from __future__ import annotations

import argparse
import json
from dataclasses import dataclass
from pathlib import Path

import pandas as pd


DATA_DIR = Path("data/okx-candles")
OUT_DIR = Path("reports/ultrashort")
SYMBOL = "ETH-USDT-SWAP"
INITIAL_EQUITY = 10_000.0
LEVERAGE = 3.0
TAKER_FEE = 0.0004
HORIZONS = (
    ("full", None),
    ("3y", pd.DateOffset(years=3)),
    ("1y", pd.DateOffset(years=1)),
    ("6m", pd.DateOffset(months=6)),
    ("3m", pd.DateOffset(months=3)),
    ("30d", pd.DateOffset(days=30)),
    ("14d", pd.DateOffset(days=14)),
)


@dataclass(frozen=True)
class Candidate:
    family: str
    bar: str
    params: dict[str, float | int | str]

    @property
    def name(self) -> str:
        body = "-".join(f"{key}{value:g}" if isinstance(value, float) else f"{key}{value}" for key, value in self.params.items())
        return f"{self.family}-{self.bar}-{body}"


def load_frame(bar: str) -> pd.DataFrame:
    frame = pd.read_csv(DATA_DIR / SYMBOL / f"{bar}.csv")
    frame["dt"] = pd.to_datetime(frame["ts"], unit="ms", utc=True)
    return frame.sort_values("ts").drop_duplicates("ts", keep="last").reset_index(drop=True)


def load_cache_summary(bars: list[str]) -> pd.DataFrame:
    rows = []
    for bar in bars:
        meta_path = DATA_DIR / SYMBOL / f"{bar}.meta.json"
        meta = json.loads(meta_path.read_text(encoding="utf-8"))
        rows.append(
            {
                "bar": bar,
                "rows": int(meta["rows"]),
                "first_time": pd.to_datetime(int(meta["first_ts"]), unit="ms", utc=True).strftime("%Y-%m-%d %H:%M"),
                "last_time": pd.to_datetime(int(meta["last_ts"]), unit="ms", utc=True).strftime("%Y-%m-%d %H:%M"),
                "history_exhausted": bool(meta["history_exhausted"]),
            }
        )
    return pd.DataFrame(rows)


def rsi(close: pd.Series, length: int) -> pd.Series:
    delta = close.diff()
    gain = delta.clip(lower=0.0).rolling(length).mean()
    loss = (-delta.clip(upper=0.0)).rolling(length).mean()
    return 100.0 - 100.0 / (1.0 + gain / loss)


def build_candidates(bars: list[str]) -> list[Candidate]:
    candidates: list[Candidate] = []
    for bar in bars:
        for window in (48, 96):
            base = {"window": window, "entry_z": 1.5, "exit_z": 0.20, "stop": 0.006, "take": 0.009, "hold": 12}
            candidates.append(Candidate("vwap_bidir", bar, base))
            candidates.append(Candidate("vwap_short", bar, base))
        for trend in (96, 192):
            candidates.append(
                Candidate(
                    "rsi_short",
                    bar,
                    {"trend": trend, "entry": 90, "exit": 45, "stop": 0.0075, "take": 0.010, "hold": 12},
                )
            )
            candidates.append(
                Candidate(
                    "rsi_bidir",
                    bar,
                    {"trend": trend, "entry": 10, "exit": 55, "stop": 0.0075, "take": 0.010, "hold": 12},
                )
            )
        for lookback in (48, 96):
            candidates.append(
                Candidate(
                    "breakdown_short",
                    bar,
                    {"lookback": lookback, "stop": 0.006, "take": 0.012, "hold": 12},
                )
            )
    return candidates


