import logging
from typing import Optional

from app.core.config import settings
from app.services.mean_reversion_strategy import calculate_adx

logger = logging.getLogger(__name__)

# Trend filter defaults (configurable via settings)
TREND_FILTER_ENABLED = getattr(settings, "TREND_FILTER_ENABLED", True)
TREND_MA_SHORT = int(getattr(settings, "TREND_MA_SHORT", 20))
TREND_MA_LONG = int(getattr(settings, "TREND_MA_LONG", 60))
TREND_TOLERANCE_PCT = float(getattr(settings, "TREND_TOLERANCE_PCT", 0.005))

# 급등 과열 필터 defaults
OVERHEAT_FILTER_ENABLED = getattr(settings, "OVERHEAT_FILTER_ENABLED", True)
OVERHEAT_3DAY_MAX = float(getattr(settings, "OVERHEAT_3DAY_MAX", 15.0))
OVERHEAT_5DAY_MAX = float(getattr(settings, "OVERHEAT_5DAY_MAX", 20.0))
OVERHEAT_10DAY_MAX = float(getattr(settings, "OVERHEAT_10DAY_MAX", 30.0))
OVERHEAT_DISPARITY_MAX = float(getattr(settings, "OVERHEAT_DISPARITY_MAX", 108.0))


def _ma(values: list, window: int) -> Optional[float]:
    try:
        if len(values) < window:
            return None
        return sum(values[-window:]) / window
    except Exception:
        return None


def _ema(values: list, window: int) -> Optional[float]:
    """EMA(Exponential Moving Average) 계산.

    SMA 시드 → 이후 지수 가중 평균 순차 적용.
    values 길이가 window 미만이면 None 반환.
    """
    try:
        if len(values) < window:
            return None
        k = 2.0 / (window + 1)
        ema = sum(values[:window]) / window
        for v in values[window:]:
            ema = v * k + ema * (1 - k)
        return ema
    except Exception:
        return None


def _passes_uptrend(closes: list, ma_short: int = TREND_MA_SHORT, ma_long: int = TREND_MA_LONG, tol: float = TREND_TOLERANCE_PCT) -> Optional[bool]:
    if not closes or len(closes) < max(ma_short, ma_long):
        return None
    ma_s = _ma(closes, ma_short)
    ma_l = _ma(closes, ma_long)
    close = closes[-1]
    if ma_s is None or ma_l is None:
        return None
    return (ma_s >= ma_l * (1 - tol)) and (close >= ma_s * (1 - tol))


def analyze_overheat(closes: list, ma20: Optional[float]) -> dict:
    """급등 과열 필터 - 단기 급등 종목 매수 차단"""
    details = {}
    reasons = []

    if len(closes) >= 4:
        gain_3d = ((closes[-1] - closes[-4]) / closes[-4]) * 100 if closes[-4] > 0 else 0
        details["gain_3d"] = round(gain_3d, 1)
        if gain_3d > OVERHEAT_3DAY_MAX:
            reasons.append(f"3일 +{gain_3d:.1f}% (>{OVERHEAT_3DAY_MAX}%)")

    if len(closes) >= 6:
        gain_5d = ((closes[-1] - closes[-6]) / closes[-6]) * 100 if closes[-6] > 0 else 0
        details["gain_5d"] = round(gain_5d, 1)
        if gain_5d > OVERHEAT_5DAY_MAX:
            reasons.append(f"5일 +{gain_5d:.1f}% (>{OVERHEAT_5DAY_MAX}%)")

    if len(closes) >= 11:
        gain_10d = ((closes[-1] - closes[-11]) / closes[-11]) * 100 if closes[-11] > 0 else 0
        details["gain_10d"] = round(gain_10d, 1)
        if gain_10d > OVERHEAT_10DAY_MAX:
            reasons.append(f"10일 +{gain_10d:.1f}% (>{OVERHEAT_10DAY_MAX}%)")

    if ma20 and ma20 > 0:
        disparity = (closes[-1] / ma20) * 100
        details["disparity_20"] = round(disparity, 1)
        if disparity > OVERHEAT_DISPARITY_MAX:
            reasons.append(f"이격도 {disparity:.1f}% (>{OVERHEAT_DISPARITY_MAX}%)")

    blocked = len(reasons) > 0
    reason = "급등과열: " + ", ".join(reasons) if blocked else ""

    return {"blocked": blocked, "reason": reason, "details": details}


def analyze_custom_signals(chart_data):
    """커스텀 매매 공식 기반 신호 분석 - 이격도 필터 적용"""
    closes = [float(item.get("stck_clpr", 0)) for item in chart_data]
    if len(closes) < 120:
        return None
    
