okx-codex-trader/scripts/search_eth_bb_squeeze_profit_protection.py

from __future__ import annotations

import argparse
import sys
from dataclasses import dataclass
from pathlib import Path

import pandas as pd

sys.path.insert(0, str(Path(__file__).resolve().parents[1]))

from okx_codex_trader.candles import align_candles_by_ts, load_candles_csv
from okx_codex_trader.models import Candle
from okx_codex_trader.research_metrics import (
    DEFAULT_COSTS,
    DEFAULT_HORIZONS,
    DEFAULT_INITIAL_EQUITY,
    DEFAULT_PRIMARY_COST,
    cost_equity_frame,
    equity_metrics,
    format_utc_ts,
    horizon_rows,
    worst_month,
)
from okx_codex_trader.sampled_report import SegmentResult, mark_to_market, trade_equity


ETH_SYMBOL = "ETH-USDT-SWAP"
BTC_SYMBOL = "BTC-USDT-SWAP"
BAR = "15m"
YEARS = 10.0
LEVERAGE = 3
INITIAL_EQUITY = DEFAULT_INITIAL_EQUITY
DATA_DIR = Path("data/okx-candles")
OUTPUT_DIR = Path("reports/eth-exploration")
PRIMARY_COST = DEFAULT_PRIMARY_COST
COSTS = DEFAULT_COSTS
HORIZONS = DEFAULT_HORIZONS


@dataclass(frozen=True)
class Variant:
    band_length: int
    bandwidth_lookback: int
    bandwidth_quantile: float
    side_mode: str
    btc_filter: str
    eth_vol_cap: float | None
    cooldown_bars: int
    stop_loss_pct: float
    middle_exit_buffer_pct: float
    middle_exit_confirm_bars: int
    breakeven_trigger_pct: float | None
    breakeven_lock_pct: float
    trail_trigger_pct: float | None
    trail_giveback_pct: float | None
    max_giveback_trigger_pct: float | None
    max_giveback_pct: float | None

    @property
    def name(self) -> str:
        vol = "none" if self.eth_vol_cap is None else f"{self.eth_vol_cap:g}"
        be = "none" if self.breakeven_trigger_pct is None else f"{self.breakeven_trigger_pct:g}-{self.breakeven_lock_pct:g}"
        trail = "none" if self.trail_trigger_pct is None else f"{self.trail_trigger_pct:g}-{self.trail_giveback_pct:g}"
        gb = "none" if self.max_giveback_trigger_pct is None else f"{self.max_giveback_trigger_pct:g}-{self.max_giveback_pct:g}"
        return (
            f"bb-squeeze-protect-l{self.band_length}-bw{self.bandwidth_lookback}"
            f"-q{self.bandwidth_quantile:g}-sl{self.stop_loss_pct:g}"
            f"-{self.side_mode}-{self.btc_filter}-vc{vol}-cd{self.cooldown_bars}"
            f"-mxbuf{self.middle_exit_buffer_pct:g}-mxc{self.middle_exit_confirm_bars}"
            f"-be{be}-tr{trail}-gb{gb}"
        )


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


def close_position(
    *,
    trades: list[dict[str, object]],
    exits: list[dict[str, object]],
    position: dict[str, object],
    candle: Candle,
    exit_price: float,
    reason: str,
) -> tuple[float, bool]:
    margin_used = float(position["margin_used"])
    exit_equity = trade_equity(
        side=str(position["side"]),
        margin_used=margin_used,
        entry_price=float(position["entry_price"]),
        exit_price=exit_price,
        leverage=LEVERAGE,
    )
    pnl = exit_equity - margin_used
    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(pnl, 4),
            "return_pct": round(pnl / margin_used * 100.0, 4),
            "cost_weight": 1.0,
            "exit_reason": reason,
            "mfe_pct": round(float(position["mfe_pct"]) * 100.0, 4),
        }
    )
    exits.append({"ts": candle.ts, "price": exit_price, "side": position["side"]})
    return exit_equity, pnl > 0.0