def signal_columns(frame: pd.DataFrame, candidate: Candidate) -> tuple[pd.Series, pd.Series]:
    close = frame["close"]
    params = candidate.params
    if candidate.family in ("vwap_bidir", "vwap_short"):
        window = int(params["window"])
        volume = frame["volume"]
        vwap = (close * volume).rolling(window).sum() / volume.rolling(window).sum()
        stdev = close.rolling(window).std(ddof=0)
        zscore = (close - vwap) / stdev
        entry = pd.Series("", index=frame.index, dtype=object)
        entry.loc[zscore >= float(params["entry_z"])] = "short"
        if candidate.family == "vwap_bidir":
            entry.loc[zscore <= -float(params["entry_z"])] = "long"
        return entry, zscore.abs() <= float(params["exit_z"])
    if candidate.family in ("rsi_short", "rsi_bidir"):
        trend = close.rolling(int(params["trend"])).mean()
        value = rsi(close, 2)
        entry = pd.Series("", index=frame.index, dtype=object)
        entry.loc[(close < trend) & (value >= float(params["entry"]))] = "short"
        if candidate.family == "rsi_bidir":
            entry.loc[(close > trend) & (value <= float(params["entry"]))] = "long"
        return entry, (value <= 100.0 - float(params["exit"])) | (value >= float(params["exit"]))
    lookback = int(params["lookback"])
    prior_low = frame["low"].shift(1).rolling(lookback).min()
    entry = pd.Series("", index=frame.index, dtype=object)
    entry.loc[close < prior_low] = "short"
    return entry, pd.Series(False, index=frame.index)


def close_return(side: str, entry: float, exit_price: float) -> float:
    price_return = exit_price / entry - 1.0 if side == "long" else entry / exit_price - 1.0
    return LEVERAGE * price_return - LEVERAGE * TAKER_FEE * (1.0 + exit_price / entry)


def mark_return(side: str, entry: float, close: float) -> float:
    price_return = close / entry - 1.0 if side == "long" else entry / close - 1.0
    return LEVERAGE * price_return - LEVERAGE * TAKER_FEE


def backtest(frame: pd.DataFrame, candidate: Candidate) -> tuple[pd.Series, pd.DataFrame]:
    entry_signal, exit_signal = signal_columns(frame, candidate)
    warmup = max(int(value) for key, value in candidate.params.items() if key in {"window", "trend", "lookback"}) + 2
    equity = INITIAL_EQUITY
    position: dict[str, object] | None = None
    pending_entry = ""
    pending_exit = False
    curve: list[tuple[pd.Timestamp, float]] = []
    trades: list[dict[str, object]] = []

    rows = list(frame.itertuples(index=False))
    for index in range(warmup, len(rows)):
        candle = rows[index]
        if pending_exit and position is not None:
            net = close_return(str(position["side"]), float(position["entry"]), float(candle.open))
            equity *= 1.0 + net
            trades.append({"entry_time": position["entry_time"], "exit_time": candle.dt, "side": position["side"], "return": net})
            position = None
            pending_exit = False
        if pending_entry and position is None and equity > 0.0:
            position = {"side": pending_entry, "entry": float(candle.open), "entry_index": index, "entry_time": candle.dt}
            pending_entry = ""

        mark = equity
        if position is not None:
            side = str(position["side"])
            entry = float(position["entry"])
            stop = float(candidate.params["stop"])
            take = float(candidate.params["take"])
            stop_price = entry * (1.0 - stop if side == "long" else 1.0 + stop)
            take_price = entry * (1.0 + take if side == "long" else 1.0 - take)
            stop_hit = candle.low <= stop_price if side == "long" else candle.high >= stop_price
            take_hit = candle.high >= take_price if side == "long" else candle.low <= take_price
            if stop_hit or take_hit:
                exit_price = stop_price if stop_hit else take_price
                net = close_return(side, entry, exit_price)
                equity *= 1.0 + net
                trades.append({"entry_time": position["entry_time"], "exit_time": candle.dt, "side": side, "return": net})
                position = None
                mark = equity
            else:
                mark = equity * (1.0 + mark_return(side, entry, float(candle.close)))

        curve.append((candle.dt, mark))
        if index == len(rows) - 1 or equity <= 0.0:
            continue
        next_entry = str(entry_signal.iloc[index])
        if position is not None:
            reverse = bool(next_entry) and next_entry != position["side"]
            stale = index - int(position["entry_index"]) >= int(candidate.params["hold"])
            if bool(exit_signal.iloc[index]) or reverse or stale:
                pending_exit = True
                pending_entry = next_entry if reverse else ""
        elif next_entry:
            pending_entry = next_entry

    if position is not None:
        final = rows[-1]
        net = close_return(str(position["side"]), float(position["entry"]), float(final.close))
        equity *= 1.0 + net
        trades.append({"entry_time": position["entry_time"], "exit_time": final.dt, "side": position["side"], "return": net})
        curve.append((final.dt, equity))

    return pd.Series(dict(curve)).sort_index(), pd.DataFrame(trades)


