36a3e2b4a1
Changes: - Added a new API endpoint for managing permanent subscriptions, allowing users to enable or disable subscriptions dynamically. - Implemented a function to fill candle data from Kiwoom, ensuring that only relevant data is inserted into the database. - Introduced a mechanism to handle master subscription states, improving the management of subscription statuses. - Updated the database schema to include new fields for managing subscription states and order book filtering. Impact: - These enhancements improve the flexibility and reliability of the trading system, allowing for better management of subscriptions and order book data, while reducing the risk of data inconsistencies. 히스토리 align 제거 븅신같은 초기설계 아예 제거 진입모드에 구멍메움 호가진입을 켜도 호가가 안들어올때 호가 안보고 그냥 사버림
201 lines
7.5 KiB
Python
201 lines
7.5 KiB
Python
#!/usr/bin/env python3
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"""
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optuna_tail_tpe_space.py — 꼬리 Optuna 연속(TPE) 탐색 공간
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기존 ``_tail_grids()`` categorical 유지. ``skip_hts_scan_dupes=False`` 고정.
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비율 축은 엔진과 동일(소수, UI% 아님) — evaluate/apply 경로와 맞춤.
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multivariate TPE: 모든 축을 **먼저** suggest 한 뒤, 제약(ATR min/max·래칫 오름차순 등)만
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맨 끝에서 TrialPruned. 중간 prune 시 뒤쪽 키가 trial마다 빠져 independent sampling 경고가 난다.
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"""
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from __future__ import annotations
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from typing import Any, Dict, List, Optional
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import optuna
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from kis_trader.backtest.optuna_tpe_common import (
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RATCHET_TPE_AXIS_KEYS,
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r1,
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r2,
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r3,
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r4,
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suggest_ratchet_tiers_pct,
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)
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# 래칫: 꼬리는 소폭 % (기존 메뉴 0.3~2.0 대역) — 숫자축 + 조립 문자열
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# 휩쏘: 실매 DB 기본(enabled=false, subbar=30, lookback=90, dip=0.003, recovery=0.001) 그리드 포함
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_TAIL_BASE_KEYS: List[str] = [
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"entry_mode",
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"cand_limit",
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"max_daily_change",
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"min_drop_rate",
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"min_recovery_ratio",
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"tail_ratio_min",
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"tail_pct_min",
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"max_rec_3m",
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"shoulder_min_high",
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"shoulder_cut_pct",
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"stop_atr_mult",
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"target_atr_mult",
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"atr_sl_min_pct",
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"atr_sl_max_pct",
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"atr_tp_min_pct",
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"atr_tp_max_pct",
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"tail_vol_mult",
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"tail_vol_win",
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"limit_atr_mult",
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"symbol_daily_loss_limit_pct",
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"symbol_daily_loss_limit_krw",
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"reentry_min_edge_krw",
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"reentry_require_nonneg",
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"max_daily",
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"cooldown_min",
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"bar_chg_min_pct",
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"bar_chg_max_pct",
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"rsi_threshold",
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"pattern_pin",
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"pattern_engulfing",
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"pattern_piercing",
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"pattern_harami",
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"pattern_doji",
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"pattern_morning_star",
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# 당일손익 다단트레일(trail_tiers/drop/arm) — 운영 리스크 손잡이. TPE·apply 탐색 제외(DB/UI 고정).
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"max_loss_krw",
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"whipsaw_enabled",
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"whipsaw_subbar_sec",
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"whipsaw_lookback_sec",
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"whipsaw_dip_pct",
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"whipsaw_recovery_tol_pct",
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]
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TAIL_TPE_AXIS_KEYS: List[str] = list(_TAIL_BASE_KEYS) + list(RATCHET_TPE_AXIS_KEYS)
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def tail_tpe_axis_keys() -> List[str]:
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return list(TAIL_TPE_AXIS_KEYS)
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def normalize_tpe_tail_entry_mode(raw: Optional[Any] = None) -> str:
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"""TPE는 진입모드를 탐색하지 않고 스터디마다 고정. align | limit_atr."""
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s = str(raw or "").strip().lower()
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if s in ("limit_atr", "limit", "atr_limit"):
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return "limit_atr"
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return "align"
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def suggest_tail_params_tpe(
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trial: optuna.Trial,
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entry_mode: Optional[str] = None,
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) -> Dict[str, Any]:
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combo: Dict[str, Any] = {}
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# 한 스터디=한 모드. categorical 혼입 금지(웹 체크 2개면 잡 2개 순차).
