okx-codex-trader/scripts/explore_ultrashort.py

from __future__ import annotations

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

import pandas as pd

from okx_codex_trader.bbmr_report import BBMRConfig, run_bbmr_segment
from okx_codex_trader.bbsb_report import BBSBConfig, run_bbsb_segment
from okx_codex_trader.donchian_report import DonchianConfig, run_donchian_segment
from okx_codex_trader.ema_pullback_report import EMAPullbackConfig, run_ema_pullback_segment
from okx_codex_trader.models import Candle
from okx_codex_trader.okx_client import OkxClient
from okx_codex_trader.rsi2_report import RSI2Config, _compute_rsi, run_rsi2_segment
from okx_codex_trader.sampled_report import SegmentResult, mark_to_market, sample_segments, trade_equity


SYMBOLS = ("BTC-USDT-SWAP", "ETH-USDT-SWAP")
BARS = ("1m", "3m", "5m")
ANALYSIS_BARS = ("3m", "5m", "15m")
HISTORY_LIMIT = 4000
SEGMENTS = 8
WINDOW_SIZE = 240
LEVERAGE = 3
INITIAL_EQUITY = 10_000.0
ROBUST_HISTORY_LIMIT = 50_000
GROSS_RETURN_NOTE = "Gross-return backtest only: fees, slippage, and funding rates are excluded."
MINUTES_PER_YEAR = 365 * 24 * 60
CANDLE_CACHE_DIR = Path("data/okx-candles")
CANDLE_BAR_MS = {
    "1m": 60_000,
    "3m": 180_000,
    "5m": 300_000,
    "15m": 900_000,
    "30m": 1_800_000,
    "1H": 3_600_000,
    "4H": 14_400_000,
    "1D": 86_400_000,
}


@dataclass(frozen=True)
class Candidate:
    name: str
    warmup_bars: int
    run: object


@dataclass(frozen=True)
class PairCandidate:
    name: str
    warmup_bars: int
    run: object


def _format_ts(ts: int) -> str:
    return pd.to_datetime(ts, unit="ms", utc=True).strftime("%Y-%m-%d %H:%M")


def candle_cache_file(cache_dir: Path, symbol: str, bar: str) -> Path:
    return cache_dir / symbol / f"{bar}.csv"


def candle_cache_meta_file(cache_dir: Path, symbol: str, bar: str) -> Path:
    return cache_dir / symbol / f"{bar}.meta.json"


def load_cached_candles(cache_dir: Path, symbol: str, bar: str) -> tuple[list[Candle], bool]:
    cache_file = candle_cache_file(cache_dir, symbol, bar)
    if not cache_file.exists():
        return [], False
    frame = pd.read_csv(cache_file)
    candles = [
        Candle(
            symbol=symbol,
            ts=int(row.ts),
            open=float(row.open),
            high=float(row.high),
            low=float(row.low),
            close=float(row.close),
            volume=float(row.volume),
        )
        for row in frame.itertuples(index=False)
    ]
    meta_file = candle_cache_meta_file(cache_dir, symbol, bar)
    history_exhausted = False
    if meta_file.exists():
        with meta_file.open("r", encoding="utf-8") as handle:
            history_exhausted = bool(json.load(handle).get("history_exhausted"))
    return candles, history_exhausted


def save_cached_candles(cache_dir: Path, symbol: str, bar: str, candles: list[Candle], history_exhausted: bool) -> None:
    cache_file = candle_cache_file(cache_dir, symbol, bar)
    cache_file.parent.mkdir(parents=True, exist_ok=True)
    frame = pd.DataFrame(
        [
            {
                "ts": candle.ts,
                "open": candle.open,
                "high": candle.high,
                "low": candle.low,
                "close": candle.close,
                "volume": candle.volume,
            }
            for candle in sorted(candles, key=lambda candle: candle.ts)
        ]
    ).drop_duplicates("ts", keep="last")
    frame.to_csv(cache_file, index=False)
    meta = {
        "symbol": symbol,
        "bar": bar,
        "history_exhausted": history_exhausted,
        "rows": len(frame),
        "first_ts": int(frame["ts"].iloc[0]) if len(frame) else None,
        "last_ts": int(frame["ts"].iloc[-1]) if len(frame) else None,
    }
    with candle_cache_meta_file(cache_dir, symbol, bar).open("w", encoding="utf-8") as handle:
        json.dump(meta, handle, separators=(",", ":"))


def latest_bridge_count(cached: list[Candle], latest_last_ts: int, interval: int) -> int:
    bridge_from_ts = max(candle.ts for candle in cached)
    for left, right in zip(cached, cached[1:]):
        if right.ts - left.ts != interval:
            bridge_from_ts = left.ts
    return ((latest_last_ts - bridge_from_ts) // interval) + 1


def get_candles_cached(
    client: OkxClient,
    symbol: str,
    bar: str,
    limit: int,
    cache_dir: Path = CANDLE_CACHE_DIR,
) -> list[Candle]:
    cached, history_exhausted = load_cached_candles(cache_dir, symbol, bar)
    if cached and (len(cached) >= limit or history_exhausted):
        latest = client.get_candles(symbol, bar, min(300, limit))
        interval = CANDLE_BAR_MS[bar]
        if latest:
            latest_last_ts = max(candle.ts for candle in latest)
            needed_latest_count = latest_bridge_count(cached, latest_last_ts, interval)
            if needed_latest_count > len(latest):
                latest = client.get_candles(symbol, bar, needed_latest_count)
        merged = {candle.ts: candle for candle in cached}
        for candle in latest:
            merged[candle.ts] = candle
        candles = sorted(merged.values(), key=lambda candle: candle.ts)
        save_cached_candles(cache_dir, symbol, bar, candles, history_exhausted)
        return candles[-limit:] if len(candles) >= limit else candles

    fetched = client.get_candles(symbol, bar, limit)
    history_exhausted = len(fetched) < limit
    save_cached_candles(cache_dir, symbol, bar, fetched, history_exhausted)
    return fetched


def align_pair_candles(left: list[Candle], right: list[Candle]) -> tuple[list[Candle], list[Candle]]:
    right_by_ts = {candle.ts: candle for candle in right}
    left_aligned: list[Candle] = []
    right_aligned: list[Candle] = []
    for candle in left:
        other = right_by_ts.get(candle.ts)
        if other is None:
            continue
        left_aligned.append(candle)
        right_aligned.append(other)
    return left_aligned, right_aligned


def _trade(
    *,
    trades: list[dict[str, object]],
    exits: list[dict[str, object]],
    position: dict[str, object],
    candle: Candle,
    exit_price: float,
    leverage: int,
) -> tuple[float, bool]:
    exit_equity = trade_equity(
        side=str(position["side"]),
        margin_used=float(position["margin_used"]),
        entry_price=float(position["entry_price"]),
        exit_price=exit_price,
        leverage=leverage,
    )
    trades.append(
        {
            "side": "Long" if position["side"] == "long" else "Short",
            "entry_time": _format_ts(int(position["entry_time"])),
            "exit_time": _format_ts(candle.ts),
            "entry_price": round(float(position["entry_price"]), 4),
            "exit_price": round(exit_price, 4),
            "pnl": round(exit_equity - float(position["margin_used"]), 4),
            "return_pct": round((exit_equity - float(position["margin_used"])) / float(position["margin_used"]) * 100, 4),
        }
    )
    exits.append({"ts": candle.ts, "price": exit_price, "side": position["side"]})
    return exit_equity, exit_equity > float(position["margin_used"])


def run_range_momentum_segment(
    *,
    candles: list[Candle],
    leverage: int,
    warmup_bars: int,
    lookback: int,
    take_profit_pct: float,
    stop_loss_pct: float,
) -> SegmentResult:
    highs = pd.Series([candle.high for candle in candles], dtype=float)
    lows = pd.Series([candle.low for candle in candles], dtype=float)
    entry_high = highs.shift(1).rolling(lookback).max().tolist()
    entry_low = lows.shift(1).rolling(lookback).min().tolist()
    equity = INITIAL_EQUITY
    ending_equity = equity
    peak_equity = equity
    max_drawdown = 0.0
    wins = 0
    trades: list[dict[str, object]] = []
    entries: list[dict[str, object]] = []
    exits: list[dict[str, object]] = []
    equity_curve: list[dict[str, float | int]] = []
    position: dict[str, object] | None = None
    pending_entry_side: str | None = None

    for index in range(warmup_bars, len(candles)):
        candle = candles[index]
        if pending_entry_side is not None and position is None and equity > 0.0:
            position = {
                "side": pending_entry_side,
                "entry_time": candle.ts,
                "entry_price": candle.open,
                "entry_index": index,
                "margin_used": equity,
                "stop_price": candle.open * (1 - stop_loss_pct if pending_entry_side == "long" else 1 + stop_loss_pct),
                "take_profit_price": candle.open * (1 + take_profit_pct if pending_entry_side == "long" else 1 - take_profit_pct),
            }
            entries.append({"ts": candle.ts, "price": candle.open, "side": pending_entry_side})
            pending_entry_side = None

        current_equity = equity
        if position is not None and index > int(position["entry_index"]):
            stop_hit = (
                position["side"] == "long" and candle.low <= float(position["stop_price"])
            ) or (
                position["side"] == "short" and candle.high >= float(position["stop_price"])
            )
            take_hit = (
                position["side"] == "long" and candle.high >= float(position["take_profit_price"])
            ) or (
                position["side"] == "short" and candle.low <= float(position["take_profit_price"])
            )
            if stop_hit or take_hit:
                exit_price = float(position["stop_price"] if stop_hit else position["take_profit_price"])
                equity, won = _trade(
                    trades=trades,
                    exits=exits,
                    position=position,
                    candle=candle,
                    exit_price=exit_price,
                    leverage=leverage,
                )
                wins += 1 if won else 0
                current_equity = equity
                position = None

        if position is not None:
            current_equity = mark_to_market(
                side=str(position["side"]),
                margin_used=float(position["margin_used"]),
                entry_price=float(position["entry_price"]),
                mark_price=candle.close,
                leverage=leverage,
            )

        peak_equity = max(peak_equity, current_equity)
        max_drawdown = max(max_drawdown, (peak_equity - current_equity) / peak_equity)
        equity_curve.append({"ts": candle.ts, "equity": current_equity, "close": candle.close})
        ending_equity = current_equity
        if index == len(candles) - 1 or position is not None or equity <= 0.0:
            continue
        if entry_high[index] == entry_high[index] and candle.close > float(entry_high[index]):
            pending_entry_side = "long"
        elif entry_low[index] == entry_low[index] and candle.close < float(entry_low[index]):
            pending_entry_side = "short"

    trade_count = len(trades)
    return SegmentResult(
        trade_count=trade_count,
        total_return=(ending_equity - INITIAL_EQUITY) / INITIAL_EQUITY,
        win_rate=wins / trade_count if trade_count else 0.0,
        max_drawdown=max_drawdown,
        trades=trades,
        open_position=position,
        candles=candles[warmup_bars:],
        equity_curve=equity_curve,
        entries=entries,
        exits=exits,
    )


def run_vwap_reversion_segment(
    *,
    candles: list[Candle],
    leverage: int,
    warmup_bars: int,
    window: int,
    entry_z: float,
    exit_z: float,
    stop_loss_pct: float,
) -> SegmentResult:
    closes = pd.Series([candle.close for candle in candles], dtype=float)
    volumes = pd.Series([candle.volume for candle in candles], dtype=float)
    vwap = (closes * volumes).rolling(window).sum() / volumes.rolling(window).sum()
    deviation = ((closes - vwap) / vwap).tolist()
    stdev = pd.Series(deviation, dtype=float).rolling(window).std(ddof=0).tolist()
    zscore = [
        float("nan") if dev != dev or std != std or std == 0.0 else dev / std
        for dev, std in zip(deviation, stdev)
    ]
    equity = INITIAL_EQUITY
    ending_equity = equity
    peak_equity = equity
    max_drawdown = 0.0
    wins = 0
    trades: list[dict[str, object]] = []
    entries: list[dict[str, object]] = []
    exits: list[dict[str, object]] = []
    equity_curve: list[dict[str, float | int]] = []
    position: dict[str, object] | None = None
    pending_entry_side: str | None = None
    pending_exit = False

    for index in range(warmup_bars, len(candles)):
        candle = candles[index]
        if pending_exit and position is not None:
            equity, won = _trade(
                trades=trades,
                exits=exits,
                position=position,
                candle=candle,
                exit_price=candle.open,
                leverage=leverage,
            )
            wins += 1 if won else 0
            position = None
            pending_exit = False
        if pending_entry_side is not None and position is None and equity > 0.0:
            position = {
                "side": pending_entry_side,
                "entry_time": candle.ts,
                "entry_price": candle.open,
                "margin_used": equity,
                "stop_price": candle.open * (1 - stop_loss_pct if pending_entry_side == "long" else 1 + stop_loss_pct),
            }
            entries.append({"ts": candle.ts, "price": candle.open, "side": pending_entry_side})
            pending_entry_side = None

        current_equity = equity
        if position is not None:
            stop_hit = (
                position["side"] == "long" and candle.low <= float(position["stop_price"])
            ) or (
                position["side"] == "short" and candle.high >= float(position["stop_price"])
            )
            if stop_hit:
                equity, won = _trade(
                    trades=trades,
                    exits=exits,
                    position=position,
                    candle=candle,
                    exit_price=float(position["stop_price"]),
                    leverage=leverage,
                )
                wins += 1 if won else 0
                current_equity = equity
                position = None

        if position is not None:
            current_equity = mark_to_market(
                side=str(position["side"]),
                margin_used=float(position["margin_used"]),
                entry_price=float(position["entry_price"]),
                mark_price=candle.close,
                leverage=leverage,
            )

        peak_equity = max(peak_equity, current_equity)
        max_drawdown = max(max_drawdown, (peak_equity - current_equity) / peak_equity)
        equity_curve.append({"ts": candle.ts, "equity": current_equity, "close": candle.close})
        ending_equity = current_equity
        if index == len(candles) - 1 or equity <= 0.0:
            continue
        current_z = zscore[index]
        if current_z != current_z:
            continue
        if position is not None:
            if (position["side"] == "long" and current_z >= -exit_z) or (
                position["side"] == "short" and current_z <= exit_z
            ):
                pending_exit = True
            continue
        if current_z <= -entry_z:
            pending_entry_side = "long"
        elif current_z >= entry_z:
            pending_entry_side = "short"

    trade_count = len(trades)
    return SegmentResult(
        trade_count=trade_count,
        total_return=(ending_equity - INITIAL_EQUITY) / INITIAL_EQUITY,
        win_rate=wins / trade_count if trade_count else 0.0,
        max_drawdown=max_drawdown,
        trades=trades,
        open_position=position,
        candles=candles[warmup_bars:],
        equity_curve=equity_curve,
        entries=entries,
        exits=exits,
    )


def run_rsi2_side_segment(
    *,
    candles: list[Candle],
    leverage: int,
    warmup_bars: int,
    config: RSI2Config,
    side_mode: str,
) -> SegmentResult:
    result = run_rsi2_segment(
        candles=candles,
        leverage=leverage,
        warmup_bars=warmup_bars,
        config=config,
    )
    if side_mode == "both":
        return result

    closes = pd.Series([candle.close for candle in candles], dtype=float)
    trend = closes.rolling(config.trend_sma).mean().tolist()
    rsi_values = _compute_rsi(closes, config.rsi_length)

    equity = config.initial_equity
    ending_equity = equity
    peak_equity = equity
    max_drawdown = 0.0
    wins = 0
    trades: list[dict[str, object]] = []
    entries: list[dict[str, object]] = []
    exits: list[dict[str, object]] = []
    equity_curve: list[dict[str, float | int]] = []
    position: dict[str, object] | None = None
    pending_entry_side: str | None = None
    pending_exit = False
    allowed_side = "long" if side_mode == "long" else "short"

    for index in range(warmup_bars, len(candles)):
        candle = candles[index]
        if pending_exit and position is not None:
            equity, won = _trade(
                trades=trades,
                exits=exits,
                position=position,
                candle=candle,
                exit_price=candle.open,
                leverage=leverage,
            )
            wins += 1 if won else 0
            position = None
            pending_exit = False
        if pending_entry_side is not None and position is None and equity > 0.0:
            position = {
                "side": pending_entry_side,
                "entry_time": candle.ts,
                "entry_price": candle.open,
                "margin_used": equity,
            }
            entries.append({"ts": candle.ts, "price": candle.open, "side": pending_entry_side})
            pending_entry_side = None

        current_equity = equity
        if position is not None:
            current_equity = mark_to_market(
                side=str(position["side"]),
                margin_used=float(position["margin_used"]),
                entry_price=float(position["entry_price"]),
                mark_price=candle.close,
                leverage=leverage,
            )
        peak_equity = max(peak_equity, current_equity)
        max_drawdown = max(max_drawdown, (peak_equity - current_equity) / peak_equity)
        equity_curve.append({"ts": candle.ts, "equity": current_equity, "close": candle.close})
        ending_equity = current_equity
        if index == len(candles) - 1 or equity <= 0.0:
            continue

