from __future__ import annotations

from dataclasses import dataclass

import numpy as np
import pandas as pd

from app.indicators.common import (
    IndicatorSignal,
    alma,
    calc_size,
    compute_fractals,
    epoch_ms,
    ha_rsi,
    ohlc_frame,
)


@dataclass(frozen=True)
class LtfParams:
    fractal_n: int = 5
    alma_length: int = 500
    engulf_threshold: float = 0.9
    rsi_6h_overbought: float = 75.0
    rsi_6h_oversold: float = 20.0
    tp1_rr: float = 1.75
    tp2_rr: float = 2.0
    tp3_rr: float = 4.0
    risk_usd: float = 40.0
    cooldown_ms: int = 360 * 60 * 1000
    engulf_reset_ms: int = 12 * 60 * 60 * 1000
    tf_minutes: int = 15
    visual_timeframe: str = "15"


def _merge_asof(df_15m: pd.DataFrame, df_6h: pd.DataFrame, columns: list[str]) -> pd.DataFrame:
    left = pd.DataFrame({"ts": df_15m.index})
    right = pd.DataFrame({"ts": df_6h.index})
    for col in columns:
        right[col] = df_6h[col].to_numpy()
    left = left.sort_values("ts")
    right = right.sort_values("ts")
    merged = pd.merge_asof(left, right, on="ts", direction="backward")
    merged = merged.set_index("ts")
    return merged.reindex(df_15m.index)


def _compute_6h(
    df_6h: pd.DataFrame,
    *,
    engulf_threshold: float,
    forming: pd.Series | None,
) -> pd.DataFrame:
    df = ohlc_frame(df_6h)
    df["ha_rsi"] = ha_rsi(df["open"], df["high"], df["low"], df["close"], 14)
    df["h6_close"] = df["close"]
    df["h6_prev_close"] = df["close"].shift(1)
    df["h6_open"] = df["open"]
    df["h6_prev_open"] = df["open"].shift(1)

    body2 = (df["h6_prev_open"] - df["h6_prev_close"]).abs()
    bull_min = df["h6_prev_close"] + body2 * engulf_threshold
    bear_min = df["h6_prev_close"] - body2 * engulf_threshold
    bull_engulf = (
        (df["h6_close"] > df["h6_open"])
        & (df["h6_close"] >= bull_min)
        & (df["h6_prev_close"] < df["h6_prev_open"])
    )
    bear_engulf = (
        (df["h6_close"] < df["h6_open"])
        & (df["h6_close"] <= bear_min)
        & (df["h6_prev_close"] > df["h6_prev_open"])
    )

    n = len(df)
    is_forming = np.zeros(n, dtype=bool)
    if forming is not None:
        is_forming = forming.reindex(df.index).fillna(False).to_numpy(dtype=bool)

    state = np.empty(n, dtype=object)
    state[:] = ""
    engulf_time = np.full(n, np.nan)
    cur_state = ""
    cur_time = np.nan
    ts_ms = epoch_ms(df.index)
    bull_a = bull_engulf.fillna(False).to_numpy(dtype=bool)
    bear_a = bear_engulf.fillna(False).to_numpy(dtype=bool)

    for i in range(n):
        ts = ts_ms[i]
        if not np.isnan(cur_time) and (ts - cur_time) > 12 * 60 * 60 * 1000:
            cur_state = ""
            cur_time = np.nan

        if is_forming[i]:
            state[i] = cur_state
            engulf_time[i] = cur_time
            continue

        if bull_a[i]:
            cur_state = "bull"
            cur_time = ts
        elif bear_a[i]:
            cur_state = "bear"
            cur_time = ts
        else:
            cur_state = ""
            cur_time = np.nan

        state[i] = cur_state
        engulf_time[i] = cur_time

    df["engulf_state"] = state
    df["engulf_time"] = engulf_time
    return df


def attach_forming_6h(df_6h_closed: pd.DataFrame, df_15m: pd.DataFrame) -> pd.DataFrame:
    """Rebuild the in-progress 6h candle from closed 15m bars (Pine request.security)."""
    closed = ohlc_frame(df_6h_closed)
    ltf = ohlc_frame(df_15m)
    if ltf.empty:
        closed["_forming"] = False
        return closed

    period = ltf.index[-1].floor("6h")
    window = ltf.loc[ltf.index >= period]
    if window.empty:
        closed["_forming"] = False
        return closed

