0780b2cdd0
Changes: - Added new API endpoints for continuing and confirming Optuna jobs, allowing for better management of ongoing studies. - Introduced detailed logging for tick feed tracking and order book processing, improving traceability of vendor performance during backtests. - Updated database schema to include new fields for managing Optuna study results, enhancing the ability to track study progress and outcomes. - Refactored existing functions to utilize the new logging and tracking features, ensuring consistency across the backtesting framework. Impact: - These enhancements improve the robustness and transparency of the Optuna backtesting process, facilitating better analysis and optimization of trading strategies.
620 lines
24 KiB
Python
620 lines
24 KiB
Python
#!/usr/bin/env python3
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"""kis_trader/backtest/optuna_scalping.py — 스캘핑(Reversal) Optuna (Grid add-on)."""
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from __future__ import annotations
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import json
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import logging
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import os
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import time
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from dataclasses import dataclass, field
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from datetime import datetime
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from typing import Any, Dict, List, Optional
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import optuna
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from optuna.samplers import RandomSampler, TPESampler
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from database import TradeDB
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from kis_trader.backtest import scalping_backtest_common as sbc
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from kis_trader.backtest.breakout_tick_loader import (
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load_breakout_ticks_by_code,
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tick_coverage_stats,
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)
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from kis_trader.backtest.optuna_search_space import scalp_grid_axis_keys, suggest_scalp_params
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from kis_trader.backtest.optuna_scalping_tpe_space import (
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scalp_tpe_axis_keys,
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suggest_scalp_params_tpe,
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)
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from kis_trader.backtest.optuna_mode_combo import enrich_out_data_with_mode_combo
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from kis_trader.backtest.optuna_common import (
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announce_optuna_json_path,
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build_optuna_result_tiers,
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pick_gated_apply_trial,
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release_shared_tick_store,
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set_optuna_trial_stability_attrs,
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stability_fields_from_trial_attrs,
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)
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from kis_trader.backtest.param_search_cli_common import (
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apply_session_to_fixed,
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combo_passes_search_filters,
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format_session_hm,
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)
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from kis_trader.backtest.param_search_scalping import (
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SCALP_GRID_AXIS_HINTS_KO,
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_fixed_defaults,
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_load_candles_for_search,
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_scalp_grids,
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_ui_to_engine_params,
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apply_params_to_db,
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evaluate_scalp_param_combo,
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)
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from kis_trader.backtest.tail_param_search import _results_dir_for_write
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from kis_trader.engine import scalping_engine as se
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from kis_trader.engine.indicator_cache import attach_indicator_caches_to_params
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from kis_trader.utils.env import get_env_bool, get_env_float
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logger = logging.getLogger("param_search_optuna")
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_FAIL_OBJECTIVE = -1e18
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@dataclass
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class ScalpSearchContext:
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start: str
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end: str
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mode: str
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base_fixed: Dict[str, Any]
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codes_candles: Dict[str, List[Dict]]
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universe_by_slot: Optional[Dict[str, List[str]]]
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universe_source: str
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fee_rate: float
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sell_tax: float
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slot_money: float
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max_stocks: int
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total_budget_krw: float
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period_days: int
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portfolio: Dict[str, Any]
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grid_keys: List[str]
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start_key: str = ""
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end_key: str = ""