        current_rsi = rsi_values[index]
        current_trend = trend[index]
        if current_rsi != current_rsi or current_trend != current_trend:
            continue
        if position is not None:
            if (position["side"] == "long" and current_rsi >= config.exit_rsi) or (
                position["side"] == "short" and current_rsi <= config.exit_rsi
            ):
                pending_exit = True
            continue
        if allowed_side == "long" and candle.close > float(current_trend) and current_rsi <= config.rsi_long_threshold:
            pending_entry_side = "long"
        elif allowed_side == "short" and candle.close < float(current_trend) and current_rsi >= config.rsi_short_threshold:
            pending_entry_side = "short"

    trade_count = len(trades)
    return SegmentResult(
        trade_count=trade_count,
        total_return=(ending_equity - config.initial_equity) / config.initial_equity,
        win_rate=(wins / trade_count) if trade_count else 0.0,
        max_drawdown=max_drawdown,
        trades=trades,
        open_position=position,
        candles=candles[warmup_bars:],
        equity_curve=equity_curve,
        entries=entries,
        exits=exits,
    )


def run_rsi2_long_guarded_segment(
    *,
    candles: list[Candle],
    leverage: int,
    warmup_bars: int,
    trend_sma: int,
    rsi_threshold: float,
    exit_rsi: float,
    stop_loss_pct: float,
    max_hold_bars: int,
) -> SegmentResult:
    closes = pd.Series([candle.close for candle in candles], dtype=float)
    trend = closes.rolling(trend_sma).mean().tolist()
    rsi_values = _compute_rsi(closes, 2)

    equity = INITIAL_EQUITY
    ending_equity = equity
    peak_equity = equity
    max_drawdown = 0.0
    wins = 0
    trades: list[dict[str, object]] = []
    entries: list[dict[str, object]] = []
    exits: list[dict[str, object]] = []
    equity_curve: list[dict[str, float | int]] = []
    position: dict[str, object] | None = None
    pending_entry = False
    pending_exit = False

    for index in range(warmup_bars, len(candles)):
        candle = candles[index]
        if pending_exit and position is not None:
            equity, won = _trade(
                trades=trades,
                exits=exits,
                position=position,
                candle=candle,
                exit_price=candle.open,
                leverage=leverage,
            )
            wins += 1 if won else 0
            position = None
            pending_exit = False
        if pending_entry and position is None and equity > 0.0:
            position = {
                "side": "long",
                "entry_time": candle.ts,
                "entry_price": candle.open,
                "entry_index": index,
                "margin_used": equity,
                "stop_price": candle.open * (1 - stop_loss_pct),
            }
            entries.append({"ts": candle.ts, "price": candle.open, "side": "long"})
            pending_entry = False

        current_equity = equity
        if position is not None and index > int(position["entry_index"]) and candle.low <= float(position["stop_price"]):
            equity, won = _trade(
                trades=trades,
                exits=exits,
                position=position,
                candle=candle,
                exit_price=float(position["stop_price"]),
                leverage=leverage,
            )
            wins += 1 if won else 0
            current_equity = equity
            position = None

        if position is not None:
            current_equity = mark_to_market(
                side="long",
                margin_used=float(position["margin_used"]),
                entry_price=float(position["entry_price"]),
                mark_price=candle.close,
                leverage=leverage,
            )
        peak_equity = max(peak_equity, current_equity)
        max_drawdown = max(max_drawdown, (peak_equity - current_equity) / peak_equity)
        equity_curve.append({"ts": candle.ts, "equity": current_equity, "close": candle.close})
        ending_equity = current_equity
        if index == len(candles) - 1 or equity <= 0.0:
            continue

        current_rsi = rsi_values[index]
        current_trend = trend[index]
        if current_rsi != current_rsi or current_trend != current_trend:
            continue
        if position is not None:
            held_bars = index - int(position["entry_index"])
            if current_rsi >= exit_rsi or held_bars >= max_hold_bars:
                pending_exit = True
            continue
        if candle.close > float(current_trend) and current_rsi <= rsi_threshold:
            pending_entry = True

    trade_count = len(trades)
    return SegmentResult(
        trade_count=trade_count,
        total_return=(ending_equity - INITIAL_EQUITY) / INITIAL_EQUITY,
        win_rate=(wins / trade_count) if trade_count else 0.0,
        max_drawdown=max_drawdown,
        trades=trades,
        open_position=position,
        candles=candles[warmup_bars:],
        equity_curve=equity_curve,
        entries=entries,
        exits=exits,
    )


def run_ma_cross_segment(
    *,
    candles: list[Candle],
    leverage: int,
    warmup_bars: int,
    fast: int,
    slow: int,
    side_mode: str,
) -> SegmentResult:
    closes = pd.Series([candle.close for candle in candles], dtype=float)
    fast_ma = closes.rolling(fast).mean().tolist()
    slow_ma = closes.rolling(slow).mean().tolist()
    equity = INITIAL_EQUITY
    ending_equity = equity
    peak_equity = equity
    max_drawdown = 0.0
    wins = 0
    trades: list[dict[str, object]] = []
    entries: list[dict[str, object]] = []
    exits: list[dict[str, object]] = []
    equity_curve: list[dict[str, float | int]] = []
    position: dict[str, object] | None = None
    pending_entry_side: str | None = None
    pending_exit = False

    for index in range(warmup_bars, len(candles)):
        candle = candles[index]
        if pending_exit and position is not None:
            equity, won = _trade(
                trades=trades,
                exits=exits,
                position=position,
                candle=candle,
                exit_price=candle.open,
                leverage=leverage,
            )
            wins += 1 if won else 0
            position = None
            pending_exit = False
        if pending_entry_side is not None and position is None and equity > 0.0:
            position = {
                "side": pending_entry_side,
                "entry_time": candle.ts,
                "entry_price": candle.open,
                "margin_used": equity,
            }
            entries.append({"ts": candle.ts, "price": candle.open, "side": pending_entry_side})
            pending_entry_side = None

        current_equity = equity
        if position is not None:
            current_equity = mark_to_market(
                side=str(position["side"]),
                margin_used=float(position["margin_used"]),
                entry_price=float(position["entry_price"]),
                mark_price=candle.close,
                leverage=leverage,
            )
        peak_equity = max(peak_equity, current_equity)
        max_drawdown = max(max_drawdown, (peak_equity - current_equity) / peak_equity)
        equity_curve.append({"ts": candle.ts, "equity": current_equity, "close": candle.close})
        ending_equity = current_equity
        if index == len(candles) - 1 or equity <= 0.0:
            continue

        current_fast = fast_ma[index]
        current_slow = slow_ma[index]
        previous_fast = fast_ma[index - 1]
        previous_slow = slow_ma[index - 1]
        if current_fast != current_fast or current_slow != current_slow or previous_fast != previous_fast or previous_slow != previous_slow:
            continue
        crossed_up = previous_fast <= previous_slow and current_fast > current_slow
        crossed_down = previous_fast >= previous_slow and current_fast < current_slow
        if position is not None:
            if (position["side"] == "long" and crossed_down) or (position["side"] == "short" and crossed_up):
                pending_exit = True
            continue
        if crossed_up and side_mode in {"both", "long"}:
            pending_entry_side = "long"
        elif crossed_down and side_mode in {"both", "short"}:
            pending_entry_side = "short"

    trade_count = len(trades)
    return SegmentResult(
        trade_count=trade_count,
        total_return=(ending_equity - INITIAL_EQUITY) / INITIAL_EQUITY,
        win_rate=wins / trade_count if trade_count else 0.0,
        max_drawdown=max_drawdown,
        trades=trades,
        open_position=position,
        candles=candles[warmup_bars:],
        equity_curve=equity_curve,
        entries=entries,
        exits=exits,
    )


def run_trend_rsi_bb_long_segment(
    *,
    candles: list[Candle],
    leverage: int,
    warmup_bars: int,
    trend_sma: int,
    band_length: int,
    std_multiplier: float,
    rsi_threshold: float,
    exit_rsi: float,
    stop_loss_pct: float,
) -> SegmentResult:
    closes = pd.Series([candle.close for candle in candles], dtype=float)
    trend = closes.rolling(trend_sma).mean().tolist()
    middle = closes.rolling(band_length).mean().tolist()
    stdev = closes.rolling(band_length).std(ddof=0).tolist()
    lower = [
        float("nan") if middle_value != middle_value or std_value != std_value else middle_value - std_multiplier * std_value
        for middle_value, std_value in zip(middle, stdev)
    ]
    rsi_values = _compute_rsi(closes, 2)

    equity = INITIAL_EQUITY
    ending_equity = equity
    peak_equity = equity
    max_drawdown = 0.0
    wins = 0
    trades: list[dict[str, object]] = []
    entries: list[dict[str, object]] = []
    exits: list[dict[str, object]] = []
    equity_curve: list[dict[str, float | int]] = []
    position: dict[str, object] | None = None
    pending_entry = False
    pending_exit = False

    for index in range(warmup_bars, len(candles)):
        candle = candles[index]
        if pending_exit and position is not None:
            equity, won = _trade(
                trades=trades,
                exits=exits,
                position=position,
                candle=candle,
                exit_price=candle.open,
                leverage=leverage,
            )
            wins += 1 if won else 0
            position = None
            pending_exit = False
        if pending_entry and position is None and equity > 0.0:
            position = {
                "side": "long",
                "entry_time": candle.ts,
                "entry_price": candle.open,
                "entry_index": index,
                "margin_used": equity,
                "stop_price": candle.open * (1 - stop_loss_pct),
            }
            entries.append({"ts": candle.ts, "price": candle.open, "side": "long"})
            pending_entry = False

        current_equity = equity
        if position is not None and index > int(position["entry_index"]) and candle.low <= float(position["stop_price"]):
            equity, won = _trade(
                trades=trades,
                exits=exits,
                position=position,
                candle=candle,
                exit_price=float(position["stop_price"]),
                leverage=leverage,
            )
            wins += 1 if won else 0
            current_equity = equity
            position = None

        if position is not None:
            current_equity = mark_to_market(
                side="long",
                margin_used=float(position["margin_used"]),
                entry_price=float(position["entry_price"]),
                mark_price=candle.close,
                leverage=leverage,
            )

        peak_equity = max(peak_equity, current_equity)
        max_drawdown = max(max_drawdown, (peak_equity - current_equity) / peak_equity)
        equity_curve.append({"ts": candle.ts, "equity": current_equity, "close": candle.close})
        ending_equity = current_equity
        if index == len(candles) - 1 or equity <= 0.0:
            continue

        current_rsi = rsi_values[index]
        current_trend = trend[index]
        current_middle = middle[index]
        current_lower = lower[index]
        if current_rsi != current_rsi or current_trend != current_trend or current_middle != current_middle or current_lower != current_lower:
            continue
        if position is not None:
            if current_rsi >= exit_rsi or candle.close >= float(current_middle):
                pending_exit = True
            continue
        if candle.close > float(current_trend) and candle.close <= float(current_lower) and current_rsi <= rsi_threshold:
            pending_entry = True

    trade_count = len(trades)
    return SegmentResult(
        trade_count=trade_count,
        total_return=(ending_equity - INITIAL_EQUITY) / INITIAL_EQUITY,
        win_rate=wins / trade_count if trade_count else 0.0,
        max_drawdown=max_drawdown,
        trades=trades,
        open_position=position,
        candles=candles[warmup_bars:],
        equity_curve=equity_curve,
        entries=entries,
        exits=exits,
    )


def run_regime_hybrid_segment(
    *,
    candles: list[Candle],
    leverage: int,
    warmup_bars: int,
    trend_sma: int,
    regime_lookback: int,
    neutral_ma_distance: float,
    rsi_long_threshold: float,
    rsi_exit: float,
    bb_length: int,
    bb_std: float,
    bb_bandwidth_lookback: int,
    stop_loss_pct: float,
) -> SegmentResult:
    closes = pd.Series([candle.close for candle in candles], dtype=float)
    trend = closes.rolling(trend_sma).mean().tolist()
    rsi_values = _compute_rsi(closes, 2)
    middle = closes.rolling(bb_length).mean().tolist()
    stdev = closes.rolling(bb_length).std(ddof=0).tolist()
    upper = [
        float("nan") if middle_value != middle_value or std_value != std_value else middle_value + bb_std * std_value
        for middle_value, std_value in zip(middle, stdev)
    ]
    lower = [
        float("nan") if middle_value != middle_value or std_value != std_value else middle_value - bb_std * std_value
        for middle_value, std_value in zip(middle, stdev)
    ]
    bandwidth = [
        float("nan") if upper_value != upper_value or lower_value != lower_value or middle_value != middle_value or middle_value == 0.0 else (upper_value - lower_value) / middle_value
        for upper_value, lower_value, middle_value in zip(upper, lower, middle)
    ]

    equity = INITIAL_EQUITY
    ending_equity = equity
    peak_equity = equity
    max_drawdown = 0.0
    wins = 0
    trades: list[dict[str, object]] = []
    entries: list[dict[str, object]] = []
    exits: list[dict[str, object]] = []
    equity_curve: list[dict[str, float | int]] = []
    position: dict[str, object] | None = None
    pending_entry: dict[str, object] | None = None
    pending_exit = False

    for index in range(warmup_bars, len(candles)):
        candle = candles[index]
        if pending_exit and position is not None:
            equity, won = _trade(
                trades=trades,
                exits=exits,
                position=position,
                candle=candle,
                exit_price=candle.open,
                leverage=leverage,
            )
            wins += 1 if won else 0
            position = None
            pending_exit = False
        if pending_entry is not None and position is None and equity > 0.0:
            side = str(pending_entry["side"])
            position = {
                "side": side,
                "entry_time": candle.ts,
                "entry_price": candle.open,
                "entry_index": index,
                "margin_used": equity,
                "stop_price": candle.open * (1 - stop_loss_pct if side == "long" else 1 + stop_loss_pct),
                "mode": str(pending_entry["mode"]),
            }
            entries.append({"ts": candle.ts, "price": candle.open, "side": side})
            pending_entry = None

        current_equity = equity
        if position is not None and index > int(position["entry_index"]):
            stop_hit = (
                position["side"] == "long" and candle.low <= float(position["stop_price"])
            ) or (
                position["side"] == "short" and candle.high >= float(position["stop_price"])
            )
            if stop_hit:
                equity, won = _trade(
                    trades=trades,
                    exits=exits,
                    position=position,
                    candle=candle,
                    exit_price=float(position["stop_price"]),
                    leverage=leverage,
                )
                wins += 1 if won else 0
                current_equity = equity
                position = None

        if position is not None:
            current_equity = mark_to_market(
                side=str(position["side"]),
                margin_used=float(position["margin_used"]),
                entry_price=float(position["entry_price"]),
                mark_price=candle.close,
                leverage=leverage,
            )

        peak_equity = max(peak_equity, current_equity)
        max_drawdown = max(max_drawdown, (peak_equity - current_equity) / peak_equity)
        equity_curve.append({"ts": candle.ts, "equity": current_equity, "close": candle.close})
        ending_equity = current_equity
        if index == len(candles) - 1 or equity <= 0.0:
            continue

        current_trend = trend[index]
        current_rsi = rsi_values[index]
        current_middle = middle[index]
        current_upper = upper[index]
        current_lower = lower[index]
        if (
            current_trend != current_trend
            or current_rsi != current_rsi
            or current_middle != current_middle
            or current_upper != current_upper
            or current_lower != current_lower
        ):
            continue

        if position is not None:
            if position["mode"] == "rsi" and (current_rsi >= rsi_exit or candle.close < float(current_trend)):
                pending_exit = True
            elif position["mode"] == "bbmr" and (
                (position["side"] == "long" and candle.close >= float(current_middle))
                or (position["side"] == "short" and candle.close <= float(current_middle))
            ):
                pending_exit = True
            continue

        regime_return = candle.close / candles[index - regime_lookback].close - 1.0
        ma_distance = candle.close / float(current_trend) - 1.0
        if candle.close > float(current_trend) and regime_return > 0.0 and current_rsi <= rsi_long_threshold:
            pending_entry = {"side": "long", "mode": "rsi"}
            continue

        previous_bandwidths = [value for value in bandwidth[max(0, index - bb_bandwidth_lookback) : index] if value == value]
        if abs(ma_distance) > neutral_ma_distance or len(previous_bandwidths) < bb_bandwidth_lookback:
            continue
        if bandwidth[index] == bandwidth[index] and bandwidth[index] <= pd.Series(previous_bandwidths, dtype=float).median():
            if candle.close < float(current_lower):
                pending_entry = {"side": "long", "mode": "bbmr"}
            elif candle.close > float(current_upper):
                pending_entry = {"side": "short", "mode": "bbmr"}

    trade_count = len(trades)
    return SegmentResult(
        trade_count=trade_count,
        total_return=(ending_equity - INITIAL_EQUITY) / INITIAL_EQUITY,
        win_rate=wins / trade_count if trade_count else 0.0,
        max_drawdown=max_drawdown,
        trades=trades,
        open_position=position,
        candles=candles[warmup_bars:],
        equity_curve=equity_curve,
        entries=entries,
        exits=exits,
    )