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


def protection_exit(position: dict[str, object], candle: Candle, variant: Variant) -> tuple[float, str] | None:
    side = str(position["side"])
    entry_price = float(position["entry_price"])
    mfe = float(position["mfe_pct"])
    stop_price = float(position["stop_price"])
    if variant.breakeven_trigger_pct is not None and mfe >= variant.breakeven_trigger_pct:
        be_stop = entry_price * (1.0 + variant.breakeven_lock_pct if side == "long" else 1.0 - variant.breakeven_lock_pct)
        stop_price = max(stop_price, be_stop) if side == "long" else min(stop_price, be_stop)
    if variant.trail_trigger_pct is not None and variant.trail_giveback_pct is not None and mfe >= variant.trail_trigger_pct:
        if side == "long":
            trail_stop = entry_price * (1.0 + mfe - variant.trail_giveback_pct)
            stop_price = max(stop_price, trail_stop)
        else:
            trail_stop = entry_price * (1.0 - mfe + variant.trail_giveback_pct)
            stop_price = min(stop_price, trail_stop)
    if side == "long":
        if candle.open <= stop_price:
            return candle.open, "protect_gap" if stop_price != float(position["stop_price"]) else "stop_gap"
        if candle.low <= stop_price:
            return stop_price, "profit_protect" if stop_price != float(position["stop_price"]) else "stop"
    else:
        if candle.open >= stop_price:
            return candle.open, "protect_gap" if stop_price != float(position["stop_price"]) else "stop_gap"
        if candle.high >= stop_price:
            return stop_price, "profit_protect" if stop_price != float(position["stop_price"]) else "stop"
    if variant.max_giveback_trigger_pct is None or variant.max_giveback_pct is None or mfe < variant.max_giveback_trigger_pct:
        return None
    if side == "long":
        close_profit = candle.close / entry_price - 1.0
    else:
        close_profit = entry_price / candle.close - 1.0
    if close_profit <= mfe - variant.max_giveback_pct:
        return candle.close, "giveback_close"
    return None


def run_variant(eth: list[Candle], btc: list[Candle], variant: Variant) -> tuple[SegmentResult, dict[str, int]]:
    eth_close = pd.Series([candle.close for candle in eth], dtype=float)
    btc_close = pd.Series([candle.close for candle in btc], dtype=float)
    middle_series = eth_close.rolling(variant.band_length).mean()
    stdev_series = eth_close.rolling(variant.band_length).std(ddof=0)
    upper_values = middle_series + 2.0 * stdev_series
    lower_values = middle_series - 2.0 * stdev_series
    middle = middle_series.tolist()
    upper = upper_values.tolist()
    lower = lower_values.tolist()
    bandwidth = ((upper_values - lower_values) / middle_series).tolist()
    threshold = pd.Series(bandwidth, dtype=float).rolling(variant.bandwidth_lookback).quantile(variant.bandwidth_quantile).tolist()
    btc_sma = btc_close.rolling(480).mean().tolist()
    btc_momentum = (btc_close / btc_close.shift(96) - 1.0).tolist()
    eth_realized_vol = eth_close.pct_change().rolling(96).std(ddof=0).tolist()
    warmup_bars = max(variant.band_length, variant.bandwidth_lookback, 480, 96)

    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
    middle_exit_streak = 0
    cooldown_until = -1
    exit_counts = {"stop_exits": 0, "protect_exits": 0, "giveback_exits": 0, "signal_exits": 0}

    for index in range(warmup_bars, len(eth)):
        candle = eth[index]
        if pending_exit and position is not None:
            equity, won = close_position(
                trades=trades,
                exits=exits,
                position=position,
                candle=candle,
                exit_price=candle.open,
                reason="signal_middle",
            )
            wins += int(won)
            exit_counts["signal_exits"] += 1
            position = None
            pending_exit = False
            middle_exit_streak = 0
            cooldown_until = index + variant.cooldown_bars
        if pending_entry_side is not None and position is None and equity > 0.0:
            entry_price = candle.open
            position = {
                "side": pending_entry_side,
                "entry_time": candle.ts,
                "entry_price": entry_price,
                "margin_used": equity,
                "stop_price": entry_price * (1.0 - variant.stop_loss_pct if pending_entry_side == "long" else 1.0 + variant.stop_loss_pct),
                "mfe_pct": 0.0,
            }
            entries.append({"ts": candle.ts, "price": entry_price, "side": pending_entry_side})
            pending_entry_side = None

