"""ORB strategy evaluation with IS/OOS validation and streaming intraday fetch."""
from __future__ import annotations

import argparse
import asyncio
import json
import statistics
import sys
import uuid
from datetime import datetime
from pathlib import Path
from typing import Any

import yaml

from apps.intraday_bt.oracle import make_intraday_oracle_client
from libs.common.config import get_settings
from libs.intraday.domain import (
    BacktestParams,
    CacheParams,
    IntradayConfig,
    IntradayMetrics,
    ORBStrategyParams,
    OutputParams,
    SweepResult,
    UniverseParams,
)
from libs.oracle_client import OracleClient

from apps.intraday_bt.orb_research import (
    build_orb_research_context,
    force_simple_returns,
    generate_walk_forward_windows,
    simulate_orb_overrides,
    split_trading_days,
)
from apps.intraday_bt.sweep import (
    SweepConfig,
    apply_overrides,
    generate_combinations,
    load_sweep_config,
)
from apps.intraday_bt.run import get_trading_days


def compute_degradation(is_sharpe: float | None, oos_sharpe: float | None) -> float:
    if is_sharpe is None or is_sharpe <= 0:
        return 0.0
    if oos_sharpe is None:
        return 0.0
    return oos_sharpe / is_sharpe


async def run_sweep_on_period(
    sweep: SweepConfig,
    context,
    client: OracleClient,
    trading_days: list[str],
    progress_prefix: str = "",
) -> list[SweepResult]:
    combos = generate_combinations(sweep)
    results: list[SweepResult] = []

    for i, overrides in enumerate(combos):
        _, metrics = await simulate_orb_overrides(
            context,
            client,
            overrides,
            trading_days,
            run_id=f"sw{i:04d}",
        )
        results.append(SweepResult(params=overrides, metrics=metrics))
        sys.stdout.write(
            f"\r  {progress_prefix}[{i+1}/{len(combos)}] "
            f"Sharpe={metrics.sharpe_ratio or 0:.2f} "
            f"Ret={(metrics.total_return_pct or 0)*100:.0f}%"
        )
        sys.stdout.flush()
    print()

    results.sort(
        key=lambda r: (
            r.metrics.sharpe_ratio or float("-inf"),
            r.metrics.total_return_pct or -999,
        ),
        reverse=True,
    )
    return results


async def run_single_config_on_period(
    base_config: IntradayConfig,
    overrides: dict[str, Any],
    context,
    client: OracleClient,
    trading_days: list[str],
    *,
    run_id: str = "",
) -> IntradayMetrics:
    _, metrics = await simulate_orb_overrides(
        context,
        client,
        overrides,
        trading_days,
        run_id=run_id or str(uuid.uuid4())[:8],
    )
    return metrics


async def _run_split_eval(
    sweep: SweepConfig,
    context,
    client: OracleClient,
    train_days: list[str],
    test_days: list[str],
    top_n: int,
    *,
    oos_sort: bool = False,
) -> dict[str, Any]:
    print(f"\n  ── Phase 1: Sweep on Train ({train_days[0]}→{train_days[-1]}, {len(train_days)}d) ──")
    is_results = await run_sweep_on_period(
        sweep, context, client, train_days, progress_prefix="IS "
    )

    print(f"\n  Top {top_n} IS configs (by Sharpe):")
    print(f"  {'#':<3} {'Sharpe':>7} {'Return':>8} {'WR':>6} {'PF':>6} {'Trades':>7} {'DD':>7}  Params")
    for i, result in enumerate(is_results[:top_n]):
        metrics = result.metrics
        print(
            f"  {i+1:<3} {metrics.sharpe_ratio or 0:>7.2f} "
            f"{(metrics.total_return_pct or 0)*100:>7.0f}% "
            f"{(metrics.win_rate or 0)*100:>5.1f}% "
            f"{metrics.profit_factor or 0:>6.2f} "
            f"{metrics.total_trades or 0:>7} "
            f"{abs((metrics.max_drawdown_pct or 0)*100):>6.2f}%  "
            f"{_format_params(result.params)}"
        )

    print(f"\n  ── Phase 2: Validate on Test ({test_days[0]}→{test_days[-1]}, {len(test_days)}d) ──")
    oos_results: list[tuple[SweepResult, IntradayMetrics]] = []
    for i, is_result in enumerate(is_results[:top_n]):
        oos_metrics = await run_single_config_on_period(
            sweep.base_config,
            is_result.params,
            context,
            client,
            test_days,
            run_id=f"oos{i:02d}",
        )
        oos_results.append((is_result, oos_metrics))
        sys.stdout.write(
            f"\r  OOS [{i+1}/{top_n}] "
            f"Sharpe={oos_metrics.sharpe_ratio or 0:.2f} "
            f"Ret={(oos_metrics.total_return_pct or 0)*100:.0f}%"
        )
        sys.stdout.flush()
    print()

