"""
Technical Indicators

보조지표 계산 함수들
"""
from typing import List, Optional, Tuple
import logging

logger = logging.getLogger(__name__)


def sma(prices: List[float], period: int) -> Optional[float]:
    """단순 이동평균 (Simple Moving Average)"""
    if len(prices) < period:
        return None
    return sum(prices[-period:]) / period


def ema(prices: List[float], period: int, prev_ema: Optional[float] = None) -> Optional[float]:
    """지수 이동평균 (Exponential Moving Average)"""
    if len(prices) < period:
        return None
    
    k = 2 / (period + 1)
    if prev_ema is None:
        # 초기값은 SMA
        prev_ema = sum(prices[-period:]) / period
    
    return prices[-1] * k + prev_ema * (1 - k)


def rsi(prices: List[float], period: int = 14) -> Optional[float]:
    """
RSI (Relative Strength Index)
    
    Args:
        prices: 종가 리스트
        period: 기간 (기본 14)
    
    Returns:
        0~100 사이 RSI 값
    """
    if len(prices) < period + 1:
        return None
    
    gains, losses = [], []
    for i in range(-period, 0):
        delta = prices[i] - prices[i - 1]
        gains.append(max(delta, 0))
        losses.append(max(-delta, 0))
    
    avg_gain = sum(gains) / period
    avg_loss = sum(losses) / period
    
    if avg_loss == 0:
        return 100.0
    
    rs = avg_gain / avg_loss
    return 100 - (100 / (1 + rs))


def bollinger_bands(prices: List[float], period: int = 20, std_mult: float = 2.0) -> Optional[Tuple[float, float, float]]:
    """
    볼린저 밴드
    
    Returns:
        (upper, middle, lower) 또는 None
    """
    if len(prices) < period:
        return None
    
    window = prices[-period:]
    middle = sum(window) / period
    variance = sum((p - middle) ** 2 for p in window) / period
    std = variance ** 0.5
    
    upper = middle + std_mult * std
    lower = middle - std_mult * std
    
    return (upper, middle, lower)


def macd(prices: List[float], fast: int = 12, slow: int = 26, signal: int = 9) -> Optional[Tuple[float, float, float]]:
    """
    MACD (Moving Average Convergence Divergence)
    
    Returns:
        (macd_line, signal_line, histogram) 또는 None
    """
    if len(prices) < slow:
        return None
    
    # EMA 계산 (재귀적으로 계산)
    def calc_ema(data: List[float], period: int) -> float:
        k = 2 / (period + 1)
        ema_val = sum(data[:period]) / period
        for p in data[period:]:
            ema_val = p * k + ema_val * (1 - k)
        return ema_val
    
    ema_fast = calc_ema(prices, fast)
    ema_slow = calc_ema(prices, slow)
    macd_line = ema_fast - ema_slow
    
    # Signal line = MACD의 EMA(9)
    # 실제로는 과거 MACD 값들 필요 (간략화)
    signal_line = macd_line  # TODO: 정확한 구현 필요
    histogram = macd_line - signal_line
    
    return (macd_line, signal_line, histogram)


def atr(highs: List[float], lows: List[float], closes: List[float], period: int = 14) -> Optional[float]:
    """
    ATR (Average True Range)
    
    Args:
        highs: 고가 리스트
        lows: 저가 리스트
        closes: 종가 리스트
        period: 기간
    
    Returns:
        ATR 값
    """
    if len(highs) < period + 1 or len(lows) < period + 1 or len(closes) < period + 1:
        return None
    
    trs = []
    for i in range(1, len(highs)):
        h = highs[i]
        l = lows[i]
        cp = closes[i - 1]
        tr = max(h - l, abs(h - cp), abs(l - cp))
        trs.append(tr)
    
    if len(trs) < period:
        return None
    
    return sum(trs[-period:]) / period


def vwap(prices: List[float], volumes: List[float]) -> Optional[float]:
    """
    VWAP (Volume Weighted Average Price)
    
    Returns:
        VWAP 값
    """
    if len(prices) != len(volumes) or not prices:
        return None
    
    total_volume = sum(volumes)
    if total_volume == 0:
        return None
    
    total_pv = sum(p * v for p, v in zip(prices, volumes))
    return total_pv / total_volume