from __future__ import annotations import argparse import sys from collections import Counter 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.models import Candle 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 = 10_000.0 DATA_DIR = Path("data/okx-candles") OUTPUT_DIR = Path("reports/eth-exploration") PRIMARY_COST = "maker_taker" COSTS = ( ("maker_maker", 0.0012), ("maker_taker", 0.0021), ("taker_taker", 0.0030), ) HORIZONS = ( ("full", None), ("3y", pd.DateOffset(years=3)), ("1y", pd.DateOffset(years=1)), ("6m", pd.DateOffset(months=6)), ("3m", pd.DateOffset(months=3)), ("30d", pd.DateOffset(days=30)), ) @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 trend_momentum_bars: int trend_middle_buffer_pct: float trend_middle_confirm_bars: int neutral_middle_buffer_pct: float neutral_middle_confirm_bars: int protect_trigger_pct: float protect_lock_pct: float protect_trail_giveback_pct: float protect_middle_confirm_bars: int fast_vol_pct: float fast_bandwidth_ratio: float fast_middle_buffer_pct: float fast_middle_confirm_bars: int max_hold_bars: int @property def name(self) -> str: vol = "none" if self.eth_vol_cap is None else f"{self.eth_vol_cap:g}" return ( f"eth-adaptive-state-exit-l{self.band_length}-bw{self.bandwidth_lookback}" f"-q{self.bandwidth_quantile:g}-{self.side_mode}-{self.btc_filter}-vc{vol}" f"-sl{self.stop_loss_pct:g}-tr{self.trend_momentum_bars}" f"-tb{self.trend_middle_buffer_pct:g}-tc{self.trend_middle_confirm_bars}" f"-nb{self.neutral_middle_buffer_pct:g}-nc{self.neutral_middle_confirm_bars}" f"-pt{self.protect_trigger_pct:g}-pl{self.protect_lock_pct:g}" f"-pg{self.protect_trail_giveback_pct:g}-pc{self.protect_middle_confirm_bars}" f"-fv{self.fast_vol_pct:g}-fr{self.fast_bandwidth_ratio:g}" f"-fb{self.fast_middle_buffer_pct:g}-fc{self.fast_middle_confirm_bars}" f"-mh{self.max_hold_bars}" ) def _format_ts(ts: int) -> str: return pd.to_datetime(ts, unit="ms", utc=True).strftime("%Y-%m-%d %H:%M") def _load_candles(symbol: str, bar: str) -> list[Candle]: frame = pd.read_csv(DATA_DIR / symbol / f"{bar}.csv") return [ 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) ] def _align_pair(left: list[Candle], right: list[Candle]) -> tuple[list[Candle], list[Candle]]: right_by_ts = {candle.ts: candle for candle in right} left_out: list[Candle] = [] right_out: list[Candle] = [] for candle in left: other = right_by_ts.get(candle.ts) if other is not None: left_out.append(candle) right_out.append(other) return left_out, right_out 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_ts": int(position["entry_time"]), "exit_ts": candle.ts, "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, "entry_state": str(position["state"]), "mfe_pct": round(float(position["mfe_pct"]) * 100.0, 4), "hold_bars": int(position["hold_bars"]), } ) 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 close_profit(side: str, entry_price: float, close: float) -> float: if side == "long": return close / entry_price - 1.0 return entry_price / close - 1.0 def hard_stop_exit(position: dict[str, object], candle: Candle) -> tuple[float, str] | None: side = str(position["side"]) stop = float(position["stop_price"]) if side == "long": if candle.open <= stop: return candle.open, "stop_gap" if candle.low <= stop: return stop, "stop" else: if candle.open >= stop: return candle.open, "stop_gap" if candle.high >= stop: return stop, "stop" return None def protection_stop_exit(position: dict[str, object], candle: Candle, variant: Variant) -> tuple[float, str] | None: if str(position["state"]) != "protect": return None side = str(position["side"]) entry_price = float(position["entry_price"]) mfe = float(position["mfe_pct"]) if side == "long": lock_stop = entry_price * (1.0 + variant.protect_lock_pct) trail_stop = entry_price * (1.0 + mfe - variant.protect_trail_giveback_pct) stop = max(float(position["stop_price"]), lock_stop, trail_stop) if candle.open <= stop: return candle.open, "protect_gap" if candle.low <= stop: return stop, "protect_trail" else: lock_stop = entry_price * (1.0 - variant.protect_lock_pct) trail_stop = entry_price * (1.0 - mfe + variant.protect_trail_giveback_pct) stop = min(float(position["stop_price"]), lock_stop, trail_stop) if candle.open >= stop: return candle.open, "protect_gap" if candle.high >= stop: return stop, "protect_trail" return None def adverse_middle(position: dict[str, object], candle: Candle, middle: float, buffer_pct: float) -> bool: if position["side"] == "long": return candle.close < middle * (1.0 - buffer_pct) return candle.close > middle * (1.0 + buffer_pct) def trend_continues(position: dict[str, object], candle: Candle, middle: float, momentum: float, required_bars: int) -> bool: if position["side"] == "long": above_middle = candle.close > middle same_direction = momentum > 0.0 else: above_middle = candle.close < middle same_direction = momentum < 0.0 if above_middle and same_direction: position["trend_bars"] = int(position["trend_bars"]) + 1 else: position["trend_bars"] = 0 return int(position["trend_bars"]) >= required_bars def update_state( *, position: dict[str, object], candle: Candle, middle: float, momentum: float, realized_vol: float, bandwidth: float, threshold: float, variant: Variant, ) -> None: position["hold_bars"] = int(position["hold_bars"]) + 1 position["mfe_pct"] = max(float(position["mfe_pct"]), favorable_move(str(position["side"]), float(position["entry_price"]), candle)) if float(position["mfe_pct"]) >= variant.protect_trigger_pct: position["state"] = "protect" return if realized_vol >= variant.fast_vol_pct or bandwidth >= threshold * variant.fast_bandwidth_ratio: position["state"] = "fast" return if trend_continues(position, candle, middle, momentum, variant.trend_momentum_bars): position["state"] = "trend" else: position["state"] = "neutral" def state_signal_exit(position: dict[str, object], candle: Candle, middle: float, variant: Variant) -> str | None: state = str(position["state"]) if state == "trend": buffer_pct = variant.trend_middle_buffer_pct confirm_bars = variant.trend_middle_confirm_bars elif state == "protect": buffer_pct = 0.0 confirm_bars = variant.protect_middle_confirm_bars elif state == "fast": buffer_pct = variant.fast_middle_buffer_pct confirm_bars = variant.fast_middle_confirm_bars else: buffer_pct = variant.neutral_middle_buffer_pct confirm_bars = variant.neutral_middle_confirm_bars if adverse_middle(position, candle, middle, buffer_pct): position["middle_exit_streak"] = int(position["middle_exit_streak"]) + 1 else: position["middle_exit_streak"] = 0 if int(position["middle_exit_streak"]) >= confirm_bars: return f"{state}_middle" if int(position["hold_bars"]) >= variant.max_hold_bars: return "time_exit" 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() eth_momentum = (eth_close / eth_close.shift(variant.band_length // 2) - 1.0).