"""
Construction sweep: holdings count, buffer (hysteresis), rebalance interval and
weighting for a fixed score, reported per ~5-year block.

    PYTHONPATH=. uv run python -m research.lab.construction --market us --combo overnight_mom+rec_126
"""

import argparse
import itertools
import warnings

import numpy as np
import pandas as pd

from research.lab.baselines import baseline_scores
from research.lab.factor_scan import load, phase_avg
from research.lab.factors import FACTORS, zrank
from research.lab.stepwise import SUBPERIODS

warnings.filterwarnings("ignore")


def combo_score(p, combo: str, market: str) -> pd.DataFrame:
    if combo.startswith("baseline:"):
        return baseline_scores(p, market)[combo.split(":", 1)[1]]
    parts = combo.split("+")
    return sum(zrank(FACTORS[n](p), p.member) for n in parts) / len(parts)


def block_stats(p, score, subs, **kw) -> dict:
    per = [phase_avg(p, score, a, b, **kw) for a, b in subs]
    return {"sharpe": np.mean([x["sharpe"] for x in per]), "min_sh": min(x["sharpe"] for x in per),
            "cagr": np.mean([x["cagr"] for x in per]), "mdd": min(x["mdd"] for x in per),
            "turn": np.mean([x["turnover"] for x in per]),
            "blocks": " ".join(f"{x['sharpe']:.2f}/{x['cagr']*100:.0f}%" for x in per)}


def main() -> None:
    ap = argparse.ArgumentParser()
    ap.add_argument("--market", default="us")
    ap.add_argument("--combo", nargs="+", required=True)
    ap.add_argument("--tops", nargs="*", type=int, default=[10, 15, 20, 30])
    ap.add_argument("--buffers", nargs="*", type=float, default=[0, 0.5, 1.0, 2.0])
    ap.add_argument("--everys", nargs="*", type=int, default=[5, 10])
    ap.add_argument("--weightings", nargs="*", default=["equal", "invvol"])
    args = ap.parse_args()
    mk = "us" if args.market == "us" else "cn"
    p, _ = load(args.market)
    subs = SUBPERIODS[mk]
    for combo in args.combo:
        s = combo_score(p, combo, args.market)
        print(f"=== {combo}")
        for top, buf, every, wt in itertools.product(args.tops, args.buffers, args.everys, args.weightings):
            st = block_stats(p, s, subs, top_n=top, every=every, buffer=buf, weighting=wt)
            print(f"  top{top:<3d} buf{buf:<4} every{every:<3d} {wt:7s} sh {st['sharpe']:.2f} min {st['min_sh']:.2f} "
                  f"cagr {st['cagr']*100:5.1f}% worstMDD {st['mdd']*100:5.1f}% turn {st['turn']:4.1f}x  [{st['blocks']}]",
                  flush=True)


if __name__ == "__main__":
    main()