    print(f"\n  {'='*80}")
    print("  IS/OOS Comparison (Target: OOS Sharpe ≥ 60% of IS Sharpe)")
    print(f"  {'='*80}")
    print(
        f"  {'#':<3} {'IS Sharpe':>10} {'OOS Sharpe':>11} {'Retain':>8} "
        f"{'IS Ret':>8} {'OOS Ret':>8} {'OOS DD':>7}  {'Verdict':>8}  Params"
    )

    report_rows: list[dict[str, Any]] = []
    for i, (is_result, oos_metrics) in enumerate(oos_results):
        is_sharpe = is_result.metrics.sharpe_ratio or 0.0
        oos_sharpe = oos_metrics.sharpe_ratio or 0.0
        retain = compute_degradation(is_sharpe, oos_sharpe)
        verdict = "PASS" if retain >= 0.60 else "WARN" if retain >= 0.40 else "FAIL"
        print(
            f"  {i+1:<3} {is_sharpe:>10.2f} {oos_sharpe:>11.2f} {retain*100:>7.0f}% "
            f"{(is_result.metrics.total_return_pct or 0)*100:>7.0f}% "
            f"{(oos_metrics.total_return_pct or 0)*100:>7.0f}% "
            f"{abs((oos_metrics.max_drawdown_pct or 0)*100):>6.2f}%  "
            f"{verdict:>6}  {_format_params(is_result.params)}"
        )
        report_rows.append({
            "rank": i + 1,
            "params": is_result.params,
            "is_sharpe": is_sharpe,
            "oos_sharpe": oos_sharpe,
            "retention_pct": round(retain * 100, 1),
            "is_return_pct": round((is_result.metrics.total_return_pct or 0) * 100, 1),
            "oos_return_pct": round((oos_metrics.total_return_pct or 0) * 100, 1),
            "oos_max_dd_pct": round(abs((oos_metrics.max_drawdown_pct or 0) * 100), 2),
            "oos_trades": oos_metrics.total_trades,
            "oos_win_rate": round((oos_metrics.win_rate or 0) * 100, 1),
            "verdict": verdict,
        })

    if oos_sort:
        report_rows.sort(key=lambda row: row["oos_sharpe"], reverse=True)
        for i, row in enumerate(report_rows, start=1):
            row["rank"] = i
        print("\n  [Re-ranked by OOS Sharpe]")

    retentions = [row["retention_pct"] for row in report_rows]
    avg_retain = statistics.mean(retentions) if retentions else 0.0
    median_retain = statistics.median(retentions) if retentions else 0.0
    pass_count = sum(1 for row in report_rows if row["verdict"] == "PASS")

    print("\n  Summary:")
    print(f"    Avg retention:    {avg_retain:.0f}%")
    print(f"    Median retention: {median_retain:.0f}%")
    print(f"    Passed (≥60%):    {pass_count}/{len(report_rows)}")

    if avg_retain >= 60:
        print("\n  Overall: ROBUST — strategy generalizes well to unseen data")
    elif avg_retain >= 40:
        print("\n  Overall: FRAGILE — moderate overfitting detected, caution advised")
    else:
        print("\n  Overall: OVERFIT — strategy does not generalize, parameter re-tuning needed")

    return {
        "mode": "split",
        "train_period": f"{train_days[0]} → {train_days[-1]}",
        "train_days": len(train_days),
        "test_period": f"{test_days[0]} → {test_days[-1]}",
        "test_days": len(test_days),
        "total_sweep_combos": sweep.total_combinations,
        "top_n": len(report_rows),
        "results": report_rows,
        "avg_retention_pct": round(avg_retain, 1),
        "median_retention_pct": round(median_retain, 1),
        "pass_count": pass_count,
    }


async def _run_walk_forward_eval(
    sweep: SweepConfig,
    context,
    client: OracleClient,
    windows: list[tuple[list[str], list[str]]],
) -> dict[str, Any]:
    wf_results: list[dict[str, Any]] = []

    for window_idx, (train_days, test_days) in enumerate(windows, start=1):
        print(
            f"\n  ── Window {window_idx}/{len(windows)}: "
            f"Train {train_days[0]}→{train_days[-1]} | "
            f"Test {test_days[0]}→{test_days[-1]} ──"
        )
        is_results = await run_sweep_on_period(
            sweep, context, client, train_days, progress_prefix=f"W{window_idx} IS "
        )
        if not is_results:
            continue