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_reason: str | None = None cooldown_until = -1 exit_counts: Counter[str] = Counter() for index in range(warmup_bars, len(eth)): candle = eth[index] if pending_exit_reason is not None and position is not None: equity, won = close_position( trades=trades, exits=exits, position=position, candle=candle, exit_price=candle.open, reason=pending_exit_reason, ) wins += int(won) exit_counts[pending_exit_reason] += 1 position = None pending_exit_reason = None 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, "state": "neutral", "trend_bars": 0, "middle_exit_streak": 0, "hold_bars": 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 = hard_stop_exit(position, candle) if risk_exit is None: values = (middle[index], bandwidth[index], threshold[index], eth_realized_vol[index], eth_momentum[index]) if not any(value != value for value in values): update_state( position=position, candle=candle, middle=float(middle[index]), momentum=float(eth_momentum[index]), realized_vol=float(eth_realized_vol[index]), bandwidth=float(bandwidth[index]), threshold=float(threshold[index]), variant=variant, ) risk_exit = protection_stop_exit(position, candle, variant) if risk_exit is None: signal_reason = state_signal_exit(position, candle, float(middle[index]), variant) if signal_reason is not None: pending_exit_reason = signal_reason 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) exit_counts[reason] += 1 current_equity = equity position = None pending_exit_reason = None cooldown_until = index + variant.cooldown_bars 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 or 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" trade_count = len(trades) result = 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[warmup_bars:], equity_curve=equity_curve, entries=entries, exits=exits, ) return result, dict(exit_counts) def cost_equity_frame(result: SegmentResult, cost: 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 - cost * float(trade.get("cost_weight", 1.0)) rows.append({"ts": pd.to_datetime(int(trade["exit_ts"]), unit="ms", utc=True), "equity": equity}) return pd.DataFrame(rows) def max_drawdown(values: list[float]) -> float: peak = values[0] dd = 0.0 for value in values: peak = max(peak, value) dd = max(dd, (peak - value) / peak if peak else 0.0) return dd def equity_metrics(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 = (1.0 + total_return) ** (1.0 / years) - 1.0 if total_return > -1.0 and years > 0.0 else 0.0 dd = max_drawdown([float(value) for value in frame["equity"]]) return { "net_total_return": total_return, "net_annualized_return": annualized, "net_max_drawdown": dd, "net_calmar": annualized / dd if dd else 0.0, } def trade_stats(trades: list[dict[str, object]]) -> dict[str, float | int]: if not trades: return { "trades": 0, "win_rate": 0.0, "profit_factor": 0.0, "payoff_ratio": 0.0, "avg_return_pct": 0.0, "avg_mfe_pct": 0.0, "avg_hold_bars": 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 { "trades": len(trades), "win_rate": len(wins) / len(trades), "profit_factor": sum(wins) / sum(losses) if losses else 0.0, "payoff_ratio": (sum(wins) / len(wins)) / (sum(losses) / len(losses)) if wins and losses else 0.0, "avg_return_pct": sum(returns) / len(returns), "avg_mfe_pct": sum(float(trade["mfe_pct"]) for trade in trades) / len(trades), "avg_hold_bars": sum(int(trade["hold_bars"]) for trade in trades) / len(trades), } def exit_reason_counts(trades: list[dict[str, object]]) -> dict[str, int]: counts = Counter(str(trade["exit_reason"]) for trade in trades) return { "stop_exits": counts["stop"] + counts["stop_gap"], "protect_exits": counts["protect_trail"] + counts["protect_gap"], "trend_middle_exits": counts["trend_middle"], "protect_middle_exits": counts["protect_middle"], "fast_middle_exits": counts["fast_middle"], "neutral_middle_exits": counts["neutral_middle"], "time_exits": counts["time_exit"], } def horizon_frame(frame: pd.DataFrame, first_ts: int, last_ts: int, offset: pd.DateOffset | None) -> tuple[pd.DataFrame, int, str, str]: end_time = pd.to_datetime(last_ts, unit="ms", utc=True) if offset is None: start_time = pd.to_datetime(first_ts, unit="ms", utc=True) return frame[["ts", "equity"]].copy(), first_ts, start_time.strftime("%Y-%m-%d %H:%M"), end_time.strftime("%Y-%m-%d %H:%M") cutoff = end_time - offset before = frame[frame["ts"] <= cutoff] if len(before): start_equity = float(before["equity"].iloc[-1]) after = frame[frame["ts"] > cutoff] out = pd.concat([pd.DataFrame([{"ts": cutoff, "equity": start_equity}]), after[["ts", "equity"]]], ignore_index=True) else: out = frame[["ts", "equity"]].copy() cutoff = pd.Timestamp(out["ts"].iloc[0]) return out, int(cutoff.timestamp() * 1000), cutoff.strftime("%Y-%m-%d %H:%M"), end_time.strftime("%Y-%m-%d %H:%M") def horizon_rows(result: SegmentResult, frame: pd.DataFrame, first_ts: int, last_ts: int) -> list[dict[str, object]]: rows: list[dict[str, object]] = [] for label, offset in HORIZONS: sliced_frame, start_ts, start_text, end_text = horizon_frame(frame, first_ts, last_ts, offset) trades = [trade for trade in result.trades if int(trade["exit_ts"]) >= start_ts] rows.append( { "horizon": label, "horizon_start": start_text, "horizon_end": end_text, **equity_metrics(sliced_frame, start_ts, last_ts), **trade_stats(trades), **exit_reason_counts(trades), } ) return rows def worst_month(frame: pd.DataFrame) -> tuple[str, float]: monthly = frame.set_index("ts")["equity"].resample("ME").last().ffill().pct_change().dropna() if not len(monthly): return "", 0.0 idx = monthly.idxmin() return idx.strftime("%Y-%m"), float(monthly.loc[idx]) def build_variants() -> list[Variant]: bases = ( (48, 960, 0.25, "both", "none", 0.006, 0.010), (48, 960, 0.25, "both", "none", 0.006, 0.012), (96, 960, 0.25, "both", "btc-up", 0.006, 0.012), (96, 960, 0.25, "both", "btc-up-momo", 0.006, 0.012), (96, 480, 0.15, "both", "none", 0.006, 0.010), (48, 960, 0.25, "long", "btc-up", 0.006, 0.010), ) exits = ( (2, 0.0015, 2, 0.0005, 1, 0.006, 0.0000, 0.006, 1, 0.0070, 1.40, 0.0, 1, 192), (2, 0.0025, 3, 0.0010, 1, 0.008, 0.0005, 0.007, 1, 0.0065, 1.30, 0.0, 1, 288), (3, 0.0035, 3, 0.0010, 2, 0.010, 0.0010, 0.008, 2, 0.0060, 1.25, 0.0, 1, 384), (3, 0.0045, 4, 0.0015, 2, 0.012, 0.0010, 0.010, 2, 0.0060, 1.20, 0.0, 1, 480), (2, 0.0020, 2, 0.0000, 1, 0.006, 0.0005, 0.005, 1, 0.0055, 1.15, 0.0, 1, 192), (4, 0.0050, 4, 0.0015, 2, 0.015, 0.0015, 0.012, 2, 0.0065, 1.35, 0.0, 1, 576), ) variants: list[Variant] = [] for band_length, lookback, quantile, side_mode, btc_filter, vol_cap, stop_loss in bases: for exit_spec in exits: 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, trend_momentum_bars=exit_spec[0], trend_middle_buffer_pct=exit_spec[1], trend_middle_confirm_bars=exit_spec[2], neutral_middle_buffer_pct=exit_spec[3], neutral_middle_confirm_bars=exit_spec[4], protect_trigger_pct=exit_spec[5], protect_lock_pct=exit_spec[6], protect_trail_giveback_pct=exit_spec[7], protect_middle_confirm_bars=exit_spec[8], fast_vol_pct=exit_spec[9], fast_bandwidth_ratio=exit_spec[10], fast_middle_buffer_pct=exit_spec[11], fast_middle_confirm_bars=exit_spec[12], max_hold_bars=exit_spec[13], ) ) 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, requested_years: float, command: str) -> str: primary = summary[summary["cost"] == PRIMARY_COST] top = primary.head(10) 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) ) lines = [ "# ETH BB squeeze adaptive state-exit exploration", "", f"Run command: `{command}`", f"Requested years: {requested_years:g}", f"Actual continuous local history: `{_format_ts(first_ts)}` to `{_format_ts(last_ts)}`.", "", "State machine:", "- `trend`: ETH move remains aligned with the breakout and close stays on the favorable side of the BB middle; middle exit uses a wider buffer and more confirmations.", "- `protect`: trade reaches floating-profit trigger; a lock/trailing stop and faster middle exit protect profit.", "- `fast`: realized volatility or bandwidth expansion crosses the variant threshold; middle exit is immediate.", "- `neutral`: default state, between trend continuation and protection.", "", "Top 10 by maker_taker full-period Calmar:", markdown_table( top[ [ "name", "trades", "win_rate", "net_total_return", "net_annualized_return", "net_max_drawdown", "net_calmar", "profit_factor", "payoff_ratio", "avg_return_pct", "avg_mfe_pct", "protect_exits", "trend_middle_exits", "fast_middle_exits", "neutral_middle_exits", ] ] ), "", "Horizon leaders:", markdown_table( horizon_top[ [ "horizon", "name", "trades", "win_rate", "net_total_return", "net_annualized_return", "net_max_drawdown", "net_calmar", "profit_factor", "payoff_ratio", "protect_exits", "fast_middle_exits", ] ] ), ] return "\n".join(lines) + "\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(ETH_SYMBOL, args.bar) btc = _load_candles(BTC_SYMBOL, args.bar) eth, btc = _align_pair(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: print(f"skip {index}/{len(variants)} {variant.name}", flush=True) continue stats = trade_stats(result.trades) reason_counts = exit_reason_counts(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": "eth_adaptive_state_exit", "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, "trend_momentum_bars": variant.trend_momentum_bars, "trend_middle_buffer_pct": variant.trend_middle_buffer_pct, "trend_middle_confirm_bars": variant.trend_middle_confirm_bars, "neutral_middle_buffer_pct": variant.neutral_middle_buffer_pct, "neutral_middle_confirm_bars": variant.neutral_middle_confirm_bars, "protect_trigger_pct": variant.protect_trigger_pct, "protect_lock_pct": variant.protect_lock_pct, "protect_trail_giveback_pct": variant.protect_trail_giveback_pct, "protect_middle_confirm_bars": variant.protect_middle_confirm_bars, "fast_vol_pct": variant.fast_vol_pct, "fast_bandwidth_ratio": variant.fast_bandwidth_ratio, "fast_middle_buffer_pct": variant.fast_middle_buffer_pct, "fast_middle_confirm_bars": variant.fast_middle_confirm_bars, "max_hold_bars": variant.max_hold_bars, "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, "gross_total_return": result.total_return, "gross_max_drawdown_mark_to_market": result.max_drawdown, "worst_month": month, "worst_month_return": month_return, **stats, **reason_counts, **metrics, } summary_rows.append(row) for horizon_row in horizon_rows(result, frame, eth[0].ts, eth[-1].ts): horizon_rows_out.append( { "family": "eth_adaptive_state_exit", "cost": cost_name, "symbol": ETH_SYMBOL, "signal_symbol": BTC_SYMBOL if variant.btc_filter != "none" else "", "bar": args.bar, "name": variant.name, **horizon_row, } ) print(f"done {index}/{len(variants)} {variant.name} exits={exit_counts}", 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-adaptive-state-exit-summary.csv" horizon_path = args.output_dir / "eth-adaptive-state-exit-horizon.csv" report_path = args.output_dir / "eth-adaptive-state-exit-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=summary, horizon=horizon, first_ts=eth[0].ts, last_ts=eth[-1].ts, requested_years=args.years, command=command), encoding="utf-8", ) print(primary.head(10).to_string(index=False)) return 0 if __name__ == "__main__": raise SystemExit(main())