"""ORB strategy overfitting analysis with streaming intraday simulation."""
from __future__ import annotations

import argparse
import asyncio
import json
import statistics
import sys
import time
from pathlib import Path
from typing import Any

import yaml

from apps.intraday_bt.oracle import make_intraday_oracle_client
from rich import box
from rich.console import Console
from rich.panel import Panel
from rich.progress import BarColumn, Progress, SpinnerColumn, TextColumn, TimeElapsedColumn

from libs.backtest.domain import SplitResult, WalkForwardSummary
from libs.common.config import get_settings
from libs.intraday.domain import ORBStrategyParams
from libs.oracle_client import OracleClient

from apps.intraday_bt.orb_research import (
    build_orb_research_context,
    force_simple_returns,
    generate_walk_forward_windows,
    resolve_orb_config,
    simulate_orb_period,
    split_trading_days,
)
from apps.intraday_bt.run import _latest_backtest_date

_console = Console(width=120)

_DEFAULT_SPLIT_DATE = "2026-01-01"
_DEFAULT_START_DATE = "2022-01-01"
_WF_TRAIN_DAYS = 252
_WF_TEST_DAYS = 63


async def run_is_oos_test(
    context,
    client: OracleClient,
    config,
    split_date: str,
) -> dict[str, Any]:
    train_days, test_days = split_trading_days(context.trading_days, split_date=split_date)
    if not train_days or not test_days:
        return {"verdict": "SKIP", "notes": "Not enough data to split"}

    orb_params = config.orb_strategy or ORBStrategyParams()
    is_metrics = await simulate_orb_period(context, client, orb_params, train_days, run_id="is")
    oos_metrics = await simulate_orb_period(context, client, orb_params, test_days, run_id="oos")
    is_sharpe = is_metrics.sharpe_ratio or 0.0
    oos_sharpe = oos_metrics.sharpe_ratio or 0.0
    retention = 0.0 if is_sharpe <= 0 else oos_sharpe / is_sharpe

    if retention >= 0.60:
        verdict = "PASS"
    elif retention >= 0.40:
        verdict = "WARN"
    else:
        verdict = "FAIL"

    return {
        "verdict": verdict,
        "is_sharpe": round(is_sharpe, 3),
        "oos_sharpe": round(oos_sharpe, 3),
        "retention_pct": round(retention * 100, 1),
        "is_period": f"{train_days[0]} → {train_days[-1]} ({len(train_days)} days)",
        "oos_period": f"{test_days[0]} → {test_days[-1]} ({len(test_days)} days)",
    }


def summarize_is_oos_from_results(
    is_result: SplitResult,
    oos_result: SplitResult,
    *,
    is_period: str | None = None,
    oos_period: str | None = None,
) -> dict[str, Any]:
    """Reuse already-computed train/test split results for IS/OOS retention."""
    is_sharpe = is_result.sharpe_ratio or 0.0
    oos_sharpe = oos_result.sharpe_ratio or 0.0
    retention = 0.0 if is_sharpe <= 0 else oos_sharpe / is_sharpe

    if retention >= 0.60:
        verdict = "PASS"
    elif retention >= 0.40:
        verdict = "WARN"
    else:
        verdict = "FAIL"

    payload = {
        "verdict": verdict,
        "is_sharpe": round(is_sharpe, 3),
        "oos_sharpe": round(oos_sharpe, 3),
        "retention_pct": round(retention * 100, 1),
        "source": "split_results",
    }
    if is_period is not None:
        payload["is_period"] = is_period
    if oos_period is not None:
        payload["oos_period"] = oos_period
    return payload


async def run_walk_forward_test(
    context,
    client: OracleClient,
    config,
    train_days: int = _WF_TRAIN_DAYS,
    test_days: int = _WF_TEST_DAYS,
) -> dict[str, Any]:
    windows = generate_walk_forward_windows(context.trading_days, train_days, test_days)
    if len(windows) < 2:
        return {"verdict": "SKIP", "notes": f"Need ≥ {train_days + 2 * test_days} days, got {len(context.trading_days)}"}

