"""
CSV 数据加载器 — 支持 MT5 History Center 格式 (quant data manager 导出)

功能:
  - 加载 M1 CSV (无表头 9 列格式)
  - 多周期重采样 (M5/M15/M30/H1/H4/D1)
  - 品种自动识别 (从文件名)
  - spread 自动转 slippage (百分比)
  - 时区处理 (UTC+2/UTC-3 等, 从文件名提取)

CSV 格式 (MT5 History Center 标准):
  date,time,open,high,low,close,vol,vol_real,spread
  2021.07.06,01:00,1791.37,1791.37,1790.55,1791.27,25850.0,25850.0,98

用法:
  from app.data_loader import load_csv, find_csv_for_symbol, compute_slippage

  # 自动查找 + 加载
  path = find_csv_for_symbol("XAUUSD")
  df = load_csv(path, timeframe="H1")

  # 显式指定文件
  df = load_csv("data/XAUUSD.csv", symbol="XAUUSD", timeframe="H1")

  # 计算 slippage (注入 PyBacktestConfig)
  slippage = compute_slippage(df["spread"], df["close"], "XAUUSD")
"""

from __future__ import annotations

import os
import re
from datetime import timedelta, timezone
from typing import Optional

import numpy as np
import pandas as pd


# ============================================================================
# 品种配置 — 点值 (最小变动单位)
# ============================================================================

SYMBOL_TICK_SIZE = {
    "XAUUSD": 0.01,       # 黄金: 0.01 美元/点
    "XAGUSD": 0.001,      # 白银
    "EURUSD": 0.00001,    # 5 位报价
    "GBPUSD": 0.00001,
    "AUDUSD": 0.00001,
    "NZDUSD": 0.00001,
    "USDCAD": 0.00001,
    "USDCHF": 0.00001,
    "EURJPY": 0.001,      # 3 位报价 (JPY 货币对)
    "USDJPY": 0.001,
    "GBPJPY": 0.001,
    "AUDJPY": 0.001,
    "EURGBP": 0.00001,
    "EURAUD": 0.00001,
    "EURCHF": 0.00001,
    "GBPCHF": 0.00001,
    "CHFJPY": 0.001,
}

# MT5 周期 → pandas 频率
TIMEFRAME_MAP = {
    "M1": "1min",
    "M5": "5min",
    "M15": "15min",
    "M30": "30min",
    "H1": "1h",
    "H2": "2h",
    "H4": "4h",
    "H6": "6h",
    "H8": "8h",
    "H12": "12h",
    "D1": "1D",
    "W1": "1W",
    "MN1": "1ME",
}


# ============================================================================
# 文件名解析
# ============================================================================

# 已知品种列表 (按长度降序匹配, 避免 GBPUSD 匹配 USD)
_KNOWN_SYMBOLS = sorted(SYMBOL_TICK_SIZE.keys(), key=len, reverse=True)


def extract_symbol_from_filename(filename: str) -> Optional[str]:
    """
    从文件名提取品种代码

    文件名示例:
      2021.7.6-2026.07.03M1XAUUSD_TICK_UTCPlus02.csv → XAUUSD
      2021.7.6-2026.07.03_M1USDJPY_TICK_UTCPlus02.csv → USDJPY
      2021.7.6-2026.07.03EURUSD_M1_TICK_UTCPlus02.csv → EURUSD
    """
    name = os.path.basename(filename).upper()
    for sym in _KNOWN_SYMBOLS:
        if sym in name:
            return sym
    return None


def extract_timezone_from_filename(filename: str) -> timezone:
    """
    从文件名提取时区

    文件名示例:
      ...UTCPlus02  → UTC+2 (MT5 标准服务器时间)
      ...UTCPlus03  → UTC+3 (夏令时)
      ...UTCMINUS05 → UTC-5
    """
    name = os.path.basename(filename).upper()
    m = re.search(r"UTCPLUS(\d+)", name)
    if m:
        return timezone(timedelta(hours=int(m.group(1))))
    m = re.search(r"UTCMINUS(\d+)", name)
    if m:
        return timezone(timedelta(hours=-int(m.group(1))))
    return timezone.utc


# ============================================================================
# CSV 加载
# ============================================================================

def load_csv(
    file_path: str,
    symbol: Optional[str] = None,
    timeframe: str = "M1",
    drop_weekend: bool = False,
) -> pd.DataFrame:
    """
    加载 MT5 History Center 格式 CSV

    参数:
        file_path: CSV 文件路径
        symbol: 品种代码 (None 时自动从文件名识别)
        timeframe: 目标周期 (M1/M5/M15/M30/H1/H4/D1/W1/MN1)
        drop_weekend: 是否过滤周末行 (默认 False, 因为外汇 CSV 已无周末数据)

    返回:
        DataFrame, 列: time, open, high, low, close, tick_volume, spread
        time 列为 datetime64[ns, tz]
    """
    if not os.path.exists(file_path):
        raise FileNotFoundError(f"CSV 文件不存在: {file_path}")

    # 自动识别品种
    if symbol is None:
        symbol = extract_symbol_from_filename(file_path)
    # 解析时区
    tz = extract_timezone_from_filename(file_path)