        current_equity = equity
        if position is not None:
            risk_exit = protection_exit(position, candle, variant)
            if risk_exit is not None:
                exit_price, reason = risk_exit
                equity, won = close_position(
                    trades=trades,
                    exits=exits,
                    position=position,
                    candle=candle,
                    exit_price=exit_price,
                    reason=reason,
                )
                wins += int(won)
                if reason.startswith("stop"):
                    exit_counts["stop_exits"] += 1
                elif reason == "giveback_close":
                    exit_counts["giveback_exits"] += 1
                else:
                    exit_counts["protect_exits"] += 1
                current_equity = equity
                position = None
                middle_exit_streak = 0
                cooldown_until = index + variant.cooldown_bars
        if position is not None:
            position["mfe_pct"] = max(float(position["mfe_pct"]), favorable_move(str(position["side"]), float(position["entry_price"]), candle))
        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(eth) - 1 or equity <= 0.0:
            continue

        values = (middle[index], upper[index], lower[index], bandwidth[index], threshold[index], btc_sma[index], btc_momentum[index], eth_realized_vol[index])
        if any(value != value for value in values):
            continue
        if position is not None:
            middle_exit = (
                position["side"] == "long" and candle.close < float(middle[index]) * (1.0 - variant.middle_exit_buffer_pct)
            ) or (
                position["side"] == "short" and candle.close > float(middle[index]) * (1.0 + variant.middle_exit_buffer_pct)
            )
            middle_exit_streak = middle_exit_streak + 1 if middle_exit else 0
            if middle_exit_streak >= variant.middle_exit_confirm_bars:
                pending_exit = True
            continue
        if index < cooldown_until:
            continue
        if variant.eth_vol_cap is not None and float(eth_realized_vol[index]) > variant.eth_vol_cap:
            continue
        if variant.btc_filter == "btc-up" and not (btc_close.iloc[index] > float(btc_sma[index])):
            continue
        if variant.btc_filter == "btc-up-momo" and not (
            btc_close.iloc[index] > float(btc_sma[index]) and float(btc_momentum[index]) > 0.0
        ):
            continue
        if bandwidth[index] <= threshold[index]:
            if candle.close > float(upper[index]):
                pending_entry_side = "long"
            elif variant.side_mode == "both" and candle.close < float(lower[index]):
                pending_entry_side = "short"

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


def trade_stats(trades: list[dict[str, object]]) -> dict[str, float]:
    if not trades:
        return {"avg_mfe_pct": 0.0, "avg_return_pct": 0.0, "payoff_ratio": 0.0, "profit_factor": 0.0}
    returns = [float(trade["return_pct"]) for trade in trades]
    wins = [value for value in returns if value > 0.0]
    losses = [-value for value in returns if value < 0.0]
    return {
        "avg_mfe_pct": sum(float(trade["mfe_pct"]) for trade in trades) / len(trades),
        "avg_return_pct": sum(returns) / len(returns),
        "payoff_ratio": (sum(wins) / len(wins)) / (sum(losses) / len(losses)) if wins and losses else 0.0,
        "profit_factor": sum(wins) / sum(losses) if losses else 0.0,
    }


def build_variants() -> list[Variant]:
    base_specs = (
        (48, 960, 0.25, "both", "none", 0.006, 0.01, 0.0005, 1),
        (48, 960, 0.25, "both", "none", 0.006, 0.01, 0.0010, 1),
        (48, 960, 0.25, "both", "none", 0.006, 0.012, 0.0005, 1),
        (96, 960, 0.25, "both", "btc-up", 0.006, 0.012, 0.001, 1),
        (96, 960, 0.25, "both", "btc-up-momo", 0.006, 0.012, 0.001, 1),
        (96, 480, 0.15, "both", "none", 0.006, 0.01, 0.001, 1),
    )
    protections = [
        (None, 0.0, None, None, None, None),
        (0.004, 0.000, None, None, None, None),
        (0.006, 0.000, None, None, None, None),
        (0.008, 0.001, None, None, None, None),
        (0.004, 0.000, 0.010, 0.006, None, None),
        (0.006, 0.000, 0.012, 0.006, None, None),
        (0.008, 0.001, 0.015, 0.008, None, None),
        (0.004, 0.000, None, None, 0.010, 0.006),
        (0.006, 0.000, None, None, 0.012, 0.007),
        (0.008, 0.001, None, None, 0.015, 0.010),
        (0.004, 0.000, 0.010, 0.006, 0.012, 0.008),
        (0.006, 0.000, 0.012, 0.006, 0.015, 0.010),
        (0.008, 0.001, 0.015, 0.008, 0.020, 0.012),
    ]
    variants: list[Variant] = []
    for band_length, lookback, quantile, side_mode, btc_filter, vol_cap, stop_loss, mxbuf, mxc in base_specs:
        for be_trigger, be_lock, trail_trigger, trail_giveback, gb_trigger, gb_pct in protections:
            variants.append(
                Variant(
                    band_length=band_length,
                    bandwidth_lookback=lookback,
                    bandwidth_quantile=quantile,
                    side_mode=side_mode,
                    btc_filter=btc_filter,
                    eth_vol_cap=vol_cap,
                    cooldown_bars=24,
                    stop_loss_pct=stop_loss,
                    middle_exit_buffer_pct=mxbuf,
                    middle_exit_confirm_bars=mxc,
                    breakeven_trigger_pct=be_trigger,
                    breakeven_lock_pct=be_lock,
                    trail_trigger_pct=trail_trigger,
                    trail_giveback_pct=trail_giveback,
                    max_giveback_trigger_pct=gb_trigger,
                    max_giveback_pct=gb_pct,
                )
            )
    return variants