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combo["entry_mode"] = normalize_tpe_tail_entry_mode(entry_mode)
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combo["cand_limit"] = trial.suggest_categorical("cand_limit", [0, 20])
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combo["max_daily_change"] = r1(
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trial.suggest_float("max_daily_change", 10.0, 60.0, step=1.0), # 확장: 20→10, 45→60
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)
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combo["min_drop_rate"] = r3(
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trial.suggest_float("min_drop_rate", 0.005, 0.15, step=0.005), # 확장: 0.01→0.005, 0.08→0.15
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)
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combo["min_recovery_ratio"] = r2(
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trial.suggest_float("min_recovery_ratio", 0.08, 0.35, step=0.01),
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)
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combo["tail_ratio_min"] = r2(trial.suggest_float("tail_ratio_min", 0.4, 1.5, step=0.1))
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combo["tail_pct_min"] = r4(
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trial.suggest_float("tail_pct_min", 0.0005, 0.01, step=0.0005),
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)
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combo["max_rec_3m"] = r2(trial.suggest_float("max_rec_3m", 0.7, 0.98, step=0.01))
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combo["shoulder_min_high"] = r4(
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trial.suggest_float("shoulder_min_high", 0.002, 0.015, step=0.001),
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)
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combo["shoulder_cut_pct"] = r4(
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trial.suggest_float("shoulder_cut_pct", 0.0005, 0.005, step=0.0005),
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)
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combo["stop_atr_mult"] = r2(trial.suggest_float("stop_atr_mult", 0.8, 3.0, step=0.1))
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combo["target_atr_mult"] = r2(
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trial.suggest_float("target_atr_mult", 0.8, 3.5, step=0.1),
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)
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combo["atr_sl_min_pct"] = r2(trial.suggest_float("atr_sl_min_pct", 0.3, 1.0, step=0.1))
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combo["atr_sl_max_pct"] = r1(trial.suggest_float("atr_sl_max_pct", 1.0, 12.0, step=0.5)) # 확장: 8.0→12.0
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combo["atr_tp_min_pct"] = r2(trial.suggest_float("atr_tp_min_pct", 0.1, 2.0, step=0.1)) # 확장: 0.2→0.1
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combo["atr_tp_max_pct"] = r1(trial.suggest_float("atr_tp_max_pct", 1.0, 10.0, step=0.5)) # 확장: 6.0→10.0
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combo["tail_vol_mult"] = r2(trial.suggest_float("tail_vol_mult", 0.0, 4.0, step=0.1))
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combo["tail_vol_win"] = trial.suggest_int("tail_vol_win", 2, 8)
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combo["limit_atr_mult"] = r2(trial.suggest_float("limit_atr_mult", 0.8, 2.5, step=0.1))
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# 꼬리 래칫: OFF=\"off\" / gain·cut 소폭% (엔진 문자열과 동일)
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combo.update(
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suggest_ratchet_tiers_pct(
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trial,
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off_token="off",
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n_max=3,
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gain_low=0.3,
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gain_high=3.0,
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gain_step=0.1,
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cut_low=0.15,
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cut_high=0.5,
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cut_step=0.05,
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),
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)
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combo["symbol_daily_loss_limit_pct"] = r2(
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trial.suggest_float("symbol_daily_loss_limit_pct", 0.0, 3.0, step=0.5),
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)
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combo["symbol_daily_loss_limit_krw"] = trial.suggest_int(
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"symbol_daily_loss_limit_krw", 0, 80000, step=10000,
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)
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combo["reentry_min_edge_krw"] = trial.suggest_int(
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"reentry_min_edge_krw", 0, 1000, step=100,
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)
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combo["reentry_require_nonneg"] = False
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combo["max_daily"] = trial.suggest_int("max_daily", 3, 80, step=5) # 확장: 5→3, 60→80
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combo["cooldown_min"] = r1(trial.suggest_float("cooldown_min", 0.0, 30.0, step=1.0)) # 확장: 15→30
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combo["bar_chg_min_pct"] = r1(
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trial.suggest_float("bar_chg_min_pct", -15.0, -3.0, step=0.5),
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)
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combo["bar_chg_max_pct"] = r2(
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trial.suggest_float("bar_chg_max_pct", -2.0, -0.2, step=0.1),
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)
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combo["rsi_threshold"] = r1(trial.suggest_float("rsi_threshold", 70.0, 95.0, step=1.0))
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for pk in (
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"pattern_pin",
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"pattern_engulfing",
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"pattern_piercing",
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"pattern_harami",
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"pattern_doji",
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"pattern_morning_star",
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):
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combo[pk] = trial.suggest_categorical(pk, [False, True])
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combo["max_loss_krw"] = trial.suggest_int("max_loss_krw", 50000, 300000, step=25000)
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# 휩쏘 TRIGGER — 실매값(False/30/90/0.003/0.001) 포함. ON·OFF·초·% 축 분리.
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combo["whipsaw_enabled"] = trial.suggest_categorical(
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"whipsaw_enabled", [False, True],
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)
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combo["whipsaw_subbar_sec"] = trial.suggest_categorical(
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"whipsaw_subbar_sec", [15, 30, 45, 60],
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)
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combo["whipsaw_lookback_sec"] = trial.suggest_categorical(
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"whipsaw_lookback_sec", [60, 90, 120, 180],
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)
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combo["whipsaw_dip_pct"] = r4(
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trial.suggest_float("whipsaw_dip_pct", 0.001, 0.01, step=0.001),
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)
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combo["whipsaw_recovery_tol_pct"] = r4(
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trial.suggest_float("whipsaw_recovery_tol_pct", 0.0005, 0.003, step=0.0005),
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)
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combo["skip_hts_scan_dupes"] = False
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# --- 제약 prune: 모든 suggest 이후에만 (키 공간 고정) ---
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if combo["atr_sl_min_pct"] >= combo["atr_sl_max_pct"]:
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raise optuna.TrialPruned("atr_sl min>=max")
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if combo["atr_tp_min_pct"] >= combo["atr_tp_max_pct"]:
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raise optuna.TrialPruned("atr_tp min>=max")
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if combo["bar_chg_min_pct"] >= combo["bar_chg_max_pct"]:
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raise optuna.TrialPruned("bar_chg invalid")
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if combo.pop("_ratchet_ascending_ok", True) is False:
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raise optuna.TrialPruned("ratchet gain not ascending")
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return combo
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