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ticks_by_code: Any = None
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tick_rows: int = 0
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tick_backtest_meta: Dict[str, Any] = field(default_factory=dict)
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cache_holder: Dict[str, Any] = field(default_factory=dict)
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shared_tick_store: Any = None # ws_ticks 공유메모리 핸들 (종료 시 unlink)
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orderbook_by_code: Dict[str, Any] = field(default_factory=dict)
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program_by_code: Dict[str, Any] = field(default_factory=dict)
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orderbook_filter: str = "off"
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def prepare_scalp_search_context(
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start: str,
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end: str,
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mode: str,
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*,
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use_fallback_universe: bool = False,
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time_start_hm: Optional[int] = None,
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time_end_hm: Optional[int] = None,
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slot_money: Optional[float] = None,
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max_stocks: Optional[int] = None,
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total_budget_krw: Optional[float] = None,
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orderbook_filter: str = "off",
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history_source: Optional[str] = None,
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) -> Optional[ScalpSearchContext]:
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grids = _scalp_grids()
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if mode == "tpe":
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grid_axes: Dict[str, Any] = {}
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logger.info(
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"📌 mode=tpe — 연속(float/int) 탐색 (Grid categorical 미사용, TPE 가 구간 축소)"
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)
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elif mode not in grids:
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logger.error(
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"❌ 스캘핑 mode: %s (fast/trigger/exit/coarse/fine/full/wide/tpe)", mode,
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)
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return None
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else:
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grid_axes = grids[mode]
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base_fixed = _fixed_defaults()
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if mode == "tpe":
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base_fixed["skip_hts_scan_dupes"] = False
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apply_session_to_fixed(base_fixed, time_start_hm=time_start_hm, time_end_hm=time_end_hm)
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_ob_mode = (orderbook_filter or "off").strip().lower()
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if _ob_mode == "off":
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base_fixed["_orderbook_filter_enabled"] = False
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elif _ob_mode == "on":
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base_fixed["_orderbook_filter_enabled"] = True
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ob_filter_on = bool(base_fixed.get("_orderbook_filter_enabled")) or _ob_mode == "auto"
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logger.info(
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"📌 호가필터: %s (%s)",
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_ob_mode.upper(),
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"적용" if ob_filter_on else "스킵 — 코어 파라미터 순수 탐색 (실매 ORDERBOOK도 OFF 권장 정합)",
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)
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db = TradeDB()
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try:
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from kis_trader.backtest.backtest_portfolio_common import load_portfolio_env_row
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env_row = load_portfolio_env_row(db)
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finally:
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db.close()
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fee_rate, sell_tax, slot_from_env = sbc.fee_and_slot_from_env(env_row, strategy="SCALP")
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portfolio = sbc.resolve_scalp_portfolio_params(
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env_row, None, strategy="SCALP",
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slot_money=slot_money if slot_money is not None else slot_from_env,
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max_stocks=max_stocks,
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total_budget_krw=total_budget_krw,
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)
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slot_money_f = float(portfolio["slot_money"])
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max_stocks_i = int(portfolio["max_stocks"])
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total_budget_f = float(portfolio["total_budget_krw"])
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period_days = max(
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1,
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(datetime.strptime(end, "%Y-%m-%d") - datetime.strptime(start, "%Y-%m-%d")).days + 1,
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)
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logger.info(
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"💼 포트폴리오: 1회 %s원 | 동시 %d종 | 총한도 %s원 | 매매 %s",
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f"{slot_money_f:,.0f}", max_stocks_i, f"{total_budget_f:,.0f}",
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format_session_hm(base_fixed),
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)
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# grid_axes 는 상단에서 mode별 설정 (tpe=빈 dict). grids[mode] 재조회 금지.
