"""
通用工具函数
"""

import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from typing import Optional


def format_date(date_str: str, output_format: str = "%Y-%m-%d") -> str:
    """
    统一日期格式

    Args:
        date_str: 输入日期字符串（支持 YYYY-MM-DD 或 YYYYMMDD）
        output_format: 输出格式

    Returns:
        str: 格式化后的日期字符串
    """
    # 尝试解析多种格式
    for fmt in ["%Y-%m-%d", "%Y%m%d", "%Y/%m/%d"]:
        try:
            dt = datetime.strptime(date_str, fmt)
            return dt.strftime(output_format)
        except ValueError:
            continue
    raise ValueError(f"无法解析日期格式: {date_str}")


def get_date_range(
    start_date: Optional[str] = None,
    end_date: Optional[str] = None,
    lookback_days: int = 365,
) -> tuple[str, str]:
    """
    获取日期范围

    Args:
        start_date: 开始日期，None则根据lookback_days计算
        end_date: 结束日期，None则使用今天
        lookback_days: 回溯天数

    Returns:
        tuple: (start_date, end_date) 格式为 YYYY-MM-DD
    """
    if end_date is None:
        end = datetime.now()
    else:
        end = datetime.strptime(format_date(end_date), "%Y-%m-%d")

    if start_date is None:
        start = end - timedelta(days=lookback_days)
    else:
        start = datetime.strptime(format_date(start_date), "%Y-%m-%d")

    return start.strftime("%Y-%m-%d"), end.strftime("%Y-%m-%d")


def calculate_cagr(
    nav_series: pd.Series,
    method: str = "natural_days",
) -> float:
    """
    计算年化收益率（CAGR）

    Args:
        nav_series: 净值序列（index=日期）
        method: 'natural_days' 或 'trading_days'

    Returns:
        float: CAGR值
    """
    total_return = nav_series.iloc[-1] / nav_series.iloc[0]

    if method == "natural_days":
        days = (nav_series.index[-1] - nav_series.index[0]).days
        years = days / 365.0
    elif method == "trading_days":
        years = len(nav_series) / 252.0
    else:
        raise ValueError(f"不支持的CAGR计算方式: {method}")

    if years <= 0:
        return 0.0

    return total_return ** (1 / years) - 1


def calculate_max_drawdown(nav_series: pd.Series) -> tuple[float, datetime, datetime]:
    """
    计算最大回撤

    Returns:
        tuple: (最大回撤比例, 回撤起始日, 回撤结束日)
    """
    cummax = nav_series.cummax()
    drawdown = (nav_series - cummax) / cummax

    max_dd = drawdown.min()
    end_idx = drawdown.idxmin()
    start_idx = nav_series[:end_idx].idxmax()

    return max_dd, start_idx, end_idx


def calculate_sharpe(
    returns: pd.Series,
    rf: float = 0.0,
    periods: int = 252,
) -> float:
    """
    计算年化夏普比率

    Args:
        returns: 日收益率序列
        rf: 无风险利率（年化）
        periods: 年化系数

    Returns:
        float: 夏普比率
    """
    excess_returns = returns - rf / periods
    if excess_returns.std() == 0:
        return 0.0
    return excess_returns.mean() / excess_returns.std() * np.sqrt(periods)


def resample_data(
    df: pd.DataFrame,
    timeframe: str,
    time_col: str = "time",
) -> pd.DataFrame:
    """
    对数据进行重采样

    Args:
        df: 原始数据
        timeframe: 目标周期 ('1D', '1W', '1M', '1Y')
        time_col: 时间列名

    Returns:
        DataFrame: 重采样后的数据
    """
    timeframe_map = {
        "1D": "D",
        "1W": "W",
        "1M": "M",
        "3M": "3M",
        "1Y": "Y",
    }

    if timeframe not in timeframe_map:
        return df

    df = df.copy()
    if time_col in df.columns:
        df[time_col] = pd.to_datetime(df[time_col])
        df.set_index(time_col, inplace=True)

    rule = timeframe_map[timeframe]

    resampled = (
        df.resample(rule)
        .agg(
            {
                "open": "first",
                "high": "max",
                "low": "min",
                "close": "last",
                "volume": "sum",
            }
        )
        .dropna()
    )

    return resampled.reset_index()


def safe_divide(a: float, b: float, default: float = 0.0) -> float:
    """安全除法，避免除以0"""
    return a / b if b != 0 else default


def truncate_string(s: str, max_length: int = 50, suffix: str = "...") -> str:
    """截断字符串"""
    if len(s) <= max_length:
        return s
    return s[: max_length - len(suffix)] + suffix