    orb_params = config.orb_strategy or ORBStrategyParams()
    sharpes: list[float] = []
    for i, (_, wf_test) in enumerate(windows, start=1):
        metrics = await simulate_orb_period(context, client, orb_params, wf_test, run_id=f"wf{i:02d}")
        sharpe = metrics.sharpe_ratio or 0.0
        sharpes.append(sharpe)
        sys.stdout.write(f"\r  WF window {i}/{len(windows)}: test Sharpe={sharpe:.2f}  ")
        sys.stdout.flush()
    print()

    mean_sr = statistics.mean(sharpes)
    std_sr = statistics.stdev(sharpes) if len(sharpes) > 1 else 0.0
    cv = std_sr / abs(mean_sr) if abs(mean_sr) > 0.01 else float("inf")
    n_positive = sum(1 for sharpe in sharpes if sharpe > 0)
    if mean_sr > 0.5 and cv < 0.80:
        verdict = "PASS"
    elif mean_sr > 0 and cv < 1.5:
        verdict = "WARN"
    else:
        verdict = "FAIL"

    return {
        "verdict": verdict,
        "n_windows": len(windows),
        "mean_sharpe": round(mean_sr, 3),
        "std_sharpe": round(std_sr, 3),
        "cv": round(cv, 3),
        "n_positive": n_positive,
        "window_sharpes": [round(s, 3) for s in sharpes],
    }


def summarize_walk_forward_test_from_summary(wf_summary: WalkForwardSummary) -> dict[str, Any]:
    """Reuse an already-built walk-forward summary for the overfit verdict."""
    sharpes = [fold.test_metrics.sharpe_ratio or 0.0 for fold in wf_summary.folds]
    if len(sharpes) < 2:
        return {
            "verdict": "SKIP",
            "notes": f"Need ≥ 2 folds, got {len(sharpes)}",
        }

    mean_sr = statistics.mean(sharpes)
    std_sr = statistics.stdev(sharpes) if len(sharpes) > 1 else 0.0
    cv = std_sr / abs(mean_sr) if abs(mean_sr) > 0.01 else float("inf")
    n_positive = sum(1 for sharpe in sharpes if sharpe > 0)
    if mean_sr > 0.5 and cv < 0.80:
        verdict = "PASS"
    elif mean_sr > 0 and cv < 1.5:
        verdict = "WARN"
    else:
        verdict = "FAIL"

    return {
        "verdict": verdict,
        "n_windows": len(sharpes),
        "mean_sharpe": round(mean_sr, 3),
        "std_sharpe": round(std_sr, 3),
        "cv": round(cv, 3),
        "n_positive": n_positive,
        "window_sharpes": [round(s, 3) for s in sharpes],
        "source": "walk_forward_summary",
    }


async def run_param_plateau_test(
    context,
    client: OracleClient,
    config,
    quick: bool = False,
    param_names: list[str] | None = None,
) -> dict[str, Any]:
    orb_params = config.orb_strategy or ORBStrategyParams()
    n_values = 3 if quick else 5
    params_to_test = [
        ("atr_stop_multiplier", orb_params.atr_stop_multiplier, 0.4),
        ("breakeven_at_r", orb_params.breakeven_at_r, 0.4),
        ("trailing_stop_atr_multiplier", orb_params.trailing_stop_atr_multiplier, 0.4),
    ]
    if param_names is not None:
        selected = set(param_names)
        params_to_test = [item for item in params_to_test if item[0] in selected]
    results = []

    for param_name, base_value, spread in params_to_test:
        lo = base_value * (1 - spread)
        hi = base_value * (1 + spread)
        test_values = [lo + (hi - lo) * i / (n_values - 1) for i in range(n_values)]
        sharpes: list[float] = []
        for value in test_values:
            modified = orb_params.model_copy(update={param_name: round(value, 6)})
            metrics = await simulate_orb_period(
                context,
                client,
                modified,
                context.trading_days,
                run_id=f"{param_name[:4]}_{value:.4f}",
            )
            sharpe = metrics.sharpe_ratio or 0.0
            sharpes.append(sharpe)
            sys.stdout.write(f"\r  {param_name}={value:.4f} → Sharpe={sharpe:.2f}  ")
            sys.stdout.flush()
        print()
        mean_sharpe = statistics.mean(sharpes)
        plateau = max(0.0, 1.0 - statistics.stdev(sharpes) / abs(mean_sharpe)) if len(sharpes) > 1 and abs(mean_sharpe) > 0.01 else 0.0
        verdict = "PASS" if plateau >= 0.70 else "WARN" if plateau >= 0.40 else "FAIL"
        results.append({
            "param": param_name,
            "base_value": base_value,
            "test_values": [round(v, 5) for v in test_values],
            "sharpe_values": [round(s, 3) for s in sharpes],
            "plateau": round(plateau, 3),
            "verdict": verdict,
        })