def run_eth_btc_rsi_filter_segment(
    *,
    eth_candles: list[Candle],
    btc_candles: list[Candle],
    leverage: int,
    warmup_bars: int,
    eth_trend_sma: int,
    eth_rsi_threshold: float,
    eth_exit_rsi: float,
    btc_trend_sma: int,
    btc_momentum_lookback: int,
    btc_min_momentum: float,
) -> SegmentResult:
    eth_closes = pd.Series([candle.close for candle in eth_candles], dtype=float)
    btc_closes = pd.Series([candle.close for candle in btc_candles], dtype=float)
    eth_trend = eth_closes.rolling(eth_trend_sma).mean().tolist()
    eth_rsi = _compute_rsi(eth_closes, 2)
    btc_trend = btc_closes.rolling(btc_trend_sma).mean().tolist()

    equity = INITIAL_EQUITY
    ending_equity = equity
    peak_equity = equity
    max_drawdown = 0.0
    wins = 0
    trades: list[dict[str, object]] = []
    entries: list[dict[str, object]] = []
    exits: list[dict[str, object]] = []
    equity_curve: list[dict[str, float | int]] = []
    position: dict[str, object] | None = None
    pending_entry = False
    pending_exit = False

    for index in range(warmup_bars, len(eth_candles)):
        candle = eth_candles[index]
        if pending_exit and position is not None:
            equity, won = _trade(
                trades=trades,
                exits=exits,
                position=position,
                candle=candle,
                exit_price=candle.open,
                leverage=leverage,
            )
            wins += 1 if won else 0
            position = None
            pending_exit = False
        if pending_entry and position is None and equity > 0.0:
            position = {
                "side": "long",
                "entry_time": candle.ts,
                "entry_price": candle.open,
                "margin_used": equity,
            }
            entries.append({"ts": candle.ts, "price": candle.open, "side": "long"})
            pending_entry = False

        current_equity = equity
        if position is not None:
            current_equity = mark_to_market(
                side="long",
                margin_used=float(position["margin_used"]),
                entry_price=float(position["entry_price"]),
                mark_price=candle.close,
                leverage=leverage,
            )

        peak_equity = max(peak_equity, current_equity)
        max_drawdown = max(max_drawdown, (peak_equity - current_equity) / peak_equity)
        equity_curve.append({"ts": candle.ts, "equity": current_equity, "close": candle.close})
        ending_equity = current_equity
        if index == len(eth_candles) - 1 or equity <= 0.0:
            continue

        current_eth_trend = eth_trend[index]
        current_eth_rsi = eth_rsi[index]
        current_btc_trend = btc_trend[index]
        if current_eth_trend != current_eth_trend or current_eth_rsi != current_eth_rsi or current_btc_trend != current_btc_trend:
            continue
        if position is not None:
            if current_eth_rsi >= eth_exit_rsi or btc_candles[index].close < float(current_btc_trend):
                pending_exit = True
            continue

        btc_momentum = btc_candles[index].close / btc_candles[index - btc_momentum_lookback].close - 1.0
        btc_risk_on = btc_candles[index].close > float(current_btc_trend) and btc_momentum >= btc_min_momentum
        eth_pullback = candle.close > float(current_eth_trend) and current_eth_rsi <= eth_rsi_threshold
        if btc_risk_on and eth_pullback:
            pending_entry = True

    trade_count = len(trades)
    return SegmentResult(
        trade_count=trade_count,
        total_return=(ending_equity - INITIAL_EQUITY) / INITIAL_EQUITY,
        win_rate=wins / trade_count if trade_count else 0.0,
        max_drawdown=max_drawdown,
        trades=trades,
        open_position=position,
        candles=eth_candles[warmup_bars:],
        equity_curve=equity_curve,
        entries=entries,
        exits=exits,
    )


def run_eth_btc_shock_filter_segment(
    *,
    eth_candles: list[Candle],
    btc_candles: list[Candle],
    leverage: int,
    warmup_bars: int,
    eth_trend_sma: int,
    eth_rsi_threshold: float,
    eth_exit_rsi: float,
    btc_trend_sma: int,
    btc_momentum_lookback: int,
    btc_min_momentum: float,
    btc_shock_lookback: int,
    btc_max_realized_vol: float,
    btc_max_drawdown: float,
) -> SegmentResult:
    eth_closes = pd.Series([candle.close for candle in eth_candles], dtype=float)
    btc_closes = pd.Series([candle.close for candle in btc_candles], dtype=float)
    eth_trend = eth_closes.rolling(eth_trend_sma).mean().tolist()
    eth_rsi = _compute_rsi(eth_closes, 2)
    btc_trend = btc_closes.rolling(btc_trend_sma).mean().tolist()
    btc_realized_vol = btc_closes.pct_change().rolling(btc_shock_lookback).std(ddof=1).tolist()
    btc_recent_high = btc_closes.rolling(btc_shock_lookback).max().tolist()

    equity = INITIAL_EQUITY
    ending_equity = equity
    peak_equity = equity
    max_drawdown = 0.0
    wins = 0
    trades: list[dict[str, object]] = []
    entries: list[dict[str, object]] = []
    exits: list[dict[str, object]] = []
    equity_curve: list[dict[str, float | int]] = []
    position: dict[str, object] | None = None
    pending_entry = False
    pending_exit = False

    for index in range(warmup_bars, len(eth_candles)):
        candle = eth_candles[index]
        if pending_exit and position is not None:
            equity, won = _trade(
                trades=trades,
                exits=exits,
                position=position,
                candle=candle,
                exit_price=candle.open,
                leverage=leverage,
            )
            wins += 1 if won else 0
            position = None
            pending_exit = False
        if pending_entry and position is None and equity > 0.0:
            position = {
                "side": "long",
                "entry_time": candle.ts,
                "entry_price": candle.open,
                "margin_used": equity,
            }
            entries.append({"ts": candle.ts, "price": candle.open, "side": "long"})
            pending_entry = False

        current_equity = equity
        if position is not None:
            current_equity = mark_to_market(
                side="long",
                margin_used=float(position["margin_used"]),
                entry_price=float(position["entry_price"]),
                mark_price=candle.close,
                leverage=leverage,
            )

        peak_equity = max(peak_equity, current_equity)
        max_drawdown = max(max_drawdown, (peak_equity - current_equity) / peak_equity)
        equity_curve.append({"ts": candle.ts, "equity": current_equity, "close": candle.close})
        ending_equity = current_equity
        if index == len(eth_candles) - 1 or equity <= 0.0:
            continue

        current_eth_trend = eth_trend[index]
        current_eth_rsi = eth_rsi[index]
        current_btc_trend = btc_trend[index]
        current_btc_vol = btc_realized_vol[index]
        current_btc_high = btc_recent_high[index]
        if (
            current_eth_trend != current_eth_trend
            or current_eth_rsi != current_eth_rsi
            or current_btc_trend != current_btc_trend
            or current_btc_vol != current_btc_vol
            or current_btc_high != current_btc_high
        ):
            continue
        btc_drawdown = btc_candles[index].close / float(current_btc_high) - 1.0
        btc_shock_ok = current_btc_vol <= btc_max_realized_vol and btc_drawdown >= -btc_max_drawdown
        if position is not None:
            if current_eth_rsi >= eth_exit_rsi or btc_candles[index].close < float(current_btc_trend) or not btc_shock_ok:
                pending_exit = True
            continue

        btc_momentum = btc_candles[index].close / btc_candles[index - btc_momentum_lookback].close - 1.0
        btc_risk_on = (
            btc_candles[index].close > float(current_btc_trend)
            and btc_momentum >= btc_min_momentum
            and btc_shock_ok
        )
        eth_pullback = candle.close > float(current_eth_trend) and current_eth_rsi <= eth_rsi_threshold
        if btc_risk_on and eth_pullback:
            pending_entry = True

    trade_count = len(trades)
    return SegmentResult(
        trade_count=trade_count,
        total_return=(ending_equity - INITIAL_EQUITY) / INITIAL_EQUITY,
        win_rate=wins / trade_count if trade_count else 0.0,
        max_drawdown=max_drawdown,
        trades=trades,
        open_position=position,
        candles=eth_candles[warmup_bars:],
        equity_curve=equity_curve,
        entries=entries,
        exits=exits,
    )


def run_eth_btc_ratio_pullback_segment(
    *,
    eth_candles: list[Candle],
    btc_candles: list[Candle],
    leverage: int,
    warmup_bars: int,
    btc_trend_sma: int,
    btc_momentum_lookback: int,
    btc_min_momentum: float,
    ratio_length: int,
    ratio_std: float,
    ratio_rsi_threshold: float,
    stop_loss_pct: float,
) -> SegmentResult:
    eth_closes = pd.Series([candle.close for candle in eth_candles], dtype=float)
    btc_closes = pd.Series([candle.close for candle in btc_candles], dtype=float)
    ratio = eth_closes / btc_closes
    btc_trend = btc_closes.rolling(btc_trend_sma).mean().tolist()
    ratio_middle = ratio.rolling(ratio_length).mean().tolist()
    ratio_stdev = ratio.rolling(ratio_length).std(ddof=0).tolist()
    ratio_lower = [
        float("nan") if middle != middle or stdev != stdev else middle - ratio_std * stdev
        for middle, stdev in zip(ratio_middle, ratio_stdev)
    ]
    ratio_rsi = _compute_rsi(ratio, 2)

    equity = INITIAL_EQUITY
    ending_equity = equity
    peak_equity = equity
    max_drawdown = 0.0
    wins = 0
    trades: list[dict[str, object]] = []
    entries: list[dict[str, object]] = []
    exits: list[dict[str, object]] = []
    equity_curve: list[dict[str, float | int]] = []
    position: dict[str, object] | None = None
    pending_entry = False
    pending_exit = False

    for index in range(warmup_bars, len(eth_candles)):
        candle = eth_candles[index]
        if pending_exit and position is not None:
            equity, won = _trade(
                trades=trades,
                exits=exits,
                position=position,
                candle=candle,
                exit_price=candle.open,
                leverage=leverage,
            )
            wins += 1 if won else 0
            position = None
            pending_exit = False
        if pending_entry and position is None and equity > 0.0:
            position = {
                "side": "long",
                "entry_time": candle.ts,
                "entry_price": candle.open,
                "entry_index": index,
                "margin_used": equity,
                "stop_price": candle.open * (1 - stop_loss_pct),
            }
            entries.append({"ts": candle.ts, "price": candle.open, "side": "long"})
            pending_entry = False

        current_equity = equity
        if position is not None and index > int(position["entry_index"]) and candle.low <= float(position["stop_price"]):
            equity, won = _trade(
                trades=trades,
                exits=exits,
                position=position,
                candle=candle,
                exit_price=float(position["stop_price"]),
                leverage=leverage,
            )
            wins += 1 if won else 0
            current_equity = equity
            position = None

        if position is not None:
            current_equity = mark_to_market(
                side="long",
                margin_used=float(position["margin_used"]),
                entry_price=float(position["entry_price"]),
                mark_price=candle.close,
                leverage=leverage,
            )

        peak_equity = max(peak_equity, current_equity)
        max_drawdown = max(max_drawdown, (peak_equity - current_equity) / peak_equity)
        equity_curve.append({"ts": candle.ts, "equity": current_equity, "close": candle.close})
        ending_equity = current_equity
        if index == len(eth_candles) - 1 or equity <= 0.0:
            continue

        current_btc_trend = btc_trend[index]
        current_ratio_middle = ratio_middle[index]
        current_ratio_lower = ratio_lower[index]
        current_ratio_rsi = ratio_rsi[index]
        if (
            current_btc_trend != current_btc_trend
            or current_ratio_middle != current_ratio_middle
            or current_ratio_lower != current_ratio_lower
            or current_ratio_rsi != current_ratio_rsi
        ):
            continue

        btc_momentum = btc_candles[index].close / btc_candles[index - btc_momentum_lookback].close - 1.0
        btc_risk_on = btc_candles[index].close > float(current_btc_trend) and btc_momentum >= btc_min_momentum
        if position is not None:
            if not btc_risk_on or ratio.iloc[index] >= float(current_ratio_middle):
                pending_exit = True
            continue

        ratio_pullback = ratio.iloc[index] <= float(current_ratio_lower) or current_ratio_rsi <= ratio_rsi_threshold
        if btc_risk_on and ratio_pullback:
            pending_entry = True

    trade_count = len(trades)
    return SegmentResult(
        trade_count=trade_count,
        total_return=(ending_equity - INITIAL_EQUITY) / INITIAL_EQUITY,
        win_rate=wins / trade_count if trade_count else 0.0,
        max_drawdown=max_drawdown,
        trades=trades,
        open_position=position,
        candles=eth_candles[warmup_bars:],
        equity_curve=equity_curve,
        entries=entries,
        exits=exits,
    )


def run_btc_lead_eth_lag_segment(
    *,
    eth_candles: list[Candle],
    btc_candles: list[Candle],
    leverage: int,
    warmup_bars: int,
    lead_lookback: int,
    btc_return_threshold: float,
    lag_gap: float,
    max_hold_bars: int,
    stop_loss_pct: float,
    take_profit_pct: float,
) -> SegmentResult:
    equity = INITIAL_EQUITY
    ending_equity = equity
    peak_equity = equity
    max_drawdown = 0.0
    wins = 0
    trades: list[dict[str, object]] = []
    entries: list[dict[str, object]] = []
    exits: list[dict[str, object]] = []
    equity_curve: list[dict[str, float | int]] = []
    position: dict[str, object] | None = None
    pending_entry = False
    pending_exit = False

    for index in range(warmup_bars, len(eth_candles)):
        candle = eth_candles[index]
        if pending_exit and position is not None:
            equity, won = _trade(
                trades=trades,
                exits=exits,
                position=position,
                candle=candle,
                exit_price=candle.open,
                leverage=leverage,
            )
            wins += 1 if won else 0
            position = None
            pending_exit = False
        if pending_entry and position is None and equity > 0.0:
            position = {
                "side": "long",
                "entry_time": candle.ts,
                "entry_price": candle.open,
                "entry_index": index,
                "margin_used": equity,
                "stop_price": candle.open * (1.0 - stop_loss_pct),
                "take_profit_price": candle.open * (1.0 + take_profit_pct),
            }
            entries.append({"ts": candle.ts, "price": candle.open, "side": "long"})
            pending_entry = False

        current_equity = equity
        if position is not None and index > int(position["entry_index"]):
            if candle.low <= float(position["stop_price"]):
                equity, won = _trade(
                    trades=trades,
                    exits=exits,
                    position=position,
                    candle=candle,
                    exit_price=float(position["stop_price"]),
                    leverage=leverage,
                )
                wins += 1 if won else 0
                current_equity = equity
                position = None
            elif candle.high >= float(position["take_profit_price"]):
                equity, won = _trade(
                    trades=trades,
                    exits=exits,
                    position=position,
                    candle=candle,
                    exit_price=float(position["take_profit_price"]),
                    leverage=leverage,
                )
                wins += 1 if won else 0
                current_equity = equity
                position = None

        if position is not None:
            current_equity = mark_to_market(
                side="long",
                margin_used=float(position["margin_used"]),
                entry_price=float(position["entry_price"]),
                mark_price=candle.close,
                leverage=leverage,
            )

        peak_equity = max(peak_equity, current_equity)
        max_drawdown = max(max_drawdown, (peak_equity - current_equity) / peak_equity)
        equity_curve.append({"ts": candle.ts, "equity": current_equity, "close": candle.close})
        ending_equity = current_equity
        if index == len(eth_candles) - 1 or equity <= 0.0:
            continue

        if position is not None:
            if index - int(position["entry_index"]) >= max_hold_bars:
                pending_exit = True
            continue

        btc_return = btc_candles[index].close / btc_candles[index - lead_lookback].close - 1.0
        eth_return = candle.close / eth_candles[index - lead_lookback].close - 1.0
        if btc_return >= btc_return_threshold and btc_return - eth_return >= lag_gap:
            pending_entry = True

    trade_count = len(trades)
    return SegmentResult(
        trade_count=trade_count,
        total_return=(ending_equity - INITIAL_EQUITY) / INITIAL_EQUITY,
        win_rate=wins / trade_count if trade_count else 0.0,
        max_drawdown=max_drawdown,
        trades=trades,
        open_position=position,
        candles=eth_candles[warmup_bars:],
        equity_curve=equity_curve,
        entries=entries,
        exits=exits,
    )