def scoped(equity: pd.Series, trades: pd.DataFrame, offset: pd.DateOffset | None) -> tuple[pd.Series, pd.DataFrame]:
    if offset is None:
        return equity, trades
    start = equity.index[-1] - offset
    scoped_equity = equity[equity.index >= start]
    scoped_trades = trades[trades["entry_time"] >= scoped_equity.index[0]] if len(trades) else trades
    return scoped_equity, scoped_trades


def metrics(equity: pd.Series, trades: pd.DataFrame) -> dict[str, float | int]:
    total = float(equity.iloc[-1] / equity.iloc[0] - 1.0)
    years = (equity.index[-1] - equity.index[0]).total_seconds() / 31_536_000
    annual = (1.0 + total) ** (1.0 / years) - 1.0 if total > -1.0 and years > 0 else 0.0
    drawdown = float(((equity.cummax() - equity) / equity.cummax()).max())
    returns = trades["return"] if len(trades) else pd.Series(dtype=float)
    wins = returns[returns > 0.0]
    losses = returns[returns < 0.0]
    return {
        "total_return": total,
        "annualized_return": annual,
        "max_drawdown": drawdown,
        "calmar": annual / drawdown if drawdown else 0.0,
        "trades": int(len(trades)),
        "short_trades": int((trades["side"] == "short").sum()) if len(trades) else 0,
        "long_trades": int((trades["side"] == "long").sum()) if len(trades) else 0,
        "profit_factor": float(wins.sum() / abs(losses.sum())) if len(losses) else (999.0 if len(wins) else 0.0),
        "win_rate": float(len(wins) / len(returns)) if len(returns) else 0.0,
    }


def summarize(candidate: Candidate, equity: pd.Series, trades: pd.DataFrame) -> dict[str, object]:
    row: dict[str, object] = {
        "symbol": SYMBOL,
        "bar": candidate.bar,
        "family": candidate.family,
        "name": candidate.name,
        "params_json": json.dumps(candidate.params, separators=(",", ":")),
        "first_time": equity.index[0].strftime("%Y-%m-%d %H:%M"),
        "last_time": equity.index[-1].strftime("%Y-%m-%d %H:%M"),
    }
    for label, offset in HORIZONS:
        part_equity, part_trades = scoped(equity, trades, offset)
        for key, value in metrics(part_equity, part_trades).items():
            row[f"{label}_{key}"] = value
    row["recent_trigger_score"] = int(row["3m_trades"]) + int(row["30d_trades"]) * 2 + int(row["14d_trades"]) * 4
    observe = (
        int(row["3m_trades"]) >= 12
        and int(row["30d_trades"]) >= 4
        and int(row["14d_trades"]) >= 1
        and float(row["full_total_return"]) > 0.0
        and float(row["3y_total_return"]) > 0.0
        and float(row["1y_total_return"]) > 0.0
        and float(row["3y_max_drawdown"]) <= 0.35
        and float(row["1y_max_drawdown"]) <= 0.25
        and float(row["1y_profit_factor"]) >= 1.05
    )
    row["readonly_observe"] = "yes" if observe else "no"
    return row


def horizon_summary(totals: pd.DataFrame) -> pd.DataFrame:
    rows = []
    for label, _ in HORIZONS:
        returns = totals[f"{label}_total_return"]
        drawdowns = totals[f"{label}_max_drawdown"]
        trades = totals[f"{label}_trades"]
        best_index = returns.idxmax()
        rows.append(
            {
                "horizon": label,
                "positive_candidates": int((returns > 0.0).sum()),
                "non_disaster_candidates": int(((returns > -0.50) & (drawdowns < 0.60)).sum()),
                "best_total_return": float(returns.max()),
                "median_total_return": float(returns.median()),
                "worst_total_return": float(returns.min()),
                "median_max_drawdown": float(drawdowns.median()),
                "max_trades": int(trades.max()),
                "best_name": str(totals.loc[best_index, "name"]),
            }
        )
    return pd.DataFrame(rows)


def markdown_table(frame: pd.DataFrame) -> str:
    def cell(value: object) -> str:
        if isinstance(value, float):
            return f"{value:.4f}"
        return str(value).replace("|", "\\|")