    # 6h just closed and is already in the closed frame.
    if not closed.empty and closed.index[-1] == period:
        closed["_forming"] = False
        return closed

    row = pd.DataFrame(
        {
            "open": [float(window["open"].iloc[0])],
            "high": [float(window["high"].max())],
            "low": [float(window["low"].min())],
            "close": [float(window["close"].iloc[-1])],
        },
        index=pd.DatetimeIndex([period], tz="UTC"),
    )
    if "volume" in window.columns:
        row["volume"] = float(window["volume"].sum())
    row["_forming"] = True
    base = closed[closed.index < period].copy()
    base["_forming"] = False
    return pd.concat([base, row])


def evaluate_ltf(
    df_15m: pd.DataFrame,
    df_6h: pd.DataFrame,
    params: LtfParams | None = None,
    *,
    forming_6h: pd.Series | None = None,
) -> pd.DataFrame:
    """Compute LTF columns. Last row is the latest closed 15m bar."""
    p = params or LtfParams()
    df = ohlc_frame(df_15m)
    forming_flag = forming_6h
    if forming_flag is None and "_forming" in df_6h.columns:
        forming_flag = df_6h["_forming"].astype(bool)
    h6 = _compute_6h(df_6h, engulf_threshold=p.engulf_threshold, forming=forming_flag)
    if forming_flag is not None:
        closed_mask = ~forming_flag.reindex(h6.index).fillna(False)
        h6_closed = h6.loc[closed_mask]
    else:
        h6_closed = h6

    n = int(p.fractal_n)
    high = df["high"].to_numpy(dtype=float)
    low = df["low"].to_numpy(dtype=float)
    up_frac, down_frac = compute_fractals(high, low, n=n)
    df["up_fractal"] = up_frac
    df["down_fractal"] = down_frac

    up_level = np.full(len(df), np.nan)
    down_level = np.full(len(df), np.nan)
    last_up = np.nan
    last_down = np.nan
    for i in range(len(df)):
        if up_frac[i]:
            last_up = high[i - n]
        if down_frac[i]:
            last_down = low[i - n]
        up_level[i] = last_up
        down_level[i] = last_down
    df["up_fractal_level"] = up_level
    df["down_fractal_level"] = down_level

    prev_up = df["up_fractal_level"].shift(1)
    prev_down = df["down_fractal_level"].shift(1)
    df["buy_crossover"] = (
        (df["close"] > prev_up) & (df["close"].shift(1) <= prev_up.shift(1)) & prev_up.notna()
    )
    df["sell_crossover"] = (
        (df["close"] < prev_down) & (df["close"].shift(1) >= prev_down.shift(1)) & prev_down.notna()
    )

    bars_16h = max(1, int(round(1440 / p.tf_minutes)))
    high_shift = df["high"].shift(1)
    low_shift = df["low"].shift(1)
    df["high_16h"] = high_shift.rolling(bars_16h, min_periods=bars_16h).max()
    df["low_16h"] = low_shift.rolling(bars_16h, min_periods=bars_16h).min()
    df["bull_breakout"] = df["high"] > df["high_16h"]
    df["bear_breakout"] = df["low"] < df["low_16h"]

    df["alma"] = alma(df["close"], length=p.alma_length, offset=0.85, sigma=5.0)

    rsi_m = _merge_asof(df, h6, ["ha_rsi"])
    closed_src = h6_closed if not h6_closed.empty else h6
    closed_m = _merge_asof(
        df, closed_src, ["engulf_state", "engulf_time", "h6_close", "h6_prev_close"]
    )
    df["rsi_6h"] = rsi_m["ha_rsi"]
    df["h6_engulf_state"] = closed_m["engulf_state"].fillna("").astype(str)
    df["h6_closed_close"] = closed_m["h6_close"]
    df["h6_prev_close"] = closed_m["h6_prev_close"]
    engulf_time = closed_m["engulf_time"].to_numpy(dtype=float)

    ts_ms = epoch_ms(df.index)
    engulf_state = df["h6_engulf_state"].to_numpy()
    for i in range(len(df)):
        et = engulf_time[i]
        if not np.isnan(et) and (ts_ms[i] - et) > p.engulf_reset_ms:
            engulf_state[i] = ""
    df["h6_engulf_state"] = engulf_state

    bull_arr = (df["bull_breakout"] | (df["h6_engulf_state"] == "bull")).to_numpy(dtype=bool)
    bear_arr = (df["bear_breakout"] | (df["h6_engulf_state"] == "bear")).to_numpy(dtype=bool)
    last_eng = np.empty(len(df), dtype=object)
    last_eng[:] = ""
    cur = ""
    for i in range(len(df)):
        b = bull_arr[i]
        s = bear_arr[i]
        if b and not s:
            cur = "bull"
        elif s and not b:
            cur = "bear"
        last_eng[i] = cur
    df["last_eng"] = last_eng

    alma_up = (df["alma"] > df["alma"].shift(1)) & (df["alma"].shift(1) > df["alma"].shift(2))
    alma_down = (df["alma"] < df["alma"].shift(1)) & (df["alma"].shift(1) < df["alma"].shift(2))
    alma_6h_long = (df["h6_closed_close"] > df["alma"]) & (
        df["h6_prev_close"] > df["alma"].shift(1)
    )
    alma_6h_short = (df["h6_closed_close"] < df["alma"]) & (
        df["h6_prev_close"] < df["alma"].shift(1)
    )
    buy_6h_ok = df["rsi_6h"] < p.rsi_6h_overbought
    sell_6h_ok = df["rsi_6h"] > p.rsi_6h_oversold

