from skopt.space import Dimension, Integer
import logging
import logging
from datetime import datetime, timezone
from functools import reduce
from typing import List
import numpy as np
import pandas_ta as pta
import talib.abstract as ta
import technical.indicators as ftt
from pandas import DataFrame, Series
from skopt.space import Dimension, Integer
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.persistence import Trade
from freqtrade.strategy import BooleanParameter, DecimalParameter, IntParameter, merge_informative_pair
from freqtrade.strategy.interface import IStrategy
logger = logging.getLogger(__name__)
# ###############################################################################
# ###############################################################################
# @Farhad#0318 ( https://github.com/farfary/freqtrade_strategies )
#
# Based on entry signal from Al (alb#1349)
# ###############################################################################
# ###############################################################################

class MiniLambo(IStrategy):
    INTERFACE_VERSION = 3
    # Protection hyperspace params:
    protection_params = {'protection_cooldown_period': 2, 'protection_maxdrawdown_lookback_period_candles': 35, 'protection_maxdrawdown_max_allowed_drawdown': 0.097, 'protection_maxdrawdown_stop_duration_candles': 1, 'protection_maxdrawdown_trade_limit': 6, 'protection_stoplossguard_lookback_period_candles': 16, 'protection_stoplossguard_stop_duration_candles': 29, 'protection_stoplossguard_trade_limit': 3}
    protection_cooldown_period = IntParameter(low=1, high=48, default=1, space='protection', optimize=True)
    protection_maxdrawdown_lookback_period_candles = IntParameter(low=1, high=48, default=1, space='protection', optimize=True)
    protection_maxdrawdown_trade_limit = IntParameter(low=1, high=8, default=4, space='protection', optimize=True)
    protection_maxdrawdown_stop_duration_candles = IntParameter(low=1, high=48, default=1, space='protection', optimize=True)
    protection_maxdrawdown_max_allowed_drawdown = DecimalParameter(low=0.01, high=0.2, default=0.1, space='protection', optimize=True)
    protection_stoplossguard_lookback_period_candles = IntParameter(low=1, high=48, default=1, space='protection', optimize=True)
    protection_stoplossguard_trade_limit = IntParameter(low=1, high=8, default=4, space='protection', optimize=True)
    protection_stoplossguard_stop_duration_candles = IntParameter(low=1, high=48, default=1, space='protection', optimize=True)

    @property
    def protections(self):
        return [{'method': 'CooldownPeriod', 'stop_duration_candles': self.protection_cooldown_period.value}, {'method': 'MaxDrawdown', 'lookback_period_candles': self.protection_maxdrawdown_lookback_period_candles.value, 'trade_limit': self.protection_maxdrawdown_trade_limit.value, 'stop_duration_candles': self.protection_maxdrawdown_stop_duration_candles.value, 'max_allowed_drawdown': self.protection_maxdrawdown_max_allowed_drawdown.value}, {'method': 'StoplossGuard', 'lookback_period_candles': self.protection_stoplossguard_lookback_period_candles.value, 'trade_limit': self.protection_stoplossguard_trade_limit.value, 'stop_duration_candles': self.protection_stoplossguard_stop_duration_candles.value, 'only_per_pair': False}]

    class HyperOpt:

        @staticmethod
        def generate_roi_table(params: dict):
            """
            Generate the ROI table that will be used by Hyperopt
            This implementation generates the default legacy Freqtrade ROI tables.
            Change it if you need different number of steps in the generated
            ROI tables or other structure of the ROI tables.
            Please keep it aligned with parameters in the 'roi' optimization
            hyperspace defined by the roi_space method.
            """
            roi_table = {}
            roi_table[0] = 0.05
            roi_table[params['roi_t6']] = 0.04
            roi_table[params['roi_t5']] = 0.03
            roi_table[params['roi_t4']] = 0.02
            roi_table[params['roi_t3']] = 0.01
            roi_table[params['roi_t2']] = 0.0001
            roi_table[params['roi_t1']] = -10
            return roi_table

