#!/usr/bin/env python3
"""Parameter sweep utility for Money Line backtests."""
from __future__ import annotations

import argparse
from itertools import product
from multiprocessing import Pool
from pathlib import Path
from typing import List, Sequence

import sys
import time

PROJECT_ROOT = Path(__file__).resolve().parents[1]
if str(PROJECT_ROOT) not in sys.path:
    sys.path.append(str(PROJECT_ROOT))

import pandas as pd

from backtester.data_loader import load_csv
from backtester.indicators.money_line import MoneyLineInputs
from backtester.simulator import TradeConfig, simulate_money_line


_DATA_SLICE = None
_TRADE_CONFIG = None
_GRID_KEYS: Sequence[str] = []


def parse_float_list(value: str) -> List[float]:
    return [float(item) for item in value.split(",") if item]


def parse_int_list(value: str) -> List[int]:
    return [int(item) for item in value.split(",") if item]


def build_arg_parser() -> argparse.ArgumentParser:
    parser = argparse.ArgumentParser(description="Run Money Line parameter sweeps against a CSV dataset")
    parser.add_argument("--csv", type=Path, required=True, help="Path to CSV with OHLCV data")
    parser.add_argument("--symbol", required=True, help="Symbol label (e.g., SOL-PERP)")
    parser.add_argument("--timeframe", default="5", help="Timeframe label (for reporting only)")
    parser.add_argument("--start", help="Optional ISO start timestamp filter", default=None)
    parser.add_argument("--end", help="Optional ISO end timestamp filter", default=None)
    parser.add_argument("--position-size", type=float, default=8100.0, help="Notional position size per trade")
    parser.add_argument("--max-bars", type=int, default=288, help="Max bars to hold a trade (default 288 = 24h on 5m)")
    parser.add_argument("--top", type=int, default=10, help="How many results to show")
    parser.add_argument(
        "--output",
        type=Path,
        help="Optional CSV to store every combination's metrics",
    )
    parser.add_argument(
        "--flip-thresholds",
        default="0.5,0.6,0.7",
        help="Comma separated flip threshold percentages",
    )
    parser.add_argument(
        "--ma-gap-thresholds",
        default="0.25,0.35,0.45",
        help="Comma separated MA gap thresholds",
    )
    parser.add_argument(
        "--momentum-adx",
        default="22,24,26",
        help="Comma separated ADX minimums for momentum dots",
    )
    parser.add_argument(
        "--momentum-long-pos",
        default="65,70",
        help="Comma separated maximum price position for long momentum entries",
    )
    parser.add_argument(
        "--momentum-short-pos",
        default="30,35",
        help="Comma separated minimum price position for short momentum entries",
    )
    parser.add_argument(
        "--cooldown-bars",
        default="2,3,4",
        help="Comma separated cooldown bar counts between primary flips",
    )
    parser.add_argument(
        "--momentum-spacing",
        default="3,4,5",
        help="Comma separated spacing (bars) between momentum signals",
    )
    parser.add_argument(
        "--momentum-cooldown",
        default="2,3",
        help="Comma separated cooldown (bars) after primary signal before momentum allowed",
    )
    parser.add_argument(
        "--workers",
        type=int,
        default=1,
        help="Number of worker processes (use >1 for multi-core sweeps)",
    )
    parser.add_argument(
        "--limit",
        type=int,
        help="Optional number of combinations to run (preview mode)",
    )
    return parser


def run_sweep(args: argparse.Namespace) -> pd.DataFrame:
    data_slice = load_csv(args.csv, args.symbol, args.timeframe, start=args.start, end=args.end)
    trade_config = TradeConfig(position_size=args.position_size, max_bars_per_trade=args.max_bars)

    grids = {
        "flip_threshold_percent": parse_float_list(args.flip_thresholds),
        "ma_gap_threshold": parse_float_list(args.ma_gap_thresholds),
        "momentum_min_adx": parse_float_list(args.momentum_adx),
        "momentum_long_max_pos": parse_float_list(args.momentum_long_pos),
        "momentum_short_min_pos": parse_float_list(args.momentum_short_pos),
        "cooldown_bars": parse_int_list(args.cooldown_bars),
        "momentum_spacing": parse_int_list(args.momentum_spacing),
        "momentum_cooldown": parse_int_list(args.momentum_cooldown),
    }

    combos = list(product(*grids.values()))
    total_combos = len(combos)
    print(f"Evaluating {total_combos} combinations...")

