# -*- coding: utf-8 -*-
"""
自适应三分位趋势策略 - 核心逻辑
趋势过滤、动态阈值、信号确认、市场状态
"""

from typing import List, Dict, Optional, Tuple
import sys
import os
ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
if ROOT_DIR not in sys.path:
    sys.path.insert(0, ROOT_DIR)

from adaptive_third_strategy.config import (
    MIN_BODY_ATR_RATIO,
    MIN_VOLATILITY_PERCENT,
    EMA_SHORT,
    EMA_LONG_FAST,
    EMA_LONG_SLOW,
    EMA_MID_FAST,
    EMA_MID_SLOW,
    ATR_PERIOD,
    VOLATILITY_COEF_CLAMP,
    BASE_COEF,
    TREND_FAVOR_COEF,
    TREND_AGAINST_COEF,
    TREND_MODE,
    CONFIRM_REQUIRED,
    VOLUME_MA_PERIOD,
    VOLUME_RATIO_THRESHOLD,
    REVERSE_BREAK_MULT,
    MIN_BARS_SINCE_ENTRY,
    FORBIDDEN_PERIODS,
    ATR_PAUSE_MULT,
    STRONG_TREND_COEF,
    RANGE_COEF,
    HIGH_VOL_EXTRA_CONFIRM,
)
from adaptive_third_strategy.indicators import (
    get_ema_atr_from_klines,
    align_higher_tf_ema,
    ema,
)


def get_body_size(candle: Dict) -> float:
    return abs(float(candle["open"]) - float(candle["close"]))


def is_bullish(candle: Dict) -> bool:
    return float(candle["close"]) > float(candle["open"])


def get_min_body_threshold(price: float, atr_value: Optional[float]) -> float:
    """有效K线最小实体 = max(ATR*0.1, 价格*0.05%)"""
    min_vol = price * MIN_VOLATILITY_PERCENT
    if atr_value is not None and atr_value > 0:
        min_vol = max(min_vol, atr_value * MIN_BODY_ATR_RATIO)
    return min_vol


def find_valid_prev_bar(
    all_data: List[Dict],
    current_idx: int,
    atr_series: List[Optional[float]],
    min_body_override: Optional[float] = None,
) -> Tuple[Optional[int], Optional[Dict]]:
    """从当前索引往前找实体>=阈值的K线。阈值 = max(ATR*0.1, 价格*0.05%)"""
    if current_idx <= 0:
        return None, None
    for i in range(current_idx - 1, -1, -1):
        prev = all_data[i]
        body = get_body_size(prev)
        price = float(prev["close"])
        atr_val = atr_series[i] if i < len(atr_series) else None
        th = min_body_override if min_body_override is not None else get_min_body_threshold(price, atr_val)
        if body >= th:
            return i, prev
    return None, None


def get_trend(
    klines_5m: List[Dict],
    idx_5m: int,
    ema_5m: List[Optional[float]],
    ema_15m_align: List[Dict],
    ema_60m_align: List[Dict],
) -> str:
    """
    多时间框架趋势。返回 "long" / "short" / "neutral"。
    长期：1h EMA50 vs EMA200；中期：15m EMA20 vs EMA50；短期：5m close vs EMA9。
    ema_*_align: 与 5m 对齐的列表，每项 {"ema_fast", "ema_slow"}。
    """
    if idx_5m >= len(klines_5m):
        return "neutral"
    curr = klines_5m[idx_5m]
    close_5 = float(curr["close"])
    # 短期
    ema9 = ema_5m[idx_5m] if idx_5m < len(ema_5m) else None
    short_bull = (ema9 is not None and close_5 > ema9)
    # 中期
    mid = ema_15m_align[idx_5m] if idx_5m < len(ema_15m_align) else {}
    mid_bull = (mid.get("ema_fast") is not None and mid.get("ema_slow") is not None
                and mid["ema_fast"] > mid["ema_slow"])
    # 长期
    long_d = ema_60m_align[idx_5m] if idx_5m < len(ema_60m_align) else {}
    long_bull = (long_d.get("ema_fast") is not None and long_d.get("ema_slow") is not None
                 and long_d["ema_fast"] > long_d["ema_slow"])