        best_is = is_results[0]
        is_sharpe = best_is.metrics.sharpe_ratio or 0.0
        oos_metrics = await run_single_config_on_period(
            sweep.base_config,
            best_is.params,
            context,
            client,
            test_days,
            run_id=f"wf{window_idx:02d}",
        )
        oos_sharpe = oos_metrics.sharpe_ratio or 0.0
        retain = compute_degradation(is_sharpe, oos_sharpe)
        print(
            f"  Best: IS Sharpe={is_sharpe:.2f} → OOS Sharpe={oos_sharpe:.2f} "
            f"(retain {retain*100:.0f}%)  {_format_params(best_is.params)}"
        )
        wf_results.append({
            "window": window_idx,
            "train_period": f"{train_days[0]} → {train_days[-1]}",
            "test_period": f"{test_days[0]} → {test_days[-1]}",
            "best_params": best_is.params,
            "is_sharpe": is_sharpe,
            "oos_sharpe": oos_sharpe,
            "retention_pct": round(retain * 100, 1),
            "oos_return_pct": round((oos_metrics.total_return_pct or 0) * 100, 1),
            "oos_trades": oos_metrics.total_trades,
            "oos_win_rate": round((oos_metrics.win_rate or 0) * 100, 1),
        })

    retentions = [row["retention_pct"] for row in wf_results]
    oos_sharpes = [row["oos_sharpe"] for row in wf_results]
    avg_retain = statistics.mean(retentions) if retentions else 0.0
    median_oos = statistics.median(oos_sharpes) if oos_sharpes else 0.0
    positive_oos = sum(1 for sharpe in oos_sharpes if sharpe > 0)

    print(f"\n  {'='*70}")
    print(f"  Walk-Forward Summary ({len(wf_results)} windows)")
    print(f"  {'='*70}")
    print(f"  {'Window':<8} {'Train':>26} {'Test':>26} {'IS Sh':>6} {'OOS Sh':>7} {'Retain':>7}")
    for row in wf_results:
        print(
            f"  {row['window']:<8} {row['train_period']:>26} {row['test_period']:>26} "
            f"{row['is_sharpe']:>6.2f} {row['oos_sharpe']:>7.2f} {row['retention_pct']:>6.0f}%"
        )

    print(f"\n  Avg OOS Sharpe:     {statistics.mean(oos_sharpes) if oos_sharpes else 0:.2f}")
    print(f"  Median OOS Sharpe:  {median_oos:.2f}")
    print(f"  Avg retention:      {avg_retain:.0f}%")
    print(f"  Positive OOS:       {positive_oos}/{len(wf_results)}")

    if wf_results:
        serialized = [json.dumps(row["best_params"], sort_keys=True) for row in wf_results]
        unique_params = len(set(serialized))
        print(f"  Unique best configs: {unique_params}/{len(wf_results)}")
        if unique_params <= len(wf_results) * 0.5:
            print("  → Parameter stability: GOOD (same params often selected)")
        else:
            print("  → Parameter stability: POOR (different params per window)")

    return {
        "mode": "walk_forward",
        "n_windows": len(wf_results),
        "windows": wf_results,
        "avg_oos_sharpe": round(statistics.mean(oos_sharpes), 2) if oos_sharpes else 0,
        "median_oos_sharpe": round(median_oos, 2),
        "avg_retention_pct": round(avg_retain, 1),
        "positive_oos_count": positive_oos,
    }


async def evaluate(
    config: IntradayConfig,
    sweep_path: str,
    split_date: str | None = None,
    train_ratio: float | None = None,
    top_n: int = 5,
    walk_forward: bool = False,
    wf_train_days: int = 252,
    wf_test_days: int = 63,
    oos_sort: bool = False,
) -> dict[str, Any]:
    settings = get_settings()

    config = force_simple_returns(config)
    sweep = load_sweep_config(sweep_path, config)
    print(f"\n{'='*65}")
    print("  ORB Strategy Evaluation — IS/OOS Validation")
    print(f"{'='*65}")
    print(f"\n  Sweep: {sweep.total_combinations} parameter combinations")
    print(f"  Sweep params: {list(sweep.sweep_params.keys())}")

    async with make_intraday_oracle_client(settings) as client:
        print("\n[1/4] Building shared ORB research context...")
        resolved_days = await get_trading_days(
            client,
            config.backtest.start_date,
            config.backtest.end_date,
            config.backtest.lookback_trading_days,
        )
        if not resolved_days:
            raise ValueError("No trading days resolved for evaluation window")
        start_date = resolved_days[0]
        end_date = resolved_days[-1]

        context = await build_orb_research_context(
            config,
            start_date,
            end_date,
            client,
            print_progress=True,
        )
        print(
            f"  Shared context: {len(context.trading_days)} days, "
            f"{context.total_pairs} ticker-day pairs"
        )

