"""
技术指标计算模块 - 纯 pandas/numpy 实现所有指标
所有函数返回 DataFrame/Series，可直接拼接到主 DataFrame
"""
import numpy as np
import pandas as pd


# ============================================================
# 辅助函数
# ============================================================

def _ema(series: pd.Series, period: int) -> pd.Series:
    return series.ewm(span=period, adjust=False).mean()


def _sma(series: pd.Series, period: int) -> pd.Series:
    return series.rolling(window=period).mean()


def _wma(series: pd.Series, period: int) -> pd.Series:
    weights = np.arange(1, period + 1, dtype=float)
    return series.rolling(window=period).apply(
        lambda x: np.dot(x, weights) / weights.sum(), raw=True
    )


def _true_range(high: pd.Series, low: pd.Series, close: pd.Series) -> pd.Series:
    prev_close = close.shift(1)
    tr1 = high - low
    tr2 = (high - prev_close).abs()
    tr3 = (low - prev_close).abs()
    return pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)


def _atr(high, low, close, period: int) -> pd.Series:
    tr = _true_range(high, low, close)
    return _ema(tr, period)


# ============================================================
# 布林带 (Bollinger Bands)
# ============================================================

def bollinger_bands(close: pd.Series, period: int = 20, std_dev: float = 2.0):
    mid = _sma(close, period)
    std = close.rolling(window=period).std()
    upper = mid + std_dev * std
    lower = mid - std_dev * std
    # 百分比位置 %B
    pct_b = (close - lower) / (upper - lower)
    return mid, upper, lower, pct_b


# ============================================================
# 肯特纳通道 (Keltner Channel)
# ============================================================

def keltner_channel(high, low, close, period: int = 20, atr_mult: float = 1.5):
    mid = _ema(close, period)
    atr = _atr(high, low, close, period)
    upper = mid + atr_mult * atr
    lower = mid - atr_mult * atr
    pct = (close - lower) / (upper - lower)
    return mid, upper, lower, pct


# ============================================================
# 唐奇安通道 (Donchian Channel)
# ============================================================

def donchian_channel(high, low, period: int = 20):
    upper = high.rolling(window=period).max()
    lower = low.rolling(window=period).min()
    mid = (upper + lower) / 2
    pct = (high.combine(low, lambda a, b: (a + b) / 2) - lower) / (upper - lower)
    return mid, upper, lower, pct


# ============================================================
# EMA 交叉
# ============================================================

def ema_cross(close, fast_period: int = 9, slow_period: int = 21):
    fast = _ema(close, fast_period)
    slow = _ema(close, slow_period)
    diff = fast - slow
    return fast, slow, diff


# ============================================================
# MACD
# ============================================================

def macd(close, fast: int = 12, slow: int = 26, signal: int = 9):
    ema_fast = _ema(close, fast)
    ema_slow = _ema(close, slow)
    macd_line = ema_fast - ema_slow
    signal_line = _ema(macd_line, signal)
    histogram = macd_line - signal_line
    return macd_line, signal_line, histogram


# ============================================================
# ADX (Average Directional Index)
# ============================================================

def adx(high, low, close, period: int = 14):
    prev_high = high.shift(1)
    prev_low = low.shift(1)

    plus_dm = np.where((high - prev_high) > (prev_low - low),
                       np.maximum(high - prev_high, 0), 0)
    minus_dm = np.where((prev_low - low) > (high - prev_high),
                        np.maximum(prev_low - low, 0), 0)

    plus_dm = pd.Series(plus_dm, index=high.index)
    minus_dm = pd.Series(minus_dm, index=high.index)

    atr = _atr(high, low, close, period)
    plus_di = 100 * _ema(plus_dm, period) / atr
    minus_di = 100 * _ema(minus_dm, period) / atr

    dx = 100 * (plus_di - minus_di).abs() / (plus_di + minus_di).replace(0, 1e-10)
    adx_val = _ema(dx, period)
    return adx_val, plus_di, minus_di


# ============================================================
# Supertrend
# ============================================================

def supertrend(high, low, close, period: int = 10, multiplier: float = 3.0):
    hl2 = (high + low) / 2
    atr = _atr(high, low, close, period)

    ub = (hl2 + multiplier * atr).values.copy()
    lb = (hl2 - multiplier * atr).values.copy()
    c = close.values
    n = len(c)

    direction = np.ones(n, dtype=np.int8)
    st = np.empty(n)
    st[0] = lb[0]

    for i in range(1, n):
        if c[i] > ub[i - 1]:
            direction[i] = 1
        elif c[i] < lb[i - 1]:
            direction[i] = -1
        else:
            direction[i] = direction[i - 1]

        if direction[i] == 1:
            if direction[i - 1] == 1:
                lb[i] = max(lb[i], lb[i - 1])
            st[i] = lb[i]
        else:
            if direction[i - 1] == -1:
                ub[i] = min(ub[i], ub[i - 1])
            st[i] = ub[i]

    return (pd.Series(st, index=close.index),
            pd.Series(direction, index=close.index))