def build_candidates() -> list[Candidate]:
    candidates: list[Candidate] = []
    candidates.append(Candidate("bbmr-default", 69, lambda candles, leverage, warmup_bars: run_bbmr_segment(candles=candles, leverage=leverage, warmup_bars=warmup_bars, config=BBMRConfig())))
    candidates.append(Candidate("bbsb-default", 69, lambda candles, leverage, warmup_bars: run_bbsb_segment(candles=candles, leverage=leverage, warmup_bars=warmup_bars, config=BBSBConfig())))
    for entry_window in (8, 12, 20):
        for exit_window in (4, 6, 10):
            for stop in (0.004, 0.008, 0.012):
                config = DonchianConfig(entry_window=entry_window, exit_window=exit_window, stop_loss_pct=stop)
                candidates.append(Candidate(f"donchian-e{entry_window}-x{exit_window}-s{stop}", max(entry_window, exit_window), lambda candles, leverage, warmup_bars, config=config: run_donchian_segment(candles=candles, leverage=leverage, warmup_bars=warmup_bars, config=config)))
    for trend in (30, 50, 80):
        for long_threshold, short_threshold in ((8.0, 92.0), (12.0, 88.0), (18.0, 82.0)):
            config = RSI2Config(trend_sma=trend, rsi_length=2, rsi_long_threshold=long_threshold, rsi_short_threshold=short_threshold, exit_rsi=50.0)
            candidates.append(Candidate(f"rsi2-t{trend}-l{long_threshold}-s{short_threshold}", max(trend, 3), lambda candles, leverage, warmup_bars, config=config: run_rsi2_segment(candles=candles, leverage=leverage, warmup_bars=warmup_bars, config=config)))
    for fast, slow in ((8, 21), (13, 34), (20, 50)):
        for stop in (0.003, 0.006, 0.01):
            config = EMAPullbackConfig(fast_ema=fast, slow_ema=slow, stop_buffer_pct=stop)
            candidates.append(Candidate(f"ema-pullback-f{fast}-s{slow}-b{stop}", max(fast, slow), lambda candles, leverage, warmup_bars, config=config: run_ema_pullback_segment(candles=candles, leverage=leverage, warmup_bars=warmup_bars, config=config)))
    for lookback in (6, 10, 16):
        for take, stop in ((0.004, 0.003), (0.006, 0.004), (0.01, 0.005)):
            candidates.append(Candidate(f"range-momo-l{lookback}-tp{take}-sl{stop}", lookback, lambda candles, leverage, warmup_bars, lookback=lookback, take=take, stop=stop: run_range_momentum_segment(candles=candles, leverage=leverage, warmup_bars=warmup_bars, lookback=lookback, take_profit_pct=take, stop_loss_pct=stop)))
    for window in (24, 48, 72):
        for entry_z in (1.5, 2.0, 2.5):
            candidates.append(Candidate(f"vwap-revert-w{window}-z{entry_z}", window * 2, lambda candles, leverage, warmup_bars, window=window, entry_z=entry_z: run_vwap_reversion_segment(candles=candles, leverage=leverage, warmup_bars=warmup_bars, window=window, entry_z=entry_z, exit_z=0.2, stop_loss_pct=0.006)))
    return candidates


def evaluate_candidate(candidate: Candidate, candles: list[Candle]) -> dict[str, object]:
    sampled = sample_segments(
        candles=candles,
        segments=SEGMENTS,
        window_size=WINDOW_SIZE,
        warmup_bars=candidate.warmup_bars,
    )
    results = [
        candidate.run(
            candles=candles[segment.context_start : segment.report_end],
            leverage=LEVERAGE,
            warmup_bars=candidate.warmup_bars,
        )
        for segment in sampled
    ]
    returns = [result.total_return for result in results]
    return {
        "name": candidate.name,
        "avg_return": sum(returns) / len(returns),
        "median_return": float(pd.Series(returns).median()),
        "worst_return": min(returns),
        "best_return": max(returns),
        "trades": sum(result.trade_count for result in results),
        "win_rate": sum(result.win_rate for result in results) / len(results),
        "max_drawdown": max(result.max_drawdown for result in results),
    }


def evaluate_candidate_all_windows(
    *,
    candidate: Candidate,
    candles: list[Candle],
    window_size: int,
    leverage: int,
) -> dict[str, object]:
    rows = evaluate_candidate_window_rows(
        candidate=candidate,
        candles=candles,
        window_size=window_size,
        leverage=leverage,
    )
    return summarize_window_rows(rows, candidate.name)


def evaluate_candidate_window_rows(
    *,
    candidate: Candidate,
    candles: list[Candle],
    window_size: int,
    leverage: int,
) -> list[dict[str, object]]:
    block_size = candidate.warmup_bars + window_size
    context_starts = list(range(0, len(candles) - block_size + 1, window_size))
    rows: list[dict[str, object]] = []
    for start in context_starts:
        result = candidate.run(
            candles=candles[start : start + block_size],
            leverage=leverage,
            warmup_bars=candidate.warmup_bars,
        )
        report_start = start + candidate.warmup_bars
        report_end = start + block_size - 1
        rows.append(
            {
                "window_start_ts": candles[report_start].ts,
                "window_end_ts": candles[report_end].ts,
                "total_return": result.total_return,
                "trade_count": result.trade_count,
                "win_rate": result.win_rate,
                "max_drawdown": result.max_drawdown,
                "trades": result.trades,
            }
        )
    return rows


def evaluate_pair_candidate_window_rows(
    *,
    candidate: PairCandidate,
    eth_candles: list[Candle],
    btc_candles: list[Candle],
    window_size: int,
    leverage: int,
) -> list[dict[str, object]]:
    block_size = candidate.warmup_bars + window_size
    context_starts = list(range(0, len(eth_candles) - block_size + 1, window_size))
    rows: list[dict[str, object]] = []
    for start in context_starts:
        result = candidate.run(
            eth_candles=eth_candles[start : start + block_size],
            btc_candles=btc_candles[start : start + block_size],
            leverage=leverage,
            warmup_bars=candidate.warmup_bars,
        )
        report_start = start + candidate.warmup_bars
        report_end = start + block_size - 1
        rows.append(
            {
                "window_start_ts": eth_candles[report_start].ts,
                "window_end_ts": eth_candles[report_end].ts,
                "total_return": result.total_return,
                "trade_count": result.trade_count,
                "win_rate": result.win_rate,
                "max_drawdown": result.max_drawdown,
                "trades": result.trades,
            }
        )
    return rows


def summarize_window_rows(rows: list[dict[str, object]], name: str = "") -> dict[str, object]:
    returns = [float(row["total_return"]) for row in rows]
    trade_returns = [
        float(trade["return_pct"]) / 100.0
        for row in rows
        for trade in row["trades"]
    ]
    winning_trade_returns = [value for value in trade_returns if value > 0.0]
    losing_trade_returns = [value for value in trade_returns if value < 0.0]
    avg_win_return = sum(winning_trade_returns) / len(winning_trade_returns) if winning_trade_returns else 0.0
    avg_loss_return_abs = abs(sum(losing_trade_returns) / len(losing_trade_returns)) if losing_trade_returns else 0.0
    gross_profit = sum(winning_trade_returns)
    gross_loss_abs = abs(sum(losing_trade_returns))
    series = pd.Series(returns, dtype=float)
    sample_count = len(returns)
    std = float(series.std(ddof=1)) if sample_count > 1 else 0.0
    ci_half_width = 1.96 * std / sqrt(sample_count) if sample_count > 1 else 0.0
    return {
        "name": name,
        "sample_count": sample_count,
        "avg_return": float(series.mean()),
        "ci95_low": float(series.mean() - ci_half_width),
        "ci95_high": float(series.mean() + ci_half_width),
        "median_return": float(series.median()),
        "positive_window_rate": float((series > 0).mean()),
        "worst_return": float(series.min()),
        "p10_return": float(series.quantile(0.10)),
        "p90_return": float(series.quantile(0.90)),
        "best_return": float(series.max()),
        "trades": sum(int(row["trade_count"]) for row in rows),
        "avg_trades_per_window": sum(int(row["trade_count"]) for row in rows) / sample_count,
        "win_rate": sum(float(row["win_rate"]) for row in rows) / sample_count,
        "trade_win_rate": len(winning_trade_returns) / len(trade_returns) if trade_returns else 0.0,
        "avg_trade_return": sum(trade_returns) / len(trade_returns) if trade_returns else 0.0,
        "avg_win_return": avg_win_return,
        "avg_loss_return_abs": avg_loss_return_abs,
        "payoff_ratio": avg_win_return / avg_loss_return_abs if avg_loss_return_abs else 0.0,
        "profit_factor": gross_profit / gross_loss_abs if gross_loss_abs else 0.0,
        "expectancy_per_trade": sum(trade_returns) / len(trade_returns) if trade_returns else 0.0,
        "max_drawdown": max(float(row["max_drawdown"]) for row in rows),
        "return_drawdown_ratio": float(series.mean()) / max(float(row["max_drawdown"]) for row in rows) if max(float(row["max_drawdown"]) for row in rows) else 0.0,
    }


def sort_robust_results(frame: pd.DataFrame) -> pd.DataFrame:
    return frame.sort_values(["ci95_low", "avg_return"], ascending=False)


def add_cost_metrics(frame: pd.DataFrame, roundtrip_cost_on_margin: float) -> pd.DataFrame:
    frame = frame.copy()
    cost = frame["avg_trades_per_window"] * roundtrip_cost_on_margin
    frame["roundtrip_cost_on_margin"] = roundtrip_cost_on_margin
    frame["net_avg_return"] = frame["avg_return"] - cost
    frame["net_ci95_low"] = frame["ci95_low"] - cost
    frame["net_ci95_high"] = frame["ci95_high"] - cost
    frame["breakeven_roundtrip_cost_on_margin"] = frame["avg_return"] / frame["avg_trades_per_window"]
    return frame


def sort_cost_results(frame: pd.DataFrame) -> pd.DataFrame:
    return frame.sort_values(["net_ci95_low", "net_avg_return"], ascending=False)


def max_drawdown_from_equity(equity_values: list[float]) -> float:
    peak = equity_values[0]
    max_drawdown = 0.0
    for equity in equity_values:
        peak = max(peak, equity)
        if peak > 0.0:
            max_drawdown = max(max_drawdown, (peak - equity) / peak)
    return max_drawdown


def cost_adjusted_trade_equity_frame(result: SegmentResult, roundtrip_cost_on_margin: float) -> pd.DataFrame:
    rows = [{"ts": pd.to_datetime(result.equity_curve[0]["ts"], unit="ms", utc=True), "equity": INITIAL_EQUITY}]
    equity = INITIAL_EQUITY
    for trade in result.trades:
        equity *= 1.0 + float(trade["return_pct"]) / 100.0 - roundtrip_cost_on_margin
        rows.append({"ts": pd.to_datetime(str(trade["exit_time"]), utc=True), "equity": equity})
    return pd.DataFrame(rows)


def annualized_metrics_from_equity(frame: pd.DataFrame, first_ts: int, last_ts: int) -> dict[str, float]:
    years = (last_ts - first_ts) / 86_400_000 / 365
    total_return = float(frame["equity"].iloc[-1] / frame["equity"].iloc[0] - 1.0)
    annualized_return = (1.0 + total_return) ** (1.0 / years) - 1.0 if total_return > -1.0 and years > 0.0 else 0.0
    max_drawdown = max_drawdown_from_equity([float(value) for value in frame["equity"]])
    daily = frame.set_index("ts")["equity"].resample("1D").last().ffill()
    daily_returns = daily.pct_change().dropna()
    daily_std = float(daily_returns.std(ddof=1)) if len(daily_returns) > 1 else 0.0
    sharpe = float(daily_returns.mean()) / daily_std * sqrt(365) if daily_std else 0.0
    return {
        "net_total_return": total_return,
        "net_annualized_return": annualized_return,
        "net_max_drawdown": max_drawdown,
        "net_calmar": annualized_return / max_drawdown if max_drawdown else 0.0,
        "net_sharpe_daily": sharpe,
    }


def recent_horizon_metrics_from_equity(
    frame: pd.DataFrame,
    last_ts: int,
    horizons: tuple[tuple[str, pd.DateOffset], ...],
) -> pd.DataFrame:
    rows: list[dict[str, object]] = []
    end_time = pd.to_datetime(last_ts, unit="ms", utc=True)
    for label, offset in horizons:
        cutoff = end_time - offset
        before_cutoff = frame[frame["ts"] <= cutoff]
        if len(before_cutoff):
            start_equity = float(before_cutoff["equity"].iloc[-1])
            start_time = cutoff
            after_cutoff = frame[frame["ts"] > cutoff]
            horizon_frame = pd.concat(
                [
                    pd.DataFrame([{"ts": start_time, "equity": start_equity}]),
                    after_cutoff[["ts", "equity"]],
                ],
                ignore_index=True,
            )
        else:
            horizon_frame = frame[["ts", "equity"]].copy()
            start_time = pd.Timestamp(horizon_frame["ts"].iloc[0])

        metrics = annualized_metrics_from_equity(
            horizon_frame,
            int(start_time.timestamp() * 1000),
            last_ts,
        )
        rows.append(
            {
                "horizon": label,
                "horizon_start": start_time.strftime("%Y-%m-%d %H:%M"),
                "horizon_end": end_time.strftime("%Y-%m-%d %H:%M"),
                "horizon_days": (end_time - start_time).total_seconds() / 86_400,
                **metrics,
            }
        )
    return pd.DataFrame(rows)


def build_rsi2_candidate(trend: int, long_threshold: float, short_threshold: float) -> Candidate:
    config = RSI2Config(
        trend_sma=trend,
        rsi_length=2,
        rsi_long_threshold=long_threshold,
        rsi_short_threshold=short_threshold,
        exit_rsi=50.0,
    )
    return Candidate(
        f"rsi2-t{trend}-l{long_threshold}-s{short_threshold}",
        max(trend, 3),
        lambda candles, leverage, warmup_bars, config=config: run_rsi2_segment(
            candles=candles,
            leverage=leverage,
            warmup_bars=warmup_bars,
            config=config,
        ),
    )


def build_rsi2_side_candidate(
    trend: int,
    long_threshold: float,
    short_threshold: float,
    exit_rsi: float,
    side_mode: str,
) -> Candidate:
    config = RSI2Config(
        trend_sma=trend,
        rsi_length=2,
        rsi_long_threshold=long_threshold,
        rsi_short_threshold=short_threshold,
        exit_rsi=exit_rsi,
    )
    return Candidate(
        f"rsi2-{side_mode}-t{trend}-l{long_threshold}-s{short_threshold}-x{exit_rsi}",
        max(trend, 3),
        lambda candles, leverage, warmup_bars, config=config, side_mode=side_mode: run_rsi2_side_segment(
            candles=candles,
            leverage=leverage,
            warmup_bars=warmup_bars,
            config=config,
            side_mode=side_mode,
        ),
    )


def build_rsi2_long_guarded_candidate(
    trend: int,
    rsi_threshold: float,
    exit_rsi: float,
    stop_loss_pct: float,
    max_hold_bars: int,
) -> Candidate:
    return Candidate(
        f"rsi2-long-guarded-t{trend}-l{rsi_threshold}-x{exit_rsi}-sl{stop_loss_pct}-mh{max_hold_bars}",
        max(trend, 3),
        lambda candles, leverage, warmup_bars, trend=trend, rsi_threshold=rsi_threshold, exit_rsi=exit_rsi, stop_loss_pct=stop_loss_pct, max_hold_bars=max_hold_bars: run_rsi2_long_guarded_segment(
            candles=candles,
            leverage=leverage,
            warmup_bars=warmup_bars,
            trend_sma=trend,
            rsi_threshold=rsi_threshold,
            exit_rsi=exit_rsi,
            stop_loss_pct=stop_loss_pct,
            max_hold_bars=max_hold_bars,
        ),
    )


def build_ma_cross_candidate(fast: int, slow: int, side_mode: str) -> Candidate:
    return Candidate(
        f"ma-cross-{side_mode}-f{fast}-s{slow}",
        slow,
        lambda candles, leverage, warmup_bars, fast=fast, slow=slow, side_mode=side_mode: run_ma_cross_segment(
            candles=candles,
            leverage=leverage,
            warmup_bars=warmup_bars,
            fast=fast,
            slow=slow,
            side_mode=side_mode,
        ),
    )


def build_trend_rsi_bb_long_candidate(
    trend_sma: int,
    band_length: int,
    std_multiplier: float,
    rsi_threshold: float,
    exit_rsi: float,
    stop_loss_pct: float,
) -> Candidate:
    return Candidate(
        f"trend-rsi-bb-long-t{trend_sma}-b{band_length}-m{std_multiplier}-r{rsi_threshold}-x{exit_rsi}-sl{stop_loss_pct}",
        max(trend_sma, band_length, 3),
        lambda candles, leverage, warmup_bars, trend_sma=trend_sma, band_length=band_length, std_multiplier=std_multiplier, rsi_threshold=rsi_threshold, exit_rsi=exit_rsi, stop_loss_pct=stop_loss_pct: run_trend_rsi_bb_long_segment(
            candles=candles,
            leverage=leverage,
            warmup_bars=warmup_bars,
            trend_sma=trend_sma,
            band_length=band_length,
            std_multiplier=std_multiplier,
            rsi_threshold=rsi_threshold,
            exit_rsi=exit_rsi,
            stop_loss_pct=stop_loss_pct,
        ),
    )


