"""
Optuna 目标函数。

将策略实例化 → 回测 → 提取指标，包装为 Optuna objective。
"""

import optuna
import pandas as pd

from backtest.base import BaseStrategy
from backtest.vectorbt.engine import VectorBTEngine
from optimizer.space import SearchSpace

# 指标提取器：从 BacktestReport 取对应字段
_METRIC_EXTRACTORS = {
    "sharpe": lambda r: r.sharpe_ratio,
    "cagr": lambda r: r.cagr,
    "calmar": lambda r: r.calmar_ratio,
    "total_return": lambda r: r.total_return,
    "return_over_dd": lambda r: abs(r.total_return / r.max_drawdown) if r.max_drawdown != 0 else 0.0,
    "win_rate": lambda r: r.win_rate,
    "profit_factor": lambda r: r.profit_factor,
}


class Objective:
    """
    Optuna 目标函数（可调用）。

    用法:
        obj = Objective(SMACrossStrategy, sma_cross_space, price_df, factor_df, metric="sharpe")
        study = optuna.create_study(direction="maximize")
        study.optimize(obj, n_trials=100)
    """

    def __init__(
        self,
        strategy_class: type[BaseStrategy],
        search_space: SearchSpace,
        price_df: pd.DataFrame,
        factor_df: pd.DataFrame | None = None,
        bt_engine: VectorBTEngine | None = None,
        metric: str = "sharpe",
    ):
        self.strategy_class = strategy_class
        self.search_space = search_space
        self.price_df = price_df
        self.factor_df = factor_df if factor_df is not None else price_df
        self.bt_engine = bt_engine or VectorBTEngine()
        self.metric = metric
        self._extractor = _METRIC_EXTRACTORS.get(metric)
        if self._extractor is None:
            raise ValueError(f"不支持的指标: '{metric}'。可选: {list(_METRIC_EXTRACTORS)}")

    def __call__(self, trial: optuna.Trial) -> float:
        params = self.search_space.suggest(trial)

        try:
            strategy = self.strategy_class(**params)
        except TypeError:
            # 过滤不匹配的参数
            valid = {k: v for k, v in params.items()
                     if k in self.strategy_class.__init__.__code__.co_varnames}
            strategy = self.strategy_class(**valid)

        report = self.bt_engine.run(strategy, self.price_df, self.factor_df)

        value = self._extractor(report)  # type: ignore

        # 无效值处理
        if value is None or (isinstance(value, float) and (pd.isna(value) or value == float("inf"))):
            return float("-inf")

        return float(value)