        @staticmethod
        def roi_space() -> List[Dimension]:
            """
            Values to search for each ROI steps
            Override it if you need some different ranges for the parameters in the
            'roi' optimization hyperspace.
            Please keep it aligned with the implementation of the
            generate_roi_table method.
            """
            return [Integer(240, 720, name='roi_t1'), Integer(120, 240, name='roi_t2'), Integer(90, 120, name='roi_t3'), Integer(60, 90, name='roi_t4'), Integer(30, 60, name='roi_t5'), Integer(1, 30, name='roi_t6')]
    # ROI table:
    minimal_roi = {'0': 0.05, '15': 0.04, '51': 0.03, '81': 0.02, '112': 0.01, '154': 0.0001, '400': -10}
    # Stoploss:
    stoploss = -0.1
    # Trailing stop:
    trailing_stop = False
    trailing_stop_positive = 0.3207
    trailing_stop_positive_offset = 0.3849
    trailing_only_offset_is_reached = False
    timeframe = '1m'
    use_exit_signal = False
    exit_profit_only = False
    ignore_roi_if_entry_signal = False
    use_custom_stoploss = True
    process_only_new_candles = True
    startup_candle_count = 200
    plot_config = {'main_plot': {'ema_14': {'color': '#888b48', 'type': 'line'}, 'entry_exit': {'exit_tag': {'color': 'red'}, 'entry_tag': {'color': 'blue'}}}, 'subplots': {'rsi4': {'rsi_4': {'color': '#888b48', 'type': 'line'}}, 'rsi14': {'rsi_14': {'color': '#888b48', 'type': 'line'}}, 'cti': {'cti': {'color': '#573892', 'type': 'line'}}, 'ewo': {'EWO': {'color': '#573892', 'type': 'line'}}, 'pct_change': {'pct_change': {'color': '#26782f', 'type': 'line'}}}}
    # Buy hyperspace params:
    entry_params = {'lambo2_pct_change_high_period': 109, 'lambo2_pct_change_high_ratio': -0.235, 'lambo2_pct_change_low_period': 20, 'lambo2_pct_change_low_ratio': -0.06, 'lambo2_ema_14_factor': 0.981, 'lambo2_rsi_14_limit': 39, 'lambo2_rsi_21_limit': 39, 'lambo2_rsi_4_limit': 44}
    # lambo2
    lambo2_ema_14_factor = DecimalParameter(0.8, 1.2, decimals=3, default=entry_params['lambo2_ema_14_factor'], space='entry', optimize=False)
    lambo2_rsi_4_limit = IntParameter(5, 60, default=entry_params['lambo2_rsi_4_limit'], space='entry', optimize=False)
    lambo2_rsi_14_limit = IntParameter(5, 60, default=entry_params['lambo2_rsi_14_limit'], space='entry', optimize=False)
    lambo2_rsi_21_limit = IntParameter(5, 60, default=entry_params['lambo2_rsi_21_limit'], space='entry', optimize=False)
    lambo2_pct_change_low_period = IntParameter(1, 60, default=entry_params['lambo2_pct_change_low_period'], space='entry', optimize=True)
    lambo2_pct_change_low_ratio = DecimalParameter(low=-0.2, high=-0.01, decimals=3, default=entry_params['lambo2_pct_change_low_ratio'], space='entry', optimize=True)
    lambo2_pct_change_high_period = IntParameter(1, 180, default=entry_params['lambo2_pct_change_high_period'], space='entry', optimize=True)
    lambo2_pct_change_high_ratio = DecimalParameter(low=-0.3, high=-0.01, decimals=3, default=entry_params['lambo2_pct_change_high_ratio'], space='entry', optimize=True)

    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, '1h') for pair in pairs]
        informative_pairs += [('BTC/USDT', '1m')]
        informative_pairs += [('BTC/USDT', '1d')]
        return informative_pairs

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
        sl_new = 1
        if current_profit > 0.2:
            sl_new = 0.05
        elif current_profit > 0.1:
            sl_new = 0.03
        elif current_profit > 0.06:
            sl_new = 0.02
        elif current_profit > 0.03:
            sl_new = 0.015
        elif current_profit > 0.015:
            sl_new = 0.0075
        return sl_new

