8fbba264ba
- Optuna web jobs/TPE/apply snapshot·틱로더 정합, jobs limit·감사로그 - 백테 UI 호가모드·후보 적용 흐름, feed_collect_stats API/탭 - 가설검증·교차검증 룰, 4전략 스모크·OB slot41 진단 스크립트 Co-authored-by: Cursor <cursoragent@cursor.com>
103 lines
3.9 KiB
Python
103 lines
3.9 KiB
Python
#!/usr/bin/env python3
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"""
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optuna_scalping_tpe_space.py — 스캘핑 Optuna 연속(TPE) 탐색 공간
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기존 ``_scalp_grids()`` categorical 유지. ``use_macd_cross=False``, ``skip_hts=False``.
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"""
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from __future__ import annotations
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from typing import Any, Dict, List
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import optuna
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from kis_trader.backtest.optuna_tpe_common import (
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ORDERBOOK_TPE_AXIS_KEYS,
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WHIPSAW_TPE_AXIS_KEYS,
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r1,
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r2,
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r3,
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suggest_orderbook_entry_tpe,
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suggest_whipsaw_tpe,
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)
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# 매수 시간창(time_*) 은 TPE 제외 — 운영 DB 고정값 사용(과적합·apply 후 진입 잠금 방지).
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# 호가·휩쏘: 본 trial 엔진 평가 (사후「필터후」근사 대체).
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_SCALP_BASE_KEYS: List[str] = [
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"rsi_period",
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"rsi_oversold",
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"rsi_overbought",
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"sl_pct",
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"tp_pct",
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"tp_max_pct",
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"drop_rate",
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"shoulder_min_high",
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"shoulder_cut_pct",
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"cooldown_min",
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"high_chase_thr",
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"max_daily_chg",
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"min_price",
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"vol_mult",
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"use_defense_filters",
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"require_reversal_candle",
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"max_loss_krw",
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"min_drop_pct_for_loss_cut",
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"min_margin",
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"min_hold_sec",
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"max_daily",
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]
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SCALP_TPE_AXIS_KEYS: List[str] = (
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list(_SCALP_BASE_KEYS) + list(ORDERBOOK_TPE_AXIS_KEYS) + list(WHIPSAW_TPE_AXIS_KEYS)
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)
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def scalp_tpe_axis_keys() -> List[str]:
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return list(SCALP_TPE_AXIS_KEYS)
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def suggest_scalp_params_tpe(trial: optuna.Trial) -> Dict[str, Any]:
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combo: Dict[str, Any] = {}
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combo["rsi_period"] = trial.suggest_int("rsi_period", 3, 14)
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combo["rsi_oversold"] = r1(trial.suggest_float("rsi_oversold", 15.0, 30.0, step=0.5))
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combo["rsi_overbought"] = r1(trial.suggest_float("rsi_overbought", 65.0, 85.0, step=0.5))
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if combo["rsi_oversold"] >= combo["rsi_overbought"]:
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raise optuna.TrialPruned("rsi oversold >= overbought")
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combo["sl_pct"] = r2(trial.suggest_float("sl_pct", 1.0, 6.0, step=0.1)) # 확장: 1.5→1.0, 4.5→6.0
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combo["tp_pct"] = r2(trial.suggest_float("tp_pct", 0.5, 8.0, step=0.1)) # 확장: 1.5→0.5, 4.0→8.0
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combo["tp_max_pct"] = r2(trial.suggest_float("tp_max_pct", 1.0, 12.0, step=0.5)) # 확장: 2.0→1.0, 8.0→12.0
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if combo["tp_max_pct"] + 1e-9 < combo["tp_pct"]:
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raise optuna.TrialPruned("tp_max < tp")
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combo["drop_rate"] = r2(trial.suggest_float("drop_rate", 0.5, 10.0, step=0.1)) # 확장: 1.0→0.5, 6.0→10.0
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combo["shoulder_min_high"] = r2(
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trial.suggest_float("shoulder_min_high", 0.3, 4.0, step=0.1),
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)
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combo["shoulder_cut_pct"] = r2(
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trial.suggest_float("shoulder_cut_pct", 0.05, 0.6, step=0.05),
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)
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combo["cooldown_min"] = trial.suggest_int("cooldown_min", 0, 15)
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combo["high_chase_thr"] = r3(
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trial.suggest_float("high_chase_thr", 0.95, 1.0, step=0.005),
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)
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combo["max_daily_chg"] = r1(trial.suggest_float("max_daily_chg", 5.0, 60.0, step=1.0)) # 확장: 10→5, 50→60
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combo["min_price"] = trial.suggest_int("min_price", 1000, 8000, step=500)
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combo["vol_mult"] = r2(trial.suggest_float("vol_mult", 0.0, 2.0, step=0.1))
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combo["use_defense_filters"] = trial.suggest_categorical(
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"use_defense_filters", [False, True],
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)
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combo["require_reversal_candle"] = trial.suggest_categorical(
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"require_reversal_candle", [False, True],
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)
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combo["max_loss_krw"] = trial.suggest_int("max_loss_krw", 50000, 300000, step=25000)
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combo["min_drop_pct_for_loss_cut"] = r3(
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trial.suggest_float("min_drop_pct_for_loss_cut", 0.005, 0.03, step=0.001),
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)
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combo["min_margin"] = r2(trial.suggest_float("min_margin", 0.05, 0.5, step=0.05))
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combo["min_hold_sec"] = trial.suggest_int("min_hold_sec", 0, 300, step=10) # 확장: 120→300초
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combo["max_daily"] = trial.suggest_int("max_daily", 5, 150, step=5) # 확장: 10→5, 100→150
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combo["use_macd_cross"] = False
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combo["skip_hts_scan_dupes"] = False
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combo.update(suggest_orderbook_entry_tpe(trial))
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combo.update(suggest_whipsaw_tpe(trial))
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return combo
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