61bec4bd1d
ㅇ Changes: - Introduced the DART strategy to the trading system, including its configuration and integration into the existing framework. - Updated the database schema to include DART-specific tables for disclosures and watchlists. - Enhanced the backtesting and parameter search functionalities to support the DART strategy. - Implemented new rules for browser verification and API interactions to ensure compliance with the updated DART strategy. Impact: - These additions expand the trading capabilities of the system, allowing for more comprehensive analysis and execution of DART-related strategies, while maintaining system integrity and performance.
105 lines
3.0 KiB
Python
105 lines
3.0 KiB
Python
#!/usr/bin/env python3
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"""kis_trader/backtest/optuna_dart.py — DART Optuna TPE."""
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from __future__ import annotations
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import logging
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from datetime import datetime
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from typing import Any, Dict, Optional
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import optuna
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from optuna.samplers import TPESampler
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from database import TradeDB
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from kis_trader.backtest.optuna_common import (
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announce_optuna_json_path,
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resolve_optuna_storage_url,
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)
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from kis_trader.backtest.param_search_dart import (
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_dart_grids,
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apply_params_to_db,
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evaluate_dart_param_combo,
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)
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from kis_trader.engine import dart_engine as de
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logger = logging.getLogger("param_search_optuna")
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_FAIL = -1e18
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def prepare_dart_search_context(start: str, end: str, mode: str) -> Optional[Dict[str, Any]]:
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grids = _dart_grids()
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if mode not in grids:
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logger.error("DART mode: %s (fast/coarse/fine)", mode)
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return None
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db = TradeDB()
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try:
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env_row = db.get_merged_env_snapshot() or {}
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finally:
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db.close()
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return {
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"start": start,
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"end": end,
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"mode": mode,
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"grid": grids[mode],
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"env_row": env_row,
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"base": de.get_dart_defaults_from_db(env_row=env_row),
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}
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def run_dart_optuna(
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ctx: Dict[str, Any],
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*,
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n_trials: int,
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storage_url: Optional[str] = None,
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study_name: Optional[str] = None,
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min_trades: int = 1,
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sampler_name: str = "tpe",
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seed: int = 42,
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show_progress: bool = False,
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**_kwargs: Any,
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) -> optuna.Study:
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grid: Dict[str, Any] = ctx["grid"]
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storage = storage_url or resolve_optuna_storage_url()
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name = study_name or "dart_%s_%s" % (
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ctx["mode"], datetime.now().strftime("%Y%m%d_%H%M%S"),
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)
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sampler = TPESampler(seed=seed) if sampler_name != "random" else optuna.samplers.RandomSampler(seed=seed)
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study = optuna.create_study(
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study_name=name,
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storage=storage,
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direction="maximize",
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sampler=sampler,
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load_if_exists=True,
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)
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def objective(trial: optuna.Trial) -> float:
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params = dict(ctx["base"])
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for axis, values in grid.items():
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params[axis] = trial.suggest_categorical(axis, list(values))
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res = evaluate_dart_param_combo(
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params, start=ctx["start"], end=ctx["end"], env_row=ctx["env_row"],
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)
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n = int(res.get("trade_count") or 0)
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pnl = float(res.get("total_pnl") or 0)
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trial.set_user_attr("trade_count", n)
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trial.set_user_attr("total_pnl", pnl)
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trial.set_user_attr("win_rate", float(res.get("win_rate") or 0))
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if n < min_trades:
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return _FAIL
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return pnl
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study.optimize(objective, n_trials=n_trials, show_progress_bar=show_progress)
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announce_optuna_json_path("dart", study)
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return study
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def apply_best_dart_trial(study: optuna.Study) -> bool:
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try:
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best = study.best_trial
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except Exception:
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return False
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if float(best.value or _FAIL) <= 0:
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logger.warning("DART best PnL<=0 — DB 미적용")
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return False
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apply_params_to_db(dict(best.params))
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return True
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