    if TREND_MODE == "aggressive":
        return "long" if short_bull else "short"
    if TREND_MODE == "conservative":
        if mid_bull and short_bull:
            return "long"
        if not mid_bull and not short_bull:
            return "short"
        return "neutral"
    # strict
    if long_bull and mid_bull and short_bull:
        return "long"
    if not long_bull and not mid_bull and not short_bull:
        return "short"
    return "neutral"


def get_dynamic_trigger_levels(
    prev: Dict,
    atr_value: float,
    trend: str,
    market_state: str = "normal",
) -> Tuple[Optional[float], Optional[float]]:
    """
    动态三分位触发价。
    波动率系数 = clamp(实体/ATR, 0.3, 3.0)，调整系数 = 0.33 * 波动率系数。
    顺势方向 ×0.8，逆势 ×1.2。
    """
    body = get_body_size(prev)
    if body < 1e-6 or atr_value <= 0:
        return None, None
    vol_coef = body / atr_value
    vol_coef = max(VOLATILITY_COEF_CLAMP[0], min(VOLATILITY_COEF_CLAMP[1], vol_coef))
    adj = BASE_COEF * vol_coef
    if market_state == "strong_trend":
        adj = STRONG_TREND_COEF
    elif market_state == "range":
        adj = RANGE_COEF
    p_close = float(prev["close"])
    if trend == "long":
        long_adj = adj * TREND_FAVOR_COEF
        short_adj = adj * TREND_AGAINST_COEF
    elif trend == "short":
        long_adj = adj * TREND_AGAINST_COEF
        short_adj = adj * TREND_FAVOR_COEF
    else:
        long_adj = short_adj = adj
    long_trigger = p_close + body * long_adj
    short_trigger = p_close - body * short_adj
    return long_trigger, short_trigger


def check_signal_confirm(
    curr: Dict,
    direction: str,
    trigger_price: float,
    all_data: List[Dict],
    current_idx: int,
    volume_ma: Optional[float],
    required: int = CONFIRM_REQUIRED,
) -> int:
    """
    确认条件计数：收盘价确认、成交量确认、动量确认。
    返回满足的个数。
    """
    count = 0
    c_close = float(curr["close"])
    c_volume = float(curr.get("volume", 0))
    # 1. 收盘价确认
    if direction == "long" and c_close >= trigger_price:
        count += 1
    elif direction == "short" and c_close <= trigger_price:
        count += 1
    # 2. 成交量确认
    if volume_ma is not None and volume_ma > 0 and c_volume >= volume_ma * VOLUME_RATIO_THRESHOLD:
        count += 1
    # 3. 动量确认：当前K线实体方向与信号一致
    if direction == "long" and is_bullish(curr):
        count += 1
    elif direction == "short" and not is_bullish(curr):
        count += 1
    return count


def in_forbidden_period(ts_sec: int) -> bool:
    """是否在禁止交易时段（按 UTC+8 小时:分）"""
    from datetime import datetime, timezone
    try:
        dt = datetime.fromtimestamp(ts_sec, tz=timezone.utc)
    except Exception:
        dt = datetime.utcfromtimestamp(ts_sec)
    # 转 UTC+8
    hour = (dt.hour + 8) % 24
    minute = dt.minute
    for h1, m1, h2, m2 in FORBIDDEN_PERIODS:
        t1 = h1 * 60 + m1
        t2 = h2 * 60 + m2
        t = hour * 60 + minute
        if t1 <= t < t2:
            return True
    return False


