refactor: enhance Optuna backtesting framework, optimize orderbook filtering, and update database management utilities.
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kis_trader/backtest/optuna_whipsaw_recommend.py
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267
kis_trader/backtest/optuna_whipsaw_recommend.py
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"""
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kis_trader/backtest/optuna_whipsaw_recommend.py
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=================================================
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Optuna 차트 캔들 최적화 완료 후, 후처리로 고속 휩쏘 파라미터 탐색을 수행하여
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전략별 최적의 휩쏘 필터 수치(Consensus)를 도출하고
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Optuna out_data 및 Apply 패치에 자동으로 결합하는 모듈입니다.
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"""
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from __future__ import annotations
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import logging
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from dataclasses import dataclass
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from datetime import datetime, timedelta
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from typing import Any, Dict, List, Optional, Tuple
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import optuna
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from database import TradeDB
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from kis_trader.engine.whipsaw_filter import whipsaw_reject_for_signal
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logger = logging.getLogger("OptunaWhipsawRecommend")
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optuna.logging.set_verbosity(optuna.logging.WARNING)
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@dataclass
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class TradeInfo:
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code: str
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name: str
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buy_dt: datetime
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buy_price: float
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actual_pnl: float
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actual_profit_rate: float
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ticks: List[Dict[str, Any]]
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def recommend_whipsaw_parameters(
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*,
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strategy: str = "MOMENTUM",
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n_trials: int = 500,
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days: int = 7,
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log: Optional[logging.Logger] = None,
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) -> Dict[str, Any]:
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lg = log or logger
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strat_upper = str(strategy).strip().upper()
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db = TradeDB()
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try:
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now = datetime.now()
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start_date = (now - timedelta(days=days)).strftime("%Y-%m-%d")
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buys = db.conn.execute(
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"""
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SELECT id, code, name, buy_date, buy_price, realized_pnl, profit_rate
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FROM trade_history
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WHERE strategy=%s AND DATE(buy_date) >= %s
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ORDER BY buy_date
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""",
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(strat_upper, start_date),
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).fetchall()
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trades: List[TradeInfo] = []
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for b in buys:
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code = b["code"]
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raw_dt = b["buy_date"]
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if isinstance(raw_dt, str):
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try:
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buy_dt = datetime.strptime(raw_dt, "%Y-%m-%d %H:%M:%S")
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except ValueError:
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buy_dt = datetime.fromisoformat(raw_dt)
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else:
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buy_dt = raw_dt
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buy_price = float(b["buy_price"] or 0)
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pnl = float(b["realized_pnl"] or 0)
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rate = float(b["profit_rate"] or 0)
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start_tick_time = (buy_dt - timedelta(seconds=180)).strftime("%Y%m%d%H%M%S")
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end_tick_time = buy_dt.strftime("%Y%m%d%H%M%S")
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ticks = db.get_ws_ticks(code, market="KR", start_tick_time=start_tick_time, end_tick_time=end_tick_time)
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if not ticks:
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continue
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trades.append(
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TradeInfo(
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code=code,
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name=str(b.get("name") or code),
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buy_dt=buy_dt,
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buy_price=buy_price,
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actual_pnl=pnl,
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actual_profit_rate=rate,
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ticks=ticks,
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)
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)
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finally:
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db.close()
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if len(trades) < 3:
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lg.warning("⚠️ [%s] 휩쏘 연산 가능한 실제 틱 보유 매수 건수(%s건)가 부족하여 최적화 생략.", strat_upper, len(trades))
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return {"ok": False, "reason": "not_enough_trades", "trade_count": len(trades)}
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orig_cnt = len(trades)
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orig_win = sum(1 for t in trades if t.actual_pnl > 0) / orig_cnt * 100.0
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orig_pnl = sum(t.actual_pnl for t in trades)
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orig_rate = sum(t.actual_profit_rate for t in trades) / orig_cnt
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def _sim_trade(tr: TradeInfo, p: Dict[str, Any]) -> Tuple[float, float, str]:
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params_for_eval = {
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"whipsaw_filter_enabled": True,
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"whipsaw_subbar_sec": p["subbar_sec"],
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"whipsaw_lookback_sec": p["lookback_sec"],
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"whipsaw_dip_pct": p["dip_pct"],
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"whipsaw_recovery_tol_pct": p.get("recov_pct", 0.0),
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}
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sig_bar = {"low": tr.buy_price, "dt": tr.buy_dt}
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reject_reason, _ = whipsaw_reject_for_signal(
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params=params_for_eval,
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strategy=strat_upper,
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signal_bar=sig_bar,
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current_price=tr.buy_price,
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ticks=tr.ticks
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)
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if reject_reason:
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return (0.0, 0.0, "ENTRY_REJECTED")
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return (tr.actual_pnl, tr.actual_profit_rate, "ORIGINAL")
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def _calc_suite(p: Dict[str, Any]) -> Tuple[int, float, float, float]:
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t_cnt = 0
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w_cnt = 0
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tot_pnl = 0.0
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tot_rate = 0.0
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for t in trades:
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pnl, rate, rtype = _sim_trade(t, p)
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if rtype != "ENTRY_REJECTED":
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t_cnt += 1
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tot_pnl += pnl
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tot_rate += rate
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if pnl > 0:
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w_cnt += 1
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w_rate = (w_cnt / t_cnt * 100.0) if t_cnt > 0 else 0.0
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avg_r = (tot_rate / t_cnt) if t_cnt > 0 else 0.0
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return t_cnt, w_rate, tot_pnl, avg_r
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valid_records: List[Dict[str, Any]] = []
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def obj_func(trial: optuna.Trial) -> float:
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params = {
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"subbar_sec": trial.suggest_categorical("subbar_sec", [10, 15, 20, 30, 45, 60]),
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"lookback_sec": trial.suggest_categorical("lookback_sec", [30, 45, 60, 90, 120, 180]),
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"dip_pct": trial.suggest_float("dip_pct", 0.001, 0.010, step=0.001),
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}
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cnt, win_r, pnl, rate = _calc_suite(params)
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if cnt < max(3, int(orig_cnt * 0.3)):
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return -999999999.0
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w_p = (pnl / 100000.0)
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w_w = win_r * 2.0
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score = w_p + w_w
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if win_r >= 60.0:
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score += (win_r - 60.0) * 1.5
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valid_records.append({"score": score, "pnl": pnl, "win_rate": win_r, "count": cnt, "rate": rate, "params": params})
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return score
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study = optuna.create_study(direction="maximize")
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study.optimize(obj_func, n_trials=n_trials)
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valid_records.sort(key=lambda x: x["score"], reverse=True)
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top5 = valid_records[: min(5, len(valid_records))]
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if not top5:
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return {"ok": False, "reason": "no_valid_trials"}
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# Consensus 도출
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best = top5[0]
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avg_subbar = int(sum(r["params"]["subbar_sec"] for r in top5) / len(top5))
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avg_lookback = int(sum(r["params"]["lookback_sec"] for r in top5) / len(top5))
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avg_dip = round(sum(r["params"]["dip_pct"] for r in top5) / len(top5), 4)
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cons_params = {
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"subbar_sec": avg_subbar,
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"lookback_sec": avg_lookback,
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"dip_pct": avg_dip,
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}
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c_cnt, c_win, c_pnl, c_rate = _calc_suite(cons_params)
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lg.info(
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"⚡ [휩쏘 필터 합의 추천] 전략=%s (모수=%d건, %d회 탐색) | subbar=%d lookback=%d dip=%.3f | 승률: %.1f%% 손익: %.0f원",
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strat_upper,
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len(trades),
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n_trials,
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avg_subbar,
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avg_lookback,
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avg_dip,
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c_win,
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c_pnl,
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)
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return {
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"ok": True,
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"strategy": strat_upper,
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"n_trials": n_trials,
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"trade_count": len(trades),
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"orig_stats": {"count": orig_cnt, "win_rate": round(orig_win, 1), "pnl": orig_pnl, "avg_rate": round(orig_rate, 2)},
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"recommended_stats": {"count": c_cnt, "win_rate": round(c_win, 1), "pnl": c_pnl, "avg_rate": round(c_rate, 2), "pnl_diff": c_pnl - orig_pnl},
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"params": {
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"whipsaw_filter_enabled": True,
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"whipsaw_subbar_sec": avg_subbar,
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"whipsaw_lookback_sec": avg_lookback,
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"whipsaw_dip_pct": avg_dip,
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},
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}
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def attach_whipsaw_recommend(
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out_data: Dict[str, Any],
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*,
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log: Optional[logging.Logger] = None,
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) -> Dict[str, Any]:
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"""out_data에 휩쏘 필터 추천 결과를 첨부."""
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lg = log or logger
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strat = str(out_data.get("strategy") or "MOMENTUM").strip().upper()
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rec = recommend_whipsaw_parameters(strategy=strat, n_trials=500, log=lg)
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out_data["whipsaw_recommend"] = rec
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mc = out_data.get("mode_combo")
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if isinstance(mc, dict):
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mc["whipsaw_recommend"] = rec
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if not rec.get("ok"):
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lg.info("⚡ [휩쏘 필터 합의 추천] 생략 — %s", rec.get("reason") or "n/a")
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return out_data
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_WHIPSAW_DB_SKIP_STRATS = {"TAIL", "SHORT", "BREAKOUT"}
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def build_whipsaw_env_patch(rec: Dict[str, Any]) -> Dict[str, str]:
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"""휩쏘 추천 결과를 DB env 패치 dict로 변환."""
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if not rec or not rec.get("ok"):
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return {}
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strat = str(rec.get("strategy") or "").strip().upper()
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pfx = "TAIL" if strat in ("SHORT", "TAIL") else strat
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p = rec.get("params", {})
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if not pfx or not p:
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return {}
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if strat in _WHIPSAW_DB_SKIP_STRATS:
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logger.info(
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"🚫 [%s] 휩쏘 필터 DB 적용 차단 (전략 특성상 UI 표시만) — 수치: subbar=%s lookback=%s dip=%s",
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strat,
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p.get('whipsaw_subbar_sec'),
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p.get('whipsaw_lookback_sec'),
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p.get('whipsaw_dip_pct'),
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)
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return {}
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patch = {
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f"{pfx}_WHIPSAW_FILTER_ENABLED": "true",
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f"{pfx}_WHIPSAW_SUBBAR_SEC": str(p["whipsaw_subbar_sec"]),
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f"{pfx}_WHIPSAW_LOOKBACK_SEC": str(p["whipsaw_lookback_sec"]),
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f"{pfx}_WHIPSAW_DIP_PCT": str(p["whipsaw_dip_pct"]),
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}
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return patch
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