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rsi_cands = grid_axes.get("rsi_period") or [base_fixed.get("rsi_period") or 7]
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try:
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rsi_period = max(int(float(x)) for x in rsi_cands)
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except (TypeError, ValueError):
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rsi_period = int(base_fixed.get("rsi_period") or 7)
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if mode == "tpe":
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# TPE 상한까지 RSI 로드 (3~14)
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rsi_period = max(rsi_period, 14)
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from kis_trader.backtest.universe_history_source import (
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resolve_backtest_universe_history_source,
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)
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_hs = resolve_backtest_universe_history_source(history_source)
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base_fixed["_universe_history_source"] = _hs
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try:
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from kis_trader.backtest.optuna_feed_trace import log_bt_feed_chain_banner
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log_bt_feed_chain_banner(context="Optuna-SCALP")
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except Exception:
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pass
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codes_candles = _load_candles_for_search(
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start, end, rsi_period, history_source=_hs,
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)
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if not codes_candles:
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logger.error("❌ 캔들 데이터 없음")
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return None
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logger.info("✅ 데이터 로드: %s종목 (history=%s)", len(codes_candles), _hs)
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# ── 틱재생 — history_source=ls 이면 ls_ws_ticks ──
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start_key = (start.replace("-", "") + "0000") if start else "202601010000"
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end_key = (end.replace("-", "") + "2359") if end else "999912312359"
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ticks_by_code: Dict[str, Any] = {}
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tick_rows = 0
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tick_backtest_meta: Dict[str, Any] = {}
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engine_probe = _ui_to_engine_params(base_fixed)
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engine_probe["backtest_tick_fallback_ohlc"] = False
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base_fixed["backtest_tick_fallback_ohlc"] = False
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if sbc._scalp_backtest_wants_ticks(engine_probe):
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_tick_db = TradeDB()
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try:
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if _hs in ("ls", "ls_condition", "ls_ws"):
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from kis_trader.backtest.ls_history_loaders import load_ls_ticks_by_code
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ticks_by_code, tick_rows = load_ls_ticks_by_code(
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_tick_db, start_key, end_key, set(codes_candles.keys()),
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)
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_tick_lbl = "ls_ws_ticks"
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else:
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ticks_by_code, tick_rows = load_breakout_ticks_by_code(
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_tick_db, start_key, end_key, set(codes_candles.keys()),
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)
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_tick_lbl = "ws_ticks"
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tick_backtest_meta = tick_coverage_stats(codes_candles, ticks_by_code)
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tick_backtest_meta["ws_tick_rows_loaded"] = tick_rows
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tick_backtest_meta["tick_table"] = _tick_lbl
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cov = tick_backtest_meta.get("tick_bar_coverage_pct", 0)
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logger.info(
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"✅ %s %s건 | 분봉 커버리지 %s%% (%s/%s종목)",
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_tick_lbl,
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f"{tick_rows:,}",
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cov,
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tick_backtest_meta.get("tick_codes_with_data", 0),
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tick_backtest_meta.get("tick_codes_total", 0),