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['ema_14'] = ta.EMA(dataframe, timeperiod=14)
        dataframe['rsi_4'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_21'] = ta.RSI(dataframe, timeperiod=21)
        dataframe['rsi_100'] = ta.RSI(dataframe, timeperiod=100)
        dataframe['cti'] = pta.cti(dataframe['close'], length=20)
        dataframe['ewo'] = EWO(dataframe, 50, 200)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        dataframe.loc[:, 'entry_tag'] = ''
        # & (dataframe['rsi_21'] > int(self.lambo2_rsi_21_limit.value))
        lambo2 = (dataframe['close'] < dataframe['ema_14'] * self.lambo2_ema_14_factor.value) & (dataframe['rsi_4'] < int(self.lambo2_rsi_4_limit.value)) & (dataframe['rsi_14'] < int(self.lambo2_rsi_14_limit.value)) & (dataframe['close'].pct_change(periods=self.lambo2_pct_change_low_period.value) < float(self.lambo2_pct_change_low_ratio.value)) & (dataframe['close'].pct_change(periods=self.lambo2_pct_change_high_period.value) > float(self.lambo2_pct_change_high_ratio.value))
        dataframe.loc[lambo2, 'entry_tag'] += 'lambo2 '
        conditions.append(lambo2)
        dataframe.loc[reduce(lambda x, y: x | y, conditions), 'entry'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        return dataframe

    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs) -> bool:
        trade.exit_reason = f'{exit_reason} ({trade.entry_tag})'
        return True

def bollinger_bands(stock_price, window_size, num_of_std):
    rolling_mean = stock_price.rolling(window=window_size).mean()
    rolling_std = stock_price.rolling(window=window_size).std()
    lower_band = rolling_mean - rolling_std * num_of_std
    return (np.nan_to_num(rolling_mean), np.nan_to_num(lower_band))

def ha_typical_price(bars):
    res = (bars['ha_high'] + bars['ha_low'] + bars['ha_close']) / 3.0
    return Series(index=bars.index, data=res)

def pct_change(a, b):
    return (b - a) / a

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 MiniLambo_TBS(MiniLambo):
    process_only_new_candles = True
    custom_info_trail_entry = dict()
    # Trailing entry parameters
    trailing_entry_order_enabled = True
    trailing_expire_seconds = 1800
    # If the current candle goes above min_uptrend_trailing_profit % before trailing_expire_seconds_uptrend seconds, entry the coin
    trailing_entry_uptrend_enabled = False
    trailing_expire_seconds_uptrend = 90
    min_uptrend_trailing_profit = 0.02
    debug_mode = True
    trailing_entry_max_stop = 0.02  # stop trailing entry if current_price > starting_price * (1+trailing_entry_max_stop)
    trailing_entry_max_entry = 0.0  # entry if price between uplimit (=min of serie (current_price * (1 + trailing_entry_offset())) and (start_price * 1+trailing_entry_max_entry))
    init_trailing_dict = {'trailing_entry_order_started': False, 'trailing_entry_order_uplimit': 0, 'start_trailing_price': 0, 'entry_tag': None, 'start_trailing_time': None, 'offset': 0, 'allow_trailing': False}

    def trailing_entry(self, pair, reinit=False):
        # returns trailing entry info for pair (init if necessary)
        if not pair in self.custom_info_trail_entry:
            self.custom_info_trail_entry[pair] = dict()
        if reinit or not 'trailing_entry' in self.custom_info_trail_entry[pair]:
            self.custom_info_trail_entry[pair]['trailing_entry'] = self.init_trailing_dict.copy()
        return self.custom_info_trail_entry[pair]['trailing_entry']

    def trailing_entry_info(self, pair: str, current_price: float):
        # current_time live, dry run
        current_time = datetime.now(timezone.utc)
        if not self.debug_mode:
            return
        trailing_entry = self.trailing_entry(pair)
        duration = 0
        try:
            duration = current_time - trailing_entry['start_trailing_time']
        except TypeError:
            duration = 0
        finally:
            logger.info(f"pair: {pair} : start: {trailing_entry['start_trailing_price']:.4f}, duration: {duration}, current: {current_price:.4f}, uplimit: {trailing_entry['trailing_entry_order_uplimit']:.4f}, profit: {self.current_trailing_profit_ratio(pair, current_price) * 100:.2f}%, offset: {trailing_entry['offset']}")

