from __future__ import annotations

import argparse
from dataclasses import dataclass
from pathlib import Path

import pandas as pd

from okx_codex_trader.candles import align_candles_by_ts, load_candles_csv
from okx_codex_trader.models import Candle
from okx_codex_trader.research_metrics import (
    DEFAULT_COSTS,
    DEFAULT_INITIAL_EQUITY,
    DEFAULT_PRIMARY_COST,
    cost_equity_frame,
    equity_metrics,
    format_utc_ts,
    trade_stats,
)
from okx_codex_trader.sampled_report import SegmentResult, mark_to_market, trade_equity
from okx_codex_trader.time_rules import entry_allowed, is_us_open_window


ETH_SYMBOL = "ETH-USDT-SWAP"
BTC_SYMBOL = "BTC-USDT-SWAP"
DATA_DIR = Path("data/okx-candles")
OUTPUT_DIR = Path("reports/eth-exploration")
INITIAL_EQUITY = DEFAULT_INITIAL_EQUITY
PRIMARY_COST = DEFAULT_PRIMARY_COST
COSTS = DEFAULT_COSTS
LEVERAGE = 3

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)),
    ("21d", pd.DateOffset(days=21)),
)


@dataclass(frozen=True)
class Position:
    side: str
    entry_ts: int
    entry_price: float
    margin_used: float
    stop_price: float
    take_price: float
    mfe_pct: float


def fmt(ts: int) -> str:
    return format_utc_ts(ts)


def close_trade(trades: list[dict[str, object]], position: Position, candle: Candle, exit_price: float, reason: str) -> tuple[float, bool]:
    exit_equity = trade_equity(
        side=position.side,
        margin_used=position.margin_used,
        entry_price=position.entry_price,
        exit_price=exit_price,
        leverage=LEVERAGE,
    )
    pnl = exit_equity - position.margin_used
    trades.append(
        {
            "side": "Long" if position.side == "long" else "Short",
            "entry_time": fmt(position.entry_ts),
            "exit_time": fmt(candle.ts),
            "exit_ts": candle.ts,
            "entry_price": round(position.entry_price, 4),
            "exit_price": round(exit_price, 4),
            "pnl": round(pnl, 4),
            "return_pct": round(pnl / position.margin_used * 100.0, 4),
            "cost_weight": 1.0,
            "exit_reason": reason,
            "mfe_pct": round(position.mfe_pct * 100.0, 4),
        }
    )
    return exit_equity, pnl > 0.0


def favorable_move(side: str, entry_price: float, candle: Candle) -> float:
    if side == "long":
        return candle.high / entry_price - 1.0
    return entry_price / candle.low - 1.0


def risk_exit(position: Position, candle: Candle, use_breakeven: bool) -> tuple[float, str] | None:
    stop = position.stop_price
    reason = "stop"
    if use_breakeven and position.mfe_pct >= 0.008:
        breakeven = position.entry_price * (1.001 if position.side == "long" else 0.999)
        stop = max(stop, breakeven) if position.side == "long" else min(stop, breakeven)
        reason = "breakeven"
    if position.side == "long":
        if candle.open <= stop:
            return candle.open, f"{reason}_gap" if reason != "stop" else "stop_gap"
        if candle.open >= position.take_price:
            return candle.open, "take_gap"
        if candle.low <= stop:
            return stop, reason
        if candle.high >= position.take_price:
            return position.take_price, "take_profit"
    else:
        if candle.open >= stop:
            return candle.open, f"{reason}_gap" if reason != "stop" else "stop_gap"
        if candle.open <= position.take_price:
            return candle.open, "take_gap"
        if candle.high >= stop:
            return stop, reason
        if candle.low <= position.take_price:
            return position.take_price, "take_profit"
    return None


def build_15m_signals(eth: list[Candle], btc: list[Candle]) -> pd.DataFrame:
    eth_close = pd.Series([c.close for c in eth], dtype=float)
    btc_close = pd.Series([c.close for c in btc], dtype=float)
    middle = eth_close.rolling(96).mean()
    stdev = eth_close.rolling(96).std(ddof=0)
    upper = middle + 2.0 * stdev
    lower = middle - 2.0 * stdev
    bandwidth = (upper - lower) / middle
    threshold = bandwidth.rolling(960).quantile(0.25)
    btc_sma = btc_close.rolling(480).mean()
    eth_vol = eth_close.pct_change().rolling(96).std(ddof=0)
    return pd.DataFrame(
        {
            "ts": [c.ts for c in eth],
            "middle": middle,
            "upper": upper,
            "lower": lower,
            "bandwidth": bandwidth,
            "threshold": threshold,
            "btc_close": btc_close,
            "btc_sma": btc_sma,
            "eth_vol": eth_vol,
        }
    )