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)
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if tick_rows <= 0:
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logger.warning(
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"⚠️ ws_ticks 없음 — SCALP Optuna 가 OHLC만 사용 "
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"(틱 수집 후 재탐색, FALLBACK_OHLC 기본 OFF)"
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)
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else:
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_fb = get_env_bool("SCALP_BACKTEST_TICK_FALLBACK_OHLC", False)
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logger.info(
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"📌 틱재생(ws_ticks): ON — OHLC 폴백 %s",
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"ON" if _fb else "OFF",
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)
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finally:
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_tick_db.close()
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shared_tick_store = None
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if get_env_bool("OPTUNA_PARAM_SEARCH_SHARED_TICKS", True) and ticks_by_code:
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from kis_trader.backtest.shared_ticks import build_shared_ticks_view
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_view, shared_tick_store = build_shared_ticks_view(ticks_by_code, enabled=True)
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if shared_tick_store is not None:
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import atexit as _atexit
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_atexit.register(shared_tick_store.unlink)
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logger.info("📦 ws_ticks 공유메모리 ON (Optuna) — dict 사본 제거, RAM 절감")
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ticks_by_code = _view
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import gc as _gc
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_gc.collect()
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try:
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import ctypes as _ctypes
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_ctypes.CDLL("libc.so.6").malloc_trim(0)
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except Exception:
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pass
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start_ymd = start.replace("-", "") if start else ""
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end_ymd = end.replace("-", "") if end else ""
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universe_by_slot = None
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universe_source = "sim"
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fallback_sim_interval = 5
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if not use_fallback_universe and start_ymd and end_ymd:
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history, src, n_slots, scan_iv = sbc.resolve_scalp_universe(
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start_ymd, end_ymd, use_saved_history=True, strategy_id="SCALP",
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history_source=_hs,
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)
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if history:
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universe_by_slot = history
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universe_source = src
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base_fixed["scan_interval_min"] = scan_iv
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avg = sum(len(v) for v in history.values()) / max(1, n_slots)
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logger.info(
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"✅ 유니버스: SCALP 이력 src=%s | %d슬롯 · 평균 %.1f종목",
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src, n_slots, avg,
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)
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if universe_by_slot is None:
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universe_top_n = int(os.environ.get("UPDATE_UNIVERSE_TOP_N", "20"))
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universe_min_score = float(os.environ.get("UPDATE_UNIVERSE_MIN_SCORE", "4.0"))
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universe_by_slot = se.build_universe_simulation(
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codes_candles,
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top_n=universe_top_n,
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min_score=universe_min_score,
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scan_interval_min=fallback_sim_interval,
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)
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base_fixed["scan_interval_min"] = fallback_sim_interval
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universe_source = "sim"
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logger.info("📌 유니버스: 개미털기 시뮬 fallback (%d분)", fallback_sim_interval)
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else:
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base_fixed["scan_interval_min"] = 1
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cache_holder: Dict[str, Any] = {}
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attach_indicator_caches_to_params(cache_holder, codes_candles)
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# 호가·프로그램 스냅샷 (필터 ON + 그리드 스윕/재생용)
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orderbook_by_code: Dict[str, Any] = {}
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program_by_code: Dict[str, Any] = {}
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_ob_axes = ("max_spread_pct", "min_bid_ask_ratio", "ask_max_mult")
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_ob_sweeping = any(len(set(grid_axes.get(k) or [])) > 1 for k in _ob_axes)
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if ob_filter_on:
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from kis_trader.backtest.trigger_snapshot_loader import load_trigger_snapshots_by_code
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_ob_db = TradeDB()
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try:
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engine_probe["_orderbook_filter_enabled"] = True
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orderbook_by_code, program_by_code, trigger_snap_meta = load_trigger_snapshots_by_code(
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_ob_db, start_key, end_key, set(codes_candles.keys()),
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engine_params=engine_probe, strategy="SCALP",
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)
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ob_rows = int(trigger_snap_meta.get("ws_orderbook_rows_loaded") or 0)
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pg_rows = int(trigger_snap_meta.get("ws_program_rows_loaded") or 0)
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logger.info(
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"✅ TRIGGER 스냅샷 ws_orderbook %s건 | ws_program %s건%s",
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f"{ob_rows:,}", f"{pg_rows:,}",
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" (호가축 스윕)" if _ob_sweeping else "",
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)
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if ob_rows <= 0:
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logger.warning(
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"⚠️ ws_orderbook 거의 없음 — 호가필터 ON 이어도 스냅샷 없으면 통과(미차단). "
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"수집 늘린 뒤 재탐색 권장."
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)
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finally:
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_ob_db.close()
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return ScalpSearchContext(
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start=start,
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end=end,
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mode=mode,
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base_fixed=base_fixed,
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codes_candles=codes_candles,
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universe_by_slot=universe_by_slot,
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universe_source=universe_source,
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fee_rate=fee_rate,
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sell_tax=sell_tax,
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slot_money=slot_money_f,
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max_stocks=max_stocks_i,
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total_budget_krw=total_budget_f,
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period_days=period_days,
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portfolio=portfolio,
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grid_keys=scalp_tpe_axis_keys() if mode == "tpe" else scalp_grid_axis_keys(mode),
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start_key=start_key,
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end_key=end_key,
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ticks_by_code=ticks_by_code,
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tick_rows=int(tick_rows),
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tick_backtest_meta=tick_backtest_meta,
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cache_holder=cache_holder,
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shared_tick_store=shared_tick_store,
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orderbook_by_code=orderbook_by_code,
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program_by_code=program_by_code,
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orderbook_filter=_ob_mode,
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)
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def _make_sampler(name: str, seed: Optional[int]):
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n = (name or "tpe").strip().lower()
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if n == "random":
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return RandomSampler(seed=seed)