    def current_trailing_profit_ratio(self, pair: str, current_price: float) -> float:
        trailing_entry = self.trailing_entry(pair)
        if trailing_entry['trailing_entry_order_started']:
            return (trailing_entry['start_trailing_price'] - current_price) / trailing_entry['start_trailing_price']
        else:
            return 0

    def trailing_entry_offset(self, dataframe, pair: str, current_price: float):
        # return rebound limit before a entry in % of initial price, function of current price return None to stop
        # trailing entry (will start again at next entry signal) return 'forceentry' to force immediate entry (example with
        # 0.5%. initial price : 100 (uplimit is 100.5), 2nd price : 99 (no entry, uplimit updated to 99.5), 3price 98 (
        # no entry uplimit updated to 98.5), 4th price 99 -> BUY
        current_trailing_profit_ratio = self.current_trailing_profit_ratio(pair, current_price)
        last_candle = dataframe.iloc[-1]
        adapt = abs(last_candle['perc_norm'])
        default_offset = 0.004 * (1 + adapt)  # NOTE: default_offset 0.003 <--> 0.006
        # default_offset = adapt*0.01
        trailing_entry = self.trailing_entry(pair)
        if not trailing_entry['trailing_entry_order_started']:
            return default_offset
        # example with duration and indicators
        # dry run, live only
        last_candle = dataframe.iloc[-1]
        current_time = datetime.now(timezone.utc)
        trailing_duration = current_time - trailing_entry['start_trailing_time']
        if trailing_duration.total_seconds() > self.trailing_expire_seconds:
            if current_trailing_profit_ratio > 0 and last_candle['entry'] == 1:
                # more than 1h, price under first signal, entry signal still active -> entry
                return 'forceentry'
            else:
                # wait for next signal
                return None
        elif self.trailing_entry_uptrend_enabled and trailing_duration.total_seconds() < self.trailing_expire_seconds_uptrend and (current_trailing_profit_ratio < -1 * self.min_uptrend_trailing_profit):
            # less than 90s and price is rising, entry
            return 'forceentry'
        if current_trailing_profit_ratio < 0:
            # current price is higher than initial price
            return default_offset
        trailing_entry_offset = {0.06: 0.02, 0.03: 0.01, 0: default_offset}
        for key in trailing_entry_offset:
            if current_trailing_profit_ratio > key:
                return trailing_entry_offset[key]
        return default_offset
    # end of trailing entry parameters
    # -----------------------------------------------------

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe = super().populate_indicators(dataframe, metadata)
        self.trailing_entry(metadata['pair'])
        return dataframe