    ma20 = sum(closes[-20:]) / 20
    ma60 = sum(closes[-60:]) / 60
    ma120 = sum(closes[-120:]) / 120
    close = closes[-1]
    
    disparity = (close / ma20) * 100 if ma20 else 100

    if len(closes) >= 4:
        price_3days_ago = closes[-4]
        recent_gain = ((close - price_3days_ago) / price_3days_ago) * 100 if price_3days_ago > 0 else 0
        if recent_gain > 10:
            return {"signal": None, "description": f"급등 종목 추격 금지 (3일 +{recent_gain:.1f}%)"}

    if ma20 > ma60 > ma120 and close > ma20:
        if disparity <= 102:
            return {"signal": "buy", "description": "정배열 눌림목 매수 (이격도 안정)"}
        else:
            return {"signal": None, "description": f"정배열이나 이격 과열 ({disparity:.1f}%)"}

    elif ma20 < ma60 < ma120 and close < ma20:
        return {"signal": "sell", "description": "역배열 하락 매도타점"}

    recent_lows = [float(item.get("stck_lwpr", 0)) for item in chart_data[-5:]]
    if any(low < ma20 for low in recent_lows) and close > ma20:
        if disparity <= 101:
            return {"signal": "buy", "description": "눌림목 진입 매수타점"}
        else:
            return {"signal": None, "description": f"눌림목이나 이격 과열 ({disparity:.1f}%)"}
    
    stop_loss_line = ma60
    take_profit_line = ma20 * 1.05
    if close < stop_loss_line:
        return {"signal": "exit", "description": "손절라인 이탈"}
    if close > take_profit_line:
        return {"signal": "take_profit", "description": "익절라인 돌파"}
    
    return None


def analyze_macd(chart_data: list) -> dict:
    if len(chart_data) < 26:
        return {"signal": None, "description": "데이터 부족"}
    
    prices = [float(item.get("stck_clpr", 0)) for item in chart_data[-26:]]
    
    if len(prices) < 26:
        return {"signal": None, "description": "데이터 부족"}
    
    def calculate_ema(prices, period):
        ema = [sum(prices[:period]) / period]
        multiplier = 2 / (period + 1)
        for price in prices[period:]:
            ema.append((price - ema[-1]) * multiplier + ema[-1])
        return ema
    
    ema12 = calculate_ema(prices, 12)
    ema26 = calculate_ema(prices, 26)
    
    if len(ema12) < 2 or len(ema26) < 2:
        return {"signal": None, "description": "데이터 부족"}
    
    macd = [ema12[i] - ema26[i] for i in range(len(ema26))]
    signal = calculate_ema(macd, 9)
    
    if len(signal) < 2:
        return {"signal": None, "description": "데이터 부족"}
    
    if macd[-2] <= signal[-2] and macd[-1] > signal[-1]:
        return {"signal": "buy", "description": "MACD 골든크로스"}
    elif macd[-2] >= signal[-2] and macd[-1] < signal[-1]:
        return {"signal": "sell", "description": "MACD 데드크로스"}
    
    return {"signal": None, "description": "신호 없음"}


def analyze_rsi(chart_data: list) -> dict:
    """RSI 분석 + 볼린저 하단 결합 진입 신호

    [2026-03 수정] RSI < 30 구간에서도 반등 확인 시 BUY 신호 발생
    - 기존: rsi < 30 → signal=None (항상 반등 대기만)
    - 개선: rsi < 30 AND rsi > rsi_prev(반등 시작) AND BB 하단 근처 → BUY
      → 이유: RSI 30 이하에서 반등이 시작된 시점이 핵심 진입 기회이며,
               30~45 구간으로 올라오기까지 기다리면 이미 3~5% 상승 후 진입
    """
    rsi_period = settings.RSI_PERIOD  # 기본값 14
    if len(chart_data) < rsi_period + 1:
        return {"signal": None, "description": "데이터 부족"}
    
    prices = [float(item.get("stck_clpr", 0)) for item in chart_data[-(rsi_period+1):]]
    gains = []
    losses = []
    for i in range(1, len(prices)):
        change = prices[i] - prices[i-1]
        if change > 0:
            gains.append(change)
            losses.append(0)
        else:
            gains.append(0)
            losses.append(abs(change))
    
    avg_gain = sum(gains[-rsi_period:]) / rsi_period
    avg_loss = sum(losses[-rsi_period:]) / rsi_period
    
    if avg_loss == 0:
        rsi = 100
    else:
        rs = avg_gain / avg_loss
        rsi = 100 - (100 / (1 + rs))
    