def build_regime_hybrid_candidate(
    trend_sma: int,
    regime_lookback: int,
    neutral_ma_distance: float,
    rsi_long_threshold: float,
    rsi_exit: float,
    bb_std: float,
    stop_loss_pct: float,
) -> Candidate:
    return Candidate(
        f"regime-hybrid-t{trend_sma}-r{regime_lookback}-n{neutral_ma_distance}-l{rsi_long_threshold}-x{rsi_exit}-m{bb_std}-sl{stop_loss_pct}",
        max(trend_sma, regime_lookback, 20, 50, 3),
        lambda candles, leverage, warmup_bars, trend_sma=trend_sma, regime_lookback=regime_lookback, neutral_ma_distance=neutral_ma_distance, rsi_long_threshold=rsi_long_threshold, rsi_exit=rsi_exit, bb_std=bb_std, stop_loss_pct=stop_loss_pct: run_regime_hybrid_segment(
            candles=candles,
            leverage=leverage,
            warmup_bars=warmup_bars,
            trend_sma=trend_sma,
            regime_lookback=regime_lookback,
            neutral_ma_distance=neutral_ma_distance,
            rsi_long_threshold=rsi_long_threshold,
            rsi_exit=rsi_exit,
            bb_length=20,
            bb_std=bb_std,
            bb_bandwidth_lookback=50,
            stop_loss_pct=stop_loss_pct,
        ),
    )


def build_eth_btc_rsi_filter_candidate(
    eth_trend_sma: int,
    eth_rsi_threshold: float,
    eth_exit_rsi: float,
    btc_trend_sma: int,
    btc_momentum_lookback: int,
    btc_min_momentum: float,
) -> PairCandidate:
    return PairCandidate(
        f"eth-btc-rsi-filter-et{eth_trend_sma}-l{eth_rsi_threshold}-x{eth_exit_rsi}-bt{btc_trend_sma}-bm{btc_momentum_lookback}-br{btc_min_momentum}",
        max(eth_trend_sma, btc_trend_sma, btc_momentum_lookback, 3),
        lambda eth_candles, btc_candles, leverage, warmup_bars, eth_trend_sma=eth_trend_sma, eth_rsi_threshold=eth_rsi_threshold, eth_exit_rsi=eth_exit_rsi, btc_trend_sma=btc_trend_sma, btc_momentum_lookback=btc_momentum_lookback, btc_min_momentum=btc_min_momentum: run_eth_btc_rsi_filter_segment(
            eth_candles=eth_candles,
            btc_candles=btc_candles,
            leverage=leverage,
            warmup_bars=warmup_bars,
            eth_trend_sma=eth_trend_sma,
            eth_rsi_threshold=eth_rsi_threshold,
            eth_exit_rsi=eth_exit_rsi,
            btc_trend_sma=btc_trend_sma,
            btc_momentum_lookback=btc_momentum_lookback,
            btc_min_momentum=btc_min_momentum,
        ),
    )


def build_eth_btc_shock_filter_candidate(
    eth_trend_sma: int,
    eth_rsi_threshold: float,
    eth_exit_rsi: float,
    btc_trend_sma: int,
    btc_momentum_lookback: int,
    btc_min_momentum: float,
    btc_shock_lookback: int,
    btc_max_realized_vol: float,
    btc_max_drawdown: float,
) -> PairCandidate:
    return PairCandidate(
        f"eth-btc-shock-filter-et{eth_trend_sma}-l{eth_rsi_threshold}-x{eth_exit_rsi}-bt{btc_trend_sma}-bm{btc_momentum_lookback}-br{btc_min_momentum}-sw{btc_shock_lookback}-sv{btc_max_realized_vol}-sd{btc_max_drawdown}",
        max(eth_trend_sma, btc_trend_sma, btc_momentum_lookback, btc_shock_lookback + 1, 3),
        lambda eth_candles, btc_candles, leverage, warmup_bars, eth_trend_sma=eth_trend_sma, eth_rsi_threshold=eth_rsi_threshold, eth_exit_rsi=eth_exit_rsi, btc_trend_sma=btc_trend_sma, btc_momentum_lookback=btc_momentum_lookback, btc_min_momentum=btc_min_momentum, btc_shock_lookback=btc_shock_lookback, btc_max_realized_vol=btc_max_realized_vol, btc_max_drawdown=btc_max_drawdown: run_eth_btc_shock_filter_segment(
            eth_candles=eth_candles,
            btc_candles=btc_candles,
            leverage=leverage,
            warmup_bars=warmup_bars,
            eth_trend_sma=eth_trend_sma,
            eth_rsi_threshold=eth_rsi_threshold,
            eth_exit_rsi=eth_exit_rsi,
            btc_trend_sma=btc_trend_sma,
            btc_momentum_lookback=btc_momentum_lookback,
            btc_min_momentum=btc_min_momentum,
            btc_shock_lookback=btc_shock_lookback,
            btc_max_realized_vol=btc_max_realized_vol,
            btc_max_drawdown=btc_max_drawdown,
        ),
    )


def build_eth_btc_ratio_pullback_candidate(
    btc_trend_sma: int,
    btc_momentum_lookback: int,
    btc_min_momentum: float,
    ratio_length: int,
    ratio_std: float,
    ratio_rsi_threshold: float,
    stop_loss_pct: float,
) -> PairCandidate:
    return PairCandidate(
        f"eth-btc-ratio-pullback-bt{btc_trend_sma}-bm{btc_momentum_lookback}-br{btc_min_momentum}-rl{ratio_length}-rs{ratio_std}-rr{ratio_rsi_threshold}-sl{stop_loss_pct}",
        max(btc_trend_sma, btc_momentum_lookback, ratio_length, 3),
        lambda eth_candles, btc_candles, leverage, warmup_bars, btc_trend_sma=btc_trend_sma, btc_momentum_lookback=btc_momentum_lookback, btc_min_momentum=btc_min_momentum, ratio_length=ratio_length, ratio_std=ratio_std, ratio_rsi_threshold=ratio_rsi_threshold, stop_loss_pct=stop_loss_pct: run_eth_btc_ratio_pullback_segment(
            eth_candles=eth_candles,
            btc_candles=btc_candles,
            leverage=leverage,
            warmup_bars=warmup_bars,
            btc_trend_sma=btc_trend_sma,
            btc_momentum_lookback=btc_momentum_lookback,
            btc_min_momentum=btc_min_momentum,
            ratio_length=ratio_length,
            ratio_std=ratio_std,
            ratio_rsi_threshold=ratio_rsi_threshold,
            stop_loss_pct=stop_loss_pct,
        ),
    )


def build_btc_lead_eth_lag_candidate(
    lead_lookback: int,
    btc_return_threshold: float,
    lag_gap: float,
    max_hold_bars: int,
    stop_loss_pct: float,
    take_profit_pct: float,
) -> PairCandidate:
    return PairCandidate(
        f"btc-lead-eth-lag-lb{lead_lookback}-br{btc_return_threshold}-gap{lag_gap}-mh{max_hold_bars}-sl{stop_loss_pct}-tp{take_profit_pct}",
        lead_lookback,
        lambda eth_candles, btc_candles, leverage, warmup_bars, lead_lookback=lead_lookback, btc_return_threshold=btc_return_threshold, lag_gap=lag_gap, max_hold_bars=max_hold_bars, stop_loss_pct=stop_loss_pct, take_profit_pct=take_profit_pct: run_btc_lead_eth_lag_segment(
            eth_candles=eth_candles,
            btc_candles=btc_candles,
            leverage=leverage,
            warmup_bars=warmup_bars,
            lead_lookback=lead_lookback,
            btc_return_threshold=btc_return_threshold,
            lag_gap=lag_gap,
            max_hold_bars=max_hold_bars,
            stop_loss_pct=stop_loss_pct,
            take_profit_pct=take_profit_pct,
        ),
    )


def history_bars_for_years(bar: str, years: float) -> int:
    if not bar.endswith("m"):
        raise ValueError("minute bar is required")
    minutes = int(bar[:-1])
    if minutes <= 0:
        raise ValueError("minute bar is required")
    return int(MINUTES_PER_YEAR * years / minutes)


def build_strategy_timeframe_candidates() -> list[Candidate]:
    return [
        Candidate("bbmr-default", 69, lambda candles, leverage, warmup_bars: run_bbmr_segment(candles=candles, leverage=leverage, warmup_bars=warmup_bars, config=BBMRConfig())),
        Candidate("bbsb-default", 69, lambda candles, leverage, warmup_bars: run_bbsb_segment(candles=candles, leverage=leverage, warmup_bars=warmup_bars, config=BBSBConfig())),
        Candidate("donchian-e12-x6-s0.008", 12, lambda candles, leverage, warmup_bars: run_donchian_segment(candles=candles, leverage=leverage, warmup_bars=warmup_bars, config=DonchianConfig(entry_window=12, exit_window=6, stop_loss_pct=0.008))),
        build_rsi2_candidate(50, 3.0, 97.0),
        Candidate("ema-pullback-f13-s34-b0.006", 34, lambda candles, leverage, warmup_bars: run_ema_pullback_segment(candles=candles, leverage=leverage, warmup_bars=warmup_bars, config=EMAPullbackConfig(fast_ema=13, slow_ema=34, stop_buffer_pct=0.006))),
        Candidate("range-momo-l10-tp0.006-sl0.004", 10, lambda candles, leverage, warmup_bars: run_range_momentum_segment(candles=candles, leverage=leverage, warmup_bars=warmup_bars, lookback=10, take_profit_pct=0.006, stop_loss_pct=0.004)),
        Candidate("vwap-revert-w72-z2.0-sl0.006", 144, lambda candles, leverage, warmup_bars: run_vwap_reversion_segment(candles=candles, leverage=leverage, warmup_bars=warmup_bars, window=72, entry_z=2.0, exit_z=0.2, stop_loss_pct=0.006)),
    ]


def summarize_periods(frame: pd.DataFrame, period: str, roundtrip_cost_on_margin: float) -> pd.DataFrame:
    period_frame = frame.copy()
    period_frame["period"] = pd.to_datetime(period_frame["window_end_ts"], unit="ms", utc=True).dt.tz_localize(None).dt.to_period(period).astype(str)
    grouped = (
        period_frame.groupby("period", as_index=False)
        .agg(
            window_count=("total_return", "size"),
            avg_return=("total_return", "mean"),
            median_return=("total_return", "median"),
            positive_window_rate=("total_return", lambda values: float((values > 0.0).mean())),
            worst_return=("total_return", "min"),
            best_return=("total_return", "max"),
            trades=("trade_count", "sum"),
            avg_trades_per_window=("trade_count", "mean"),
            avg_window_win_rate=("win_rate", "mean"),
            max_drawdown=("max_drawdown", "max"),
        )
        .sort_values("period")
    )
    grouped["net_avg_return"] = grouped["avg_return"] - grouped["avg_trades_per_window"] * roundtrip_cost_on_margin
    return grouped


def summarize_equity_periods(result: SegmentResult, period: str) -> pd.DataFrame:
    frame = pd.DataFrame(result.equity_curve)
    frame["period"] = pd.to_datetime(frame["ts"], unit="ms", utc=True).dt.tz_localize(None).dt.to_period(period).astype(str)
    grouped = (
        frame.groupby("period", as_index=False)
        .agg(
            start_equity=("equity", "first"),
            end_equity=("equity", "last"),
            min_equity=("equity", "min"),
            max_equity=("equity", "max"),
            bars=("equity", "size"),
        )
        .sort_values("period")
    )
    grouped["return"] = grouped["end_equity"] / grouped["start_equity"] - 1.0
    grouped["drawdown_from_period_high"] = (grouped["max_equity"] - grouped["min_equity"]) / grouped["max_equity"]
    return grouped


def summarize_cost_adjusted_trade_equity_periods(
    result: SegmentResult,
    period: str,
    roundtrip_cost_on_margin: float,
) -> pd.DataFrame:
    frame = cost_adjusted_trade_equity_frame(result, roundtrip_cost_on_margin)
    frame["period"] = frame["ts"].dt.tz_localize(None).dt.to_period(period).astype(str)
    grouped = (
        frame.groupby("period", as_index=False)
        .agg(
            start_equity=("equity", "first"),
            end_equity=("equity", "last"),
            min_equity=("equity", "min"),
            max_equity=("equity", "max"),
            trades=("equity", lambda values: max(len(values) - 1, 0)),
        )
        .sort_values("period")
    )
    grouped["return"] = grouped["end_equity"] / grouped["start_equity"] - 1.0
    grouped["drawdown_from_period_high"] = (grouped["max_equity"] - grouped["min_equity"]) / grouped["max_equity"]
    return grouped


def add_market_regime_columns(candles: list[Candle], rows: list[dict[str, object]], roundtrip_cost_on_margin: float) -> pd.DataFrame:
    index_by_ts = {candle.ts: index for index, candle in enumerate(candles)}
    output_rows: list[dict[str, object]] = []
    closes = pd.Series([candle.close for candle in candles], dtype=float)
    long_ma = closes.rolling(240).mean().tolist()
    for row in rows:
        start = index_by_ts[int(row["window_start_ts"])]
        end = index_by_ts[int(row["window_end_ts"])]
        window_closes = closes.iloc[start : end + 1]
        returns = window_closes.pct_change().dropna()
        ma_value = long_ma[end]
        close_value = float(window_closes.iloc[-1])
        output_rows.append(
            {
                **{key: value for key, value in row.items() if key != "trades"},
                "net_return": float(row["total_return"]) - int(row["trade_count"]) * roundtrip_cost_on_margin,
                "market_return": close_value / float(window_closes.iloc[0]) - 1.0,
                "realized_vol": float(returns.std(ddof=1)) if len(returns) > 1 else 0.0,
                "ma240_distance": close_value / float(ma_value) - 1.0 if ma_value == ma_value and ma_value else 0.0,
            }
        )
    frame = pd.DataFrame(output_rows)
    frame["market_return_bucket"] = pd.qcut(frame["market_return"], 3, labels=["down", "flat", "up"], duplicates="drop")
    frame["realized_vol_bucket"] = pd.qcut(frame["realized_vol"], 3, labels=["low", "mid", "high"], duplicates="drop")
    frame["ma240_distance_bucket"] = pd.qcut(frame["ma240_distance"], 3, labels=["below", "near", "above"], duplicates="drop")
    return frame


def summarize_regime_columns(frame: pd.DataFrame) -> pd.DataFrame:
    summaries: list[pd.DataFrame] = []
    for column in ("market_return_bucket", "realized_vol_bucket", "ma240_distance_bucket"):
        grouped = (
            frame.groupby(["symbol", "bar", "name", column], observed=True, as_index=False)
            .agg(
                sample_count=("net_return", "size"),
                avg_net_return=("net_return", "mean"),
                median_net_return=("net_return", "median"),
                positive_window_rate=("net_return", lambda values: float((values > 0.0).mean())),
                worst_net_return=("net_return", "min"),
                best_net_return=("net_return", "max"),
                avg_trades=("trade_count", "mean"),
                avg_market_return=("market_return", "mean"),
                avg_realized_vol=("realized_vol", "mean"),
                avg_ma240_distance=("ma240_distance", "mean"),
            )
            .rename(columns={column: "regime"})
        )
        grouped.insert(3, "regime_type", column.removesuffix("_bucket"))
        summaries.append(grouped)
    return pd.concat(summaries, ignore_index=True).sort_values(
        ["name", "regime_type", "avg_net_return"],
        ascending=[True, True, False],
    )


def main() -> int:
    client = OkxClient()
    candidates = build_candidates()
    rows: list[dict[str, object]] = []
    for symbol in SYMBOLS:
        for bar in BARS:
            candles = get_candles_cached(client, symbol, bar, HISTORY_LIMIT)
            for candidate in candidates:
                metrics = evaluate_candidate(candidate, candles)
                rows.append({"symbol": symbol, "bar": bar, **metrics})
            print(f"done {symbol} {bar}")
    frame = pd.DataFrame(rows)
    frame["score"] = frame["avg_return"] - frame["max_drawdown"] * 0.25
    columns = ["symbol", "bar", "name", "avg_return", "median_return", "worst_return", "best_return", "trades", "win_rate", "max_drawdown", "score"]
    print(frame.sort_values(["avg_return", "median_return"], ascending=False)[columns].head(30).to_string(index=False))
    frame.to_csv("ultrashort-exploration.csv", index=False)
    return 0