    if args.limit is not None and args.limit > 0 and args.limit < total_combos:
        combos = combos[: args.limit]
        print(f"Preview mode: running first {len(combos)} combos (out of {total_combos})")

    keys = list(grids.keys())
    worker_count = args.workers if args.workers and args.workers > 0 else 1
    total_to_run = len(combos)
    progress = ProgressBar(total=total_to_run)

    if total_to_run == 0:
        print("No combinations to evaluate with current configuration.")
        return pd.DataFrame()

    if worker_count <= 1:
        records = []
        for idx, combo in enumerate(combos, start=1):
            records.append(_evaluate_combo(combo, keys, data_slice, trade_config))
            progress.update(idx)
    else:
        print(f"Using {worker_count} worker processes")
        with Pool(
            processes=worker_count,
            initializer=_init_worker,
            initargs=(data_slice, trade_config, keys),
        ) as pool:
            records = []
            for idx, record in enumerate(pool.imap_unordered(_worker_eval, combos), start=1):
                records.append(record)
                progress.update(idx)

    progress.finish()

    elapsed = progress.elapsed
    combos_run = len(combos)
    avg_time = elapsed / combos_run if combos_run else 0
    print(
        f"Processed {combos_run} combos in {elapsed:.1f}s (avg {avg_time:.2f}s per combo)"
    )
    if combos_run < total_combos:
        projected = avg_time * total_combos
        print(
            f"Estimated full sweep ({total_combos} combos) would take ~{projected/60:.1f} minutes with current settings"
        )

    return pd.DataFrame(records)


def _init_worker(data_slice, trade_config, keys):
    global _DATA_SLICE, _TRADE_CONFIG, _GRID_KEYS
    _DATA_SLICE = data_slice
    _TRADE_CONFIG = trade_config
    _GRID_KEYS = keys


def _worker_eval(combo):
    return _evaluate_combo(combo, _GRID_KEYS, _DATA_SLICE, _TRADE_CONFIG)


def _evaluate_combo(combo, keys, data_slice, trade_config):
    params = dict(zip(keys, combo))
    inputs = MoneyLineInputs(**params)
    result = simulate_money_line(data_slice.data, data_slice.symbol, inputs=inputs, config=trade_config)
    trades = result.trades
    pnl = result.total_pnl
    win_rate = result.win_rate * 100
    max_dd = result.max_drawdown
    avg_trade = result.average_pnl
    profit_factor = _profit_factor(trades)
    return {
        **params,
        "trades": len(trades),
        "total_pnl": pnl,
        "win_rate": win_rate,
        "avg_pnl": avg_trade,
        "max_drawdown": max_dd,
        "profit_factor": profit_factor,
    }


class ProgressBar:
    def __init__(self, total: int, bar_length: int = 40) -> None:
        self.total = max(total, 0)
        self.bar_length = bar_length
        self.start_time = time.time()
        self._done = False

    def update(self, count: int) -> None:
        if self.total <= 0:
            return
        count = min(count, self.total)
        percent = count / self.total
        filled = int(self.bar_length * percent)
        bar = "#" * filled + "-" * (self.bar_length - filled)
        elapsed = time.time() - self.start_time
        rate = elapsed / count if count else 0
        remaining = rate * (self.total - count) if rate else 0
        
        # Format elapsed time
        elapsed_hours = int(elapsed // 3600)
        elapsed_mins = int((elapsed % 3600) // 60)
        elapsed_str = f"{elapsed_hours}h {elapsed_mins}m" if elapsed_hours > 0 else f"{elapsed_mins}m"
        
        # Format remaining time
        remaining_hours = int(remaining // 3600)
        remaining_mins = int((remaining % 3600) // 60)
        remaining_str = f"{remaining_hours}h {remaining_mins}m" if remaining_hours > 0 else f"{remaining_mins}m"
        
        sys.stdout.write(
            f"\r[{bar}] {percent*100:5.1f}% ({count}/{self.total}) | "
            f"Elapsed {elapsed_str:>7} | ETA {remaining_str:>7}"
        )
        sys.stdout.flush()
        if count >= self.total:
            self._done = True
            sys.stdout.write("\n")

    def finish(self) -> None:
        if not self._done and self.total > 0:
            self.update(self.total)

    @property
    def elapsed(self) -> float:
        return time.time() - self.start_time


def _profit_factor(trades) -> float:
    gains = [t.realized_pnl for t in trades if t.realized_pnl > 0]
    losses = [-t.realized_pnl for t in trades if t.realized_pnl < 0]
    total_gain = sum(gains)
    total_loss = sum(losses)
    if total_loss == 0:
        return float("inf") if total_gain > 0 else 0.0
    return total_gain / total_loss


def main() -> int:
    parser = build_arg_parser()
    args = parser.parse_args()
    df = run_sweep(args)
    df = df.sort_values("total_pnl", ascending=False)

    top_n = df.head(args.top)
    print("\n=== TOP RESULTS ===")
    for idx, row in top_n.iterrows():
        print(
            f"PnL ${row['total_pnl']:.2f} | Trades {row['trades']} | Win {row['win_rate']:.2f}% | "
            f"DD ${row['max_drawdown']:.2f} | PF {row['profit_factor']:.2f} | params {row.drop(['trades','total_pnl','win_rate','avg_pnl','max_drawdown','profit_factor']).to_dict()}"
        )

    if args.output:
        args.output.parent.mkdir(parents=True, exist_ok=True)
        df.to_csv(args.output, index=False)
        print(f"\nSaved all {len(df)} results to {args.output}")

    return 0


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