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


def markdown_table(frame: pd.DataFrame) -> str:
    columns = list(frame.columns)
    rows = [columns, ["---" for _ in columns]]
    for record in frame.to_dict("records"):
        rows.append([record[column] for column in columns])
    return "\n".join("| " + " | ".join(format_cell(value) for value in row) + " |" for row in rows)


def write_report(summary: pd.DataFrame, horizon: pd.DataFrame, first_ts: int, last_ts: int, command: str) -> str:
    primary = summary[summary["cost"] == PRIMARY_COST]
    top = primary.head(10)
    baseline = primary[primary["breakeven_trigger_pct"].isna() & primary["trail_trigger_pct"].isna() & primary["max_giveback_trigger_pct"].isna()]
    baseline_top = baseline.sort_values(["net_calmar", "net_annualized_return"], ascending=[False, False]).head(5)
    horizon_top = (
        horizon[horizon["cost"] == PRIMARY_COST]
        .sort_values(["horizon", "net_calmar", "net_annualized_return"], ascending=[True, False, False])
        .groupby("horizon", observed=True)
        .head(3)
    )
    return "\n".join(
        [
            "# ETH BB squeeze profit-protection exploration",
            "",
            f"Run command: `{command}`",
            f"Actual continuous local history: `{_format_ts(first_ts)}` to `{_format_ts(last_ts)}`.",
            "",
            "The entry logic matches existing BB squeeze families. Variants only change exits after a trade has floating profit: breakeven stop, trailing protection, and close-based giveback.",
            "",
            "Top 10 by maker_taker Calmar:",
            markdown_table(
                top[
                    [
                        "name",
                        "trades",
                        "net_total_return",
                        "net_annualized_return",
                        "net_max_drawdown",
                        "net_calmar",
                        "avg_mfe_pct",
                        "avg_return_pct",
                        "profit_factor",
                        "stop_exits",
                        "protect_exits",
                        "giveback_exits",
                        "signal_exits",
                    ]
                ]
            ),
            "",
            "Best no-protection baselines in same grid:",
            markdown_table(
                baseline_top[
                    [
                        "name",
                        "trades",
                        "net_annualized_return",
                        "net_max_drawdown",
                        "net_calmar",
                        "avg_mfe_pct",
                        "avg_return_pct",
                        "profit_factor",
                    ]
                ]
            ),
            "",
            "Recent horizon leaders:",
            markdown_table(
                horizon_top[
                    [
                        "horizon",
                        "name",
                        "trades",
                        "net_total_return",
                        "net_annualized_return",
                        "net_max_drawdown",
                        "net_calmar",
                    ]
                ]
            ),
        ]
    ) + "\n"