def focus_vwap() -> int:
    client = OkxClient()
    candles = get_candles_cached(client, "ETH-USDT-SWAP", "3m", HISTORY_LIMIT)
    rows: list[dict[str, object]] = []
    for seed in (3, 7, 11, 17, 23):
        for window in (56, 64, 72, 80, 96):
            for entry_z in (1.6, 1.8, 2.0, 2.2, 2.4):
                for stop in (0.004, 0.006, 0.008):
                    warmup_bars = window * 2
                    sampled = sample_segments(
                        candles=candles,
                        segments=SEGMENTS,
                        window_size=WINDOW_SIZE,
                        warmup_bars=warmup_bars,
                        seed=seed,
                    )
                    results = [
                        run_vwap_reversion_segment(
                            candles=candles[segment.context_start : segment.report_end],
                            leverage=LEVERAGE,
                            warmup_bars=warmup_bars,
                            window=window,
                            entry_z=entry_z,
                            exit_z=0.2,
                            stop_loss_pct=stop,
                        )
                        for segment in sampled
                    ]
                    returns = [result.total_return for result in results]
                    rows.append(
                        {
                            "seed": seed,
                            "window": window,
                            "entry_z": entry_z,
                            "stop": stop,
                            "avg_return": sum(returns) / len(returns),
                            "median_return": float(pd.Series(returns).median()),
                            "worst_return": min(returns),
                            "best_return": max(returns),
                            "trades": sum(result.trade_count for result in results),
                            "win_rate": sum(result.win_rate for result in results) / len(results),
                            "max_drawdown": max(result.max_drawdown for result in results),
                        }
                    )
    frame = pd.DataFrame(rows)
    grouped = (
        frame.groupby(["window", "entry_z", "stop"], as_index=False)
        .agg(
            avg_return=("avg_return", "mean"),
            median_return=("median_return", "mean"),
            worst_seed_avg=("avg_return", "min"),
            worst_window_return=("worst_return", "min"),
            trades=("trades", "mean"),
            win_rate=("win_rate", "mean"),
            max_drawdown=("max_drawdown", "max"),
        )
        .sort_values(["avg_return", "worst_seed_avg"], ascending=False)
    )
    print(grouped.head(30).to_string(index=False))
    grouped.to_csv("ultrashort-vwap-focus.csv", index=False)
    return 0


def robust_vwap(history_limit: int, window_size: int) -> int:
    client = OkxClient()
    rows: list[dict[str, object]] = []
    candidates = [
        Candidate(
            f"vwap-revert-w{window}-z{entry_z}-sl{stop}",
            window * 2,
            lambda candles, leverage, warmup_bars, window=window, entry_z=entry_z, stop=stop: run_vwap_reversion_segment(
                candles=candles,
                leverage=leverage,
                warmup_bars=warmup_bars,
                window=window,
                entry_z=entry_z,
                exit_z=0.2,
                stop_loss_pct=stop,
            ),
        )
        for window in (56, 64, 72, 80, 96)
        for entry_z in (1.6, 1.8, 2.0, 2.2, 2.4)
        for stop in (0.004, 0.006, 0.008)
    ]
    for symbol in SYMBOLS:
        candles = get_candles_cached(client, symbol, "3m", history_limit)
        for candidate in candidates:
            metrics = evaluate_candidate_all_windows(
                candidate=candidate,
                candles=candles,
                window_size=window_size,
                leverage=LEVERAGE,
            )
            rows.append({"symbol": symbol, "bar": "3m", "history_bars": len(candles), **metrics})
        print(f"done robust {symbol} 3m {len(candles)} bars")
    frame = pd.DataFrame(rows)
    columns = [
        "symbol",
        "bar",
        "history_bars",
        "name",
        "sample_count",
        "avg_return",
        "ci95_low",
        "ci95_high",
        "median_return",
        "positive_window_rate",
        "worst_return",
        "p10_return",
        "p90_return",
        "best_return",
        "trades",
        "avg_trades_per_window",
        "win_rate",
        "trade_win_rate",
        "avg_trade_return",
        "avg_win_return",
        "avg_loss_return_abs",
        "payoff_ratio",
        "profit_factor",
        "expectancy_per_trade",
        "max_drawdown",
        "return_drawdown_ratio",
    ]
    frame = sort_robust_results(frame)
    print(GROSS_RETURN_NOTE)
    print(frame[columns].head(30).to_string(index=False))
    frame.to_csv("ultrashort-vwap-robust.csv", index=False)
    return 0


def robust_all(history_limit: int, window_size: int) -> int:
    client = OkxClient()
    candidates = build_candidates()
    rows: list[dict[str, object]] = []
    for symbol in SYMBOLS:
        for bar in BARS:
            candles = get_candles_cached(client, symbol, bar, history_limit)
            for candidate in candidates:
                metrics = evaluate_candidate_all_windows(
                    candidate=candidate,
                    candles=candles,
                    window_size=window_size,
                    leverage=LEVERAGE,
                )
                rows.append({"symbol": symbol, "bar": bar, "history_bars": len(candles), **metrics})
            print(f"done robust all {symbol} {bar} {len(candles)} bars")
    frame = pd.DataFrame(rows)
    columns = [
        "symbol",
        "bar",
        "history_bars",
        "name",
        "sample_count",
        "avg_return",
        "ci95_low",
        "ci95_high",
        "median_return",
        "positive_window_rate",
        "worst_return",
        "p10_return",
        "p90_return",
        "best_return",
        "trades",
        "avg_trades_per_window",
        "win_rate",
        "trade_win_rate",
        "avg_trade_return",
        "avg_win_return",
        "avg_loss_return_abs",
        "payoff_ratio",
        "profit_factor",
        "expectancy_per_trade",
        "max_drawdown",
        "return_drawdown_ratio",
    ]
    frame = sort_robust_results(frame)
    print(GROSS_RETURN_NOTE)
    print(frame[columns].head(40).to_string(index=False))
    frame.to_csv("ultrashort-robust-all.csv", index=False)
    return 0


def robust_rsi2_cost_search(history_limit: int, window_size: int, roundtrip_cost_on_margin: float) -> int:
    client = OkxClient()
    candidates = [
        build_rsi2_candidate(trend, long_threshold, short_threshold)
        for trend in (30, 50, 80, 120, 160, 240)
        for long_threshold, short_threshold in (
            (3.0, 97.0),
            (5.0, 95.0),
            (8.0, 92.0),
            (10.0, 90.0),
            (12.0, 88.0),
            (15.0, 85.0),
            (18.0, 82.0),
        )
    ]
    rows: list[dict[str, object]] = []
    for symbol in SYMBOLS:
        for bar in ("3m", "5m", "15m"):
            candles = get_candles_cached(client, symbol, bar, history_limit)
            for candidate in candidates:
                metrics = evaluate_candidate_all_windows(
                    candidate=candidate,
                    candles=candles,
                    window_size=window_size,
                    leverage=LEVERAGE,
                )
                rows.append({"symbol": symbol, "bar": bar, "history_bars": len(candles), **metrics})
            print(f"done robust rsi2 cost {symbol} {bar} {len(candles)} bars")
    frame = sort_cost_results(add_cost_metrics(pd.DataFrame(rows), roundtrip_cost_on_margin))
    columns = [
        "symbol",
        "bar",
        "history_bars",
        "name",
        "sample_count",
        "avg_return",
        "ci95_low",
        "avg_trades_per_window",
        "breakeven_roundtrip_cost_on_margin",
        "roundtrip_cost_on_margin",
        "net_avg_return",
        "net_ci95_low",
        "net_ci95_high",
        "median_return",
        "positive_window_rate",
        "worst_return",
        "trade_win_rate",
        "avg_trade_return",
        "avg_win_return",
        "avg_loss_return_abs",
        "payoff_ratio",
        "profit_factor",
        "expectancy_per_trade",
        "max_drawdown",
        "return_drawdown_ratio",
    ]
    print(GROSS_RETURN_NOTE)
    print(frame[columns].head(40).to_string(index=False))
    frame.to_csv("ultrashort-rsi2-cost-search.csv", index=False)
    return 0


def rsi2_period_analysis(
    *,
    symbol: str,
    bar: str,
    history_limit: int,
    window_size: int,
    roundtrip_cost_on_margin: float,
    trend: int,
    long_threshold: float,
    short_threshold: float,
) -> int:
    client = OkxClient()
    candles = get_candles_cached(client, symbol, bar, history_limit)
    candidate = build_rsi2_candidate(trend, long_threshold, short_threshold)
    rows = evaluate_candidate_window_rows(
        candidate=candidate,
        candles=candles,
        window_size=window_size,
        leverage=LEVERAGE,
    )
    window_frame = pd.DataFrame(rows).drop(columns=["trades"])
    window_frame["window_start"] = pd.to_datetime(window_frame["window_start_ts"], unit="ms", utc=True).dt.strftime("%Y-%m-%d %H:%M")
    window_frame["window_end"] = pd.to_datetime(window_frame["window_end_ts"], unit="ms", utc=True).dt.strftime("%Y-%m-%d %H:%M")
    window_frame["net_return"] = window_frame["total_return"] - window_frame["trade_count"] * roundtrip_cost_on_margin
    window_frame.insert(0, "name", candidate.name)
    window_frame.insert(0, "history_bars", len(candles))
    window_frame.insert(0, "bar", bar)
    window_frame.insert(0, "symbol", symbol)

    monthly = summarize_periods(window_frame, "M", roundtrip_cost_on_margin)
    quarterly = summarize_periods(window_frame, "Q", roundtrip_cost_on_margin)
    full_result = candidate.run(candles=candles, leverage=LEVERAGE, warmup_bars=candidate.warmup_bars)
    equity_monthly = summarize_equity_periods(full_result, "M")
    equity_quarterly = summarize_equity_periods(full_result, "Q")
    net_trade_equity_monthly = summarize_cost_adjusted_trade_equity_periods(full_result, "M", roundtrip_cost_on_margin)
    net_trade_equity_quarterly = summarize_cost_adjusted_trade_equity_periods(full_result, "Q", roundtrip_cost_on_margin)
    window_frame.to_csv("ultrashort-rsi2-window-distribution.csv", index=False)
    monthly.to_csv("ultrashort-rsi2-monthly.csv", index=False)
    quarterly.to_csv("ultrashort-rsi2-quarterly.csv", index=False)
    equity_monthly.to_csv("ultrashort-rsi2-equity-monthly.csv", index=False)
    equity_quarterly.to_csv("ultrashort-rsi2-equity-quarterly.csv", index=False)
    net_trade_equity_monthly.to_csv("ultrashort-rsi2-net-trade-equity-monthly.csv", index=False)
    net_trade_equity_quarterly.to_csv("ultrashort-rsi2-net-trade-equity-quarterly.csv", index=False)

    summary = add_cost_metrics(pd.DataFrame([summarize_window_rows(rows, candidate.name)]), roundtrip_cost_on_margin)
    first_candle = _format_ts(candles[0].ts)
    last_candle = _format_ts(candles[-1].ts)
    print(f"actual data range UTC: {first_candle} -> {last_candle}; bars={len(candles)}")
    print(
        summary[
            [
                "sample_count",
                "avg_return",
                "ci95_low",
                "avg_trades_per_window",
                "roundtrip_cost_on_margin",
                "net_avg_return",
                "net_ci95_low",
                "positive_window_rate",
                "trades",
                "trade_win_rate",
                "avg_win_return",
                "avg_loss_return_abs",
                "payoff_ratio",
                "profit_factor",
                "max_drawdown",
            ]
        ].to_string(index=False)
    )
    print("monthly")
    print(monthly.to_string(index=False))
    print("quarterly")
    print(quarterly.to_string(index=False))
    print("equity monthly")
    print(equity_monthly.to_string(index=False))
    print("equity quarterly")
    print(equity_quarterly.to_string(index=False))
    print("cost-adjusted closed-trade equity monthly")
    print(net_trade_equity_monthly.to_string(index=False))
    print("cost-adjusted closed-trade equity quarterly")
    print(net_trade_equity_quarterly.to_string(index=False))
    return 0


def strategy_timeframe_analysis(
    *,
    symbols: tuple[str, ...],
    bars: tuple[str, ...],
    years: float,
    window_size: int,
    roundtrip_cost_on_margin: float,
    period: str,
) -> int:
    client = OkxClient()
    candidates = build_strategy_timeframe_candidates()
    summary_rows: list[dict[str, object]] = []
    period_frames: list[pd.DataFrame] = []
    availability_rows: list[dict[str, object]] = []
    for symbol in symbols:
        for bar in bars:
            requested_bars = history_bars_for_years(bar, years)
            candles = get_candles_cached(client, symbol, bar, requested_bars)
            first_ts = candles[0].ts
            last_ts = candles[-1].ts
            availability_rows.append(
                {
                    "symbol": symbol,
                    "bar": bar,
                    "requested_years": years,
                    "requested_bars": requested_bars,
                    "actual_bars": len(candles),
                    "first_candle": _format_ts(first_ts),
                    "last_candle": _format_ts(last_ts),
                    "actual_days": (last_ts - first_ts) / 86_400_000,
                    "complete_requested_range": len(candles) >= requested_bars,
                }
            )
            for candidate in candidates:
                rows = evaluate_candidate_window_rows(
                    candidate=candidate,
                    candles=candles,
                    window_size=window_size,
                    leverage=LEVERAGE,
                )
                summary = add_cost_metrics(pd.DataFrame([summarize_window_rows(rows, candidate.name)]), roundtrip_cost_on_margin).iloc[0].to_dict()
                summary_rows.append(
                    {
                        "symbol": symbol,
                        "bar": bar,
                        "requested_bars": requested_bars,
                        "actual_bars": len(candles),
                        "first_candle": _format_ts(first_ts),
                        "last_candle": _format_ts(last_ts),
                        **summary,
                    }
                )
                window_frame = pd.DataFrame(rows).drop(columns=["trades"])
                period_frame = summarize_periods(window_frame, period, roundtrip_cost_on_margin)
                period_frame.insert(0, "name", candidate.name)
                period_frame.insert(0, "bar", bar)
                period_frame.insert(0, "symbol", symbol)
                period_frames.append(period_frame)
                print(f"done strategy timeframe {symbol} {bar} {candidate.name} windows={len(rows)}")
    availability = pd.DataFrame(availability_rows)
    summary_frame = sort_cost_results(pd.DataFrame(summary_rows))
    period_summary = pd.concat(period_frames, ignore_index=True)
    availability.to_csv("ultrashort-strategy-timeframe-availability.csv", index=False)
    summary_frame.to_csv("ultrashort-strategy-timeframe-summary.csv", index=False)
    period_summary.to_csv("ultrashort-strategy-timeframe-periods.csv", index=False)
    print("availability")
    print(availability.to_string(index=False))
    print("summary")
    print(
        summary_frame[
            [
                "symbol",
                "bar",
                "name",
                "actual_bars",
                "sample_count",
                "trades",
                "net_avg_return",
                "net_ci95_low",
                "positive_window_rate",
                "trade_win_rate",
                "payoff_ratio",
                "profit_factor",
                "max_drawdown",
            ]
        ].head(50).to_string(index=False)
    )
    return 0


def rsi2_variant_search(
    *,
    symbol: str,
    bar: str,
    years: float,
    window_size: int,
    roundtrip_cost_on_margin: float,
) -> int:
    client = OkxClient()
    requested_bars = history_bars_for_years(bar, years)
    candles = get_candles_cached(client, symbol, bar, requested_bars)
    candidates = [
        build_rsi2_side_candidate(trend, long_threshold, short_threshold, exit_rsi, side_mode)
        for trend in (50, 120, 240, 480)
        for long_threshold, short_threshold in (
            (2.0, 98.0),
            (3.0, 97.0),
            (5.0, 95.0),
        )
        for exit_rsi in (45.0, 55.0)
        for side_mode in ("both", "long", "short")
    ]
    rows: list[dict[str, object]] = []
    for candidate in candidates:
        metrics = evaluate_candidate_all_windows(
            candidate=candidate,
            candles=candles,
            window_size=window_size,
            leverage=LEVERAGE,
        )
        rows.append(
            {
                "symbol": symbol,
                "bar": bar,
                "requested_bars": requested_bars,
                "actual_bars": len(candles),
                "first_candle": _format_ts(candles[0].ts),
                "last_candle": _format_ts(candles[-1].ts),
                **metrics,
            }
        )
        print(f"done rsi2 variant {candidate.name}")
    frame = sort_cost_results(add_cost_metrics(pd.DataFrame(rows), roundtrip_cost_on_margin))
    frame.to_csv("ultrashort-rsi2-variant-search.csv", index=False)
    print(
        frame[
            [
                "symbol",
                "bar",
                "name",
                "sample_count",
                "trades",
                "net_avg_return",
                "net_ci95_low",
                "positive_window_rate",
                "trade_win_rate",
                "avg_win_return",
                "avg_loss_return_abs",
                "payoff_ratio",
                "profit_factor",
                "max_drawdown",
            ]
        ].head(50).to_string(index=False)
    )
    return 0


def build_best_known_candidates() -> list[Candidate]:
    bbmr_config = BBMRConfig(band_length=20, std_multiplier=2.5, bandwidth_lookback=50, stop_loss_pct=0.005)
    return [
        Candidate(
            "bbmr-l20-m2.5-sl0.005",
            50,
            lambda candles, leverage, warmup_bars, config=bbmr_config: run_bbmr_segment(
                candles=candles,
                leverage=leverage,
                warmup_bars=warmup_bars,
                config=config,
            ),
        ),
        build_rsi2_side_candidate(50, 3.0, 97.0, 45.0, "long"),
        build_rsi2_side_candidate(50, 3.0, 97.0, 55.0, "long"),
        build_rsi2_side_candidate(240, 2.0, 98.0, 55.0, "long"),
        build_trend_rsi_bb_long_candidate(480, 20, 2.0, 5.0, 45.0, 0.005),
        build_trend_rsi_bb_long_candidate(240, 20, 2.5, 5.0, 55.0, 0.008),
        build_regime_hybrid_candidate(240, 240, 0.015, 2.0, 55.0, 2.5, 0.008),
        build_regime_hybrid_candidate(50, 240, 0.02, 3.0, 55.0, 2.5, 0.005),
    ]


