from __future__ import annotations

import json
import multiprocessing as mp
import sys
from concurrent.futures import ProcessPoolExecutor, as_completed
from dataclasses import asdict
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 strategy32.scripts.run_current_relaxed_hybrid_experiment import (
    BASELINE_PATH,
    BEST_CASH_OVERLAY,
    CACHE_PATH,
    CURRENT_OVERHEAT_OVERRIDES,
    OUT_JSON as SEARCH_OUT_JSON,
    RELAXED_OVERHEAT_OVERRIDES,
    WINDOWS,
    YEAR_PERIODS,
    YTD_START,
    HybridSwitchCandidate,
    _compose_hybrid_curve,
)
from strategy32.research.soft_router import build_cash_overlay_period_components, load_component_bundle, score_candidate, segment_metrics


OUT_JSON = Path("/tmp/strategy32_current_relaxed_hybrid_exact.json")

BEST_SEARCH_CANDIDATE = HybridSwitchCandidate(
    positive_regimes=("MOMENTUM_EXPANSION", "EUPHORIC_BREAKOUT"),
    core_score_min=0.60,
    breadth_persist_min=0.50,
    funding_persist_min=0.55,
    panic_max=0.20,
    choppy_max=0.40,
    distribution_max=0.30,
)


def _baseline_summary() -> dict[str, object]:
    payload = json.loads(BASELINE_PATH.read_text(encoding="utf-8"))
    variants = payload["variants"]
    return {name: variants[name]["results"] for name in ("current_overheat", "relaxed_overheat")}


def _period_specs(latest_bar: pd.Timestamp) -> list[tuple[str, str, pd.Timestamp, pd.Timestamp]]:
    specs: list[tuple[str, str, pd.Timestamp, pd.Timestamp]] = []
    for days, label in WINDOWS:
        specs.append(("window", label, latest_bar - pd.Timedelta(days=days), latest_bar))
    for label, start, end_exclusive in YEAR_PERIODS:
        specs.append(("year", label, start, min(latest_bar, end_exclusive - pd.Timedelta(seconds=1))))
    specs.append(("year", "2026_YTD", YTD_START, latest_bar))
    return specs


def _period_worker(
    cache_path: str,
    candidate_payload: dict[str, object],
    kind: str,
    label: str,
    start_text: str,
    end_text: str,
) -> tuple[str, str, dict[str, float], list[dict[str, object]]]:
    bundle, _ = load_component_bundle(cache_path)
    candidate = HybridSwitchCandidate(**candidate_payload)
    start = pd.Timestamp(start_text)
    end = pd.Timestamp(end_text)
    current = build_cash_overlay_period_components(
        bundle=bundle,
        eval_start=start,
        eval_end=end,
        profile_name=BEST_CASH_OVERLAY.regime_profile,
        core_filter=BEST_CASH_OVERLAY.core_filter,
        cap_engine=BEST_CASH_OVERLAY.cap_engine,
        chop_engine=BEST_CASH_OVERLAY.chop_engine,
        dist_engine=BEST_CASH_OVERLAY.dist_engine,
        core_config_overrides=CURRENT_OVERHEAT_OVERRIDES,
    )
    relaxed = build_cash_overlay_period_components(
        bundle=bundle,
        eval_start=start,
        eval_end=end,
        profile_name=BEST_CASH_OVERLAY.regime_profile,
        core_filter=BEST_CASH_OVERLAY.core_filter,
        cap_engine=BEST_CASH_OVERLAY.cap_engine,
        chop_engine=BEST_CASH_OVERLAY.chop_engine,
        dist_engine=BEST_CASH_OVERLAY.dist_engine,
        core_config_overrides=RELAXED_OVERHEAT_OVERRIDES,
    )
    curve, rows = _compose_hybrid_curve(
        current_components=current,
        relaxed_components=relaxed,
        switch_candidate=candidate,
    )
    latest_rows: list[dict[str, object]] = []
    if label == "2026_YTD":
        latest_rows = rows.tail(5).assign(timestamp=lambda df: df["timestamp"].astype(str)).to_dict(orient="records")
    return kind, label, segment_metrics(curve, start, end), latest_rows


def main() -> None:
    if SEARCH_OUT_JSON.exists():
        payload = json.loads(SEARCH_OUT_JSON.read_text(encoding="utf-8"))
        if payload.get("search_top"):
            best_candidate = HybridSwitchCandidate(**payload["search_top"][0]["candidate"])
        else:
            best_candidate = BEST_SEARCH_CANDIDATE
    else:
        best_candidate = BEST_SEARCH_CANDIDATE

    _, latest_bar = load_component_bundle(CACHE_PATH)
    window_results: dict[str, dict[str, float]] = {}
    year_results: dict[str, dict[str, float]] = {}
    latest_rows: list[dict[str, object]] = []

    specs = _period_specs(latest_bar)
    ctx = mp.get_context("fork")
    with ProcessPoolExecutor(max_workers=min(6, len(specs)), mp_context=ctx) as executor:
        future_map = {
            executor.submit(
                _period_worker,
                CACHE_PATH,
                asdict(best_candidate),
                kind,
                label,
                str(start),
                str(end),
            ): (kind, label)
            for kind, label, start, end in specs
        }
        for future in as_completed(future_map):
            kind, label = future_map[future]
            kind_result, label_result, metrics, latest = future.result()
            if kind_result == "window":
                window_results[label_result] = metrics
            else:
                year_results[label_result] = metrics
            if latest:
                latest_rows = latest
            print(f"[done] {label_result}", flush=True)

    score, negative_years, mdd_violations = score_candidate(
        {label: window_results[label] for _, label in WINDOWS},
        {label: year_results[label] for label, _, _ in YEAR_PERIODS},
    )
    payload = {
        "analysis": "current_relaxed_hybrid_exact",
        "latest_bar": str(latest_bar),
        "candidate": asdict(best_candidate),
        "score": score,
        "negative_years": negative_years,
        "mdd_violations": mdd_violations,
        "windows": {label: window_results[label] for _, label in WINDOWS},
        "years": year_results,
        "latest_rows": latest_rows,
        "baselines": _baseline_summary(),
    }
    OUT_JSON.write_text(json.dumps(payload, indent=2), encoding="utf-8")
    print(json.dumps(payload, indent=2))
    print(f"[saved] {OUT_JSON}")


if __name__ == "__main__":
    main()