import datetime
from typing import List, Tuple
import numpy as np  # noqa
import pandas as pd  # noqa
pd.options.mode.chained_assignment = None
from pandas import DataFrame, Series
from technical.util import resample_to_interval, resampled_merge
from freqtrade.strategy import IStrategy, merge_informative_pair
from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter


import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from collections import deque


class PlotConfig():

    def __init__(self):
        self.config = {
            'main_plot': {
                resample('bollinger_upperband') : {'color': 'rgba(4,137,122,0.7)'},
                resample('kc_upperband') : {'color': 'rgba(4,146,250,0.7)'},
                resample('kc_middleband') : {'color': 'rgba(4,146,250,0.7)'},
                resample('kc_lowerband') : {'color': 'rgba(4,146,250,0.7)'},
                resample('bollinger_lowerband') : {
                    'color': 'rgba(4,137,122,0.7)',
                    'fill_to': resample('bollinger_upperband'),
                    'fill_color': 'rgba(4,137,122,0.07)'
                    },
                resample('ema9') : {'color': 'purple'},
                resample('ema20') : {'color': 'yellow'},
                resample('ema50') : {'color': 'red'},
                resample('ema200') : {'color': 'white'},
            },
            'subplots': {
                "ATR" : {
                    resample('atr'):{'color':'firebrick'}
                }
            }
        }

    def add_pivots_in_config(self):
        self.config['main_plot']["pivot_lows"] = {
            "plotly": {
                'mode': 'markers',
                'marker': {
                    'symbol': 'diamond-open',
                    'size': 11,
                    'line': {
                        'width': 2
                    },
                    'color': 'olive'
                }
            }
        }
        self.config['main_plot']["pivot_highs"] = {
            "plotly": {
                'mode': 'markers',
                'marker': {
                    'symbol': 'diamond-open',
                    'size': 11,
                    'line': {
                        'width': 2
                    },
                    'color': 'violet'
                }
            }
        }
        self.config['main_plot']["pivot_highs"] = {
            "plotly": {
                'mode': 'markers',
                'marker': {
                    'symbol': 'diamond-open',
                    'size': 11,
                    'line': {
                        'width': 2
                    },
                    'color': 'violet'
                }
            }
        }
        return self

    def add_divergence_in_config(self, indicator:str):


























        for i in range(3):
            self.config['main_plot']["bullish_divergence_" + indicator + "_line_" + str(i)] = {
                "plotly": {
                    'mode': 'lines',
                    'line' : {
                        'color': 'green',
                        'dash' :'dash'
                    }
                }
            }   
            self.config['main_plot']["bearish_divergence_" + indicator + "_line_" + str(i)] = {
                "plotly": {
                    'mode': 'lines',
                    'line' : {
                        "color":'crimson',
                        'dash' :'dash'
                    }
                }
            } 
        return self

    def add_total_divergences_in_config(self, dataframe):
        total_bullish_divergences_count = dataframe[resample("total_bullish_divergences_count")]
        total_bullish_divergences_names = dataframe[resample("total_bullish_divergences_names")]
        self.config['main_plot'][resample("total_bullish_divergences")] = {
            "plotly": {
                'mode': 'markers+text',
                'text': total_bullish_divergences_count,
                'hovertext': total_bullish_divergences_names,
                'textfont':{'size': 11, 'color':'green'},
                'textposition':'bottom center',
                'marker': {
                    'symbol': 'diamond',
                    'size': 11,
                    'line': {
                        'width': 2
                    },
                    'color': 'green'
                }
            }
        }
        total_bearish_divergences_count = dataframe[resample("total_bearish_divergences_count")]
        total_bearish_divergences_names = dataframe[resample("total_bearish_divergences_names")]
        self.config['main_plot'][resample("total_bearish_divergences")] = {
            "plotly": {
                'mode': 'markers+text',
                'text': total_bearish_divergences_count,
                'hovertext': total_bearish_divergences_names,
                'textfont':{'size': 11, 'color':'crimson'},
                'textposition':'top center',
                'marker': {
                    'symbol': 'diamond',
                    'size': 11,
                    'line': {
                        'width': 2
                    },
                    'color': 'crimson'
                }
            }
        }
        return self
        
class HarmonicDivergence(IStrategy):
    """
    This is a strategy template to get you started.
    More information in https://www.freqtrade.io/en/latest/strategy-customization/

    You can:
        :return: a Dataframe with all mandatory indicators for the strategies
    - Rename the class name (Do not forget to update class_name)
    - Add any methods you want to build your strategy
    - Add any lib you need to build your strategy

    You must keep:
    - the lib in the section "Do not remove these libs"
    - the methods: populate_indicators, populate_buy_trend, populate_sell_trend
    You should keep:
    - timeframe, minimal_roi, stoploss, trailing_*
    """