def run_15m_baseline(eth: list[Candle], btc: list[Candle]) -> SegmentResult:
    indicators = build_15m_signals(eth, btc)
    equity = INITIAL_EQUITY
    peak = equity
    max_dd = 0.0
    wins = 0
    trades: list[dict[str, object]] = []
    equity_curve: list[dict[str, float | int]] = []
    position: Position | None = None
    pending_side: str | None = None
    pending_middle_exit = False
    cooldown_until = -1
    warmup = 960

    for index in range(warmup, len(eth)):
        candle = eth[index]
        if pending_middle_exit and position is not None:
            equity, won = close_trade(trades, position, candle, candle.open, "signal_middle")
            wins += int(won)
            position = None
            pending_middle_exit = False
            cooldown_until = index + 24
        if pending_side is not None and position is None:
            entry = candle.open
            position = Position(
                side=pending_side,
                entry_ts=candle.ts,
                entry_price=entry,
                margin_used=equity,
                stop_price=entry * (0.99 if pending_side == "long" else 1.01),
                take_price=entry * (1.03 if pending_side == "long" else 0.97),
                mfe_pct=0.0,
            )
            pending_side = None

        current = equity
        if position is not None:
            out = risk_exit(position, candle, True)
            if out is not None:
                price, reason = out
                equity, won = close_trade(trades, position, candle, price, reason)
                wins += int(won)
                current = equity
                position = None
                cooldown_until = index + 24
        if position is not None:
            position = Position(**{**position.__dict__, "mfe_pct": max(position.mfe_pct, favorable_move(position.side, position.entry_price, candle))})
            current = mark_to_market(
                side=position.side,
                margin_used=position.margin_used,
                entry_price=position.entry_price,
                mark_price=candle.close,
                leverage=LEVERAGE,
            )
        peak = max(peak, current)
        max_dd = max(max_dd, (peak - current) / peak)
        equity_curve.append({"ts": candle.ts, "equity": current, "close": candle.close})
        if index == len(eth) - 1:
            continue

        row = indicators.iloc[index]
        if any(value != value for value in (row.middle, row.upper, row.lower, row.bandwidth, row.threshold, row.btc_sma, row.eth_vol)):
            continue
        if position is not None:
            middle_exit = (position.side == "long" and candle.close < row.middle * 0.999) or (position.side == "short" and candle.close > row.middle * 1.001)
            if middle_exit and not is_us_open_window(candle.ts):
                pending_middle_exit = True
            continue
        if index < cooldown_until or row.eth_vol > 0.006 or not entry_allowed(candle.ts, "weekday") or not row.btc_close > row.btc_sma:
            continue
        if row.bandwidth <= row.threshold:
            if candle.close > row.upper:
                pending_side = "long"
            elif candle.close < row.lower:
                pending_side = "short"

    return SegmentResult(len(trades), (equity_curve[-1]["equity"] - INITIAL_EQUITY) / INITIAL_EQUITY, wins / len(trades) if trades else 0.0, max_dd, trades, position.__dict__ if position else None, eth[warmup:], equity_curve, [], [])


def run_3m_execution(eth15: list[Candle], btc15: list[Candle], eth3: list[Candle]) -> SegmentResult:
    indicators = build_15m_signals(eth15, btc15)
    three_by_window: dict[int, list[Candle]] = {}
    for candle in eth3:
        parent_ts = candle.ts - (candle.ts % 900_000)
        three_by_window.setdefault(parent_ts, []).append(candle)

    equity = INITIAL_EQUITY
    peak = equity
    max_dd = 0.0
    wins = 0
    trades: list[dict[str, object]] = []
    equity_curve: list[dict[str, float | int]] = []
    position: Position | None = None
    pending_side: str | None = None
    pending_middle_exit = False
    cooldown_until = -1
    warmup = 960

    for index in range(warmup, len(eth15)):
        candle15 = eth15[index]
        subcandles = [c for c in three_by_window.get(candle15.ts, []) if candle15.ts <= c.ts < candle15.ts + 900_000]
        if not subcandles:
            subcandles = [candle15]
        if pending_middle_exit and position is not None:
            equity, won = close_trade(trades, position, subcandles[0], subcandles[0].open, "signal_middle")
            wins += int(won)
            position = None
            pending_middle_exit = False
            cooldown_until = index + 24
        if pending_side is not None and position is None:
            entry = subcandles[0].open
            position = Position(
                side=pending_side,
                entry_ts=subcandles[0].ts,
                entry_price=entry,
                margin_used=equity,
                stop_price=entry * (0.99 if pending_side == "long" else 1.01),
                take_price=entry * (1.03 if pending_side == "long" else 0.97),
                mfe_pct=0.0,
            )
            pending_side = None