    verdicts = [result["verdict"] for result in results]
    if all(v == "PASS" for v in verdicts):
        overall = "PASS"
    elif "FAIL" in verdicts:
        overall = "FAIL"
    else:
        overall = "WARN"
    return {"verdict": overall, "params": results}


async def run_permutation_test(
    context,
    client: OracleClient,
    config,
    n_permutations: int = 30,
) -> dict[str, Any]:
    orb_params = config.orb_strategy or ORBStrategyParams()
    observed = await simulate_orb_period(context, client, orb_params, context.trading_days, run_id="perm_obs")
    observed_sharpe = observed.sharpe_ratio or 0.0
    _console.print(f"  [dim]Observed Sharpe (real ranking): {observed_sharpe:.3f}[/dim]")

    null_sharpes: list[float] = []
    with Progress(
        SpinnerColumn(),
        TextColumn("[progress.description]{task.description}"),
        BarColumn(),
        "{task.completed}/{task.total}",
        TimeElapsedColumn(),
        console=_console,
        transient=True,
    ) as progress:
        task = progress.add_task("Permutation test", total=n_permutations)
        for i in range(n_permutations):
            metrics = await simulate_orb_period(
                context,
                client,
                orb_params,
                context.trading_days,
                run_id=f"perm{i:03d}",
                shuffle_candidates_seed=42 + i,
            )
            null_sharpes.append(metrics.sharpe_ratio or 0.0)
            progress.advance(task)

    p_value = sum(1 for sr in null_sharpes if sr >= observed_sharpe) / max(len(null_sharpes), 1)
    null_sorted = sorted(null_sharpes)
    p95_idx = min(len(null_sorted) - 1, int(0.95 * (len(null_sorted) - 1))) if null_sorted else 0
    null_p95 = null_sorted[p95_idx] if null_sorted else 0.0
    null_median = statistics.median(null_sharpes) if null_sharpes else 0.0

    if p_value < 0.05:
        verdict = "PASS"
    elif p_value < 0.20:
        verdict = "WARN"
    else:
        verdict = "FAIL"
    return {
        "verdict": verdict,
        "observed_sharpe": round(observed_sharpe, 3),
        "n_permutations": n_permutations,
        "null_median": round(null_median, 3),
        "null_p95": round(null_p95, 3),
        "p_value": round(p_value, 4),
        "null_sharpes": [round(sr, 3) for sr in null_sharpes],
    }


def _vc(verdict: str) -> str:
    return {"PASS": "green", "WARN": "yellow", "FAIL": "red", "SKIP": "dim"}.get(verdict, "white")


def _verdict_tag(verdict: str) -> str:
    color = _vc(verdict)
    return f"[{color}][{verdict}][/{color}]"


def _print_report(
    config_path: Path,
    test1: dict[str, Any],
    test2: dict[str, Any],
    test3: dict[str, Any],
    test4: dict[str, Any],
    elapsed: float,
) -> None:
    weights = {"PASS": 1.0, "WARN": 0.5, "FAIL": 0.0, "SKIP": None}
    scores = [weights[t.get("verdict", "SKIP")] for t in [test1, test2, test3, test4]]
    scores = [score for score in scores if score is not None]
    overall_score = int(statistics.mean(scores) * 100) if scores else 0
    pass_count = sum(1 for t in [test1, test2, test3, test4] if t.get("verdict") == "PASS")
    overall_verdict = "PASS" if pass_count >= 3 else "WARN" if pass_count >= 2 else "FAIL"
    color = _vc(overall_verdict)

