from __future__ import annotations

from dataclasses import dataclass
from pathlib import Path

import pandas as pd


DATA_DIR = Path("data/okx-candles")
OUTPUT_DIR = Path("reports/recent-regime")
PREFIX = "recent-squeeze-refine"
INITIAL_EQUITY = 10_000.0
ROUNDTRIP_COST = 0.0021
HORIZONS = (
    ("3y", pd.DateOffset(years=3)),
    ("1y", pd.DateOffset(years=1)),
    ("6m", pd.DateOffset(months=6)),
    ("90d", pd.DateOffset(days=90)),
    ("30d", pd.DateOffset(days=30)),
    ("14d", pd.DateOffset(days=14)),
    ("7d", pd.DateOffset(days=7)),
)


@dataclass(frozen=True)
class Candidate:
    quantile: float
    trigger_bars: int
    trigger_pct: float
    stop_pct: float
    take_pct: float
    hold_bars: int
    side_mode: str
    ratio_filter: str
    cooldown_bars: int

    @property
    def name(self) -> str:
        return (
            f"sqref-q{self.quantile:g}-tb{self.trigger_bars}-tr{self.trigger_pct:g}"
            f"-sl{self.stop_pct:g}-tp{self.take_pct:g}-h{self.hold_bars}"
            f"-{self.side_mode}-{self.ratio_filter}-cd{self.cooldown_bars}"
        )


def load_frame(symbol: str) -> pd.DataFrame:
    frame = pd.read_csv(DATA_DIR / symbol / "15m.csv")
    frame["dt"] = pd.to_datetime(frame["ts"], unit="ms", utc=True)
    return frame.sort_values("ts").drop_duplicates("ts", keep="last").set_index("dt")


def aligned_frame() -> pd.DataFrame:
    eth = load_frame("ETH-USDT-SWAP").add_prefix("eth_")
    btc = load_frame("BTC-USDT-SWAP").add_prefix("btc_")
    frame = eth.join(btc, how="inner")
    frame["ratio"] = frame["eth_close"] / frame["btc_close"]
    frame = frame.dropna()
    return frame[frame.index >= frame.index[-1] - pd.DateOffset(years=3)]


def max_drawdown(equity: pd.Series) -> float:
    peak = equity.cummax()
    return float(((peak - equity) / peak).max()) if len(equity) else 0.0


def annualized(total: float, start: pd.Timestamp, end: pd.Timestamp) -> float:
    years = (end - start).total_seconds() / 31_536_000
    if years <= 0.0 or total <= -1.0:
        return -1.0 if total <= -1.0 else 0.0
    return (1.0 + total) ** (1.0 / years) - 1.0


def squeeze_series(frame: pd.DataFrame, quantile: float) -> pd.Series:
    close = frame["eth_close"]
    middle = close.rolling(96).mean()
    width = 4.0 * close.rolling(96).std(ddof=0) / middle
    return width <= width.rolling(960).quantile(quantile)


def ratio_allows(frame: pd.DataFrame, index: int, side: str, ratio_filter: str) -> bool:
    if ratio_filter == "none":
        return True
    ratio_return = frame["ratio"].iloc[index] / frame["ratio"].iloc[index - 96] - 1.0
    if ratio_filter == "weak-short":
        return side == "short" and ratio_return < -0.01
    if ratio_filter == "strong-long":
        return side == "long" and ratio_return > 0.01
    if ratio_filter == "directional":
        return bool((side == "short" and ratio_return < -0.005) or (side == "long" and ratio_return > 0.005))
    raise ValueError(f"unknown ratio_filter {ratio_filter}")


def signal_side(candidate: Candidate, frame: pd.DataFrame, squeeze: pd.Series, index: int) -> str | None:
    if not bool(squeeze.iloc[index]):
        return None
    move = frame["eth_close"].iloc[index] / frame["eth_close"].iloc[index - candidate.trigger_bars] - 1.0
    if candidate.side_mode in ("long", "both") and move >= candidate.trigger_pct and ratio_allows(frame, index, "long", candidate.ratio_filter):
        return "long"
    if candidate.side_mode in ("short", "both") and move <= -candidate.trigger_pct and ratio_allows(frame, index, "short", candidate.ratio_filter):
        return "short"
    return None


def exit_return(side: str, entry: float, exit_price: float) -> float:
    gross = exit_price / entry - 1.0 if side == "long" else entry / exit_price - 1.0
    return gross - ROUNDTRIP_COST


def mark_equity(equity: float, side: str, entry: float, mark: float) -> float:
    gross = mark / entry - 1.0 if side == "long" else entry / mark - 1.0
    return equity * (1.0 + gross)


def run_candidate(frame: pd.DataFrame, candidate: Candidate) -> tuple[pd.Series, pd.DataFrame]:
    squeeze = squeeze_series(frame, candidate.quantile)
    equity = INITIAL_EQUITY
    position: dict[str, object] | None = None
    pending_side: str | None = None
    cooldown_until = -1
    curve: list[dict[str, object]] = []
    trades: list[dict[str, object]] = []
    rows = list(frame.itertuples())
    warmup = max(960, 96, candidate.trigger_bars) + 1
    for index in range(warmup, len(rows)):
        row = rows[index]
        ts = frame.index[index]
        if pending_side is not None and position is None:
            entry = float(row.eth_open)
            position = {"side": pending_side, "entry": entry, "entry_index": index, "entry_time": ts}
            pending_side = None