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# multivariate TPE + 조건부 suggest 시 independent sampling 경고가 trial마다 폭주 → 억제
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return TPESampler(seed=seed, multivariate=True, warn_independent_sampling=False)
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def _scalp_objective_value(result: Dict[str, Any], sort_by: str) -> float:
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pnl = float(result["total_pnl"])
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if sort_by == "score":
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mdd_floor = get_env_float("SCALP_SCORE_MDD_FLOOR", 5000.0)
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mdd = float(result.get("mdd") or 0)
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return pnl / max(mdd, mdd_floor)
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if sort_by == "win_rate":
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return float(result["win_rate"])
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return pnl
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def run_scalp_optuna(
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ctx: ScalpSearchContext,
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*,
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n_trials: int,
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storage_url: str,
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study_name: str,
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min_trades: int,
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min_win_rate: float,
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min_pf: float,
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sort_by: str = "pnl",
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sampler_name: str = "tpe",
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seed: Optional[int] = None,
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n_jobs: int = 1,
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show_progress: bool = True,
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) -> optuna.Study:
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study = optuna.create_study(
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study_name=study_name,
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storage=storage_url,
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load_if_exists=True,
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direction="maximize",
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sampler=_make_sampler(sampler_name, seed),
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)
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from kis_trader.backtest.optuna_study_store import bind_study_trials, finalize_optuna_export
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bind_study_trials(study, n_trials=n_trials, log=logger)
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def objective(trial: optuna.Trial) -> float:
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if ctx.mode == "tpe":
|
|
combo = suggest_scalp_params_tpe(trial)
|
|
else:
|
|
combo = suggest_scalp_params(trial, ctx.mode)
|
|
result = evaluate_scalp_param_combo(
|
|
combo,
|
|
base_fixed=ctx.base_fixed,
|
|
grid_keys=ctx.grid_keys,
|
|
codes_candles=ctx.codes_candles,
|
|
min_trades=min_trades,
|
|
min_win_rate=min_win_rate,
|
|
min_pf=min_pf,
|
|
universe_by_slot=ctx.universe_by_slot,
|
|
slot_money=ctx.slot_money,
|
|
max_stocks=ctx.max_stocks,
|
|
total_budget_krw=ctx.total_budget_krw,
|
|
fee_rate=ctx.fee_rate,
|
|
sell_tax=ctx.sell_tax,
|
|
period_days=ctx.period_days,
|
|
cache_holder=ctx.cache_holder,
|
|
ticks_by_code=ctx.ticks_by_code,
|
|
orderbook_by_code=ctx.orderbook_by_code,
|
|
program_by_code=ctx.program_by_code,
|
|
start_key=ctx.start_key,
|
|
end_key=ctx.end_key,
|
|
)
|
|
if result is None:
|
|
trial.set_user_attr("gates_ok", False)
|
|
return _FAIL_OBJECTIVE
|
|
obj = _scalp_objective_value(result, sort_by)
|
|
trial.set_user_attr("gates_ok", True)
|
|
trial.set_user_attr("total_pnl", float(result["total_pnl"]))
|
|
trial.set_user_attr("win_rate", float(result["win_rate"]))
|
|
trial.set_user_attr("pf", float(result.get("pf") or 0))
|
|
trial.set_user_attr("mdd", float(result.get("mdd") or 0))
|
|
trial.set_user_attr(
|
|
"score",
|
|
float(obj if sort_by == "score" else _scalp_objective_value(result, "score")),
|
|
)
|
|
trial.set_user_attr("total_trades", int(result["total_trades"]))
|
|
trial.set_user_attr(
|
|
"merged_json",
|
|
json.dumps(result.get("merged_params") or {}, ensure_ascii=False),
|
|
)
|
|
set_optuna_trial_stability_attrs(trial, result)
|
|
return float(obj)
|
|
|
|
logger.info(
|
|
"🔬 Optuna SCALP | study=%s | mode=%s | trials=%d | sort=%s | universe=%s | ticks=%s",
|
|
study_name, ctx.mode, n_trials, sort_by, ctx.universe_source,
|
|
f"{ctx.tick_rows:,}",
|
|
)
|
|
t0 = time.time()
|
|
try:
|
|
study.optimize(objective, n_trials=n_trials, n_jobs=n_jobs, show_progress_bar=show_progress)
|
|
elapsed = time.time() - t0
|
|
|
|
passing: List[Dict[str, Any]] = []
|
|
for trial in study.trials:
|
|
if trial.state != optuna.trial.TrialState.COMPLETE:
|
|
continue
|
|
if not trial.user_attrs.get("gates_ok"):
|
|
continue
|
|