    # 직전 RSI 계산 (반등 확인용)
    if len(chart_data) >= rsi_period + 2:
        prev_prices = [float(item.get("stck_clpr", 0)) for item in chart_data[-(rsi_period+2):-(1)]]
        prev_gains = []
        prev_losses = []
        for i in range(1, len(prev_prices)):
            change = prev_prices[i] - prev_prices[i-1]
            if change > 0:
                prev_gains.append(change)
                prev_losses.append(0)
            else:
                prev_gains.append(0)
                prev_losses.append(abs(change))
        
        prev_avg_gain = sum(prev_gains[-rsi_period:]) / rsi_period
        prev_avg_loss = sum(prev_losses[-rsi_period:]) / rsi_period
        if prev_avg_loss == 0:
            rsi_prev = 100
        else:
            prev_rs = prev_avg_gain / prev_avg_loss
            rsi_prev = 100 - (100 / (1 + prev_rs))
    else:
        rsi_prev = rsi

    # [2026-03] RSI < 30 구간: 반등 시작 + BB 하단 근처 시 BUY
    if rsi < 30:
        rsi_rebounding = rsi > rsi_prev  # RSI 바닥 찍고 반등 중

        if rsi_rebounding:
            # 볼린저 하단 근처 확인 (-3% ~ +1.5%)
            bb_lower = None
            if len(chart_data) >= 20:
                bb_closes = [float(item.get("stck_clpr", 0)) for item in chart_data[-20:]]
                bb_mid = sum(bb_closes) / 20
                bb_std = (sum((p - bb_mid) ** 2 for p in bb_closes) / 20) ** 0.5
                bb_lower = bb_mid - 2 * bb_std

            close = float(chart_data[-1].get("stck_clpr", 0))
            if bb_lower and bb_lower * 0.97 <= close <= bb_lower * 1.015:
                return {
                    "signal": "buy",
                    "description": f"RSI 바닥 탈출 반등 ({rsi_prev:.1f}→{rsi:.1f}) + BB하단"
                }
            else:
                # BB 하단 근처는 아니지만 RSI 반등 중: 약한 신호
                return {
                    "signal": None,
                    "description": f"RSI 과매도 반등 중 ({rsi_prev:.1f}→{rsi:.1f}) - BB하단 대기"
                }
        else:
            # RSI 여전히 하락 중 → 낙하하는 칼 잡기 방지
            return {"signal": None, "description": f"RSI 과매도 심화 ({rsi:.1f}) - 반등 대기"}

    # 기존 30~45 반등 구간 로직 유지
    elif 30 <= rsi <= 45 and rsi > rsi_prev:
        return {"signal": "buy", "description": f"RSI 바닥 탈출 반등 ({rsi:.1f})"}
    elif rsi > 70:
        return {"signal": "sell", "description": f"RSI 과매수 ({rsi:.1f})"}
    elif 45 <= rsi <= 60:
        return {"signal": "buy", "description": f"RSI 안정 상승 ({rsi:.1f})"}
    
    return {"signal": None, "description": f"RSI 중립 ({rsi:.1f})"}


def analyze_bollinger(chart_data: list) -> dict:
    bb_period = settings.BOLLINGER_PERIOD  # 기본값 20
    bb_stddev = settings.BOLLINGER_STDDEV  # 기본값 2.0
    if len(chart_data) < bb_period:
        return {"signal": None, "description": "데이터 부족"}
    
    prices = [float(item.get("stck_clpr", 0)) for item in chart_data[-bb_period:]]
    ma_bb = sum(prices) / len(prices)
    variance = sum((p - ma_bb) ** 2 for p in prices) / len(prices)
    std_dev = variance ** 0.5
    
    upper_band = ma_bb + (bb_stddev * std_dev)
    lower_band = ma_bb - (bb_stddev * std_dev)
    current_price = prices[-1]
    
    if current_price < lower_band:
        return {"signal": "buy", "description": "볼린저 하한선 터치"}
    elif current_price > upper_band:
        return {"signal": "sell", "description": "볼린저 상한선 돌파"}
    
    return {"signal": None, "description": "볼린저 중립"}


def analyze_moving_average(chart_data: list) -> dict:
    ma_long = settings.MA_LONG_PERIOD  # 기본값 60
    ma_short = settings.MA_MEDIUM_PERIOD  # 기본값 20
    ma_quick = settings.MA_SHORT_PERIOD  # 기본값 5
    
    if len(chart_data) < ma_long:
        return {"signal": None, "description": "데이터 부족"}
    