        current = equity
        if position is not None:
            side = str(position["side"])
            entry = float(position["entry"])
            if side == "long":
                stop_price = entry * (1.0 - candidate.stop_pct)
                take_price = entry * (1.0 + candidate.take_pct)
                stop_hit = float(row.eth_low) <= stop_price
                take_hit = float(row.eth_high) >= take_price
            else:
                stop_price = entry * (1.0 + candidate.stop_pct)
                take_price = entry * (1.0 - candidate.take_pct)
                stop_hit = float(row.eth_high) >= stop_price
                take_hit = float(row.eth_low) <= take_price
            hold_hit = index - int(position["entry_index"]) >= candidate.hold_bars
            if stop_hit or take_hit or hold_hit:
                exit_price = stop_price if stop_hit else take_price if take_hit else float(row.eth_close)
                ret = exit_return(side, entry, exit_price)
                equity *= 1.0 + ret
                trades.append(
                    {
                        "name": candidate.name,
                        "entry_time": position["entry_time"],
                        "exit_time": ts,
                        "side": side,
                        "entry": entry,
                        "exit": exit_price,
                        "return": ret,
                        "bars": index - int(position["entry_index"]),
                    }
                )
                current = equity
                position = None
                cooldown_until = index + candidate.cooldown_bars
            else:
                current = mark_equity(equity, side, entry, float(row.eth_close))
        curve.append({"dt": ts, "equity": current})
        if index == len(rows) - 1 or position is not None or index < cooldown_until:
            continue
        side = signal_side(candidate, frame, squeeze, index)
        if side is not None:
            pending_side = side
    equity_series = pd.DataFrame(curve).set_index("dt")["equity"]
    return equity_series, pd.DataFrame(trades)


def metrics(equity: pd.Series, trades: pd.DataFrame, start: pd.Timestamp | None = None) -> dict[str, object]:
    scoped = equity if start is None else equity[equity.index >= start]
    if len(scoped) < 2:
        scoped = equity
    scoped_trades = trades if start is None or trades.empty else trades[pd.to_datetime(trades["entry_time"], utc=True) >= start]
    trade_returns = scoped_trades["return"] if len(scoped_trades) else pd.Series(dtype=float)
    wins = trade_returns[trade_returns > 0.0]
    losses = trade_returns[trade_returns < 0.0]
    total = float(scoped.iloc[-1] / scoped.iloc[0] - 1.0)
    gross_profit = float(wins.sum()) if len(wins) else 0.0
    gross_loss = abs(float(losses.sum())) if len(losses) else 0.0
    avg_win = float(wins.mean()) if len(wins) else 0.0
    avg_loss = abs(float(losses.mean())) if len(losses) else 0.0
    return {
        "start": scoped.index[0].strftime("%Y-%m-%d %H:%M"),
        "end": scoped.index[-1].strftime("%Y-%m-%d %H:%M"),
        "total_return": total,
        "annualized_return": annualized(total, scoped.index[0], scoped.index[-1]),
        "max_drawdown": max_drawdown(scoped),
        "calmar": annualized(total, scoped.index[0], scoped.index[-1]) / max_drawdown(scoped) if max_drawdown(scoped) else 0.0,
        "trades": int(len(trade_returns)),
        "win_rate": float(len(wins) / len(trade_returns)) if len(trade_returns) else 0.0,
        "profit_factor": gross_profit / gross_loss if gross_loss else (999.0 if gross_profit else 0.0),
        "payoff_ratio": avg_win / avg_loss if avg_loss else 0.0,
    }


def candidates() -> list[Candidate]:
    output: list[Candidate] = []
    for quantile in (0.15, 0.20):
        for trigger_bars in (4,):
            for trigger_pct in (0.004,):
                for stop_pct, take_pct in ((0.006, 0.012), (0.008, 0.016)):
                    for hold_bars in (48,):
                        for side_mode in ("long", "short", "both"):
                            for ratio_filter in ("none", "directional"):
                                output.append(
                                    Candidate(
                                        quantile,
                                        trigger_bars,
                                        trigger_pct,
                                        stop_pct,
                                        take_pct,
                                        hold_bars,
                                        side_mode,
                                        ratio_filter,
                                        8,
                                    )
                                )
    return output


def monthly_rows(name: str, equity: pd.Series) -> pd.DataFrame:
    monthly = equity.resample("ME").last()
    frame = pd.DataFrame(
        {
            "name": name,
            "month": monthly.index.strftime("%Y-%m"),
            "start_equity": monthly.shift(1).fillna(equity.iloc[0]).to_numpy(),
            "end_equity": monthly.to_numpy(),
        }
    )
    frame["return"] = frame["end_equity"] / frame["start_equity"] - 1.0
    return frame