def best_total_annualized(
    *,
    symbols: tuple[str, ...],
    bar: str,
    years: float,
    roundtrip_cost_on_margin: float,
) -> int:
    client = OkxClient()
    rows: list[dict[str, object]] = []
    horizon_rows: list[dict[str, object]] = []
    horizons = (
        ("3y", pd.DateOffset(years=3)),
        ("1y", pd.DateOffset(years=1)),
        ("6m", pd.DateOffset(months=6)),
        ("3m", pd.DateOffset(months=3)),
    )
    for symbol in symbols:
        candles = get_candles_cached(client, symbol, bar, history_bars_for_years(bar, years))
        for candidate in build_best_known_candidates():
            result = candidate.run(candles=candles, leverage=LEVERAGE, warmup_bars=candidate.warmup_bars)
            net_equity = cost_adjusted_trade_equity_frame(result, roundtrip_cost_on_margin)
            metrics = annualized_metrics_from_equity(net_equity, candles[0].ts, candles[-1].ts)
            gross_years = (candles[-1].ts - candles[0].ts) / 86_400_000 / 365
            gross_annualized = (1.0 + result.total_return) ** (1.0 / gross_years) - 1.0 if result.total_return > -1.0 else 0.0
            rows.append(
                {
                    "symbol": symbol,
                    "bar": bar,
                    "name": candidate.name,
                    "first_candle": _format_ts(candles[0].ts),
                    "last_candle": _format_ts(candles[-1].ts),
                    "years": gross_years,
                    "trades": result.trade_count,
                    "gross_total_return": result.total_return,
                    "gross_annualized_return": gross_annualized,
                    "gross_max_drawdown_mark_to_market": result.max_drawdown,
                    **metrics,
                }
            )
            horizon_frame = recent_horizon_metrics_from_equity(net_equity, candles[-1].ts, horizons)
            for horizon_row in horizon_frame.to_dict("records"):
                horizon_rows.append(
                    {
                        "symbol": symbol,
                        "bar": bar,
                        "name": candidate.name,
                        "first_candle": _format_ts(candles[0].ts),
                        "last_candle": _format_ts(candles[-1].ts),
                        "trades": result.trade_count,
                        **horizon_row,
                    }
                )
    frame = pd.DataFrame(rows).sort_values(["net_calmar", "net_annualized_return"], ascending=False)
    horizon_output = pd.DataFrame(horizon_rows)
    horizon_output["horizon"] = pd.Categorical(horizon_output["horizon"], categories=["3y", "1y", "6m", "3m"], ordered=True)
    horizon_output = horizon_output.sort_values(["horizon", "net_annualized_return"], ascending=[True, False])
    frame.to_csv("ultrashort-best-total-annualized.csv", index=False)
    horizon_output.to_csv("ultrashort-best-horizon-returns.csv", index=False)
    print(frame.to_string(index=False))
    print("recent horizon returns")
    print(horizon_output.to_string(index=False))
    return 0


def ma_cross_search(
    *,
    symbols: tuple[str, ...],
    bar: str,
    years: float,
    window_size: int,
    roundtrip_cost_on_margin: float,
) -> int:
    client = OkxClient()
    rows: list[dict[str, object]] = []
    for symbol in symbols:
        candles = get_candles_cached(client, symbol, bar, history_bars_for_years(bar, years))
        candidates = [
            build_ma_cross_candidate(fast, slow, side_mode)
            for fast, slow in ((12, 48), (20, 80), (30, 120), (50, 200), (80, 320))
            for side_mode in ("both", "long", "short")
        ]
        for candidate in candidates:
            metrics = evaluate_candidate_all_windows(
                candidate=candidate,
                candles=candles,
                window_size=window_size,
                leverage=LEVERAGE,
            )
            rows.append(
                {
                    "symbol": symbol,
                    "bar": bar,
                    "actual_bars": len(candles),
                    "first_candle": _format_ts(candles[0].ts),
                    "last_candle": _format_ts(candles[-1].ts),
                    **metrics,
                }
            )
            print(f"done ma cross {symbol} {candidate.name}")
    frame = sort_cost_results(add_cost_metrics(pd.DataFrame(rows), roundtrip_cost_on_margin))
    frame.to_csv("ultrashort-ma-cross-search.csv", index=False)
    print(
        frame[
            [
                "symbol",
                "bar",
                "name",
                "sample_count",
                "trades",
                "net_avg_return",
                "net_ci95_low",
                "positive_window_rate",
                "trade_win_rate",
                "payoff_ratio",
                "profit_factor",
                "max_drawdown",
            ]
        ].head(40).to_string(index=False)
    )
    return 0


def trend_rsi_bb_search(
    *,
    symbols: tuple[str, ...],
    bar: str,
    years: float,
    window_size: int,
    roundtrip_cost_on_margin: float,
) -> int:
    client = OkxClient()
    rows: list[dict[str, object]] = []
    candidates = [
        build_trend_rsi_bb_long_candidate(trend_sma, band_length, std_multiplier, rsi_threshold, exit_rsi, stop_loss_pct)
        for trend_sma in (120, 240, 480)
        for band_length in (20, 30)
        for std_multiplier in (2.0, 2.5)
        for rsi_threshold in (2.0, 3.0, 5.0)
        for exit_rsi in (45.0, 55.0)
        for stop_loss_pct in (0.005, 0.008)
    ]
    for symbol in symbols:
        candles = get_candles_cached(client, symbol, bar, history_bars_for_years(bar, years))
        for candidate in candidates:
            metrics = evaluate_candidate_all_windows(
                candidate=candidate,
                candles=candles,
                window_size=window_size,
                leverage=LEVERAGE,
            )
            rows.append(
                {
                    "symbol": symbol,
                    "bar": bar,
                    "actual_bars": len(candles),
                    "first_candle": _format_ts(candles[0].ts),
                    "last_candle": _format_ts(candles[-1].ts),
                    **metrics,
                }
            )
            print(f"done trend rsi bb {symbol} {candidate.name}")
    frame = sort_cost_results(add_cost_metrics(pd.DataFrame(rows), roundtrip_cost_on_margin))
    frame.to_csv("ultrashort-trend-rsi-bb-search.csv", index=False)
    print(
        frame[
            [
                "symbol",
                "bar",
                "name",
                "sample_count",
                "trades",
                "net_avg_return",
                "net_ci95_low",
                "positive_window_rate",
                "trade_win_rate",
                "avg_win_return",
                "avg_loss_return_abs",
                "payoff_ratio",
                "profit_factor",
                "max_drawdown",
            ]
        ].head(50).to_string(index=False)
    )
    return 0


def regime_analysis(
    *,
    symbols: tuple[str, ...],
    bar: str,
    years: float,
    window_size: int,
    roundtrip_cost_on_margin: float,
) -> int:
    client = OkxClient()
    window_frames: list[pd.DataFrame] = []
    for symbol in symbols:
        candles = get_candles_cached(client, symbol, bar, history_bars_for_years(bar, years))
        for candidate in build_best_known_candidates():
            rows = evaluate_candidate_window_rows(
                candidate=candidate,
                candles=candles,
                window_size=window_size,
                leverage=LEVERAGE,
            )
            frame = add_market_regime_columns(candles, rows, roundtrip_cost_on_margin)
            frame.insert(0, "name", candidate.name)
            frame.insert(0, "last_candle", _format_ts(candles[-1].ts))
            frame.insert(0, "first_candle", _format_ts(candles[0].ts))
            frame.insert(0, "bar", bar)
            frame.insert(0, "symbol", symbol)
            window_frames.append(frame)
            print(f"done regime {symbol} {candidate.name} windows={len(frame)}")
    windows = pd.concat(window_frames, ignore_index=True)
    summary = summarize_regime_columns(windows)
    windows.to_csv("ultrashort-regime-windows.csv", index=False)
    summary.to_csv("ultrashort-regime-summary.csv", index=False)
    print(
        summary[
            [
                "symbol",
                "bar",
                "name",
                "regime_type",
                "regime",
                "sample_count",
                "avg_net_return",
                "median_net_return",
                "positive_window_rate",
                "avg_trades",
                "avg_market_return",
                "avg_realized_vol",
                "avg_ma240_distance",
            ]
        ].to_string(index=False)
    )
    return 0


def eth_btc_signal_search(
    *,
    bar: str,
    years: float,
    window_size: int,
    roundtrip_cost_on_margin: float,
) -> int:
    client = OkxClient()
    requested_bars = history_bars_for_years(bar, years)
    eth = get_candles_cached(client, "ETH-USDT-SWAP", bar, requested_bars)
    btc = get_candles_cached(client, "BTC-USDT-SWAP", bar, requested_bars)
    eth, btc = align_pair_candles(eth, btc)
    candidates = [
        build_eth_btc_rsi_filter_candidate(eth_trend, eth_rsi, eth_exit, btc_trend, btc_momentum, btc_min_momentum)
        for eth_trend in (50, 120)
        for eth_rsi in (2.0, 3.0, 5.0)
        for eth_exit in (45.0, 55.0)
        for btc_trend in (120, 240, 480)
        for btc_momentum in (96, 240)
        for btc_min_momentum in (0.0, 0.01)
    ]
    summary_rows: list[dict[str, object]] = []
    total_rows: list[dict[str, object]] = []
    horizon_rows: list[dict[str, object]] = []
    horizons = (
        ("3y", pd.DateOffset(years=3)),
        ("1y", pd.DateOffset(years=1)),
        ("6m", pd.DateOffset(months=6)),
        ("3m", pd.DateOffset(months=3)),
    )
    for candidate in candidates:
        rows = evaluate_pair_candidate_window_rows(
            candidate=candidate,
            eth_candles=eth,
            btc_candles=btc,
            window_size=window_size,
            leverage=LEVERAGE,
        )
        summary = add_cost_metrics(pd.DataFrame([summarize_window_rows(rows, candidate.name)]), roundtrip_cost_on_margin).iloc[0].to_dict()
        summary_rows.append(
            {
                "symbol": "ETH-USDT-SWAP",
                "signal_symbol": "BTC-USDT-SWAP",
                "bar": bar,
                "actual_bars": len(eth),
                "first_candle": _format_ts(eth[0].ts),
                "last_candle": _format_ts(eth[-1].ts),
                **summary,
            }
        )

        result = candidate.run(eth_candles=eth, btc_candles=btc, leverage=LEVERAGE, warmup_bars=candidate.warmup_bars)
        net_equity = cost_adjusted_trade_equity_frame(result, roundtrip_cost_on_margin)
        metrics = annualized_metrics_from_equity(net_equity, eth[0].ts, eth[-1].ts)
        years_actual = (eth[-1].ts - eth[0].ts) / 86_400_000 / 365
        gross_annualized = (1.0 + result.total_return) ** (1.0 / years_actual) - 1.0 if result.total_return > -1.0 else 0.0
        total_rows.append(
            {
                "symbol": "ETH-USDT-SWAP",
                "signal_symbol": "BTC-USDT-SWAP",
                "bar": bar,
                "name": candidate.name,
                "first_candle": _format_ts(eth[0].ts),
                "last_candle": _format_ts(eth[-1].ts),
                "years": years_actual,
                "trades": result.trade_count,
                "gross_total_return": result.total_return,
                "gross_annualized_return": gross_annualized,
                "gross_max_drawdown_mark_to_market": result.max_drawdown,
                **metrics,
            }
        )
        horizon_frame = recent_horizon_metrics_from_equity(net_equity, eth[-1].ts, horizons)
        for horizon_row in horizon_frame.to_dict("records"):
            horizon_rows.append(
                {
                    "symbol": "ETH-USDT-SWAP",
                    "signal_symbol": "BTC-USDT-SWAP",
                    "bar": bar,
                    "name": candidate.name,
                    "trades": result.trade_count,
                    **horizon_row,
                }
            )
        print(f"done eth btc signal {candidate.name}")

    summary = sort_cost_results(pd.DataFrame(summary_rows))
    totals = pd.DataFrame(total_rows).sort_values(["net_calmar", "net_annualized_return"], ascending=False)
    horizon = pd.DataFrame(horizon_rows)
    horizon["horizon"] = pd.Categorical(horizon["horizon"], categories=["3y", "1y", "6m", "3m"], ordered=True)
    horizon = horizon.sort_values(["horizon", "net_annualized_return"], ascending=[True, False])
    summary.to_csv("ultrashort-eth-btc-signal-summary.csv", index=False)
    totals.to_csv("ultrashort-eth-btc-signal-total.csv", index=False)
    horizon.to_csv("ultrashort-eth-btc-signal-horizon.csv", index=False)
    print("window summary")
    print(
        summary[
            [
                "name",
                "sample_count",
                "trades",
                "net_avg_return",
                "net_ci95_low",
                "positive_window_rate",
                "trade_win_rate",
                "payoff_ratio",
                "profit_factor",
                "max_drawdown",
            ]
        ].head(30).to_string(index=False)
    )
    print("total")
    print(totals.head(30).to_string(index=False))
    print("horizon")
    print(horizon.head(60).to_string(index=False))
    return 0


def eth_btc_shock_filter_search(
    *,
    bar: str,
    years: float,
    window_size: int,
    roundtrip_cost_on_margin: float,
) -> int:
    client = OkxClient()
    requested_bars = history_bars_for_years(bar, years)
    eth = get_candles_cached(client, "ETH-USDT-SWAP", bar, requested_bars)
    btc = get_candles_cached(client, "BTC-USDT-SWAP", bar, requested_bars)
    eth, btc = align_pair_candles(eth, btc)
    candidates = [
        build_eth_btc_shock_filter_candidate(
            eth_trend,
            eth_rsi,
            eth_exit,
            btc_trend,
            btc_momentum,
            btc_min_momentum,
            btc_shock_lookback,
            btc_max_realized_vol,
            btc_max_drawdown,
        )
        for eth_trend in (50,)
        for eth_rsi in (3.0,)
        for eth_exit in (45.0, 55.0)
        for btc_trend in (480,)
        for btc_momentum in (240,)
        for btc_min_momentum in (0.0, 0.01)
        for btc_shock_lookback in (96, 240)
        for btc_max_realized_vol in (0.006, 0.01)
        for btc_max_drawdown in (0.03, 0.05, 0.08)
    ]
    summary_rows: list[dict[str, object]] = []
    total_rows: list[dict[str, object]] = []
    horizon_rows: list[dict[str, object]] = []
    horizons = (
        ("3y", pd.DateOffset(years=3)),
        ("1y", pd.DateOffset(years=1)),
        ("6m", pd.DateOffset(months=6)),
        ("3m", pd.DateOffset(months=3)),
    )
    for candidate in candidates:
        rows = evaluate_pair_candidate_window_rows(
            candidate=candidate,
            eth_candles=eth,
            btc_candles=btc,
            window_size=window_size,
            leverage=LEVERAGE,
        )
        summary = add_cost_metrics(pd.DataFrame([summarize_window_rows(rows, candidate.name)]), roundtrip_cost_on_margin).iloc[0].to_dict()
        summary_rows.append(
            {
                "symbol": "ETH-USDT-SWAP",
                "signal_symbol": "BTC-USDT-SWAP",
                "bar": bar,
                "actual_bars": len(eth),
                "first_candle": _format_ts(eth[0].ts),
                "last_candle": _format_ts(eth[-1].ts),
                **summary,
            }
        )

        result = candidate.run(eth_candles=eth, btc_candles=btc, leverage=LEVERAGE, warmup_bars=candidate.warmup_bars)
        net_equity = cost_adjusted_trade_equity_frame(result, roundtrip_cost_on_margin)
        metrics = annualized_metrics_from_equity(net_equity, eth[0].ts, eth[-1].ts)
        years_actual = (eth[-1].ts - eth[0].ts) / 86_400_000 / 365
        gross_annualized = (1.0 + result.total_return) ** (1.0 / years_actual) - 1.0 if result.total_return > -1.0 else 0.0
        total_rows.append(
            {
                "symbol": "ETH-USDT-SWAP",
                "signal_symbol": "BTC-USDT-SWAP",
                "bar": bar,
                "name": candidate.name,
                "first_candle": _format_ts(eth[0].ts),
                "last_candle": _format_ts(eth[-1].ts),
                "years": years_actual,
                "trades": result.trade_count,
                "gross_total_return": result.total_return,
                "gross_annualized_return": gross_annualized,
                "gross_max_drawdown_mark_to_market": result.max_drawdown,
                **metrics,
            }
        )
        horizon_frame = recent_horizon_metrics_from_equity(net_equity, eth[-1].ts, horizons)
        for horizon_row in horizon_frame.to_dict("records"):
            horizon_rows.append(
                {
                    "symbol": "ETH-USDT-SWAP",
                    "signal_symbol": "BTC-USDT-SWAP",
                    "bar": bar,
                    "name": candidate.name,
                    "trades": result.trade_count,
                    **horizon_row,
                }
            )
        print(f"done eth btc shock filter {candidate.name}")

