from logging import FATAL
from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame

import talib.abstract as ta
import numpy as np
import freqtrade.vendor.qtpylib.indicators as qtpylib
import datetime
from technical.util import resample_to_interval, resampled_merge
from datetime import datetime, timedelta
from freqtrade.persistence import Trade
from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter
import technical.indicators as ftt


def EWO(dataframe, ema_length=5, ema2_length=35):
    df = dataframe.copy()
    ema1 = ta.EMA(df, timeperiod=ema_length)
    ema2 = ta.EMA(df, timeperiod=ema2_length)
    emadif = (ema1 - ema2) / df['low'] * 100
    return emadif


class NASOSv7LongShort(IStrategy):
    INTERFACE_VERSION = 3

    minimal_roi = {
        "0": 10
    }

    # Adjust stoploss for both long and short
    stoploss = -0.15

    # Parameters for both long and short entries
    base_nb_candles_buy = IntParameter(2, 20, default=8, space='buy', optimize=True)
    base_nb_candles_sell = IntParameter(2, 25, default=16, space='sell', optimize=True)
    
    # Long buy parameters
    long_low_offset = DecimalParameter(0.9, 0.99, default=0.984, space='buy', optimize=False)
    long_low_offset_2 = DecimalParameter(0.9, 0.99, default=0.942, space='buy', optimize=False)
    
    # Short sell parameters
    short_high_offset = DecimalParameter(0.95, 1.1, default=1.084, space='sell', optimize=True)
    short_high_offset_2 = DecimalParameter(0.99, 1.5, default=1.401, space='sell', optimize=True)

    fast_ewo = 50
    slow_ewo = 200

    lookback_candles = IntParameter(1, 24, default=3, space='buy', optimize=True)

    profit_threshold = DecimalParameter(1.0, 1.03, default=1.008, space='buy', optimize=True)

    # EWO parameters for both long and short
    ewo_low = DecimalParameter(-20.0, -8.0, default=-14.378, space='buy', optimize=False)
    ewo_high = DecimalParameter(2.0, 12.0, default=2.403, space='buy', optimize=False)
    ewo_high_2 = DecimalParameter(-6.0, 12.0, default=-5.585, space='buy', optimize=False)

    rsi_buy = IntParameter(50, 100, default=72, space='buy', optimize=False)

    # Custom stoploss parameters
    pHSL = DecimalParameter(-0.200, -0.040, default=-0.15, decimals=3,
                            space='sell', optimize=False, load=True)

    pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3,
                             space='sell', optimize=False, load=True)
    pSL_1 = DecimalParameter(0.008, 0.020, default=0.014, decimals=3,
                             space='sell', optimize=False, load=True)

    pPF_2 = DecimalParameter(0.040, 0.100, default=0.024, decimals=3,
                             space='sell', optimize=False, load=True)
    pSL_2 = DecimalParameter(0.020, 0.070, default=0.022, decimals=3,
                             space='sell', optimize=False, load=True)

    trailing_stop = True
    trailing_stop_positive = 0.001
    trailing_stop_positive_offset = 0.016
    trailing_only_offset_is_reached = True

    use_exit_signal = True
    exit_profit_only = False
    exit_profit_offset = 0.01
    ignore_roi_if_entry_signal = False

    timeframe = '5m'
    inf_1h = '1h'

    process_only_new_candles = True
    startup_candle_count = 200
    use_custom_stoploss = False

    # Enable both long and short trading
    position_adjustment_enable = True
    max_entry_position_adjustment = 0
    max_dist_to_main_market_price_pct = 0.002

    plot_config = {
        'main_plot': {
            'ma_buy': {'color': 'orange'},
            'ma_sell': {'color': 'orange'},
        },
    }

    slippage_protection = {
        'retries': 3,
        'max_slippage': -0.02
    }

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                        current_rate: float, current_profit: float, **kwargs) -> float:
        HSL = self.pHSL.value
        PF_1 = self.pPF_1.value
        SL_1 = self.pSL_1.value
        PF_2 = self.pPF_2.value
        SL_2 = self.pSL_2.value

        if (current_profit > PF_2):
            sl_profit = SL_2 + (current_profit - PF_2)
        elif (current_profit > PF_1):
            sl_profit = SL_1 + ((current_profit - PF_1)*(SL_2 - SL_1)/(PF_2 - PF_1))
        else:
            sl_profit = HSL

        return stoploss_from_open(sl_profit, current_profit)

