"""
Re-run the existing factor_combo champions under the research engine, with
the same rules every new candidate gets: next-open execution, PIT universe,
costs, and averaging over rebalance phases.

    PYTHONPATH=. uv run python -m research.lab.baselines --market us
"""

import argparse
import warnings

import pandas as pd

from research.lab.factor_scan import load, phase_avg
from research.lab.report import fmt
from strategies.factor_combo import SIGNAL_REGISTRY

warnings.filterwarnings("ignore")

CHAMPIONS = {
    "us": ["rec_mfilt+deep_upvol", "ma200+mom7m+rec126", "rec_mfilt+ma200", "mom7m+rec126"],
    "cn": ["up_cap+quality_mom", "down_resil+qual_mom", "rec63+mom_gap", "up_cap+mom_gap"],
}


def baseline_scores(p, market: str) -> dict[str, pd.DataFrame]:
    mk = "us" if market == "us" else "cn"
    # factor_combo signals were built on prices masked to index membership
    px = p.close.where(p.member).ffill(limit=5)
    return {name: SIGNAL_REGISTRY[name](px) for name in CHAMPIONS[mk]}


def main() -> None:
    ap = argparse.ArgumentParser()
    ap.add_argument("--market", default="us")
    args = ap.parse_args()
    p, periods = load(args.market)
    scores = baseline_scores(p, args.market)
    for name, s in scores.items():
        for every, label in [(5, "weekly"), (21, "monthly")]:
            for per, (a, b) in periods.items():
                st = phase_avg(p, s, a, b, 10, every)
                print(f"{name:22s} top10 {label:8s} {per:8s} {fmt(st)}", flush=True)


if __name__ == "__main__":
    main()