        current = equity
        if position is not None:
            for sub in subcandles:
                out = risk_exit(position, sub, True)
                if out is not None:
                    price, reason = out
                    equity, won = close_trade(trades, position, sub, price, f"3m_{reason}")
                    wins += int(won)
                    current = equity
                    position = None
                    cooldown_until = index + 24
                    break
                position = Position(**{**position.__dict__, "mfe_pct": max(position.mfe_pct, favorable_move(position.side, position.entry_price, sub))})
        if position is not None:
            current = mark_to_market(
                side=position.side,
                margin_used=position.margin_used,
                entry_price=position.entry_price,
                mark_price=candle15.close,
                leverage=LEVERAGE,
            )
        peak = max(peak, current)
        max_dd = max(max_dd, (peak - current) / peak)
        equity_curve.append({"ts": candle15.ts, "equity": current, "close": candle15.close})
        if index == len(eth15) - 1:
            continue

        row = indicators.iloc[index]
        if any(value != value for value in (row.middle, row.upper, row.lower, row.bandwidth, row.threshold, row.btc_sma, row.eth_vol)):
            continue
        if position is not None:
            middle_exit = (position.side == "long" and candle15.close < row.middle * 0.999) or (position.side == "short" and candle15.close > row.middle * 1.001)
            if middle_exit and not is_us_open_window(candle15.ts):
                pending_middle_exit = True
            continue
        if index < cooldown_until or row.eth_vol > 0.006 or not entry_allowed(candle15.ts, "weekday") or not row.btc_close > row.btc_sma:
            continue
        if row.bandwidth <= row.threshold:
            if candle15.close > row.upper:
                pending_side = "long"
            elif candle15.close < row.lower:
                pending_side = "short"

    return SegmentResult(len(trades), (equity_curve[-1]["equity"] - INITIAL_EQUITY) / INITIAL_EQUITY, wins / len(trades) if trades else 0.0, max_dd, trades, position.__dict__ if position else None, eth15[warmup:], equity_curve, [], [])


def refined_entry(side: str, subcandles: list[Candle], mode: str) -> tuple[int, float] | None:
    if mode == "confirm_1":
        if len(subcandles) < 2:
            return None
        first = subcandles[0]
        if side == "long" and first.close > first.open:
            return subcandles[1].ts, subcandles[1].open
        if side == "short" and first.close < first.open:
            return subcandles[1].ts, subcandles[1].open
        return None
    pullback_rates = {
        "pullback_0005": 0.0005,
        "pullback_001": 0.001,
        "pullback_002": 0.002,
        "pullback_003": 0.003,
    }
    if mode in pullback_rates:
        anchor = subcandles[0].open
        rate = pullback_rates[mode]
        target = anchor * (1.0 - rate if side == "long" else 1.0 + rate)
        for offset, candle in enumerate(subcandles[:-1]):
            if side == "long" and candle.low <= target:
                return subcandles[offset + 1].ts, subcandles[offset + 1].open
            if side == "short" and candle.high >= target:
                return subcandles[offset + 1].ts, subcandles[offset + 1].open
        return None
    twap_slices = {
        "twap_2x3m": 2,
        "twap_3x3m": 3,
        "twap_4x3m": 4,
        "twap_5x3m": 5,
    }
    if mode in twap_slices:
        if not subcandles:
            return None
        slices = subcandles[: twap_slices[mode]]
        return slices[-1].ts, sum(candle.open for candle in slices) / len(slices)
    raise ValueError("entry mode is invalid")


def run_3m_entry_execution(eth15: list[Candle], btc15: list[Candle], eth3: list[Candle], entry_mode: str) -> SegmentResult:
    indicators = build_15m_signals(eth15, btc15)
    three_by_window: dict[int, list[Candle]] = {}
    for candle in eth3:
        parent_ts = candle.ts - (candle.ts % 900_000)
        three_by_window.setdefault(parent_ts, []).append(candle)

    equity = INITIAL_EQUITY
    peak = equity
    max_dd = 0.0
    wins = 0
    trades: list[dict[str, object]] = []
    equity_curve: list[dict[str, float | int]] = []
    position: Position | None = None
    pending_side: str | None = None
    pending_middle_exit = False
    cooldown_until = -1
    warmup = 960