    _console.print()
    _console.print(
        Panel(
            f"[bold]ORB OVERFITTING ANALYSIS[/bold]\n"
            f"Config: [cyan]{config_path}[/cyan]\n\n"
            f"Overall: [{color} bold]{overall_verdict}[/{color} bold]  "
            f"Score: [bold]{overall_score}/100[/bold]  ({elapsed:.0f}s)",
            box=box.DOUBLE,
            width=100,
        )
    )
    for label, payload in [
        ("1. IS/OOS Retention", test1),
        ("2. Walk-Forward Stability", test2),
        ("3. Parameter Plateau", test3),
        ("4. Candidate Ranking Permutation", test4),
    ]:
        _console.print(f"\n  [bold]{label}[/bold]  {_verdict_tag(payload.get('verdict', 'SKIP'))}")
        _console.print(f"     {json.dumps(payload, ensure_ascii=True)}")


async def _async_main(args: argparse.Namespace) -> int:
    t0 = time.time()
    config_path, config = resolve_orb_config(args.config)
    config = force_simple_returns(config)

    settings = get_settings()
    async with make_intraday_oracle_client(settings) as client:
        context = await build_orb_research_context(
            config,
            args.start,
            args.end or _latest_backtest_date().isoformat(),
            client,
            print_progress=True,
        )

        skip = set(args.skip.split(",")) if args.skip else set()
        test1: dict[str, Any] = {"verdict": "SKIP"}
        test2: dict[str, Any] = {"verdict": "SKIP"}
        test3: dict[str, Any] = {"verdict": "SKIP"}
        test4: dict[str, Any] = {"verdict": "SKIP"}

        if "is_oos" not in skip:
            _console.print("\n[bold][1/4] IS/OOS Retention...[/bold]")
            test1 = await run_is_oos_test(context, client, config, args.split_date)
        if "wf" not in skip:
            _console.print("\n[bold][2/4] Walk-Forward Stability...[/bold]")
            wf_train = 126 if args.quick else _WF_TRAIN_DAYS
            wf_test = 42 if args.quick else _WF_TEST_DAYS
            test2 = await run_walk_forward_test(context, client, config, train_days=wf_train, test_days=wf_test)
        if "plateau" not in skip:
            _console.print("\n[bold][3/4] Parameter Plateau...[/bold]")
            test3 = await run_param_plateau_test(context, client, config, quick=args.quick)
        if "perm" not in skip:
            _console.print(f"\n[bold][4/4] Candidate Permutation (N={args.permutations})...[/bold]")
            test4 = await run_permutation_test(context, client, config, n_permutations=args.permutations)

    elapsed = time.time() - t0
    _print_report(config_path, test1, test2, test3, test4, elapsed)

    if args.output_json:
        report = {
            "config": str(config_path),
            "period": f"{context.trading_days[0]} → {context.trading_days[-1]}",
            "split_date": args.split_date,
            "elapsed_seconds": round(elapsed, 1),
            "tests": {
                "is_oos": test1,
                "walk_forward": test2,
                "param_plateau": test3,
                "permutation": test4,
            },
        }
        Path(args.output_json).parent.mkdir(parents=True, exist_ok=True)
        Path(args.output_json).write_text(json.dumps(report, indent=2))
        _console.print(f"\n[dim]Report saved → {args.output_json}[/dim]")
    return 0


def main() -> None:
    parser = argparse.ArgumentParser(
        prog="fithia2 intraday-overfit-check",
        description="ORB strategy overfitting analysis (IS/OOS, WFV, param plateau, permutation)",
    )
    parser.add_argument("--config", "-c", required=True, help="YAML config path or strategy slug")
    parser.add_argument("--split-date", default=_DEFAULT_SPLIT_DATE, help=f"IS/OOS split date (default: {_DEFAULT_SPLIT_DATE})")
    parser.add_argument("--start", default=_DEFAULT_START_DATE, help=f"Start of backtest window (default: {_DEFAULT_START_DATE})")
    parser.add_argument("--end", default=None, help="End of backtest window (default: latest available)")
    parser.add_argument("--quick", action="store_true", help="Quick mode: fewer WF windows, 3-point plateau, 15 permutations")
    parser.add_argument("--permutations", type=int, default=50, help="Candidate permutation test iterations")
    parser.add_argument("--skip", default="", help="Comma-separated tests to skip: is_oos,wf,plateau,perm")
    parser.add_argument("--output-json", default=None, help="Save report as JSON to this path")
    args = parser.parse_args()
    if args.quick:
        args.permutations = 15
    raise SystemExit(asyncio.run(_async_main(args)))


if __name__ == "__main__":
    main()