# ============================================================
# RSI
# ============================================================

def rsi(close, period: int = 14):
    delta = close.diff()
    gain = delta.clip(lower=0)
    loss = (-delta).clip(lower=0)
    avg_gain = _ema(gain, period)
    avg_loss = _ema(loss, period)
    rs = avg_gain / avg_loss.replace(0, 1e-10)
    return 100 - (100 / (1 + rs))


# ============================================================
# Stochastic Oscillator
# ============================================================

def stochastic(high, low, close, k_period: int = 14, d_period: int = 3, smooth: int = 3):
    lowest_low = low.rolling(window=k_period).min()
    highest_high = high.rolling(window=k_period).max()
    raw_k = 100 * (close - lowest_low) / (highest_high - lowest_low).replace(0, 1e-10)
    k = _sma(raw_k, smooth)
    d = _sma(k, d_period)
    return k, d


# ============================================================
# CCI (Commodity Channel Index)
# ============================================================

def cci(high, low, close, period: int = 20):
    tp = (high + low + close) / 3
    sma_tp = _sma(tp, period)
    mad = tp.rolling(window=period).apply(lambda x: np.abs(x - x.mean()).mean(), raw=True)
    return (tp - sma_tp) / (0.015 * mad.replace(0, 1e-10))


# ============================================================
# Williams %R
# ============================================================

def williams_r(high, low, close, period: int = 14):
    highest_high = high.rolling(window=period).max()
    lowest_low = low.rolling(window=period).min()
    return -100 * (highest_high - close) / (highest_high - lowest_low).replace(0, 1e-10)


# ============================================================
# WMA (替代 VWMA，因为没有 volume)
# ============================================================

def wma(close, period: int = 20):
    return _wma(close, period)


# ============================================================
# 统一计算所有指标并拼接到 DataFrame
# ============================================================

def compute_all_indicators(df: pd.DataFrame, params: dict) -> pd.DataFrame:
    """
    根据参数字典计算所有指标，返回带指标列的 DataFrame
    params 示例:
    {
        'bb_period': 20, 'bb_std': 2.0,
        'kc_period': 20, 'kc_mult': 1.5,
        'dc_period': 20,
        'ema_fast': 9, 'ema_slow': 21,
        'macd_fast': 12, 'macd_slow': 26, 'macd_signal': 9,
        'adx_period': 14,
        'st_period': 10, 'st_mult': 3.0,
        'rsi_period': 14,
        'stoch_k': 14, 'stoch_d': 3, 'stoch_smooth': 3,
        'cci_period': 20,
        'wr_period': 14,
        'wma_period': 20,
    }
    """
    out = df.copy()
    h, l, c = out['high'], out['low'], out['close']

    # 布林带
    bb_mid, bb_up, bb_lo, bb_pct = bollinger_bands(c, params['bb_period'], params['bb_std'])
    out['bb_pct'] = bb_pct

    # 肯特纳通道
    _, _, _, kc_pct = keltner_channel(h, l, c, params['kc_period'], params['kc_mult'])
    out['kc_pct'] = kc_pct

    # 唐奇安通道
    _, _, _, dc_pct = donchian_channel(h, l, params['dc_period'])
    out['dc_pct'] = dc_pct

    # EMA 交叉
    _, _, ema_diff = ema_cross(c, params['ema_fast'], params['ema_slow'])
    out['ema_diff'] = ema_diff

    # MACD
    macd_l, macd_s, macd_h = macd(c, params['macd_fast'], params['macd_slow'], params['macd_signal'])
    out['macd_hist'] = macd_h

    # ADX
    adx_val, plus_di, minus_di = adx(h, l, c, params['adx_period'])
    out['adx'] = adx_val
    out['di_diff'] = plus_di - minus_di

    # Supertrend
    _, st_dir = supertrend(h, l, c, params['st_period'], params['st_mult'])
    out['st_dir'] = st_dir

    # RSI
    out['rsi'] = rsi(c, params['rsi_period'])

    # Stochastic
    stoch_k, stoch_d = stochastic(h, l, c, params['stoch_k'], params['stoch_d'], params['stoch_smooth'])
    out['stoch_k'] = stoch_k
    out['stoch_d'] = stoch_d

    # CCI
    out['cci'] = cci(h, l, c, params['cci_period'])

    # Williams %R
    out['wr'] = williams_r(h, l, c, params['wr_period'])

    # WMA
    out['wma'] = wma(c, params['wma_period'])
    out['wma_diff'] = c - out['wma']

    return out