    buy_sig = np.zeros(len(df), dtype=bool)
    sell_sig = np.zeros(len(df), dtype=bool)
    last_buy_time = np.nan
    last_sell_time = np.nan
    open_side = ""
    long_sl_px = np.nan
    short_sl_px = np.nan
    close_a = df["close"].to_numpy(dtype=float)
    low_a = df["low"].to_numpy(dtype=float)
    high_a = df["high"].to_numpy(dtype=float)
    buy_x = df["buy_crossover"].fillna(False).to_numpy(dtype=bool)
    sell_x = df["sell_crossover"].fillna(False).to_numpy(dtype=bool)
    alma_up_a = alma_up.fillna(False).to_numpy(dtype=bool)
    alma_down_a = alma_down.fillna(False).to_numpy(dtype=bool)
    alma_6h_long_a = alma_6h_long.fillna(False).to_numpy(dtype=bool)
    alma_6h_short_a = alma_6h_short.fillna(False).to_numpy(dtype=bool)
    buy_6h_a = buy_6h_ok.fillna(False).to_numpy(dtype=bool)
    sell_6h_a = sell_6h_ok.fillna(False).to_numpy(dtype=bool)
    long_sl_arr = df["low_16h"].to_numpy(dtype=float)
    short_sl_arr = df["high_16h"].to_numpy(dtype=float)

    for i in range(len(df)):
        if not np.isnan(long_sl_px) and (close_a[i] < long_sl_px or low_a[i] <= long_sl_px):
            long_sl_px = np.nan
            if open_side == "buy":
                open_side = ""
        if not np.isnan(short_sl_px) and (close_a[i] > short_sl_px or high_a[i] >= short_sl_px):
            short_sl_px = np.nan
            if open_side == "sell":
                open_side = ""

        ts = ts_ms[i]
        buy_cd = np.isnan(last_buy_time) or (ts - last_buy_time >= p.cooldown_ms)
        sell_cd = np.isnan(last_sell_time) or (ts - last_sell_time >= p.cooldown_ms)

        is_buy = (
            buy_x[i]
            and open_side != "buy"
            and last_eng[i] == "bull"
            and (alma_up_a[i] or alma_6h_long_a[i])
            and buy_cd
            and buy_6h_a[i]
        )
        is_sell = (
            sell_x[i]
            and open_side != "sell"
            and last_eng[i] == "bear"
            and (alma_down_a[i] or alma_6h_short_a[i])
            and sell_cd
            and sell_6h_a[i]
        )

        if is_buy:
            buy_sig[i] = True
            last_buy_time = ts
            open_side = "buy"
            long_sl_px = long_sl_arr[i]
            short_sl_px = np.nan
        if is_sell:
            sell_sig[i] = True
            last_sell_time = ts
            open_side = "sell"
            short_sl_px = short_sl_arr[i]
            long_sl_px = np.nan

    df["buy_sig"] = buy_sig
    df["sell_sig"] = sell_sig
    df["long_sl"] = df["low_16h"]
    df["short_sl"] = df["high_16h"]
    df["long_tp1"] = df["close"] + (df["close"] - df["long_sl"]) * p.tp1_rr
    df["long_tp2"] = df["close"] + (df["close"] - df["long_sl"]) * p.tp2_rr
    df["long_tp3"] = df["close"] + (df["close"] - df["long_sl"]) * p.tp3_rr
    df["short_tp1"] = df["close"] - (df["short_sl"] - df["close"]) * p.tp1_rr
    df["short_tp2"] = df["close"] - (df["short_sl"] - df["close"]) * p.tp2_rr
    df["short_tp3"] = df["close"] - (df["short_sl"] - df["close"]) * p.tp3_rr
    return df


def last_ltf_signal(df: pd.DataFrame, params: LtfParams | None = None) -> IndicatorSignal | None:
    if df.empty:
        return None
    p = params or LtfParams()
    last = df.iloc[-1]
    ts = int(df.index[-1].timestamp())
    close = float(last["close"])
    if bool(last["buy_sig"]):
        sl = float(last["long_sl"])
        if np.isnan(sl) or np.isnan(close):
            return None
        size = calc_size(close, sl, p.risk_usd)
        if np.isnan(size):
            return None
        return IndicatorSignal(
            strategy_id="ltf",
            side="long",
            entry=close,
            close=close,
            sl=sl,
            tp1=float(last["long_tp1"]),
            tp2=float(last["long_tp2"]),
            tp3=float(last["long_tp3"]),
            size_usd=size,
            bar_open_ts=ts,
            visual_timeframe=p.visual_timeframe,
        )
    if bool(last["sell_sig"]):
        sl = float(last["short_sl"])
        if np.isnan(sl) or np.isnan(close):
            return None
        size = calc_size(close, sl, p.risk_usd)
        if np.isnan(size):
            return None
        return IndicatorSignal(
            strategy_id="ltf",
            side="short",
            entry=close,
            close=close,
            sl=sl,
            tp1=float(last["short_tp1"]),
            tp2=float(last["short_tp2"]),
            tp3=float(last["short_tp3"]),
            size_usd=size,
            bar_open_ts=ts,
            visual_timeframe=p.visual_timeframe,
        )
    return None