    INTERFACE_VERSION = 2


    minimal_roi = {








        "0": 0.007
    }


    stoploss = -0.02

    use_custom_stoploss = False

    trailing_stop = False




    timeframe = '15m'

    process_only_new_candles = False

    use_sell_signal = True
    sell_profit_only = False
    ignore_roi_if_buy_signal = False

    startup_candle_count: int = 30

    order_types = {
        'buy': 'market',
        'sell': 'market',
        'stoploss': 'limit',
        'stoploss_on_exchange': False
    }

    order_time_in_force = {
        'buy': 'gtc',
        'sell': 'gtc'
    }

    plot_config = None

    def get_ticker_indicator(self):
        return int(self.timeframe[:-1])

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Adds several different TA indicators to the given DataFrame

        Performance Note: For the best performance be frugal on the number of indicators
        you are using. Let uncomment only the indicator you are using in your strategies
        or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
        :param dataframe: Dataframe with data from the exchange
        :param metadata: Additional information, like the currently traded pair
        :return: a Dataframe with all mandatory indicators for the strategies
        """



        informative = dataframe



        informative['rsi'] = ta.RSI(informative)

        informative['stoch'] = ta.STOCH(informative)['slowk']

        informative['roc'] = ta.ROC(informative)

        informative['uo'] = ta.ULTOSC(informative)

        informative['ao'] = qtpylib.awesome_oscillator(informative)

        informative['macd'] = ta.MACD(informative)['macd']

        informative['cci'] = ta.CCI(informative)

        informative['cmf'] = chaikin_money_flow(informative, 20)

        informative['obv'] = ta.OBV(informative)

        informative['mfi'] = ta.MFI(informative)

        informative['adx'] = ta.ADX(informative)

        informative['atr'] = qtpylib.atr(informative, window=14, exp=False)


        keltner = emaKeltner(informative)
        informative["kc_upperband"] = keltner["upper"]
        informative["kc_middleband"] = keltner["mid"]
        informative["kc_lowerband"] = keltner["lower"]

        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=20, stds=2)
        informative['bollinger_upperband'] = bollinger['upper']
        informative['bollinger_lowerband'] = bollinger['lower']

        informative['ema9'] = ta.EMA(informative, timeperiod=9)
        informative['ema20'] = ta.EMA(informative, timeperiod=20)
        informative['ema50'] = ta.EMA(informative, timeperiod=50)
        informative['ema200'] = ta.EMA(informative, timeperiod=200)        
        
        pivots = pivot_points(informative)
        informative['pivot_lows'] = pivots['pivot_lows']
        informative['pivot_highs'] = pivots['pivot_highs']





 

        initialize_divergences_lists(informative)
        add_divergences(informative, 'rsi')
        add_divergences(informative, 'stoch')
        add_divergences(informative, 'roc')
        add_divergences(informative, 'uo')
        add_divergences(informative, 'ao')
        add_divergences(informative, 'macd')
        add_divergences(informative, 'cci')
        add_divergences(informative, 'cmf')
        add_divergences(informative, 'obv')
        add_divergences(informative, 'mfi')
        add_divergences(informative, 'adx')
















        HarmonicDivergence.plot_config = (
            PlotConfig()












            .add_total_divergences_in_config(dataframe)
            .config)

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the buy signal for the given dataframe
        :param dataframe: DataFrame populated with indicators
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                (dataframe[resample('total_bullish_divergences')].shift() > 0)









                & two_bands_check(dataframe)


                & (dataframe['volume'] > 0)  # Make sure Volume is not 0
            ),
            'buy'] = 1

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the sell signal for the given dataframe
        :param dataframe: DataFrame populated with indicators
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                (dataframe['volume'] > 0)  # Make sure Volume is not 0
            ),
            'sell'] = 0
        return dataframe
        
    def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
                    current_profit: float, **kwargs):

        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        takeprofit = 999999

        
        for i in range(1,len(dataframe['close'])):
            if dataframe.iloc[-i]['date'].to_pydatetime().replace(tzinfo=datetime.timezone.utc) == trade.open_date_utc:
                buy_candle = dataframe.iloc[-i-1].squeeze()
                takeprofit = buy_candle[resample('high')] + buy_candle[resample('atr')]
                break




    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                            current_rate: float, current_profit: float, **kwargs) -> float:

        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        stoploss = 999999

        for i in range(1,len(dataframe['close'])):            
            if dataframe.iloc[-i]['date'].to_pydatetime().replace(tzinfo=datetime.timezone.utc) == trade.open_date_utc:
                buy_candle = dataframe.iloc[-i-1].squeeze()
                stoploss = buy_candle[resample('low')] - buy_candle[resample('atr')]

                break

        if stoploss < current_rate:
            return (stoploss / current_rate) - 1

        return 1

def resample(indicator):

    return indicator

def two_bands_check(dataframe):
    check = (

    ((dataframe[resample('low')] < dataframe[resample('kc_lowerband')]) & (dataframe[resample('high')] > dataframe[resample('kc_upperband')])) # 1