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


def write_report(totals: pd.DataFrame, summary: pd.DataFrame, cache: pd.DataFrame, paths: list[Path], command: str) -> str:
    selected = totals[totals["readonly_observe"] == "yes"].head(12)
    least_bad = totals.sort_values(
        ["full_total_return", "3y_total_return", "1y_total_return", "6m_total_return", "3m_total_return"],
        ascending=[False, False, False, False, False],
    ).head(12)
    recent = totals.sort_values(["recent_trigger_score", "3y_calmar", "1y_calmar"], ascending=[False, False, False]).head(12)
    cols = [
        "family",
        "bar",
        "name",
        "full_total_return",
        "full_max_drawdown",
        "full_trades",
        "3y_total_return",
        "1y_total_return",
        "6m_total_return",
        "3m_total_return",
        "30d_total_return",
        "14d_total_return",
        "3m_trades",
        "30d_trades",
        "14d_trades",
        "readonly_observe",
    ]
    observation = (
        "No read-only observation candidates passed the rule."
        if not len(selected)
        else "At least one read-only observation candidate passed the rule."
    )
    disaster = (
        "Long-term status: still disastrous. The full, 3y, and 1y windows have zero positive candidates under the original high-frequency short/bidir candidate set."
        if all(int(summary.loc[summary["horizon"] == label, "positive_candidates"].iloc[0]) == 0 for label in ("full", "3y", "1y"))
        else "Long-term status: not uniformly disastrous; at least one of full/3y/1y has a positive candidate."
    )
    return "\n".join(
        [
            "# ETH high-frequency short/bidirectional revalidation",
            "",
            f"Run command: `{command}`",
            "Scope: offline only; local refreshed 3m/5m/15m OKX ETH candle cache; no live executor, deployment, private API, or order path touched.",
            f"Cost model: taker fee `{TAKER_FEE}` each side on `{LEVERAGE:g}x` notional; entries execute on next open.",
            "",
            "Output files:",
            *[f"- `{path}`" for path in paths],
            "",
            "Cache used:",
            "",
            markdown_table(cache),
            "",
            "Windows compared: full, 3y, 1y, 6m, 3m, 30d, 14d.",
            "Read-only observation rule: 3m >= 12 trades, 30d >= 4 trades, 14d >= 1 trade, positive full/3y/1y return, 3y MDD <= 35%, 1y MDD <= 25%, and 1y profit factor >= 1.05.",
            "",
            f"Conclusion: {disaster} {observation}",
            "",
            "## Horizon Summary",
            "",
            markdown_table(summary),
            "",
            "## Read-only Observation Candidates",
            "",
            markdown_table(selected[cols]) if len(selected) else "No candidates passed the read-only observation rule.",
            "",
            "## Least-bad Long-term Rows",
            "",
            markdown_table(least_bad[cols]),
            "",
            "## Most Recently Active Rows",
            "",
            markdown_table(recent[cols]),
        ]
    ) + "\n"


def main() -> int:
    parser = argparse.ArgumentParser()
    parser.add_argument("--bars", nargs="+", default=["3m", "5m", "15m"])
    parser.add_argument("--output-dir", type=Path, default=OUT_DIR)
    args = parser.parse_args()

    rows: list[dict[str, object]] = []
    frames = {bar: load_frame(bar) for bar in args.bars}
    for candidate in build_candidates(args.bars):
        equity, trades = backtest(frames[candidate.bar], candidate)
        if len(equity) >= 2:
            rows.append(summarize(candidate, equity, trades))

    totals = pd.DataFrame(rows).sort_values(
        ["readonly_observe", "full_total_return", "3y_total_return", "1y_total_return", "recent_trigger_score"],
        ascending=[False, False, False, False, False],
    )
    summary = horizon_summary(totals)
    cache = load_cache_summary(args.bars)
    args.output_dir.mkdir(parents=True, exist_ok=True)
    totals_path = args.output_dir / "eth-highfreq-short-bidir-revalidation-candidates.csv"
    summary_path = args.output_dir / "eth-highfreq-short-bidir-revalidation-summary.csv"
    report_path = args.output_dir / "eth-highfreq-short-bidir-revalidation-report.md"
    paths = [totals_path, summary_path, report_path]
    totals.to_csv(totals_path, index=False)
    summary.to_csv(summary_path, index=False)
    command = f"rtk .venv/bin/python {Path(__file__).as_posix()} --bars {' '.join(args.bars)}"
    report_path.write_text(write_report(totals, summary, cache, paths, command), encoding="utf-8")
    print(summary.to_string(index=False))
    return 0


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