    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, **kwargs) -> bool:
        val = super().confirm_trade_entry(pair, order_type, amount, rate, time_in_force, **kwargs)
        if val:
            if self.trailing_entry_order_enabled and self.config['runmode'].value in ('live', 'dry_run'):
                val = False
                dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
                if len(dataframe) >= 1:
                    last_candle = dataframe.iloc[-1].squeeze()
                    current_price = rate
                    trailing_entry = self.trailing_entry(pair)
                    trailing_entry_offset = self.trailing_entry_offset(dataframe, pair, current_price)
                    if trailing_entry['allow_trailing']:
                        if not trailing_entry['trailing_entry_order_started'] and last_candle['entry'] == 1:
                            trailing_entry['trailing_entry_order_started'] = True
                            trailing_entry['trailing_entry_order_uplimit'] = last_candle['close']
                            trailing_entry['start_trailing_price'] = last_candle['close']
                            trailing_entry['entry_tag'] = last_candle['entry_tag']
                            trailing_entry['start_trailing_time'] = datetime.now(timezone.utc)
                            trailing_entry['offset'] = 0
                            self.trailing_entry_info(pair, current_price)
                            logger.info(f"start trailing entry for {pair} at {last_candle['close']}")
                        elif trailing_entry['trailing_entry_order_started']:
                            if trailing_entry_offset == 'forceentry':
                                # entry in custom conditions
                                val = True
                                ratio = '%.2f' % (self.current_trailing_profit_ratio(pair, current_price) * 100)
                                self.trailing_entry_info(pair, current_price)
                                logger.info(f'price OK for {pair} ({ratio} %, {current_price}), order may not be triggered if all slots are full')
                            elif trailing_entry_offset is None:
                                # stop trailing entry custom conditions
                                self.trailing_entry(pair, reinit=True)
                                logger.info(f'STOP trailing entry for {pair} because "trailing entry offset" returned None')
                            elif current_price < trailing_entry['trailing_entry_order_uplimit']:
                                # update uplimit
                                old_uplimit = trailing_entry['trailing_entry_order_uplimit']
                                self.custom_info_trail_entry[pair]['trailing_entry']['trailing_entry_order_uplimit'] = min(current_price * (1 + trailing_entry_offset), self.custom_info_trail_entry[pair]['trailing_entry']['trailing_entry_order_uplimit'])
                                self.custom_info_trail_entry[pair]['trailing_entry']['offset'] = trailing_entry_offset
                                self.trailing_entry_info(pair, current_price)
                                logger.info(f"update trailing entry for {pair} at {old_uplimit} -> {self.custom_info_trail_entry[pair]['trailing_entry']['trailing_entry_order_uplimit']}")
                            elif current_price < trailing_entry['start_trailing_price'] * (1 + self.trailing_entry_max_entry):
                                # entry ! current price > uplimit && lower thant starting price
                                val = True
                                ratio = '%.2f' % (self.current_trailing_profit_ratio(pair, current_price) * 100)
                                self.trailing_entry_info(pair, current_price)
                                logger.info(f"current price ({current_price}) > uplimit ({trailing_entry['trailing_entry_order_uplimit']}) and lower than starting price price ({trailing_entry['start_trailing_price'] * (1 + self.trailing_entry_max_entry)}). OK for {pair} ({ratio} %), order may not be triggered if all slots are full")
                            elif current_price > trailing_entry['start_trailing_price'] * (1 + self.trailing_entry_max_stop):
                                # stop trailing entry because price is too high
                                self.trailing_entry(pair, reinit=True)
                                self.trailing_entry_info(pair, current_price)
                                logger.info(f'STOP trailing entry for {pair} because of the price is higher than starting price * {1 + self.trailing_entry_max_stop}')
                            else:
                                # uplimit > current_price > max_price, continue trailing and wait for the price to go down
                                self.trailing_entry_info(pair, current_price)
                                logger.info(f'price too high for {pair} !')
                    else:
                        logger.info(f'Wait for next entry signal for {pair}')
                if val == True:
                    self.trailing_entry_info(pair, rate)
                    self.trailing_entry(pair, reinit=True)
                    logger.info(f'STOP trailing entry for {pair} because I entry it')
        return val

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe = super().populate_entry_trend(dataframe, metadata)
        if self.trailing_entry_order_enabled and self.config['runmode'].value in ('live', 'dry_run'):
            last_candle = dataframe.iloc[-1].squeeze()
            trailing_entry = self.trailing_entry(metadata['pair'])
            if last_candle['entry'] == 1:
                if not trailing_entry['trailing_entry_order_started']:
                    open_trades = Trade.get_trades([Trade.pair == metadata['pair'], Trade.is_open.is_(True)]).all()
                    if not open_trades:
                        logger.info(f"Set 'allow_trailing' to True for {metadata['pair']} to start trailing!!!")
                        # self.custom_info_trail_entry[metadata['pair']]['trailing_entry']['allow_trailing'] = True
                        trailing_entry['allow_trailing'] = True
                        initial_entry_tag = last_candle['entry_tag'] if 'entry_tag' in last_candle else 'entry signal'
                        dataframe.loc[:, 'entry_tag'] = f"{initial_entry_tag} (start trail price {last_candle['close']})"
            elif trailing_entry['trailing_entry_order_started'] == True:
                logger.info(f"Continue trailing for {metadata['pair']}. Manually trigger entry signal!!")
                dataframe.loc[:, 'entry'] = 1
                dataframe.loc[:, 'entry_tag'] = trailing_entry['entry_tag']
        # dataframe['entry'] = 1
        return dataframe