def format_cell(value: object) -> str:
    if isinstance(value, float):
        return f"{value:.6g}"
    return str(value).replace("|", "\\|")


def markdown_table(frame: pd.DataFrame) -> str:
    rows = [list(frame.columns), ["---" for _ in frame.columns]]
    rows.extend(frame.astype(object).where(pd.notna(frame), "").values.tolist())
    return "\n".join("| " + " | ".join(format_cell(value) for value in row) + " |" for row in rows)


def main() -> int:
    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
    frame = aligned_frame()
    total_rows: list[dict[str, object]] = []
    horizon_rows: list[dict[str, object]] = []
    monthly_output: list[pd.DataFrame] = []
    equity_by_name: dict[str, pd.Series] = {}
    trades_by_name: dict[str, pd.DataFrame] = {}

    for candidate in candidates():
        equity, trades = run_candidate(frame, candidate)
        full = metrics(equity, trades)
        horizons = {label: metrics(equity, trades, equity.index[-1] - offset) for label, offset in HORIZONS}
        recent_returns = [horizons[label]["total_return"] for label in ("90d", "30d", "14d", "7d")]
        row = {
            "name": candidate.name,
            "side_mode": candidate.side_mode,
            "ratio_filter": candidate.ratio_filter,
            "quantile": candidate.quantile,
            "trigger_bars": candidate.trigger_bars,
            "trigger_pct": candidate.trigger_pct,
            "stop_pct": candidate.stop_pct,
            "take_pct": candidate.take_pct,
            "hold_bars": candidate.hold_bars,
            **full,
            "return_3y": horizons["3y"]["total_return"],
            "return_1y": horizons["1y"]["total_return"],
            "return_6m": horizons["6m"]["total_return"],
            "return_90d": horizons["90d"]["total_return"],
            "return_30d": horizons["30d"]["total_return"],
            "return_14d": horizons["14d"]["total_return"],
            "return_7d": horizons["7d"]["total_return"],
            "min_recent_return": min(float(value) for value in recent_returns),
            "recent_trades": sum(int(horizons[label]["trades"]) for label in ("30d", "14d", "7d")),
        }
        row["score"] = (
            3.0 * float(row["return_30d"])
            + 2.0 * float(row["return_14d"])
            + float(row["return_7d"])
            + float(row["return_90d"])
            - 0.5 * float(row["max_drawdown"])
        )
        total_rows.append(row)
        for label, values in horizons.items():
            horizon_rows.append({"name": candidate.name, "horizon": label, **values})
        equity_by_name[candidate.name] = equity
        trades_by_name[candidate.name] = trades

    total = pd.DataFrame(total_rows).sort_values(
        ["min_recent_return", "return_30d", "return_14d", "score"],
        ascending=[False, False, False, False],
    )
    qualified = total[
        (total["return_30d"] > 0.0)
        & (total["return_14d"] > 0.0)
        & (total["return_7d"] > 0.0)
        & (total["trades"] >= 30)
        & (total["profit_factor"] >= 1.0)
    ].copy()
    top = qualified.head(12) if len(qualified) else total.head(12)
    for name in top["name"]:
        monthly_output.append(monthly_rows(str(name), equity_by_name[str(name)]))

    horizons = pd.DataFrame(horizon_rows)
    monthly = pd.concat(monthly_output, ignore_index=True) if monthly_output else pd.DataFrame(columns=["name", "month", "start_equity", "end_equity", "return"])
    total_path = OUTPUT_DIR / f"{PREFIX}-total.csv"
    qualified_path = OUTPUT_DIR / f"{PREFIX}-qualified.csv"
    horizon_path = OUTPUT_DIR / f"{PREFIX}-horizons.csv"
    monthly_path = OUTPUT_DIR / f"{PREFIX}-monthly.csv"
    report_path = OUTPUT_DIR / f"{PREFIX}-report.md"
    total.to_csv(total_path, index=False)
    qualified.to_csv(qualified_path, index=False)
    horizons.to_csv(horizon_path, index=False)
    monthly.to_csv(monthly_path, index=False)
    report_path.write_text(
        "\n".join(
            [
                "# Recent Squeeze Breakout Refine",
                "",
                "Scope: ETH 15m only, data through the local cache end, roundtrip cost 0.21% on margin.",
                "",
                "## Top Qualified",
                "",
                markdown_table(
                    top[
                        [
                            "name",
                            "total_return",
                            "max_drawdown",
                            "profit_factor",
                            "trades",
                            "return_90d",
                            "return_30d",
                            "return_14d",
                            "return_7d",
                            "min_recent_return",
                        ]
                    ]
                ),
                "",
                "## Interpretation",
                "",
                "Qualified rows require positive 30d/14d/7d returns, at least 30 full-period trades, and profit factor >= 1.0.",
                "If this table still has negative 90d rows, the pattern is recent-regime specific rather than robust across the full recent quarter.",
                "",
            ]
        ),
        encoding="utf-8",
    )
    print(report_path)
    print(top.head(8).to_string(index=False))
    return 0


if __name__ == "__main__":
    raise SystemExit(main())