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.
277 lines
10 KiB
Python
277 lines
10 KiB
Python
#!/usr/bin/env python3
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"""구 Optuna JSON에 TopN 후처리(진입/익절/손절/휩쏘)를 다시 붙여 저장.
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실매 엔진 미변경. 캔들/틱은 prepare_* 가 DB 재사용. REST 웜업은 기존 prepare 경로만.
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"""
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from __future__ import annotations
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import argparse
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import json
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import logging
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import sys
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from pathlib import Path
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from typing import Any, Callable, Dict, Optional, Tuple
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_ROOT = Path(__file__).resolve().parents[2]
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if str(_ROOT) not in sys.path:
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sys.path.insert(0, str(_ROOT))
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logger = logging.getLogger("optuna_rerun_postprocess")
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EvalFn = Callable[[Dict[str, Any]], Optional[Dict[str, Any]]]
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def _hist(data: Dict[str, Any]) -> Optional[str]:
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return (
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data.get("universe_history_source")
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or data.get("_universe_history_source")
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or data.get("history_source")
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)
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def build_replay_evaluate_fn(
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data: Dict[str, Any],
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) -> Tuple[Optional[EvalFn], Any]:
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"""JSON 메타로 실매 Optuna와 같은 evaluate_fn + ctx. 실패 시 (None, None)."""
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strat = str(data.get("strategy") or "").strip().lower()
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start = str(data.get("start") or "").strip()
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end = str(data.get("end") or "").strip()
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mode = str(data.get("mode") or "tpe").strip().lower() or "tpe"
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hist = _hist(data)
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if not start or not end:
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logger.warning("⚠️ start/end 없음 — 호가 재탐색만(체결 재실행 없음)")
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return None, None
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if strat in ("momentum", "us_momentum"):
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from kis_trader.backtest.optuna_momentum import prepare_momentum_search_context
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from kis_trader.backtest.param_search_momentum import evaluate_momentum_param_combo
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mk = "US" if strat == "us_momentum" else "KR"
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ctx = prepare_momentum_search_context(
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start, end, mode,
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history_source=hist,
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market=mk,
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symbol=str(data.get("symbol") or "") or None,
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orderbook_filter="off",
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)
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if ctx is None:
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return None, None
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def _eval(combo: Dict[str, Any]) -> Optional[Dict[str, Any]]:
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return evaluate_momentum_param_combo(
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combo,
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base_fixed=ctx.base_fixed,
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grid_keys=ctx.grid_keys,
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codes_candles=ctx.codes_candles,
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min_trades=1,
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min_win_rate=0.0,
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min_pf=0.0,
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universe_by_slot=ctx.universe_by_slot,
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slot_money=ctx.slot_money,
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max_stocks=ctx.max_stocks,
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total_budget_krw=ctx.total_budget_krw,
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fee_rate=ctx.fee_rate,
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sell_tax=ctx.sell_tax,
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period_days=ctx.period_days,
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cache_holder=ctx.cache_holder,
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ticks_by_code=ctx.ticks_by_code,
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orderbook_by_code=ctx.orderbook_by_code,
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program_by_code=ctx.program_by_code,
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log_verdict_by_code=ctx.log_verdict_by_code,
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start_key=ctx.start_key,
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end_key=ctx.end_key,
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include_trades=True,
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)
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return _eval, ctx
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if strat == "breakout":
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from kis_trader.backtest.optuna_breakout import prepare_breakout_search_context
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from kis_trader.backtest.param_search_breakout import evaluate_breakout_param_combo
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ctx = prepare_breakout_search_context(
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start, end, mode, history_source=hist, orderbook_filter="off",
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)
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if ctx is None:
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return None, None
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def _eval_b(combo: Dict[str, Any]) -> Optional[Dict[str, Any]]:
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return evaluate_breakout_param_combo(
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combo,
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base_fixed=ctx.base_fixed,
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grid_keys=ctx.grid_keys,
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codes_candles=ctx.codes_candles,
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min_trades=1,
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min_win_rate=0.0,
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min_pf=0.0,
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universe_by_slot=ctx.universe_by_slot,
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slot_money=ctx.slot_money,
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max_stocks=ctx.max_stocks,
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total_budget_krw=ctx.total_budget_krw,
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fee_rate=ctx.fee_rate,
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sell_tax=ctx.sell_tax,
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period_days=ctx.period_days,
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cache_holder=ctx.cache_holder,
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ticks_by_code=ctx.ticks_by_code,
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orderbook_by_code=ctx.orderbook_by_code,
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program_by_code=ctx.program_by_code,
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log_verdict_by_code=ctx.log_verdict_by_code,
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share_denom_by_code=ctx.share_denom_by_code,
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include_trades=True,
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)
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return _eval_b, ctx
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if strat in ("scalp", "scalping"):
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from kis_trader.backtest.optuna_scalping import prepare_scalp_search_context
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from kis_trader.backtest.param_search_scalping import evaluate_scalp_param_combo
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ctx = prepare_scalp_search_context(
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start, end, mode, history_source=hist, orderbook_filter="off",
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)
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if ctx is None:
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return None, None
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def _eval_s(combo: Dict[str, Any]) -> Optional[Dict[str, Any]]:
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return evaluate_scalp_param_combo(
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combo,
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base_fixed=ctx.base_fixed,
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grid_keys=ctx.grid_keys,