    prices = [float(item.get("stck_clpr", 0)) for item in chart_data[-ma_long:]]
    ma5 = sum(prices[-ma_quick:]) / ma_quick
    ma20 = sum(prices[-ma_short:]) / ma_short
    ma60 = sum(prices[-ma_long:]) / ma_long
    current_price = prices[-1]
    
    if ma5 > ma20 and current_price > ma5:
        return {"signal": "buy", "description": "5일선 > 20일선"}
    elif ma20 > ma60 and current_price > ma20:
        return {"signal": "buy", "description": "20일선 > 60일선"}
    elif ma5 < ma20 < ma60 and current_price < ma5:
        return {"signal": "sell", "description": "이동평균 역배열"}
    
    return {"signal": None, "description": "이동평균 중립"}


def analyze_stochastic(chart_data: list) -> dict:
    if len(chart_data) < 14:
        return {"signal": None, "description": "데이터 부족"}
    
    recent_data = chart_data[-14:]
    highs = [float(item.get("stck_hgpr", 0)) for item in recent_data]
    lows = [float(item.get("stck_lwpr", 0)) for item in recent_data]
    closes = [float(item.get("stck_clpr", 0)) for item in recent_data]
    
    highest_high = max(highs)
    lowest_low = min(lows)
    current_close = closes[-1]
    
    if highest_high == lowest_low:
        return {"signal": None, "description": "데이터 부족"}
    
    k = ((current_close - lowest_low) / (highest_high - lowest_low)) * 100
    
    if k < 30:
        return {"signal": "buy", "description": f"스토캐스틱 과매도 (%K: {k:.1f})"}
    elif k > 70:
        return {"signal": "sell", "description": f"스토캐스틱 과매수 (%K: {k:.1f})"}
    
    return {"signal": None, "description": f"스토캐스틱 중립 (%K: {k:.1f})"}


def analyze_volatility_breakout(chart_data: list, k: float = 0.5) -> dict:
    if len(chart_data) < 2:
        return {"signal": None, "description": "데이터 부족"}
    
    today = chart_data[-1]
    yesterday = chart_data[-2]
    
    current_price = float(today.get("stck_clpr", 0))
    open_price = float(today.get("stck_oprc", 0))
    
    prev_high = float(yesterday.get("stck_hgpr", 0))
    prev_low = float(yesterday.get("stck_lwpr", 0))
    prev_range = prev_high - prev_low
    
    if prev_range == 0 or open_price == 0:
        return {"signal": None, "description": "데이터 부족"}
    
    target_price = open_price + (prev_range * k)
    
    if current_price >= target_price:
        return {"signal": "buy", "description": f"변동성 돌파 발생 (목표가: {target_price:.0f})"}
    
    return {"signal": None, "description": "변동성 돌파 미달"}


def analyze_adx(chart_data: list) -> dict:
    if len(chart_data) < 29:
        return {"signal": None, "description": "데이터 부족", "adx": None}

    highs = [float(item.get("stck_hgpr", 0)) for item in chart_data]
    lows = [float(item.get("stck_lwpr", 0)) for item in chart_data]
    closes = [float(item.get("stck_clpr", 0)) for item in chart_data]

    adx = calculate_adx(highs, lows, closes)
    if adx is None:
        return {"signal": None, "description": "ADX 계산 불가", "adx": None}

    if adx < 20:
        return {"signal": "buy", "description": f"ADX({adx:.0f}<20) 횡보장 — 평균회귀 유리", "adx": adx}
    if adx > 30:
        return {"signal": "sell", "description": f"ADX({adx:.0f}>30) 강추세 — 평균회귀 위험", "adx": adx}
    return {"signal": None, "description": f"ADX({adx:.0f}) 중립", "adx": adx}


def analyze_obv(chart_data: list) -> dict:
    if len(chart_data) < 2:
        return {"signal": None, "description": "데이터 부족"}
    
    volumes = [int(item.get("acml_vol", 0)) for item in chart_data[-5:]]
    closes = [float(item.get("stck_clpr", 0)) for item in chart_data[-5:]]
    
    if len(volumes) < 2 or len(closes) < 2:
        return {"signal": None, "description": "데이터 부족"}
    
    if volumes[-1] > volumes[-2] * 1.5 and closes[-1] > closes[-2]:
        return {"signal": "buy", "description": "거래량 증가와 상승"}
    elif volumes[-1] < volumes[-2] * 0.5 and closes[-1] < closes[-2]:
        return {"signal": "sell", "description": "거래량 감소와 하락"}
    
    return {"signal": None, "description": "거래량 중립"}