from __future__ import annotations

from dataclasses import dataclass
from typing import Optional

try:  # Python 3.8+ has Literal in typing, otherwise fall back to typing_extensions
    from typing import Literal
except ImportError:  # pragma: no cover - compatibility path for Python 3.7
    from typing_extensions import Literal

import numpy as np
import pandas as pd

from backtester.math_utils import calculate_adx, calculate_atr, rma

Direction = Literal["long", "short"]


@dataclass
class MoneyLineInputs:
    atr_length: int = 14
    adx_length: int = 14
    rsi_length: int = 14
    ma_fast_length: int = 50
    ma_slow_length: int = 200
    ma_gap_threshold: float = 0.35
    flip_threshold_percent: float = 0.6
    cooldown_bars: int = 3
    momentum_spacing: int = 4
    momentum_cooldown: int = 3
    momentum_min_adx: float = 23.0
    momentum_min_volume_ratio: float = 1.0
    momentum_long_max_pos: float = 70.0
    momentum_short_min_pos: float = 30.0


@dataclass
class MoneyLineSignal:
    timestamp: pd.Timestamp
    direction: Direction
    entry_price: float
    adx: float
    atr: float
    rsi: float
    volume_ratio: float
    price_position: float
    signal_type: Literal["primary", "momentum"]


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


def rolling_volume_ratio(volume: pd.Series, length: int = 20) -> pd.Series:
    avg = volume.rolling(length).mean()
    return volume / avg


def price_position(high: pd.Series, low: pd.Series, close: pd.Series, length: int = 100) -> pd.Series:
    highest = high.rolling(length).max()
    lowest = low.rolling(length).min()
    return 100.0 * (close - lowest) / (highest - lowest)


def money_line_signals(df: pd.DataFrame, inputs: Optional[MoneyLineInputs] = None) -> list[MoneyLineSignal]:
    if inputs is None:
        inputs = MoneyLineInputs()

    data = df.copy()
    data = data.sort_index()

    data["ema_fast"] = ema(data["close"], inputs.ma_fast_length)
    data["ema_slow"] = ema(data["close"], inputs.ma_slow_length)
    data["rsi"] = rsi(data["close"], inputs.rsi_length)
    data["atr"] = calculate_atr(data, inputs.atr_length)
    data["adx"] = calculate_adx(data, inputs.adx_length)
    data["volume_ratio"] = rolling_volume_ratio(data["volume"])
    data["price_position"] = price_position(data["high"], data["low"], data["close"])

    ma_gap = 100.0 * (data["ema_fast"] - data["ema_slow"]) / data["close"]
    ma_gap_score = np.tanh(ma_gap / inputs.ma_gap_threshold)

    signals: list[MoneyLineSignal] = []
    last_direction: Optional[Direction] = None
    cooldown_remaining = 0
    momentum_cooldown = 0

    for idx in range(1, len(data)):
        row = data.iloc[idx]
        prev = data.iloc[idx - 1]
        close = row.close

        fast = row.ema_fast
        slow = row.ema_slow
        gap_score = ma_gap_score.iloc[idx]

        flip_up = prev.close <= prev.ema_fast and close > fast
        flip_down = prev.close >= prev.ema_fast and close < fast

        direction: Optional[Direction] = None
        if flip_up and gap_score > inputs.flip_threshold_percent / 100.0:
            direction = "long"
        elif flip_down and gap_score < -inputs.flip_threshold_percent / 100.0:
            direction = "short"

        if direction and cooldown_remaining == 0:
            signals.append(
                MoneyLineSignal(
                    timestamp=row.name,
                    direction=direction,
                    entry_price=float(close),
                    adx=float(row.adx),
                    atr=float(row.atr),
                    rsi=float(row.rsi),
                    volume_ratio=float(row.volume_ratio),
                    price_position=float(row.price_position),
                    signal_type="primary",
                )
            )
            last_direction = direction
            cooldown_remaining = inputs.cooldown_bars
            momentum_cooldown = inputs.momentum_cooldown
        else:
            cooldown_remaining = max(0, cooldown_remaining - 1)
            momentum_cooldown = max(0, momentum_cooldown - 1)

        if (
            last_direction
            and momentum_cooldown == 0
            and row.adx >= inputs.momentum_min_adx
            and row.volume_ratio >= inputs.momentum_min_volume_ratio
        ):
            pos = row.price_position
            if last_direction == "long" and pos <= inputs.momentum_long_max_pos:
                signals.append(
                    MoneyLineSignal(
                        timestamp=row.name,
                        direction="long",
                        entry_price=float(close),
                        adx=float(row.adx),
                        atr=float(row.atr),
                        rsi=float(row.rsi),
                        volume_ratio=float(row.volume_ratio),
                        price_position=float(row.price_position),
                        signal_type="momentum",
                    )
                )
                momentum_cooldown = inputs.momentum_spacing
            elif last_direction == "short" and pos >= inputs.momentum_short_min_pos:
                signals.append(
                    MoneyLineSignal(
                        timestamp=row.name,
                        direction="short",
                        entry_price=float(close),
                        adx=float(row.adx),
                        atr=float(row.atr),
                        rsi=float(row.rsi),
                        volume_ratio=float(row.volume_ratio),
                        price_position=float(row.price_position),
                        signal_type="momentum",
                    )
                )
                momentum_cooldown = inputs.momentum_spacing

    return signals


def rsi(series: pd.Series, length: int) -> pd.Series:
    delta = series.diff()
    gain = np.where(delta > 0, delta, 0.0)
    loss = np.where(delta < 0, -delta, 0.0)
    avg_gain = rma(pd.Series(gain), length)
    avg_loss = rma(pd.Series(loss), length)
    rs = avg_gain / avg_loss.replace(0, np.nan)
    rsi_series = 100 - (100 / (1 + rs))
    return rsi_series.fillna(50.0)