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codes_candles=ctx.codes_candles,
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min_trades=1,
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min_win_rate=0.0,
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min_pf=0.0,
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universe_by_slot=ctx.universe_by_slot,
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slot_money=ctx.slot_money,
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max_stocks=ctx.max_stocks,
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total_budget_krw=ctx.total_budget_krw,
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fee_rate=ctx.fee_rate,
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sell_tax=ctx.sell_tax,
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period_days=ctx.period_days,
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cache_holder=ctx.cache_holder,
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ticks_by_code=ctx.ticks_by_code,
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orderbook_by_code=ctx.orderbook_by_code,
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program_by_code=ctx.program_by_code,
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start_key=ctx.start_key,
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end_key=ctx.end_key,
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include_trades=True,
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)
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return _eval_s, ctx
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if strat in ("tail", "short"):
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from kis_trader.backtest.param_search_optuna import prepare_tail_search_context
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from kis_trader.backtest.tail_param_search import evaluate_tail_param_combo
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ctx = prepare_tail_search_context(
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start, end, mode, history_source=hist, orderbook_filter="off",
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)
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if ctx is None:
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return None, None
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def _eval_t(combo: Dict[str, Any]) -> Optional[Dict[str, Any]]:
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return evaluate_tail_param_combo(
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combo,
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base_params=ctx.base_params,
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candles_by_code=ctx.candles_by_code,
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fee_rate=ctx.fee_rate,
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sell_tax=ctx.sell_tax,
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min_trades=1,
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min_win_rate=0.0,
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min_pf=0.0,
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universe_by_slot=ctx.universe_by_slot,
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slot_money=ctx.slot_money,
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max_stocks=ctx.max_stocks,
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total_budget_krw=ctx.total_budget_krw,
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period_days=ctx.period_days,
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cache_holder=ctx.cache_holder,
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ticks_by_code=ctx.ticks_by_code,
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orderbook_by_code=ctx.orderbook_by_code,
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program_by_code=ctx.program_by_code,
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log_verdict_by_code=ctx.log_verdict_by_code,
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include_trades=True,
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)
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return _eval_t, ctx
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logger.warning("⚠️ 전략 %s 후처리 재실행 evaluate 미지원", strat)
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return None, None
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def rerun_postprocess_on_json(path: str, *, ob_n_trials: int = 0) -> Dict[str, Any]:
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p = Path(path)
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if not p.is_file():
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raise FileNotFoundError(str(p))
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data = json.loads(p.read_text(encoding="utf-8"))
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eval_fn, ctx = build_replay_evaluate_fn(data)
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try:
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from kis_trader.backtest.optuna_postprocess_topn import attach_topn_postprocess
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attach_topn_postprocess(
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data,
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evaluate_fn=eval_fn,
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log=logger,
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run_ob_whipsaw=True,
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ob_n_trials=int(ob_n_trials or 0),
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)
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tmp = p.with_suffix(".tmp.json")
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tmp.write_text(json.dumps(data, indent=2, ensure_ascii=False), encoding="utf-8")
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tmp.replace(p)
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topn = data.get("postprocess_topn") or {}
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n = len(topn.get("postprocess_by_anchor") or [])
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logger.info("📌 후처리 재저장 %s anchors=%d overfit=%s", p, n, topn.get("apply_overfit_pct"))
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try:
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from kis_trader.backtest.optuna_study_store import (
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count_study_states,
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save_payload,
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)
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import optuna
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from kis_trader.backtest.optuna_common import resolve_optuna_storage_url
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sname = str(data.get("optuna_study_name") or "")
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n_c = int(data.get("optuna_n_complete") or 0)
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if sname:
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try:
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st = optuna.load_study(
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study_name=sname,
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storage=resolve_optuna_storage_url(None),
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)
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n_c, _nr, n_f = count_study_states(st)
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except Exception:
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n_f = n_c
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save_payload(
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study_name=sname,
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out_data=data,
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n_complete=n_f if n_f else n_c,
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pp_status="done",
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)
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except Exception as exc:
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logger.warning("⚠️ optuna_study_result 반영 실패: %s", exc)
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return {"ok": True, "path": str(p), "anchors": n, "run_ob_whipsaw": True}
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finally:
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if ctx is not None:
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try:
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from kis_trader.backtest.optuna_common import release_shared_tick_store
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release_shared_tick_store(ctx, log=logger)
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except Exception as exc:
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logger.warning("⚠️ tick store 해제: %s", exc)
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def main() -> int:
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s [%(levelname)s] %(message)s",
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stream=sys.stdout,
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)
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ap = argparse.ArgumentParser(description="Optuna JSON TopN 후처리 재실행")
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ap.add_argument("--result-json", required=True)
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ap.add_argument("--ob-axis-trials", type=int, default=0, help="0=DB OPTUNA_OB_* trial 수")
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args = ap.parse_args()
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out = rerun_postprocess_on_json(args.result_json, ob_n_trials=int(args.ob_axis_trials or 0))
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logger.info("OK %s", out)
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return 0 if out.get("ok") else 1
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if __name__ == "__main__":
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raise SystemExit(main())
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