"""Phase 5+6 entry point: optimize GoldScalperPro on real XAUUSD bars.

Assembles ObjectiveConfig (engine + signals + search space + constraints),
runs Optuna, applies the diverse top-N selector, then runs the robustness
layers on the finalists and prints a report. This is the cycle that Phase 7
will feed into MT5 for verification.

Usage:
    python run.py [--trials 200] [--top-n 3] [--deposit 10000]
"""
from __future__ import annotations

import argparse
import sys
import time
from pathlib import Path

PROJECT = Path(__file__).resolve().parent
sys.path.insert(0, str(PROJECT))

import optuna

from shared.core.engine import SizingInputs
from shared.data.loaders import load_bars
from shared.optimizer.objective import (
    Constraints,
    ObjectiveConfig,
    build_objective,
)
from shared.optimizer.selector import select_diverse_topn
from shared.robustness.layers import stability_region
from strategies.gold_scalper_pro.instruments import XAUUSD_REAL
from strategies.gold_scalper_pro.scalper_engine import (
    ScalperConfig,
    ScalperEngine,
    config_from_params,
    engine_kwargs_from_params,
)
from strategies.gold_scalper_pro.search_space import (
    FROZEN_BASELINE,
    INT_PARAMS,
    SEARCH_SPACE,
)
from strategies.gold_scalper_pro.signals import build_signals


def find_bars_file() -> Path:
    """Auto-find the latest XAUUSD M5 parquet in data/."""
    data_dir = PROJECT / "data"
    candidates = sorted(data_dir.glob("XAUUSD_M5_*.parquet"))
    if not candidates:
        raise FileNotFoundError(f"no XAUUSD M5 parquet in {data_dir}")
    return candidates[-1]


def main() -> int:
    ap = argparse.ArgumentParser(description="Optimize GoldScalperPro.")
    ap.add_argument("--trials", type=int, default=100,
                    help="Optuna trials (default 100)")
    ap.add_argument("--top-n", type=int, default=3,
                    help="diverse finalists to select (default 3)")
    ap.add_argument("--deposit", type=float, default=10000.0,
                    help="initial deposit (default 10000)")
    ap.add_argument("--bars", type=str, default="",
                    help="path to parquet bars (blank = auto-find)")
    args = ap.parse_args()

    bars_path = Path(args.bars) if args.bars else find_bars_file()
    print(f"=== GoldScalperPro optimization ===")
    print(f"bars    : {bars_path.name}")
    print(f"trials  : {args.trials}")
    print(f"top-n   : {args.top_n}")
    print(f"deposit : {args.deposit:,.0f} USD")
    print()

    bars = load_bars(bars_path)
    print(f"loaded {len(bars):,} bars  {bars['timestamp'].iloc[0]} → {bars['timestamp'].iloc[-1]}")

    # ── Assemble the objective ────────────────────────────────────────────
    constraints = Constraints(
        min_trades=25,
        min_profit_factor=1.2,        # relaxed for first pass; tightened later
        max_equity_dd_pct=0.40,       # 40% hard cap
    )
    obj_cfg = ObjectiveConfig(
        engine=ScalperEngine(),
        bars=bars,
        instrument=XAUUSD_REAL,
        sizing=SizingInputs(),
        initial_deposit=args.deposit,
        search_space=SEARCH_SPACE,
        int_params=INT_PARAMS,
        frozen_baseline=FROZEN_BASELINE,
        constraints=constraints,
        dd_weight=1.0,
        build_signals=build_signals,
        build_engine_kwargs=engine_kwargs_from_params,
    )
    objective = build_objective(obj_cfg)

    # ── Run Optuna ─────────────────────────────────────────────────────────
    optuna.logging.set_verbosity(optuna.logging.WARNING)
    study = optuna.create_study(direction="maximize",
                                sampler=optuna.samplers.TPESampler(seed=42))
    print(f"\nrunning {args.trials} trials ...")
    t0 = time.time()
    study.optimize(objective, n_trials=args.trials, show_progress_bar=False)
    elapsed = time.time() - t0
    print(f"done in {elapsed:.1f}s  ({elapsed/args.trials:.2f}s/trial)")

    # ── Report ────────────────────────────────────────────────────────────
    best = study.best_trial
    print(f"\n=== best trial #{best.number} ===")
    print(f"  score          : {best.value:+,.2f}")
    print(f"  net profit     : {best.user_attrs['net_profit']:+,.2f}")
    print(f"  profit factor  : {best.user_attrs['profit_factor']:.2f}")
    print(f"  trades         : {best.user_attrs['total_trades']}")
    print(f"  win rate       : {best.user_attrs['win_rate']:.2%}")
    print(f"  equity DD      : {best.user_attrs['max_equity_dd']:,.2f} "
          f"({best.user_attrs['max_equity_dd_pct']:.2%})")
    print(f"  sharpe         : {best.user_attrs['sharpe']:.2f}")
    if best.user_attrs.get("violations"):
        print(f"  violations     : {best.user_attrs['violations']}")
    print("  params:")
    for k, v in best.user_attrs["params"].items():
        if k in SEARCH_SPACE:
            print(f"    {k:24s} = {v}")

    # ── Diverse top-N ─────────────────────────────────────────────────────
    print(f"\n=== diverse top-{args.top_n} finalists ===")
    finalists = select_diverse_topn(study, args.top_n, SEARCH_SPACE)
    for i, t in enumerate(finalists, 1):
        print(f"  #{i} trial {t.number}: score={t.value:+,.2f} "
              f"net={t.user_attrs['net_profit']:+,.2f} "
              f"PF={t.user_attrs['profit_factor']:.2f} "
              f"trades={t.user_attrs['total_trades']}")

    # ── Stability region (is the best on a plateau?) ─────────────────────
    print(f"\n=== stability region ===")
    sr = stability_region(study, SEARCH_SPACE)
    print(f"  passed          : {sr.get('passed')}")
    print(f"  cluster_size    : {sr.get('cluster_size')}")
    print(f"  best_in_cluster : {sr.get('best_in_cluster')}")
    if sr.get("reason"):
        print(f"  reason          : {sr['reason']}")

    print("\n=== done ===")
    print("Next: Phase 7 — generate .set/.ini for each finalist, run MT5, compare.")
    return 0


if __name__ == "__main__":
    raise SystemExit(main())