    summary = sort_cost_results(pd.DataFrame(summary_rows))
    totals = pd.DataFrame(total_rows).sort_values(["net_calmar", "net_annualized_return"], ascending=False)
    horizon = pd.DataFrame(horizon_rows)
    horizon["horizon"] = pd.Categorical(horizon["horizon"], categories=["3y", "1y", "6m", "3m"], ordered=True)
    horizon = horizon.sort_values(["horizon", "net_annualized_return"], ascending=[True, False])
    summary.to_csv("ultrashort-eth-btc-shock-filter-summary.csv", index=False)
    totals.to_csv("ultrashort-eth-btc-shock-filter-total.csv", index=False)
    horizon.to_csv("ultrashort-eth-btc-shock-filter-horizon.csv", index=False)
    print("window summary")
    print(
        summary[
            [
                "name",
                "sample_count",
                "trades",
                "net_avg_return",
                "net_ci95_low",
                "positive_window_rate",
                "trade_win_rate",
                "payoff_ratio",
                "profit_factor",
                "max_drawdown",
            ]
        ].head(30).to_string(index=False)
    )
    print("total")
    print(totals.head(30).to_string(index=False))
    print("horizon")
    print(horizon.head(60).to_string(index=False))
    return 0


def eth_btc_ratio_search(
    *,
    bar: str,
    years: float,
    window_size: int,
    roundtrip_cost_on_margin: float,
) -> int:
    client = OkxClient()
    requested_bars = history_bars_for_years(bar, years)
    eth = get_candles_cached(client, "ETH-USDT-SWAP", bar, requested_bars)
    btc = get_candles_cached(client, "BTC-USDT-SWAP", bar, requested_bars)
    eth, btc = align_pair_candles(eth, btc)
    candidates = [
        build_eth_btc_ratio_pullback_candidate(btc_trend, btc_momentum, btc_min_momentum, ratio_length, ratio_std, ratio_rsi, stop)
        for btc_trend in (480,)
        for btc_momentum in (96, 240)
        for btc_min_momentum in (0.0, 0.01)
        for ratio_length in (48, 96)
        for ratio_std in (1.5, 2.0)
        for ratio_rsi in (5.0,)
        for stop in (0.005, 0.008)
    ]
    summary_rows: list[dict[str, object]] = []
    total_rows: list[dict[str, object]] = []
    horizon_rows: list[dict[str, object]] = []
    horizons = (
        ("3y", pd.DateOffset(years=3)),
        ("1y", pd.DateOffset(years=1)),
        ("6m", pd.DateOffset(months=6)),
        ("3m", pd.DateOffset(months=3)),
    )
    for candidate in candidates:
        rows = evaluate_pair_candidate_window_rows(
            candidate=candidate,
            eth_candles=eth,
            btc_candles=btc,
            window_size=window_size,
            leverage=LEVERAGE,
        )
        summary = add_cost_metrics(pd.DataFrame([summarize_window_rows(rows, candidate.name)]), roundtrip_cost_on_margin).iloc[0].to_dict()
        summary_rows.append(
            {
                "symbol": "ETH-USDT-SWAP",
                "signal_symbol": "BTC-USDT-SWAP",
                "bar": bar,
                "actual_bars": len(eth),
                "first_candle": _format_ts(eth[0].ts),
                "last_candle": _format_ts(eth[-1].ts),
                **summary,
            }
        )
        result = candidate.run(eth_candles=eth, btc_candles=btc, leverage=LEVERAGE, warmup_bars=candidate.warmup_bars)
        net_equity = cost_adjusted_trade_equity_frame(result, roundtrip_cost_on_margin)
        metrics = annualized_metrics_from_equity(net_equity, eth[0].ts, eth[-1].ts)
        years_actual = (eth[-1].ts - eth[0].ts) / 86_400_000 / 365
        gross_annualized = (1.0 + result.total_return) ** (1.0 / years_actual) - 1.0 if result.total_return > -1.0 else 0.0
        total_rows.append(
            {
                "symbol": "ETH-USDT-SWAP",
                "signal_symbol": "BTC-USDT-SWAP",
                "bar": bar,
                "name": candidate.name,
                "first_candle": _format_ts(eth[0].ts),
                "last_candle": _format_ts(eth[-1].ts),
                "years": years_actual,
                "trades": result.trade_count,
                "gross_total_return": result.total_return,
                "gross_annualized_return": gross_annualized,
                "gross_max_drawdown_mark_to_market": result.max_drawdown,
                **metrics,
            }
        )
        horizon_frame = recent_horizon_metrics_from_equity(net_equity, eth[-1].ts, horizons)
        for horizon_row in horizon_frame.to_dict("records"):
            horizon_rows.append(
                {
                    "symbol": "ETH-USDT-SWAP",
                    "signal_symbol": "BTC-USDT-SWAP",
                    "bar": bar,
                    "name": candidate.name,
                    "trades": result.trade_count,
                    **horizon_row,
                }
            )
        print(f"done eth btc ratio {candidate.name}")

    summary = sort_cost_results(pd.DataFrame(summary_rows))
    totals = pd.DataFrame(total_rows).sort_values(["net_calmar", "net_annualized_return"], ascending=False)
    horizon = pd.DataFrame(horizon_rows)
    horizon["horizon"] = pd.Categorical(horizon["horizon"], categories=["3y", "1y", "6m", "3m"], ordered=True)
    horizon = horizon.sort_values(["horizon", "net_annualized_return"], ascending=[True, False])
    summary.to_csv("ultrashort-eth-btc-ratio-summary.csv", index=False)
    totals.to_csv("ultrashort-eth-btc-ratio-total.csv", index=False)
    horizon.to_csv("ultrashort-eth-btc-ratio-horizon.csv", index=False)
    print("window summary")
    print(
        summary[
            [
                "name",
                "sample_count",
                "trades",
                "net_avg_return",
                "net_ci95_low",
                "positive_window_rate",
                "trade_win_rate",
                "payoff_ratio",
                "profit_factor",
                "max_drawdown",
            ]
        ].head(30).to_string(index=False)
    )
    print("total")
    print(totals.head(30).to_string(index=False))
    print("horizon")
    print(horizon.head(60).to_string(index=False))
    return 0


def btc_lead_eth_lag_search(
    *,
    bar: str,
    years: float,
    window_size: int,
    roundtrip_cost_on_margin: float,
) -> int:
    client = OkxClient()
    requested_bars = history_bars_for_years(bar, years)
    eth = get_candles_cached(client, "ETH-USDT-SWAP", bar, requested_bars)
    btc = get_candles_cached(client, "BTC-USDT-SWAP", bar, requested_bars)
    eth, btc = align_pair_candles(eth, btc)
    candidates = [
        build_btc_lead_eth_lag_candidate(lead_lookback, btc_return_threshold, lag_gap, max_hold_bars, stop_loss_pct, take_profit_pct)
        for lead_lookback in (8, 16)
        for btc_return_threshold in (0.012, 0.018, 0.024)
        for lag_gap in (0.006, 0.012)
        for max_hold_bars in (8, 32)
        for stop_loss_pct in (0.006, 0.008)
        for take_profit_pct in (0.012, 0.018)
    ]
    summary_rows: list[dict[str, object]] = []
    total_rows: list[dict[str, object]] = []
    horizon_rows: list[dict[str, object]] = []
    horizons = (
        ("3y", pd.DateOffset(years=3)),
        ("1y", pd.DateOffset(years=1)),
        ("6m", pd.DateOffset(months=6)),
        ("3m", pd.DateOffset(months=3)),
    )
    for candidate in candidates:
        rows = evaluate_pair_candidate_window_rows(
            candidate=candidate,
            eth_candles=eth,
            btc_candles=btc,
            window_size=window_size,
            leverage=LEVERAGE,
        )
        summary = add_cost_metrics(pd.DataFrame([summarize_window_rows(rows, candidate.name)]), roundtrip_cost_on_margin).iloc[0].to_dict()
        summary_rows.append(
            {
                "symbol": "ETH-USDT-SWAP",
                "signal_symbol": "BTC-USDT-SWAP",
                "bar": bar,
                "actual_bars": len(eth),
                "first_candle": _format_ts(eth[0].ts),
                "last_candle": _format_ts(eth[-1].ts),
                **summary,
            }
        )

        result = candidate.run(eth_candles=eth, btc_candles=btc, leverage=LEVERAGE, warmup_bars=candidate.warmup_bars)
        net_equity = cost_adjusted_trade_equity_frame(result, roundtrip_cost_on_margin)
        metrics = annualized_metrics_from_equity(net_equity, eth[0].ts, eth[-1].ts)
        years_actual = (eth[-1].ts - eth[0].ts) / 86_400_000 / 365
        gross_annualized = (1.0 + result.total_return) ** (1.0 / years_actual) - 1.0 if result.total_return > -1.0 else 0.0
        total_rows.append(
            {
                "symbol": "ETH-USDT-SWAP",
                "signal_symbol": "BTC-USDT-SWAP",
                "bar": bar,
                "name": candidate.name,
                "first_candle": _format_ts(eth[0].ts),
                "last_candle": _format_ts(eth[-1].ts),
                "years": years_actual,
                "trades": result.trade_count,
                "gross_total_return": result.total_return,
                "gross_annualized_return": gross_annualized,
                "gross_max_drawdown_mark_to_market": result.max_drawdown,
                **metrics,
            }
        )
        horizon_frame = recent_horizon_metrics_from_equity(net_equity, eth[-1].ts, horizons)
        for horizon_row in horizon_frame.to_dict("records"):
            horizon_rows.append(
                {
                    "symbol": "ETH-USDT-SWAP",
                    "signal_symbol": "BTC-USDT-SWAP",
                    "bar": bar,
                    "name": candidate.name,
                    "trades": result.trade_count,
                    **horizon_row,
                }
            )
        print(f"done btc lead eth lag {candidate.name}")

    summary = sort_cost_results(pd.DataFrame(summary_rows))
    totals = pd.DataFrame(total_rows).sort_values(["net_calmar", "net_annualized_return"], ascending=False)
    horizon = pd.DataFrame(horizon_rows)
    horizon["horizon"] = pd.Categorical(horizon["horizon"], categories=["3y", "1y", "6m", "3m"], ordered=True)
    horizon = horizon.sort_values(["horizon", "net_annualized_return"], ascending=[True, False])
    output_prefix = f"ultrashort-btc-lead-eth-lag-{bar}"
    summary.to_csv(f"{output_prefix}-summary.csv", index=False)
    totals.to_csv(f"{output_prefix}-total.csv", index=False)
    horizon.to_csv(f"{output_prefix}-horizon.csv", index=False)
    print("window summary")
    print(
        summary[
            [
                "name",
                "sample_count",
                "trades",
                "net_avg_return",
                "net_ci95_low",
                "positive_window_rate",
                "trade_win_rate",
                "payoff_ratio",
                "profit_factor",
                "max_drawdown",
            ]
        ].head(30).to_string(index=False)
    )
    print("total")
    print(totals.head(30).to_string(index=False))
    print("horizon")
    print(horizon.head(60).to_string(index=False))
    return 0


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--focus-vwap", action="store_true")
    parser.add_argument("--robust-vwap", action="store_true")
    parser.add_argument("--robust-all", action="store_true")
    parser.add_argument("--rsi2-cost-search", action="store_true")
    parser.add_argument("--rsi2-period-analysis", action="store_true")
    parser.add_argument("--strategy-timeframe-analysis", action="store_true")
    parser.add_argument("--rsi2-variant-search", action="store_true")
    parser.add_argument("--best-total-annualized", action="store_true")
    parser.add_argument("--ma-cross-search", action="store_true")
    parser.add_argument("--trend-rsi-bb-search", action="store_true")
    parser.add_argument("--regime-analysis", action="store_true")
    parser.add_argument("--eth-btc-signal-search", action="store_true")
    parser.add_argument("--eth-btc-shock-filter-search", action="store_true")
    parser.add_argument("--eth-btc-ratio-search", action="store_true")
    parser.add_argument("--btc-lead-eth-lag-search", action="store_true")
    parser.add_argument("--history-limit", type=int, default=ROBUST_HISTORY_LIMIT)
    parser.add_argument("--window-size", type=int, default=WINDOW_SIZE)
    parser.add_argument("--roundtrip-cost-on-margin", type=float, default=0.0012)
    parser.add_argument("--symbol", default="BTC-USDT-SWAP")
    parser.add_argument("--symbols", default="BTC-USDT-SWAP")
    parser.add_argument("--bar", default="15m")
    parser.add_argument("--bars", default=",".join(ANALYSIS_BARS))
    parser.add_argument("--years", type=float, default=10.0)
    parser.add_argument("--period", default="Q")
    parser.add_argument("--rsi2-trend", type=int, default=50)
    parser.add_argument("--rsi2-long-threshold", type=float, default=3.0)
    parser.add_argument("--rsi2-short-threshold", type=float, default=97.0)
    args = parser.parse_args()
    if args.rsi2_period_analysis:
        raise SystemExit(
            rsi2_period_analysis(
                symbol=args.symbol,
                bar=args.bar,
                history_limit=args.history_limit,
                window_size=args.window_size,
                roundtrip_cost_on_margin=args.roundtrip_cost_on_margin,
                trend=args.rsi2_trend,
                long_threshold=args.rsi2_long_threshold,
                short_threshold=args.rsi2_short_threshold,
            )
        )
    if args.rsi2_cost_search:
        raise SystemExit(robust_rsi2_cost_search(args.history_limit, args.window_size, args.roundtrip_cost_on_margin))
    if args.best_total_annualized:
        raise SystemExit(
            best_total_annualized(
                symbols=tuple(value.strip() for value in args.symbols.split(",") if value.strip()),
                bar=args.bar,
                years=args.years,
                roundtrip_cost_on_margin=args.roundtrip_cost_on_margin,
            )
        )
    if args.ma_cross_search:
        raise SystemExit(
            ma_cross_search(
                symbols=tuple(value.strip() for value in args.symbols.split(",") if value.strip()),
                bar=args.bar,
                years=args.years,
                window_size=args.window_size,
                roundtrip_cost_on_margin=args.roundtrip_cost_on_margin,
            )
        )
    if args.trend_rsi_bb_search:
        raise SystemExit(
            trend_rsi_bb_search(
                symbols=tuple(value.strip() for value in args.symbols.split(",") if value.strip()),
                bar=args.bar,
                years=args.years,
                window_size=args.window_size,
                roundtrip_cost_on_margin=args.roundtrip_cost_on_margin,
            )
        )
    if args.regime_analysis:
        raise SystemExit(
            regime_analysis(
                symbols=tuple(value.strip() for value in args.symbols.split(",") if value.strip()),
                bar=args.bar,
                years=args.years,
                window_size=args.window_size,
                roundtrip_cost_on_margin=args.roundtrip_cost_on_margin,
            )
        )
    if args.eth_btc_signal_search:
        raise SystemExit(
            eth_btc_signal_search(
                bar=args.bar,
                years=args.years,
                window_size=args.window_size,
                roundtrip_cost_on_margin=args.roundtrip_cost_on_margin,
            )
        )
    if args.eth_btc_shock_filter_search:
        raise SystemExit(
            eth_btc_shock_filter_search(
                bar=args.bar,
                years=args.years,
                window_size=args.window_size,
                roundtrip_cost_on_margin=args.roundtrip_cost_on_margin,
            )
        )
    if args.eth_btc_ratio_search:
        raise SystemExit(
            eth_btc_ratio_search(
                bar=args.bar,
                years=args.years,
                window_size=args.window_size,
                roundtrip_cost_on_margin=args.roundtrip_cost_on_margin,
            )
        )
    if args.btc_lead_eth_lag_search:
        raise SystemExit(
            btc_lead_eth_lag_search(
                bar=args.bar,
                years=args.years,
                window_size=args.window_size,
                roundtrip_cost_on_margin=args.roundtrip_cost_on_margin,
            )
        )
    if args.rsi2_variant_search:
        raise SystemExit(
            rsi2_variant_search(
                symbol=args.symbol,
                bar=args.bar,
                years=args.years,
                window_size=args.window_size,
                roundtrip_cost_on_margin=args.roundtrip_cost_on_margin,
            )
        )
    if args.strategy_timeframe_analysis:
        raise SystemExit(
            strategy_timeframe_analysis(
                symbols=tuple(value.strip() for value in args.symbols.split(",") if value.strip()),
                bars=tuple(value.strip() for value in args.bars.split(",") if value.strip()),
                years=args.years,
                window_size=args.window_size,
                roundtrip_cost_on_margin=args.roundtrip_cost_on_margin,
                period=args.period,
            )
        )
    if args.robust_all:
        raise SystemExit(robust_all(args.history_limit, args.window_size))
    if args.robust_vwap:
        raise SystemExit(robust_vwap(args.history_limit, args.window_size))
    raise SystemExit(focus_vwap() if args.focus_vwap else main())