from __future__ import annotations

import argparse
import itertools
import json
from dataclasses import replace
from pathlib import Path

import numpy as np
import pandas as pd

from qfr.strategy.etf_trend import Constraints, TrendParams, UniverseAsset, run_backtest


def load_universe(config_path: Path) -> tuple[list[UniverseAsset], Constraints, str, str]:
    conf = json.loads(config_path.read_text(encoding="utf-8"))
    universe = [UniverseAsset(**a) for a in conf["assets"]]

    cons = conf.get("constraints", {})
    constraints = Constraints(
        max_positions=int(cons.get("max_positions", 4)),
        must_commodity=int(cons.get("must_include", {}).get("commodity", 0)),
        must_rates=int(cons.get("must_include", {}).get("rates", 0)),
        must_equity=int(cons.get("must_include", {}).get("equity", 0)),
    )

    risk_proxy = cons.get("risk_proxy", "510300.SH")
    rates_fallback = cons.get("rates_fallback", "511010.SH")

    return universe, constraints, risk_proxy, rates_fallback


def load_prices(raw_dir: Path, universe: list[UniverseAsset], start: str, end: str) -> dict[str, pd.DataFrame]:
    out: dict[str, pd.DataFrame] = {}
    for a in universe:
        fn = raw_dir / f"{a.ts_code.replace('.', '')}.parquet"
        df = pd.read_parquet(fn)
        df = df.copy()
        df["trade_date"] = df["trade_date"].astype(str)
        df = df[(df["trade_date"] >= start) & (df["trade_date"] <= end)]
        out[a.ts_code] = df
    return out


def perf_stats(equity: pd.Series) -> dict[str, float]:
    r = equity.pct_change().dropna()
    if r.empty:
        return {}
    ann_ret = float((equity.iloc[-1] / equity.iloc[0]) ** (252 / len(r)) - 1)
    ann_vol = float(r.std(ddof=1) * (252 ** 0.5))
    dd = float((equity / equity.cummax() - 1.0).min())
    calmar = float(ann_ret / abs(dd)) if dd < 0 else float("nan")
    return {"ann_return": ann_ret, "ann_vol": ann_vol, "max_drawdown": dd, "calmar": calmar}


def main() -> None:
    p = argparse.ArgumentParser()
    p.add_argument("--config", default="configs/etf_universe.json")
    p.add_argument("--rawdir", default="data/raw")
    p.add_argument("--start", default="20200101")
    p.add_argument("--end", default="20251231")
    p.add_argument("--out", default="data/tune_results.parquet")
    args = p.parse_args()

    config_path = Path(args.config)
    raw_dir = Path(args.rawdir)

    universe, constraints, risk_proxy, rates_fallback = load_universe(config_path)
    prices = load_prices(raw_dir, universe, args.start, args.end)

    base = TrendParams()

    # small grid to keep runtime reasonable
    fast_list = [5, 10]
    slow_list = [20, 40]
    atr_mult_list = [2.5, 3.0]
    vol_window_list = [10, 20]
    port_vol_window_list = [40, 60]
    max_positions_list = [3, 4]

    rows = []

    for sma_fast, sma_slow, atr_mult, vol_window, port_vol_window, max_positions in itertools.product(
        fast_list,
        slow_list,
        atr_mult_list,
        vol_window_list,
        port_vol_window_list,
        max_positions_list,
    ):
        if sma_fast >= sma_slow:
            continue

        params = replace(
            base,
            sma_fast=sma_fast,
            sma_slow=sma_slow,
            atr_mult=atr_mult,
            vol_window=vol_window,
            port_vol_window=port_vol_window,
            max_positions=max_positions,
            rebalance_every=1,
        )

        cons = replace(constraints, max_positions=max_positions)

        equity, _weights = run_backtest(
            prices,
            universe,
            cons,
            params,
            rates_fallback=rates_fallback,
            risk_proxy=risk_proxy,
        )

        st = perf_stats(equity["equity"])
        if not st:
            continue

        row = {
            "sma_fast": sma_fast,
            "sma_slow": sma_slow,
            "atr_mult": atr_mult,
            "vol_window": vol_window,
            "port_vol_window": port_vol_window,
            "max_positions": max_positions,
            **st,
        }
        rows.append(row)

    df = pd.DataFrame(rows)
    if df.empty:
        print("no results")
        return

    # filter by vol constraint first, then sort by ann_return
    filt = df[df["ann_vol"] <= 0.18].copy()
    if filt.empty:
        filt = df.copy()

    filt = filt.sort_values(["ann_return", "calmar"], ascending=False)

    out = Path(args.out)
    out.parent.mkdir(parents=True, exist_ok=True)
    filt.to_parquet(out, index=False)

    print("top10")
    cols = [
        "ann_return",
        "ann_vol",
        "max_drawdown",
        "calmar",
        "sma_fast",
        "sma_slow",
        "atr_mult",
        "vol_window",
        "port_vol_window",
        "max_positions",
    ]
    print(filt[cols].head(10).to_string(index=False))


if __name__ == "__main__":
    main()