    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
                           rate: float, time_in_force: str, sell_reason: str,
                           current_time: datetime, **kwargs) -> bool:
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1]

        if (last_candle is not None):
            # Additional exit confirmation for both longs and shorts
            if (sell_reason in ['sell_signal']):
                if (last_candle['hma_50']*1.149 > last_candle['ema_100']) and (last_candle['close'] < last_candle['ema_100']*0.951):
                    return False

        try:
            state = self.slippage_protection['__pair_retries']
        except KeyError:
            state = self.slippage_protection['__pair_retries'] = {}

        candle = dataframe.iloc[-1].squeeze()

        slippage = (rate / candle['close']) - 1
        if slippage < self.slippage_protection['max_slippage']:
            pair_retries = state.get(pair, 0)
            if pair_retries < self.slippage_protection['retries']:
                state[pair] = pair_retries + 1
                return False

        state[pair] = 0

        return True

    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, '1h') for pair in pairs]
        return informative_pairs

    def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        assert self.dp, "DataProvider is required for multiple timeframes."
        informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h)
        return informative_1h

    def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        for val in self.base_nb_candles_buy.range:
            dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val)

        for val in self.base_nb_candles_sell.range:
            dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val)

        dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50)
        dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100)

        dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9)

        dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo)

        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)

        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        informative_1h = self.informative_1h_indicators(dataframe, metadata)
        dataframe = merge_informative_pair(
            dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True)

        dataframe = self.normal_tf_indicators(dataframe, metadata)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dont_buy_conditions = []

        dont_buy_conditions.append(
            (
                (dataframe['close_1h'].rolling(self.lookback_candles.value).max()
                 < (dataframe['close'] * self.profit_threshold.value))
            )
        )

        # Long Entry Conditions (unchanged from original)
        dataframe.loc[
            (
                (dataframe['rsi_fast'] < 35) &
                (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.long_low_offset.value)) &
                (dataframe['EWO'] > self.ewo_high.value) &
                (dataframe['rsi'] < self.rsi_buy.value) &
                (dataframe['volume'] > 0) &
                (dataframe['close'] < (
                    dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.short_high_offset.value))
            ),
            ['enter_long', 'buy_tag']] = (1, 'ewo1')

        # Short Entry Conditions (new)
        dataframe.loc[
            (
                (dataframe['rsi_fast'] > 65) &
                (dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.short_high_offset.value)) &
                (dataframe['EWO'] < self.ewo_low.value) &
                (dataframe['rsi'] > (100 - self.rsi_buy.value)) &
                (dataframe['volume'] > 0) &
                (dataframe['close'] > (
                    dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.long_low_offset.value))
            ),
            ['enter_short', 'sell_tag']] = (1, 'short_ewo')

        if dont_buy_conditions:
            for condition in dont_buy_conditions:
                dataframe.loc[condition, 'enter_long'] = 0
                dataframe.loc[condition, 'enter_short'] = 0

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Long Exit Conditions
        long_conditions = [
            ((dataframe['close'] > dataframe['sma_9']) &
             (dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.short_high_offset_2.value)) &
             (dataframe['rsi'] > 50) &
             (dataframe['volume'] > 0) &
             (dataframe['rsi_fast'] > dataframe['rsi_slow'])
            ) |
            (
                (dataframe['close'] < dataframe['hma_50']) &
                (dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.short_high_offset.value)) &
                (dataframe['volume'] > 0) &
                (dataframe['rsi_fast'] > dataframe['rsi_slow'])
            )
        ]

        # Short Exit Conditions
        short_conditions = [
            ((dataframe['close'] < dataframe['sma_9']) &
             (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.long_low_offset.value)) &
             (dataframe['rsi'] < 50) &
             (dataframe['volume'] > 0) &
             (dataframe['rsi_fast'] < dataframe['rsi_slow'])
            ) |
            (
                (dataframe['close'] > dataframe['hma_50']) &
                (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.long_low_offset_2.value)) &
                (dataframe['volume'] > 0) &
                (dataframe['rsi_fast'] < dataframe['rsi_slow'])
            )
        ]

        # Set exit signals
        if long_conditions:
            dataframe.loc[
                reduce(lambda x, y: x | y, long_conditions),
                'exit_long'
            ] = 1

        if short_conditions:
            dataframe.loc[
                reduce(lambda x, y: x | y, short_conditions),
                'exit_short'
            ] = 1

        return dataframe