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.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), ) @dataclass(frozen=True) class StrategySpec: family: str symbol: str signal_symbol: str name: str params: dict[str, float | int | str] reentry_bars: int reentry_pullback_pct: float 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_trade(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, "entry_kind": position["entry_kind"], "exit_reason": reason, } ) exits.append({"ts": candle.ts, "price": exit_price, "side": position["side"]}) return exit_equity, pnl > 0.0 def rsi(series: pd.Series, length: int) -> pd.Series: delta = series.diff() gain = delta.clip(lower=0.0).ewm(alpha=1 / length, adjust=False).mean() loss = (-delta.clip(upper=0.0)).ewm(alpha=1 / length, adjust=False).mean() rs = gain / loss.replace(0.0, pd.NA) return 100.0 - (100.0 / (1.0 + rs)) def true_range(frame: pd.DataFrame) -> pd.Series: high = frame["high"] low = frame["low"] close = frame["close"] return pd.concat([high - low, (high - close.shift(1)).abs(), (low - close.shift(1)).abs()], axis=1).max(axis=1) def build_signals(spec: StrategySpec, frame: pd.DataFrame) -> tuple[pd.Series, pd.Series]: close = frame["close"] open_ = frame["open"] high = frame["high"] low = frame["low"] p = spec.params if spec.family == "range_momentum": lookback = int(p["lookback"]) entry_high = high.shift(1).rolling(lookback).max() entry_low = low.shift(1).rolling(lookback).min() return (close > entry_high).fillna(False), (close < entry_low).fillna(False) if spec.family == "btc_lead_eth_lag": sig_close = frame["sig_close"] lead = int(p["lead_lookback"]) btc_ret = sig_close / sig_close.shift(lead) - 1.0 eth_ret = close / close.shift(lead) - 1.0 entry = (btc_ret >= float(p["btc_return_threshold"])) & (eth_ret < btc_ret - float(p["lag_gap"])) exit_ = pd.Series(False, index=frame.index) return entry.fillna(False), exit_ if spec.family == "short_bb_upper_rejection": trend = close.rolling(int(p["trend"])).mean() mid = close.rolling(int(p["bb"])).mean() std = close.rolling(int(p["bb"])).std(ddof=0) upper = mid + std * float(p["std"]) atr = true_range(frame).rolling(int(p["atr"])).mean() entry = (close < trend) & (high >= upper) & (close < upper) & (close < open_) & atr.notna() & (atr > 0) exit_ = (close <= mid) | (close > trend) return entry.fillna(False), exit_.fillna(False) raise ValueError(f"unknown family {spec.family}") def run_strategy(candles: list[Candle], spec: StrategySpec, with_t: bool) -> tuple[SegmentResult, dict[str, int]]: frame = pd.DataFrame([c.__dict__ for c in candles]) if spec.signal_symbol != spec.symbol: signal = _load_candles(spec.signal_symbol, BAR) left, right = _align_pair(candles, signal) candles = left frame = pd.DataFrame([c.__dict__ for c in left]) frame["sig_close"] = [c.close for c in right] entry_signal, exit_signal = build_signals(spec, frame) warmup = max(int(value) for key, value in spec.params.items() if isinstance(value, int) and key not in {"max_hold"}) 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, str] | None = None pending_exit = False reentry: dict[str, object] | None = None stats = {"take_exits": 0, "reentries": 0} for index in range(warmup, len(candles)): candle = candles[index] if pending_exit and position is not None: equity, won = _close_trade(trades, exits, position, candle, candle.open, "signal_exit") wins += int(won) position = None pending_exit = False if pending_entry is not None and position is None and equity > 0.0: side = pending_entry["side"] stop = float(spec.params["stop"]) take = float(spec.params["take"]) position = { "side": side, "entry_kind": pending_entry["kind"], "entry_time": candle.ts, "entry_price": candle.open, "entry_index": index, "margin_used": equity, "stop_price": candle.open * (1.0 - stop if side == "long" else 1.0 + stop), "take_price": candle.open * (1.0 + take if side == "long" else 1.0 - take), } entries.append({"ts": candle.ts, "price": candle.open, "side": side}) stats["reentries"] += int(pending_entry["kind"] == "reentry") pending_entry = None current_equity = equity if position is not None: side = str(position["side"]) stop_hit = (side == "long" and candle.low <= float(position["stop_price"])) or (side == "short" and candle.high >= float(position["stop_price"])) take_hit = (side == "long" and candle.high >= float(position["take_price"])) or (side == "short" and candle.low <= float(position["take_price"])) if stop_hit or take_hit: reason = "stop" if stop_hit else "take" exit_price = float(position["stop_price"] if stop_hit else position["take_price"]) equity, won = _close_trade(trades, exits, position, candle, exit_price, reason) wins += int(won) current_equity = equity if take_hit and not stop_hit and with_t: stats["take_exits"] += 1 reentry = {"side": side, "until": index + spec.reentry_bars, "anchor": exit_price} 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 if position is None and reentry is not None: side = str(reentry["side"]) anchor = float(reentry["anchor"]) if index > int(reentry["until"]): reentry = None elif (side == "long" and candle.close <= anchor * (1.0 - spec.reentry_pullback_pct)) or ( side == "short" and candle.close >= anchor * (1.0 + spec.reentry_pullback_pct) ): pending_entry = {"side": side, "kind": "reentry"} reentry = None continue if position is not None: held = index - int(position["entry_index"]) if bool(exit_signal.iloc[index]) or held >= int(spec.params["max_hold"]): pending_exit = True continue if reentry is not None: continue if bool(entry_signal.iloc[index]): side = "short" if spec.family == "short_bb_upper_rejection" else "long" if spec.family == "range_momentum": side = "long" if frame.iloc[index]["close"] > frame.iloc[max(index - 1, 0)]["close"] else "short" pending_entry = {"side": side, "kind": "initial"} 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=candles[warmup:], equity_curve=equity_curve, entries=entries, exits=exits, ) return result, stats def cost_metrics(result: SegmentResult, cost: float, first_ts: int, last_ts: int) -> dict[str, float]: equity = INITIAL_EQUITY peak = equity dd = 0.0 for trade in result.trades: equity *= 1.0 + float(trade["return_pct"]) / 100.0 - cost peak = max(peak, equity) dd = max(dd, (peak - equity) / peak) years = (last_ts - first_ts) / 86_400_000 / 365 total = equity / INITIAL_EQUITY - 1.0 ann = (1.0 + total) ** (1.0 / years) - 1.0 if total > -1 and years > 0 else 0.0 return {"net_total_return": total, "net_annualized_return": ann, "net_max_drawdown": dd, "net_calmar": ann / dd if dd else 0.0} def specs() -> list[StrategySpec]: return [ StrategySpec("range_momentum", ETH_SYMBOL, ETH_SYMBOL, "range-momo-eth-l10-tp0.006-sl0.004", {"lookback": 10, "take": 0.006, "stop": 0.004, "max_hold": 9999}, 48, 0.002), StrategySpec("btc_lead_eth_lag", ETH_SYMBOL, BTC_SYMBOL, "btc-lead-eth-lag-lb6-br0.006-gap0.002-tp0.012-sl0.006", {"lead_lookback": 6, "btc_return_threshold": 0.006, "lag_gap": 0.002, "take": 0.012, "stop": 0.006, "max_hold": 12}, 48, 0.003), StrategySpec("short_bb_upper_rejection", ETH_SYMBOL, ETH_SYMBOL, "short-bb-upper-rejection-eth", {"trend": 96, "bb": 48, "atr": 48, "std": 1.5, "take": 0.010, "stop": 0.006, "max_hold": 12}, 48, 0.002), ] 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() candles = _load_candles(ETH_SYMBOL, args.bar) candles = candles[-int(args.years * 365 * 24 * 60 / 15) :] rows: list[dict[str, object]] = [] for spec in specs(): for with_t in (False, True): result, stats = run_strategy(candles, spec, with_t) for cost_name, cost in COSTS: rows.append( { "family": spec.family, "cost": cost_name, "name": spec.name + ("-t" if with_t else "-base"), "with_t": with_t, "trades": result.trade_count, "win_rate": result.win_rate, "gross_total_return": result.total_return, "gross_max_drawdown": result.max_drawdown, **stats, **cost_metrics(result, cost, candles[0].ts, candles[-1].ts), } ) output = pd.DataFrame(rows).sort_values(["cost", "net_calmar", "net_annualized_return"], ascending=[True, False, False]) args.output_dir.mkdir(parents=True, exist_ok=True) path = args.output_dir / "other-strategies-t-overlay-summary.csv" output.to_csv(path, index=False) print(output[output.cost.eq(PRIMARY_COST)].to_string(index=False)) return 0 if __name__ == "__main__": raise SystemExit(main())