    # 加载 CSV (无表头, 9 列格式)
    df = pd.read_csv(
        file_path,
        header=None,
        names=["date", "time", "open", "high", "low", "close",
               "volume", "real_volume", "spread"],
        dtype={
            "open": np.float64, "high": np.float64,
            "low": np.float64, "close": np.float64,
            "volume": np.float64, "real_volume": np.float64,
            "spread": np.int32,
        },
    )

    # 合并 date + time → datetime
    df["time"] = pd.to_datetime(
        df["date"] + " " + df["time"],
        format="%Y.%m.%d %H:%M",
        utc=False,
    )
    df["time"] = df["time"].dt.tz_localize(tz)

    # 清理列
    df = df.drop(columns=["date", "real_volume"])
    df = df.rename(columns={"volume": "tick_volume"})

    # 排序 + 去重 + 建索引
    df = df.sort_values("time").drop_duplicates(subset=["time"])
    df = df.set_index("time")

    # 可选: 过滤周末 (外汇 CSV 通常已无周末行, 此项保险用)
    if drop_weekend:
        df = df[df.index.dayofweek < 5]

    # 重采样到目标周期
    if timeframe != "M1":
        df = _resample(df, timeframe)

    return df.reset_index()


def _resample(df: pd.DataFrame, timeframe: str) -> pd.DataFrame:
    """重采样 M1 到更高周期"""
    if timeframe == "M1":
        return df

    freq = TIMEFRAME_MAP.get(timeframe)
    if not freq:
        raise ValueError(
            f"不支持的周期: {timeframe}, 可选: {', '.join(TIMEFRAME_MAP.keys())}"
        )

    resampled = df.resample(freq, label="left", closed="left").agg({
        "open": "first",
        "high": "max",
        "low": "min",
        "close": "last",
        "tick_volume": "sum",
        "spread": "mean",
    }).dropna()

    return resampled


# ============================================================================
# 点差 → slippage 转换
# ============================================================================

def compute_slippage(
    spread_series: pd.Series,
    close: pd.Series,
    symbol: str,
    fallback: float = 0.0005,
) -> float:
    """
    根据平均 spread 和价格计算 slippage (百分比)

    公式: slippage = (avg_spread × tick_size) / avg_price

    参数:
        spread_series: spread 列 (点数, 如 98 表示 98 个 tick)
        close: 收盘价 (用于计算平均价格)
        symbol: 品种代码 (决定 tick_size)
        fallback: 无法识别品种时的默认值 (默认 0.05%)

    返回:
        slippage 百分比 (如 0.00055 表示 0.055%)

    示例:
        XAUUSD: avg_spread=98, tick_size=0.01, avg_price=1791
                slippage = 98 × 0.01 / 1791 = 0.000547 ≈ 0.055%

        EURUSD: avg_spread=43, tick_size=0.00001, avg_price=1.186
                slippage = 43 × 0.00001 / 1.186 = 0.000363 ≈ 0.036%
    """
    tick_size = SYMBOL_TICK_SIZE.get(symbol)
    if tick_size is None:
        return fallback

    avg_spread = float(spread_series.mean())
    avg_price = float(close.mean())

    if avg_price == 0:
        return fallback

    spread_cost = avg_spread * tick_size
    return spread_cost / avg_price


def get_tick_size(symbol: str) -> float:
    """获取品种的 tick_size (最小变动单位)"""
    return SYMBOL_TICK_SIZE.get(symbol, 0.0001)


# ============================================================================
# 自动查找
# ============================================================================

def find_csv_for_symbol(
    symbol: str,
    data_dir: Optional[str] = None,
) -> Optional[str]:
    """
    在 data/ 目录查找匹配品种的 CSV

    匹配规则: 文件名 (大写) 包含品种代码 (大写)
    如查找 XAUUSD, 匹配 "2021.7.6-2026.07.03M1XAUUSD_TICK_UTCPlus02.csv"

    返回:
        第一个匹配的文件路径, 未找到返回 None
    """
    if data_dir is None:
        data_dir = os.path.join(
            os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
            "data",
        )

    if not os.path.exists(data_dir):
        return None

    symbol_upper = symbol.upper()
    # 优先匹配 M1 文件 (原始数据, 精度最高)
    files = [f for f in os.listdir(data_dir) if f.endswith(".csv")]
    # 先找 M1 文件
    for f in files:
        if symbol_upper in f.upper() and "M1" in f.upper():
            return os.path.join(data_dir, f)
    # 再找任意匹配
    for f in files:
        if symbol_upper in f.upper():
            return os.path.join(data_dir, f)

    return None


def list_available_symbols(data_dir: Optional[str] = None) -> list:
    """
    列出 data/ 目录下所有可用品种

    返回:
        [(symbol, filename), ...] 列表
    """
    if data_dir is None:
        data_dir = os.path.join(
            os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
            "data",
        )

    if not os.path.exists(data_dir):
        return []

    result = []
    for f in sorted(os.listdir(data_dir)):
        if not f.endswith(".csv"):
            continue
        symbol = extract_symbol_from_filename(f)
        if symbol:
            result.append((symbol, f))

    return result