merged_raw = trial.user_attrs.get("merged_json") or "{}"
|
|
try:
|
|
merged = json.loads(merged_raw)
|
|
except json.JSONDecodeError:
|
|
merged = dict(trial.params)
|
|
row = {
|
|
"params": dict(trial.params),
|
|
"merged_params": merged,
|
|
"total_trades": int(trial.user_attrs.get("total_trades") or 0),
|
|
"win_rate": float(trial.user_attrs.get("win_rate") or 0),
|
|
"total_pnl": float(trial.user_attrs.get("total_pnl") or 0),
|
|
"pf": float(trial.user_attrs.get("pf") or 0),
|
|
"mdd": float(trial.user_attrs.get("mdd") or 0),
|
|
"score": float(trial.user_attrs.get("score") or 0),
|
|
"optuna_trial_number": trial.number,
|
|
}
|
|
row.update(stability_fields_from_trial_attrs(trial))
|
|
passing.append(row)
|
|
|
|
if sort_by == "score":
|
|
passing.sort(key=lambda r: (-r["score"], -r["total_pnl"], -r["win_rate"]))
|
|
elif sort_by == "win_rate":
|
|
passing.sort(key=lambda r: (-r["win_rate"], -r["total_pnl"]))
|
|
else:
|
|
passing.sort(key=lambda r: (-r["total_pnl"], -r["win_rate"]))
|
|
|
|
tiers = build_optuna_result_tiers(passing, sort_by=sort_by)
|
|
|
|
out_data = {
|
|
"engine": "optuna",
|
|
"strategy": "scalp",
|
|
"mode": ctx.mode,
|
|
"start": ctx.start,
|
|
"end": ctx.end,
|
|
"universe_source": ctx.universe_source,
|
|
"slot_money": int(ctx.slot_money),
|
|
"max_stocks": ctx.max_stocks,
|
|
"total_budget_krw": int(ctx.total_budget_krw),
|
|
"backtest_days": ctx.period_days,
|
|
"min_trades": min_trades,
|
|
"min_win_rate": min_win_rate,
|
|
"min_pf": min_pf,
|
|
"sort_by": sort_by,
|
|
"grid_keys": ctx.grid_keys,
|
|
"grid_axis_hints": {
|
|
k: SCALP_GRID_AXIS_HINTS_KO[k]
|
|
for k in ctx.grid_keys if k in SCALP_GRID_AXIS_HINTS_KO
|
|
},
|
|
"optuna_study_name": study_name,
|
|
"optuna_storage": storage_url,
|
|
"optuna_n_trials_requested": n_trials,
|
|
"optuna_trials_completed": len(study.trials),
|
|
"optuna_best_value": study.best_value if study.best_trial else None,
|
|
"optuna_best_trial_number": study.best_trial.number if study.best_trial else None,
|
|
"elapsed_sec": round(elapsed, 1),
|
|
"ws_tick_rows_loaded": int(ctx.tick_rows),
|
|
"tick_backtest": ctx.tick_backtest_meta,
|
|
**tiers,
|
|
}
|
|
|
|
ts = datetime.now().strftime("%Y%m%d_%H%M%S")
|
|
out_path = os.path.join(_results_dir_for_write(), f"optuna_scalp_{ctx.mode}_{ts}.json")
|
|
with open(out_path, "w", encoding="utf-8") as f:
|
|
json.dump(out_data, f, indent=2, ensure_ascii=False)
|
|
announce_optuna_json_path(
|
|
out_path, strategy="scalp", mode=ctx.mode, note="중간저장(mode 전)", log=logger,
|
|
)
|
|
|
|
def _eval_mode(combo: Dict[str, Any]) -> Optional[Dict[str, Any]]:
|
|
return evaluate_scalp_param_combo(
|
|
combo,
|
|
base_fixed=ctx.base_fixed,
|
|
grid_keys=ctx.grid_keys,
|
|
codes_candles=ctx.codes_candles,
|
|
min_trades=1,
|
|
min_win_rate=0.0,
|
|
min_pf=0.0,
|
|
universe_by_slot=ctx.universe_by_slot,
|
|
slot_money=ctx.slot_money,
|
|
max_stocks=ctx.max_stocks,
|
|
total_budget_krw=ctx.total_budget_krw,
|
|
fee_rate=ctx.fee_rate,
|
|
sell_tax=ctx.sell_tax,
|
|
period_days=ctx.period_days,
|
|
cache_holder=ctx.cache_holder,
|
|
ticks_by_code=ctx.ticks_by_code,
|
|
orderbook_by_code=ctx.orderbook_by_code,
|
|
program_by_code=ctx.program_by_code,
|
|
start_key=ctx.start_key,
|
|
end_key=ctx.end_key,
|
|
include_trades=True,
|
|
)
|
|
|
|
def _save_partial(_data: Dict[str, Any]) -> None:
|
|
with open(out_path, "w", encoding="utf-8") as f:
|
|
json.dump(_data, f, indent=2, ensure_ascii=False)
|
|
announce_optuna_json_path(
|
|
out_path, strategy="scalp", mode=ctx.mode, note="mode_combo params 저장(실측 전)", log=logger,
|
|
)
|
|
|
|
def _enrich() -> None:
|
|
enrich_out_data_with_mode_combo(
|
|
out_data,
|
|
evaluate_fn=_eval_mode,
|
|
grid_keys=ctx.grid_keys,
|
|
log=logger,
|
|
on_partial_save=_save_partial,
|
|
)
|
|
with open(out_path, "w", encoding="utf-8") as f:
|
|
json.dump(out_data, f, indent=2, ensure_ascii=False)
|
|
announce_optuna_json_path(
|
|
out_path, strategy="scalp", mode=ctx.mode, note="최종 JSON", log=logger,
|
|
)
|
|
|
|
finalize_optuna_export(
|
|
study,
|
|
out_data=out_data,
|
|
out_path=out_path,
|
|
strategy="scalp",
|
|
mode=ctx.mode,
|
|
enrich_fn=_enrich,
|
|
log=logger,
|
|
)
|
|
study._kis_export_path = out_path # type: ignore[attr-defined]
|
|
return study
|
|
finally:
|
|
release_shared_tick_store(ctx, log=logger)
|
|
|
|
|
|
def apply_best_scalp_trial(study: optuna.Study) -> bool:
|
|
trial = pick_gated_apply_trial(study, sort_by="score", fail_objective=_FAIL_OBJECTIVE)
|
|
if trial is None:
|
|
logger.warning(
|
|
"⚠️ 사후게이트(results_gated) 통과 trial 없음 — DB 미적용"
|
|
)
|
|
return False
|
|
pnl = float(trial.user_attrs.get("total_pnl") or 0)
|
|
if pnl <= 0:
|
|
logger.warning("⚠️ gated trial 총손익 ≤ 0 — DB 미적용")
|
|
return False
|
|
merged_raw = trial.user_attrs.get("merged_json") or "{}"
|
|
merged = json.loads(merged_raw)
|
|
apply_params_to_db(merged)
|
|
logger.info("🚀 [Optuna apply-best] scalp gated trial #%d → env_config", trial.number)
|
|
try:
|
|
from kis_trader.backtest.optuna_daily_trail_recommend import (
|
|
apply_daily_trail_recommend_from_optuna_json,
|
|
)
|
|
apply_daily_trail_recommend_from_optuna_json(
|
|
getattr(study, "_kis_export_path", None),
|
|
strategy="scalp",
|
|
log=logger,
|
|
)
|
|
except Exception as exc:
|
|
logger.warning("⚠️ 다단트레일 추천 반영 스킵: %s", exc)
|
|
return True
|