"""Full-window audit: every registered US strategy vs TrendRider V9.

Runs the official engine path (run_market, same params as precompute) over
the current 10y common window and writes the comparison table plus the
head-to-head verdict (does V9 beat every other strategy on BOTH CAGR and
Sharpe?) to data/research/results/tr9_full_comparison.json.

    PYTHONPATH=. uv run python -m research.tr9_audit
"""
from __future__ import annotations

import json
import os

import pandas as pd

from trader import (AGENT_REVIEW_STRATEGIES, STRATEGY_REGISTRY,
                    strategy_supports_market)

import research.tr9_lab as lab

OUT = os.path.join("data", "research", "results", "tr9_full_comparison.json")
V9_NAMES = ["trend_rider_v9", "trend_rider_v9_vt24", "trend_rider_v9_vt32", "trend_rider_v9_vt36",
            "trend_rider_v10", "trend_rider_v10_vt32", "trend_rider_v10_vt36"]
NEW_NAMES = [n for n in V9_NAMES if "v10" in n]


def main() -> None:
    us_names = [n for n in STRATEGY_REGISTRY
                if strategy_supports_market(n, "us") and n not in AGENT_REVIEW_STRATEGIES
                and not n.startswith("sim_")]
    end = "2026-10-07"
    print(f"auditing {len(us_names)} US strategies (window ...{end})")
    curves = lab.run(us_names, start="2017-12-26", end=end)
    rows = []
    for name, eq in curves.items():
        m = lab.metrics(eq)
        rows.append({"name": name, **m})
    df = pd.DataFrame(rows).sort_values("cagr", ascending=False)
    pd.set_option("display.width", 200)
    print(f"{'name':44s} {'CAGR':>8s} {'vol':>7s} {'Sharpe':>7s} {'MDD':>7s}")
    for _, r in df.iterrows():
        print(f"{r['name']:44s} {r['cagr']*100:7.1f}% {r['vol']*100:6.1f}% "
              f"{r['sharpe']:7.2f} {r['mdd']*100:6.1f}%")

    others = df[~df["name"].isin(NEW_NAMES)]
    best_cagr = others.loc[others["cagr"].idxmax()]
    best_sharpe = others.loc[others["sharpe"].idxmax()]
    print(f"\nexisting best CAGR   : {best_cagr['name']} {best_cagr['cagr']*100:.1f}%")
    print(f"existing best Sharpe : {best_sharpe['name']} {best_sharpe['sharpe']:.2f}")
    verdict = {}
    for v9 in NEW_NAMES:
        row = df[df["name"] == v9].iloc[0]
        beat_cagr = bool((others["cagr"] < row["cagr"]).all())
        beat_sharpe = bool((others["sharpe"] < row["sharpe"]).all())
        verdict[v9] = {"cagr": float(row["cagr"]), "sharpe": float(row["sharpe"]),
                       "mdd": float(row["mdd"]),
                       "beats_all_cagr": beat_cagr, "beats_all_sharpe": beat_sharpe,
                       "beats_all_both": beat_cagr and beat_sharpe}
        print(f"{v9}: CAGR {row['cagr']*100:.1f}% (beats all: {beat_cagr}), "
              f"Sharpe {row['sharpe']:.2f} (beats all: {beat_sharpe})")
    with open(OUT, "w") as fh:
        json.dump({"window": {"start": "2017-12-26", "end": end},
                   "rows": rows, "verdict": verdict}, fh, indent=1)
    print("wrote", OUT)


if __name__ == "__main__":
    main()