"""
WT Bot v3 — Technical Indicators
===================================
WaveTrend, EMA, ATR — чистые функции.
Работают с raw klines от Binance API.
"""

import numpy as np
from src.config import (
    WT_CHANNEL_LEN, WT_AVG_LEN, WT_MA_LEN,
    WT_OVERSOLD, WT_OVERBOUGHT, EMA_PERIOD,
)


def _klines_to_arrays(klines):
    """Конвертирует Binance klines в numpy arrays."""
    highs = np.array([float(k[2]) for k in klines])
    lows = np.array([float(k[3]) for k in klines])
    closes = np.array([float(k[4]) for k in klines])
    volumes = np.array([float(k[5]) for k in klines])
    return highs, lows, closes, volumes


def _ema(data, period):
    """Exponential Moving Average."""
    alpha = 2 / (period + 1)
    ema = np.zeros_like(data)
    ema[0] = data[0]
    for i in range(1, len(data)):
        ema[i] = alpha * data[i] + (1 - alpha) * ema[i - 1]
    return ema


def _sma(data, period):
    """Simple Moving Average (last value)."""
    if len(data) < period:
        return None
    return np.mean(data[-period:])


# ============================================================
# WAVETREND
# ============================================================

def calc_wavetrend(klines):
    """
    WaveTrend Oscillator (LazyBear).
    Returns: (wt1_last, wt2_last) — последние значения.
    """
    if len(klines) < WT_AVG_LEN + WT_MA_LEN + 10:
        return None, None

    highs, lows, closes, _ = _klines_to_arrays(klines)
    hlc3 = (highs + lows + closes) / 3

    # ESA = EMA(hlc3, channel_len)
    esa = _ema(hlc3, WT_CHANNEL_LEN)

    # D = EMA(|hlc3 - ESA|, channel_len)
    d = _ema(np.abs(hlc3 - esa), WT_CHANNEL_LEN)

    # CI = (hlc3 - ESA) / (0.015 * D)
    d_safe = np.where(d == 0, 1, d)
    ci = (hlc3 - esa) / (0.015 * d_safe)

    # WT1 = EMA(CI, avg_len)
    wt1 = _ema(ci, WT_AVG_LEN)

    # WT2 = SMA(WT1, ma_len) — берём последнее значение
    wt2_last = float(np.mean(wt1[-WT_MA_LEN:]))
    wt1_last = float(wt1[-1])

    return wt1_last, wt2_last


def calc_wavetrend_series(klines):
    """
    WaveTrend — полные серии для детекции кросса.
    Returns: (wt1_array, wt2_array) — numpy arrays.
    """
    if len(klines) < WT_AVG_LEN + WT_MA_LEN + 10:
        return None, None

    highs, lows, closes, _ = _klines_to_arrays(klines)
    hlc3 = (highs + lows + closes) / 3

    esa = _ema(hlc3, WT_CHANNEL_LEN)
    d = _ema(np.abs(hlc3 - esa), WT_CHANNEL_LEN)
    d_safe = np.where(d == 0, 1, d)
    ci = (hlc3 - esa) / (0.015 * d_safe)
    wt1 = _ema(ci, WT_AVG_LEN)

    # WT2 = SMA(WT1, ma_len) — rolling
    wt2 = np.zeros_like(wt1)
    for i in range(WT_MA_LEN - 1, len(wt1)):
        wt2[i] = np.mean(wt1[i - WT_MA_LEN + 1:i + 1])

    return wt1, wt2


# ============================================================
# WT ZONE & CROSS DETECTION
# ============================================================

def is_in_wt_zone(wt1, wt2):
    """
    Проверка зоны.
    Returns: 1 = oversold (потенциальный long), -1 = overbought (short), 0 = neutral.
    """
    if wt1 < WT_OVERSOLD or wt2 < WT_OVERSOLD:
        return 1
    elif wt1 > WT_OVERBOUGHT or wt2 > WT_OVERBOUGHT:
        return -1
    return 0


def detect_wt_cross(wt1_series, wt2_series):
    """
    Детекция WT кросса на ПОСЛЕДНЕЙ закрытой свече.
    Используем [-2] и [-1] (предпоследняя и последняя ЗАКРЫТАЯ).

    Returns: 1 = bullish cross, -1 = bearish cross, 0 = none.
    """
    if len(wt1_series) < 3 or len(wt2_series) < 3:
        return 0

    # Используем закрытые свечи: [-3] = предпоследняя, [-2] = последняя закрытая
    # [-1] = текущая незакрытая — НЕ используем
    prev_wt1 = wt1_series[-3]
    prev_wt2 = wt2_series[-3]
    curr_wt1 = wt1_series[-2]
    curr_wt2 = wt2_series[-2]

    # Bullish cross: wt1 пересекает wt2 снизу вверх
    cross_up = prev_wt1 <= prev_wt2 and curr_wt1 > curr_wt2
    # Bearish cross: wt1 пересекает wt2 сверху вниз
    cross_down = prev_wt1 >= prev_wt2 and curr_wt1 < curr_wt2

    if cross_up:
        return 1
    elif cross_down:
        return -1
    return 0


# ============================================================
# EMA
# ============================================================

def calc_ema(klines, period=EMA_PERIOD):
    """
    EMA от close. Возвращает последнее значение.
    """
    if len(klines) < period:
        return None
    closes = np.array([float(k[4]) for k in klines])
    ema = _ema(closes, period)
    return float(ema[-1])


# ============================================================
# ATR
# ============================================================

def calc_atr_pct(klines, period=14):
    """
    ATR в процентах от цены (для скринера).
    Использует 1h klines.
    """
    if len(klines) < period + 1:
        return None

    highs, lows, closes, _ = _klines_to_arrays(klines)

    trs = []
    for i in range(1, len(highs)):
        tr = max(
            highs[i] - lows[i],
            abs(highs[i] - closes[i - 1]),
            abs(lows[i] - closes[i - 1]),
        )
        trs.append(tr)

    if len(trs) < period:
        return None

    atr = np.mean(trs[-period:])
    last_price = closes[-1]

    if last_price == 0:
        return None

    return (atr / last_price) * 100