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

    eth = load_candles_csv(DATA_DIR, ETH_SYMBOL, args.bar)
    btc = load_candles_csv(DATA_DIR, BTC_SYMBOL, args.bar)
    eth, btc = align_candles_by_ts(eth, btc)
    requested_bars = int(args.years * 365 * 24 * 60 / 15)
    eth = eth[-requested_bars:]
    btc = btc[-requested_bars:]

    summary_rows: list[dict[str, object]] = []
    horizon_rows_out: list[dict[str, object]] = []
    variants = build_variants()
    for index, variant in enumerate(variants, start=1):
        result, exit_counts = run_variant(eth, btc, variant)
        if not result.equity_curve:
            continue
        stats = trade_stats(result.trades)
        for cost_name, cost in COSTS:
            frame = cost_equity_frame(result, cost)
            metrics = equity_metrics(frame, eth[0].ts, eth[-1].ts)
            month, month_return = worst_month(frame)
            row = {
                "family": "bb_squeeze_profit_protection",
                "cost": cost_name,
                "symbol": ETH_SYMBOL,
                "signal_symbol": BTC_SYMBOL if variant.btc_filter != "none" else "",
                "bar": args.bar,
                "name": variant.name,
                "band_length": variant.band_length,
                "bandwidth_lookback": variant.bandwidth_lookback,
                "bandwidth_quantile": variant.bandwidth_quantile,
                "side_mode": variant.side_mode,
                "btc_filter": variant.btc_filter,
                "eth_vol_cap": variant.eth_vol_cap,
                "cooldown_bars": variant.cooldown_bars,
                "stop_loss_pct": variant.stop_loss_pct,
                "middle_exit_buffer_pct": variant.middle_exit_buffer_pct,
                "middle_exit_confirm_bars": variant.middle_exit_confirm_bars,
                "breakeven_trigger_pct": variant.breakeven_trigger_pct,
                "breakeven_lock_pct": variant.breakeven_lock_pct,
                "trail_trigger_pct": variant.trail_trigger_pct,
                "trail_giveback_pct": variant.trail_giveback_pct,
                "max_giveback_trigger_pct": variant.max_giveback_trigger_pct,
                "max_giveback_pct": variant.max_giveback_pct,
                "first_candle": _format_ts(eth[0].ts),
                "last_candle": _format_ts(eth[-1].ts),
                "years": (eth[-1].ts - eth[0].ts) / 86_400_000 / 365,
                "trades": result.trade_count,
                "gross_total_return": result.total_return,
                "gross_max_drawdown_mark_to_market": result.max_drawdown,
                "worst_month": month,
                "worst_month_return": month_return,
                **exit_counts,
                **stats,
                **metrics,
            }
            summary_rows.append(row)
            for horizon_row in horizon_rows(frame, eth[-1].ts, HORIZONS):
                horizon_rows_out.append(
                    {
                        "family": "bb_squeeze_profit_protection",
                        "cost": cost_name,
                        "symbol": ETH_SYMBOL,
                        "bar": args.bar,
                        "name": variant.name,
                        "trades": result.trade_count,
                        **horizon_row,
                    }
                )
        print(f"done {index}/{len(variants)} {variant.name}", flush=True)

    summary = pd.DataFrame(summary_rows).sort_values(
        ["cost", "net_calmar", "net_annualized_return", "profit_factor"],
        ascending=[True, False, False, False],
    )
    primary = summary[summary["cost"] == PRIMARY_COST]
    summary = pd.concat([primary, summary[summary["cost"] != PRIMARY_COST]], ignore_index=True)
    horizon = pd.DataFrame(horizon_rows_out)
    horizon["horizon"] = pd.Categorical(horizon["horizon"], categories=[label for label, _ in HORIZONS], ordered=True)
    horizon = horizon.sort_values(["cost", "horizon", "net_calmar", "net_annualized_return"], ascending=[True, True, False, False])

    args.output_dir.mkdir(parents=True, exist_ok=True)
    summary_path = args.output_dir / "eth-bb-squeeze-profit-protection-summary.csv"
    horizon_path = args.output_dir / "eth-bb-squeeze-profit-protection-horizon.csv"
    report_path = args.output_dir / "eth-bb-squeeze-profit-protection-report.md"
    summary.to_csv(summary_path, index=False)
    horizon.to_csv(horizon_path, index=False)
    command = f"rtk .venv/bin/python {Path(__file__).as_posix()} --bar {args.bar} --years {args.years}"
    report_path.write_text(write_report(summary, horizon, eth[0].ts, eth[-1].ts, command), encoding="utf-8")
    print(primary.head(10).to_string(index=False))
    return 0


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