - OOS_FRAC))
    f = metrics_portfolio(all_trades, len(df0), ts0); o = metrics_portfolio(all_trades, len(df0), ts0, split=split0)
    print("  " + "-" * 100)
    print(f"  {'GLOBALE BTC+ETH (8 sleeve)':<26s}{f['trades']:>6d}{f['acc']:>7.1f}{f['ret']:>+10.0f}{f['dd']:>7.1f}{f['sharpe']:>7.2f}"
          f"  | {o['acc']:>6.1f}{o['ret']:>+9.0f}{o['dd']:>7.1f}{o['sharpe']:>7.2f}")
    print("\n  Nota: ogni sleeve gira su un sotto-conto indipendente (pos 0.15); l'equity di")
    print("  portafoglio e' la media dei sotto-conti. Curve poco correlate => DD aggregato")
    print("  molto piu' basso del DD del singolo sleeve (la vera leva anti-drawdown).")


if __name__ == "__main__":
    main()