    for index in range(warmup, len(eth15)):
        candle15 = eth15[index]
        subcandles = [c for c in three_by_window.get(candle15.ts, []) if candle15.ts <= c.ts < candle15.ts + 900_000]
        if not subcandles:
            subcandles = [candle15]
        if pending_middle_exit and position is not None:
            equity, won = close_trade(trades, position, subcandles[0], subcandles[0].open, "signal_middle")
            wins += int(won)
            position = None
            pending_middle_exit = False
            cooldown_until = index + 24
        if pending_side is not None and position is None:
            entry = refined_entry(pending_side, subcandles, entry_mode)
            if entry is not None:
                entry_ts, entry_price = entry
                position = Position(
                    side=pending_side,
                    entry_ts=entry_ts,
                    entry_price=entry_price,
                    margin_used=equity,
                    stop_price=entry_price * (0.99 if pending_side == "long" else 1.01),
                    take_price=entry_price * (1.03 if pending_side == "long" else 0.97),
                    mfe_pct=0.0,
                )
            pending_side = None

        current = equity
        if position is not None:
            for sub in subcandles:
                if sub.ts < position.entry_ts:
                    continue
                out = risk_exit(position, sub, True)
                if out is not None:
                    price, reason = out
                    equity, won = close_trade(trades, position, sub, price, f"3m_{reason}")
                    wins += int(won)
                    current = equity
                    position = None
                    cooldown_until = index + 24
                    break
                position = Position(**{**position.__dict__, "mfe_pct": max(position.mfe_pct, favorable_move(position.side, position.entry_price, sub))})
        if position is not None:
            current = mark_to_market(
                side=position.side,
                margin_used=position.margin_used,
                entry_price=position.entry_price,
                mark_price=candle15.close,
                leverage=LEVERAGE,
            )
        peak = max(peak, current)
        max_dd = max(max_dd, (peak - current) / peak)
        equity_curve.append({"ts": candle15.ts, "equity": current, "close": candle15.close})
        if index == len(eth15) - 1:
            continue

        row = indicators.iloc[index]
        if any(value != value for value in (row.middle, row.upper, row.lower, row.bandwidth, row.threshold, row.btc_sma, row.eth_vol)):
            continue
        if position is not None:
            middle_exit = (position.side == "long" and candle15.close < row.middle * 0.999) or (position.side == "short" and candle15.close > row.middle * 1.001)
            if middle_exit and not is_us_open_window(candle15.ts):
                pending_middle_exit = True
            continue
        if index < cooldown_until or row.eth_vol > 0.006 or not entry_allowed(candle15.ts, "weekday") or not row.btc_close > row.btc_sma:
            continue
        if row.bandwidth <= row.threshold:
            if candle15.close > row.upper:
                pending_side = "long"
            elif candle15.close < row.lower:
                pending_side = "short"

    return SegmentResult(len(trades), (equity_curve[-1]["equity"] - INITIAL_EQUITY) / INITIAL_EQUITY, wins / len(trades) if trades else 0.0, max_dd, trades, position.__dict__ if position else None, eth15[warmup:], equity_curve, [], [])


def scoped_trades(trades: list[dict[str, object]], start: pd.Timestamp, end_ts: int) -> list[dict[str, object]]:
    return [trade for trade in trades if start < pd.to_datetime(str(trade["exit_time"]), utc=True) <= pd.to_datetime(end_ts, unit="ms", utc=True)]


def write_outputs(results: dict[str, SegmentResult], last_ts: int, output_dir: Path) -> None:
    rows: list[dict[str, object]] = []
    reason_rows: list[dict[str, object]] = []
    trade_rows: list[dict[str, object]] = []
    for name, result in results.items():
        for trade in result.trades:
            trade_rows.append({"name": name, **trade})
        for cost_name, cost in COSTS:
            frame = cost_equity_frame(result, cost)
            for label, offset in HORIZONS:
                if offset is None:
                    scoped = frame[["ts", "equity"]].copy()
                    start = pd.Timestamp(scoped["ts"].iloc[0])
                else:
                    end = pd.to_datetime(last_ts, unit="ms", utc=True)
                    start = end - offset
                    before = frame[frame["ts"] <= start]
                    start_equity = float(before["equity"].iloc[-1]) if len(before) else float(frame["equity"].iloc[0])
                    scoped = pd.concat([pd.DataFrame([{"ts": start, "equity": start_equity}]), frame[frame["ts"] > start][["ts", "equity"]]], ignore_index=True)
                trades = scoped_trades(result.trades, start, last_ts)
                rows.append(
                    {
                        "name": name,
                        "cost": cost_name,
                        "horizon": label,
                        "trades": len(trades),
                        "win_rate": sum(1 for trade in trades if float(trade["return_pct"]) > 0.0) / len(trades) if trades else 0.0,
                        **trade_stats(trades),
                        **equity_metrics(scoped, int(start.timestamp() * 1000), last_ts),
                    }
                )
        for reason, group in pd.DataFrame(result.trades).groupby("exit_reason") if result.trades else []:
            reason_rows.append({"name": name, "exit_reason": reason, "trades": len(group), **trade_stats(group.to_dict("records"))})