def get_market_state(
    atr_value: float,
    atr_avg: Optional[float],
    trend: str,
) -> str:
    """normal / strong_trend / range / high_vol"""
    if atr_avg is not None and atr_avg > 0 and atr_value >= atr_avg * ATR_PAUSE_MULT:
        return "high_vol"
    if trend in ("long", "short") and atr_avg is not None and atr_value > atr_avg * 1.2:
        return "strong_trend"
    if trend == "neutral":
        return "range"
    return "normal"


def check_trigger(
    all_data: List[Dict],
    current_idx: int,
    atr_series: List[Optional[float]],
    ema_5m: List[Optional[float]],
    ema_15m_align: List[Dict],
    ema_60m_align: List[Dict],
    volume_ma_list: Optional[List[Optional[float]]] = None,
    use_confirm: bool = True,
) -> Tuple[Optional[str], Optional[float], Optional[int], Optional[Dict]]:
    """
    检查当前K线是否产生有效信号（含趋势过滤与确认）。
    返回 (方向, 触发价, 有效前一根索引, 有效前一根K线) 或 (None, None, None, None)。
    """
    if current_idx <= 0 or current_idx >= len(all_data):
        return None, None, None, None
    curr = all_data[current_idx]
    valid_prev_idx, prev = find_valid_prev_bar(all_data, current_idx, atr_series)
    if prev is None:
        return None, None, None, None
    atr_val = atr_series[current_idx] if current_idx < len(atr_series) else None
    if atr_val is None or atr_val <= 0:
        return None, None, None, None
    trend = get_trend(
        all_data, current_idx, ema_5m, ema_15m_align, ema_60m_align,
    )
    atr_avg = None
    if atr_series:
        valid_atr = [x for x in atr_series[: current_idx + 1] if x is not None and x > 0]
        if len(valid_atr) >= ATR_PERIOD:
            atr_avg = sum(valid_atr) / len(valid_atr)
    market_state = get_market_state(atr_val, atr_avg, trend)
    if market_state == "high_vol":
        return None, None, None, None
    long_trigger, short_trigger = get_dynamic_trigger_levels(prev, atr_val, trend, market_state)
    if long_trigger is None:
        return None, None, None, None
    c_high = float(curr["high"])
    c_low = float(curr["low"])
    long_triggered = c_high >= long_trigger
    short_triggered = c_low <= short_trigger
    direction = None
    trigger_price = None
    if long_triggered and short_triggered:
        c_open = float(curr["open"])
        if abs(c_open - short_trigger) <= abs(c_open - long_trigger):
            direction, trigger_price = "short", short_trigger
        else:
            direction, trigger_price = "long", long_trigger
    elif short_triggered:
        direction, trigger_price = "short", short_trigger
    elif long_triggered:
        direction, trigger_price = "long", long_trigger
    if direction is None:
        return None, None, None, None
    # 趋势过滤：逆势不交易（可选，这里做过滤）
    if trend == "long" and direction == "short":
        return None, None, None, None
    if trend == "short" and direction == "long":
        return None, None, None, None
    # 禁止时段
    if in_forbidden_period(curr["id"]):
        return None, None, None, None
    # 信号确认
    if use_confirm and CONFIRM_REQUIRED > 0:
        vol_ma = volume_ma_list[current_idx] if volume_ma_list and current_idx < len(volume_ma_list) else None
        n = check_signal_confirm(curr, direction, trigger_price, all_data, current_idx, vol_ma, CONFIRM_REQUIRED)
        if n < CONFIRM_REQUIRED:
            return None, None, None, None
    return direction, trigger_price, valid_prev_idx, prev


def build_volume_ma(klines: List[Dict], period: int = VOLUME_MA_PERIOD) -> List[Optional[float]]:
    """前 period-1 为 None，之后为 volume 的 SMA"""
    vol = [float(k.get("volume", 0)) for k in klines]
    out: List[Optional[float]] = [None] * (period - 1)
    for i in range(period - 1, len(vol)):
        out.append(sum(vol[i - period + 1 : i + 1]) / period)
    return out