from __future__ import annotations

import copy
import itertools
import json
import sys
from pathlib import Path

import pandas as pd

PACKAGE_PARENT = Path(__file__).resolve().parents[2]
if str(PACKAGE_PARENT) not in sys.path:
    sys.path.insert(0, str(PACKAGE_PARENT))

from strategy29.backtest.window_analysis import evaluate_window_result, slice_bundle
from strategy32.backtest.simulator import Strategy32Backtester
from strategy32.config import PROFILE_V5_BASELINE, build_strategy32_config
from strategy32.data import build_strategy32_market_bundle


WINDOWS = [(30, "1m"), (365, "1y"), (1095, "3y"), (1825, "5y")]
FEATURES: list[tuple[str, str, bool]] = [
    ("no_sideways", "enable_sideways_engine", False),
    ("strong_kill_switch", "enable_strong_kill_switch", True),
    ("daily_trend_filter", "enable_daily_trend_filter", True),
    ("expanded_hedge", "enable_expanded_hedge", True),
    ("max_holding_exit", "enable_max_holding_exit", True),
]


def variant_name(enabled: list[str]) -> str:
    return "baseline_v5" if not enabled else "+".join(enabled)


def balanced_score(results: dict[str, dict[str, float | int | str]]) -> float:
    score = 0.0
    for label, weight in (("1y", 1.0), ("3y", 1.0), ("5y", 1.2)):
        annualized = float(results[label]["annualized_return"])
        drawdown = abs(float(results[label]["max_drawdown"]))
        score += weight * (annualized / max(drawdown, 0.01))
    score += 0.25 * float(results["1m"]["total_return"])
    return score


def build_variants() -> list[tuple[str, dict[str, bool], list[str]]]:
    variants: list[tuple[str, dict[str, bool], list[str]]] = [("baseline_v5", {}, [])]
    feature_names = [feature[0] for feature in FEATURES]
    for r in range(1, len(FEATURES) + 1):
        for combo in itertools.combinations(range(len(FEATURES)), r):
            overrides: dict[str, bool] = {}
            enabled: list[str] = []
            for idx in combo:
                label, attr, value = FEATURES[idx]
                overrides[attr] = value
                enabled.append(label)
            variants.append((variant_name(enabled), overrides, enabled))
    return variants


def main() -> None:
    base = build_strategy32_config(PROFILE_V5_BASELINE)
    end = pd.Timestamp("2026-03-15 00:00:00", tz="UTC")
    start = end - pd.Timedelta(days=max(days for days, _ in WINDOWS) + base.warmup_days + 14)

    print("fetching bundle...")
    bundle, latest_completed_bar, accepted_symbols, rejected_symbols, quote_by_symbol = build_strategy32_market_bundle(
        symbols=base.symbols,
        auto_discover_symbols=base.auto_discover_symbols,
        quote_assets=base.quote_assets,
        excluded_base_assets=base.excluded_base_assets,
        min_quote_volume_24h=base.discovery_min_quote_volume_24h,
        start=start,
        end=end,
        timeframe=base.timeframe,
        max_staleness_days=base.max_symbol_staleness_days,
    )
    print("latest", latest_completed_bar)

    results: dict[str, dict[str, dict[str, float | int | str]]] = {}
    summary_rows: list[dict[str, float | int | str | list[str]]] = []

    for idx, (name, overrides, enabled) in enumerate(build_variants(), start=1):
        cfg = copy.deepcopy(base)
        for attr, value in overrides.items():
            setattr(cfg, attr, value)
        variant_results = {}
        print(f"\n[{idx:02d}/32] {name}")
        for days, label in WINDOWS:
            eval_end = latest_completed_bar
            eval_start = eval_end - pd.Timedelta(days=days)
            raw_start = eval_start - pd.Timedelta(days=cfg.warmup_days)
            sliced = slice_bundle(bundle, raw_start, eval_end)
            backtester = Strategy32Backtester(cfg, sliced, trade_start=eval_start)
            backtester.engine_config.initial_capital = 1000.0
            result = backtester.run()
            metrics = evaluate_window_result(result, eval_start=eval_start, bars_per_day=backtester.engine_config.bars_per_day)
            metrics["engine_pnl"] = result.engine_pnl
            metrics["total_trades"] = result.total_trades
            variant_results[label] = metrics
            print(
                label,
                "ret",
                round(float(metrics["total_return"]) * 100, 2),
                "mdd",
                round(float(metrics["max_drawdown"]) * 100, 2),
                "sharpe",
                round(float(metrics["sharpe"]), 2),
                "trades",
                metrics["trade_count"],
            )
        score = balanced_score(variant_results)
        results[name] = variant_results
        summary_rows.append(
            {
                "name": name,
                "enabled": enabled,
                "balanced_score": score,
                "ret_1m": float(variant_results["1m"]["total_return"]),
                "ret_1y": float(variant_results["1y"]["total_return"]),
                "ret_3y": float(variant_results["3y"]["total_return"]),
                "ret_5y": float(variant_results["5y"]["total_return"]),
                "mdd_1y": float(variant_results["1y"]["max_drawdown"]),
                "mdd_3y": float(variant_results["3y"]["max_drawdown"]),
                "mdd_5y": float(variant_results["5y"]["max_drawdown"]),
            }
        )

    summary_rows.sort(key=lambda row: float(row["balanced_score"]), reverse=True)
    payload = {
        "strategy": "strategy32",
        "analysis": "v6_exhaustive_combo",
        "initial_capital": 1000.0,
        "auto_discover_symbols": base.auto_discover_symbols,
        "latest_completed_bar": str(latest_completed_bar),
        "requested_symbols": [] if base.auto_discover_symbols else base.symbols,
        "accepted_symbols": accepted_symbols,
        "rejected_symbols": rejected_symbols,
        "quote_by_symbol": quote_by_symbol,
        "timeframe": base.timeframe,
        "results": results,
        "summary": summary_rows,
    }
    out = Path("/tmp/strategy32_v6_exhaustive_combos.json")
    out.write_text(json.dumps(payload, indent=2), encoding="utf-8")
    print("\nTop 10 by balanced score")
    for row in summary_rows[:10]:
        print(
            row["name"],
            "score",
            round(float(row["balanced_score"]), 3),
            "1y",
            round(float(row["ret_1y"]) * 100, 2),
            "3y",
            round(float(row["ret_3y"]) * 100, 2),
            "5y",
            round(float(row["ret_5y"]) * 100, 2),
            "mdd5y",
            round(float(row["mdd_5y"]) * 100, 2),
        )
    print("\nwrote", out)


if __name__ == "__main__":
    main()