    output_dir.mkdir(parents=True, exist_ok=True)
    pd.DataFrame(rows).to_csv(output_dir / "bb-squeeze-15m-signal-3m-execution-horizons.csv", index=False)
    pd.DataFrame(reason_rows).to_csv(output_dir / "bb-squeeze-15m-signal-3m-execution-exits.csv", index=False)
    pd.DataFrame(trade_rows).to_csv(output_dir / "bb-squeeze-15m-signal-3m-execution-trades.csv", index=False)
    primary = pd.DataFrame(rows)
    report = "# BB squeeze 15m signal plus 3m execution test\n\n"
    report += markdown_table(primary[(primary["cost"] == PRIMARY_COST) & (primary["horizon"].isin(["full", "1y", "6m", "3m", "30d", "21d"]))])
    report += "\n"
    (output_dir / "bb-squeeze-15m-signal-3m-execution-report.md").write_text(report, encoding="utf-8")


def markdown_table(frame: pd.DataFrame) -> str:
    columns = list(frame.columns)
    lines = [
        "| " + " | ".join(columns) + " |",
        "| " + " | ".join(["---"] * len(columns)) + " |",
    ]
    for row in frame.itertuples(index=False):
        lines.append("| " + " | ".join(str(value) for value in row) + " |")
    return "\n".join(lines)


def main() -> int:
    parser = argparse.ArgumentParser()
    parser.add_argument("--years", type=float, default=10.0)
    parser.add_argument("--output-dir", type=Path, default=OUTPUT_DIR)
    args = parser.parse_args()

    eth15 = load_candles_csv(DATA_DIR, ETH_SYMBOL, "15m")
    btc15 = load_candles_csv(DATA_DIR, BTC_SYMBOL, "15m")
    eth15, btc15 = align_candles_by_ts(eth15, btc15)
    eth3 = load_candles_csv(DATA_DIR, ETH_SYMBOL, "3m")
    requested = min(int(args.years * 365 * 24 * 60 / 15), len(eth15))
    first_ts = eth15[-requested].ts
    eth15 = [c for c in eth15 if c.ts >= first_ts]
    btc15 = [c for c in btc15 if c.ts >= first_ts]
    eth3 = [c for c in eth3 if first_ts <= c.ts <= eth15[-1].ts + 900_000]
    results = {
        "15m_baseline": run_15m_baseline(eth15, btc15),
        "15m_signal_3m_risk": run_3m_execution(eth15, btc15, eth3),
        "15m_signal_3m_confirm_1": run_3m_entry_execution(eth15, btc15, eth3, "confirm_1"),
        "15m_signal_3m_pullback_0005": run_3m_entry_execution(eth15, btc15, eth3, "pullback_0005"),
        "15m_signal_3m_pullback_001": run_3m_entry_execution(eth15, btc15, eth3, "pullback_001"),
        "15m_signal_3m_pullback_002": run_3m_entry_execution(eth15, btc15, eth3, "pullback_002"),
        "15m_signal_3m_pullback_003": run_3m_entry_execution(eth15, btc15, eth3, "pullback_003"),
        "15m_signal_3m_twap_2x3m": run_3m_entry_execution(eth15, btc15, eth3, "twap_2x3m"),
        "15m_signal_3m_twap_3x3m": run_3m_entry_execution(eth15, btc15, eth3, "twap_3x3m"),
        "15m_signal_3m_twap_4x3m": run_3m_entry_execution(eth15, btc15, eth3, "twap_4x3m"),
        "15m_signal_3m_twap_5x3m": run_3m_entry_execution(eth15, btc15, eth3, "twap_5x3m"),
    }
    write_outputs(results, eth15[-1].ts, args.output_dir)
    print((args.output_dir / "bb-squeeze-15m-signal-3m-execution-report.md").as_posix())
    return 0


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