        if walk_forward:
            windows = generate_walk_forward_windows(
                context.trading_days,
                train_days=wf_train_days,
                test_days=wf_test_days,
            )
            print(
                f"\n[2/4] Walk-forward: {len(windows)} windows "
                f"(train={wf_train_days}d, test={wf_test_days}d)"
            )
            print("\n[3/4] Running streaming evaluation...")
            report = await _run_walk_forward_eval(sweep, context, client, windows)
        else:
            train_days, test_days = split_trading_days(
                context.trading_days,
                split_date=split_date,
                train_ratio=train_ratio,
            )
            print("\n[2/4] Train/Test split:")
            print(f"      Train (IS):  {train_days[0]} → {train_days[-1]} ({len(train_days)} days)")
            print(f"      Test (OOS):  {test_days[0]} → {test_days[-1]} ({len(test_days)} days)")
            print("\n[3/4] Running streaming evaluation...")
            report = await _run_split_eval(
                sweep,
                context,
                client,
                train_days,
                test_days,
                top_n,
                oos_sort=oos_sort,
            )

    return report


def _format_params(params: dict[str, Any]) -> str:
    parts = []
    for key, value in sorted(params.items()):
        short_key = key.replace("_multiplier", "").replace("_pct", "%").replace("_at_r", "R")
        if isinstance(value, float):
            parts.append(f"{short_key}={value:.2g}")
        elif value is None:
            parts.append(f"{short_key}=off")
        else:
            parts.append(f"{short_key}={value}")
    return " ".join(parts)


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(
        description="ORB strategy IS/OOS evaluation",
        formatter_class=argparse.RawDescriptionHelpFormatter,
    )
    parser.add_argument("--config", required=True, help="Base strategy config YAML")
    parser.add_argument("--sweep", required=True, help="Sweep parameter grid YAML")
    parser.add_argument("--start", help="Start date (YYYY-MM-DD)")
    parser.add_argument("--end", help="End date (YYYY-MM-DD)")
    parser.add_argument("--days", type=int, help="Lookback trading days (alternative to --start)")
    parser.add_argument("--split-date", help="Train/test split date (YYYY-MM-DD)")
    parser.add_argument("--train-ratio", type=float, help="Fraction of data for training (0.0-1.0)")
    parser.add_argument("--top-n", type=int, default=5, help="Top N configs to validate OOS")
    parser.add_argument("--walk-forward", action="store_true", help="Enable walk-forward evaluation")
    parser.add_argument("--wf-train-days", type=int, default=252, help="WF train window (trading days)")
    parser.add_argument("--wf-test-days", type=int, default=63, help="WF test window (trading days)")
    parser.add_argument("--output", help="Save report JSON to path")
    parser.add_argument("--oos-sort", action="store_true", help="Re-rank final results by OOS Sharpe")
    return parser.parse_args()


def main() -> None:
    args = parse_args()
    with open(args.config) as handle:
        raw = yaml.safe_load(handle) or {}

    orb_params = ORBStrategyParams(**(raw.get("orb_strategy", {})))
    backtest_params = BacktestParams(**(raw.get("backtest", {})))
    universe_params = UniverseParams(**(raw.get("universe", {})))
    cache_params = CacheParams(**(raw.get("cache", {})))
    output_params = OutputParams(**(raw.get("output", {})))

    if args.start:
        backtest_params.start_date = args.start
    if args.end:
        backtest_params.end_date = args.end
    if args.days:
        backtest_params.lookback_trading_days = args.days

    config = IntradayConfig(
        strategy_mode=raw.get("strategy_mode", "orb"),
        orb_strategy=orb_params,
        backtest=backtest_params,
        universe=universe_params,
        cache=cache_params,
        output=output_params,
    )

    report = asyncio.run(
        evaluate(
            config=config,
            sweep_path=args.sweep,
            split_date=args.split_date,
            train_ratio=args.train_ratio,
            top_n=args.top_n,
            walk_forward=args.walk_forward,
            wf_train_days=args.wf_train_days,
            wf_test_days=args.wf_test_days,
            oos_sort=args.oos_sort,
        )
    )

    if args.output:
        out_path = Path(args.output)
    else:
        out_dir = Path(config.output.dir)
        out_dir.mkdir(parents=True, exist_ok=True)
        ts = datetime.now().strftime("%Y%m%d_%H%M%S")
        out_path = out_dir / f"eval_{ts}.json"

    out_path.parent.mkdir(parents=True, exist_ok=True)
    out_path.write_text(json.dumps(report, indent=2))
    print(f"\n  Report saved: {out_path}")


if __name__ == "__main__":
    main()