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_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 = ( ("3y", pd.DateOffset(years=3)), ("1y", pd.DateOffset(years=1)), ("6m", pd.DateOffset(months=6)), ("3m", pd.DateOffset(months=3)), ) @dataclass(frozen=True) class Variant: band_length: int bandwidth_lookback: int bandwidth_quantile: float stop_loss_pct: float reward_risk: float exit_mode: str side_mode: str btc_filter: str eth_vol_cap: float | None dd_overlay: float | None cooldown_bars: int middle_exit_buffer_pct: float middle_exit_confirm_bars: int @property def take_profit_pct(self) -> float: return self.stop_loss_pct * self.reward_risk @property def name(self) -> str: vol = "none" if self.eth_vol_cap is None else f"{self.eth_vol_cap:g}" dd = "none" if self.dd_overlay is None else f"{self.dd_overlay:g}" return ( f"bb-squeeze-rr-l{self.band_length}-bw{self.bandwidth_lookback}" f"-q{self.bandwidth_quantile:g}-sl{self.stop_loss_pct:g}-rr{self.reward_risk:g}" f"-{self.exit_mode}-{self.side_mode}-{self.btc_filter}-vc{vol}-dd{dd}" f"-cd{self.cooldown_bars}-mxbuf{self.middle_exit_buffer_pct:g}-mxc{self.middle_exit_confirm_bars}" ) 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, } ) exits.append({"ts": candle.ts, "price": exit_price, "side": position["side"]}) return exit_equity, pnl > 0.0 def risk_exit_price(position: dict[str, object], candle: Candle) -> tuple[float, str] | None: side = str(position["side"]) stop = float(position["stop_price"]) take = float(position["take_price"]) if side == "long": if candle.open <= stop: return candle.open, "stop_gap" if candle.open >= take: return candle.open, "take_gap" stop_hit = candle.low <= stop take_hit = candle.high >= take else: if candle.open >= stop: return candle.open, "stop_gap" if candle.open <= take: return candle.open, "take_gap" stop_hit = candle.high >= stop take_hit = candle.low <= take if stop_hit: return stop, "stop" if take_hit: return take, "take_profit" 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, "take_profit_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), "take_price": entry_price * (1.0 + variant.take_profit_pct if pending_entry_side == "long" else 1.0 - variant.take_profit_pct), } 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 = risk_exit_price(position, candle) 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 else: exit_counts["take_profit_exits"] += 1 current_equity = equity position = None middle_exit_streak = 0 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: if variant.exit_mode != "fixed_rr_only": 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.dd_overlay is not None and (peak_equity - current_equity) / peak_equity > variant.dd_overlay: 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, exit_counts def build_variants() -> list[Variant]: bases = ( (96, 960, 0.25, "both", "none", 0.006, None), (96, 960, 0.25, "both", "btc-up", 0.006, 0.25), (96, 960, 0.25, "both", "btc-up-momo", 0.006, 0.25), (48, 960, 0.25, "long", "btc-up", 0.006, 0.25), (48, 960, 0.25, "both", "none", 0.006, None), (96, 480, 0.15, "both", "none", 0.006, None), ) variants: list[Variant] = [] for length, bandwidth_lookback, quantile, side_mode, btc_filter, vol_cap, dd_overlay in bases: for stop_loss_pct in (0.006, 0.008, 0.01, 0.012): for reward_risk in (1.0, 1.5, 2.0, 2.5, 3.0): for exit_mode in ("fixed_rr_only", "hybrid_signal_rr"): for middle_exit_buffer_pct, middle_exit_confirm_bars in ((0.0, 1), (0.001, 1), (0.001, 2)): variants.append( Variant( band_length=length, bandwidth_lookback=bandwidth_lookback, bandwidth_quantile=quantile, stop_loss_pct=stop_loss_pct, reward_risk=reward_risk, exit_mode=exit_mode, side_mode=side_mode, btc_filter=btc_filter, eth_vol_cap=vol_cap, dd_overlay=dd_overlay, cooldown_bars=24, middle_exit_buffer_pct=middle_exit_buffer_pct, middle_exit_confirm_bars=middle_exit_confirm_bars, ) ) 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) by_mode = primary.sort_values(["exit_mode", "net_calmar", "net_annualized_return"], ascending=[True, False, False]).groupby("exit_mode").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) ) lines = [ "# ETH BB squeeze fixed R:R 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)}`.", "", "Exit modes:", "- `fixed_rr_only`: fixed stop-loss and take-profit only.", "- `hybrid_signal_rr`: fixed stop-loss/take-profit plus original middle-band signal exit.", "", "Top 10 by maker_taker Calmar:", markdown_table( top[ [ "name", "exit_mode", "trades", "stop_loss_pct", "take_profit_pct", "reward_risk", "net_total_return", "net_annualized_return", "net_max_drawdown", "net_calmar", "worst_month_return", ] ] ), "", "Top by exit mode:", markdown_table( by_mode[ [ "exit_mode", "name", "trades", "net_annualized_return", "net_max_drawdown", "net_calmar", "worst_month_return", ] ] ), "", "Recent horizon leaders:", markdown_table( horizon_top[ [ "horizon", "name", "exit_mode", "trades", "net_total_return", "net_annualized_return", "net_max_drawdown", "net_calmar", ] ] ), ] 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_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: print(f"skip {index}/{len(variants)} {variant.name}") continue 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_fixed_rr", "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, "stop_loss_pct": variant.stop_loss_pct, "take_profit_pct": variant.take_profit_pct, "reward_risk": variant.reward_risk, "exit_mode": variant.exit_mode, "side_mode": variant.side_mode, "btc_filter": variant.btc_filter, "eth_vol_cap": variant.eth_vol_cap, "dd_overlay": variant.dd_overlay, "cooldown_bars": variant.cooldown_bars, "middle_exit_buffer_pct": variant.middle_exit_buffer_pct, "middle_exit_confirm_bars": variant.middle_exit_confirm_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, "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, **metrics, } summary_rows.append(row) for horizon_row in horizon_rows(frame, eth[-1].ts, HORIZONS): horizon_rows_out.append( { "family": "bb_squeeze_fixed_rr", "cost": cost_name, "symbol": ETH_SYMBOL, "signal_symbol": BTC_SYMBOL if variant.btc_filter != "none" else "", "bar": args.bar, "name": variant.name, "exit_mode": variant.exit_mode, "trades": result.trade_count, **horizon_row, } ) print(f"done {index}/{len(variants)} {variant.name}") summary = pd.DataFrame(summary_rows).sort_values( ["cost", "net_calmar", "worst_month_return", "net_annualized_return"], 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-fixed-rr-summary.csv" horizon_path = args.output_dir / "eth-bb-squeeze-fixed-rr-horizon.csv" report_path = args.output_dir / "eth-bb-squeeze-fixed-rr-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())