    )
    return ~check

def ema_cross_check(dataframe):
    dataframe['ema20_50_cross'] = qtpylib.crossed_below(dataframe[resample('ema20')],dataframe[resample('ema50')])
    dataframe['ema20_200_cross'] = qtpylib.crossed_below(dataframe[resample('ema20')],dataframe[resample('ema200')])
    dataframe['ema50_200_cross'] = qtpylib.crossed_below(dataframe[resample('ema50')],dataframe[resample('ema200')])
    return ~(
        dataframe['ema20_50_cross'] 
        | dataframe['ema20_200_cross'] 
        | dataframe['ema50_200_cross'] 
        )

def green_candle(dataframe):
    return dataframe[resample('open')] < dataframe[resample('close')]

def keltner_middleband_check(dataframe):
    return (dataframe[resample('low')] < dataframe[resample('kc_middleband')]) & (dataframe[resample('high')] > dataframe[resample('kc_middleband')])

def keltner_lowerband_check(dataframe):
    return (dataframe[resample('low')] < dataframe[resample('kc_lowerband')]) & (dataframe[resample('high')] > dataframe[resample('kc_lowerband')])

def bollinger_lowerband_check(dataframe):
    return (dataframe[resample('low')] < dataframe[resample('bollinger_lowerband')]) & (dataframe[resample('high')] > dataframe[resample('bollinger_lowerband')])

def bollinger_keltner_check(dataframe):
    return (dataframe[resample('bollinger_lowerband')] < dataframe[resample('kc_lowerband')]) & (dataframe[resample('bollinger_upperband')] > dataframe[resample('kc_upperband')])

def ema_check(dataframe):
    check = (
        (dataframe[resample('ema9')] < dataframe[resample('ema20')])
        & (dataframe[resample('ema20')] < dataframe[resample('ema50')])
        & (dataframe[resample('ema50')] < dataframe[resample('ema200')]))
    return ~check

def initialize_divergences_lists(dataframe: DataFrame):
    dataframe["total_bullish_divergences"] = np.empty(len(dataframe['close'])) * np.nan
    dataframe["total_bullish_divergences_count"] = np.empty(len(dataframe['close'])) * np.nan
    dataframe["total_bullish_divergences_count"] = [0 if x != x else x for x in dataframe["total_bullish_divergences_count"]]
    dataframe["total_bullish_divergences_names"] = np.empty(len(dataframe['close'])) * np.nan
    dataframe["total_bullish_divergences_names"] = ['' if x != x else x for x in dataframe["total_bullish_divergences_names"]]
    dataframe["total_bearish_divergences"] = np.empty(len(dataframe['close'])) * np.nan
    dataframe["total_bearish_divergences_count"] = np.empty(len(dataframe['close'])) * np.nan
    dataframe["total_bearish_divergences_count"] = [0 if x != x else x for x in dataframe["total_bearish_divergences_count"]]
    dataframe["total_bearish_divergences_names"] = np.empty(len(dataframe['close'])) * np.nan
    dataframe["total_bearish_divergences_names"] = ['' if x != x else x for x in dataframe["total_bearish_divergences_names"]]

def add_divergences(dataframe: DataFrame, indicator: str):
    (bearish_divergences, bearish_lines, bullish_divergences, bullish_lines) = divergence_finder_dataframe(dataframe, indicator)
    dataframe['bearish_divergence_' + indicator + '_occurence'] = bearish_divergences


    dataframe['bullish_divergence_' + indicator + '_occurence'] = bullish_divergences



def divergence_finder_dataframe(dataframe: DataFrame, indicator_source: str) -> Tuple[pd.Series, pd.Series]:
    bearish_lines = [np.empty(len(dataframe['close'])) * np.nan]
    bearish_divergences = np.empty(len(dataframe['close'])) * np.nan
    bullish_lines = [np.empty(len(dataframe['close'])) * np.nan]
    bullish_divergences = np.empty(len(dataframe['close'])) * np.nan
    low_iterator = []
    high_iterator = []

    for index, row in enumerate(dataframe.itertuples(index=True, name='Pandas')):
        if np.isnan(row.pivot_lows):
            low_iterator.append(0 if len(low_iterator) == 0 else low_iterator[-1])
        else:
            low_iterator.append(index)
        if np.isnan(row.pivot_highs):
            high_iterator.append(0 if len(high_iterator) == 0 else high_iterator[-1])
        else:
            high_iterator.append(index)

    for index, row in enumerate(dataframe.itertuples(index=True, name='Pandas')):

        bearish_occurence = bearish_divergence_finder(dataframe,
            dataframe[indicator_source],
            high_iterator,
            index)

        if bearish_occurence != None:
            (prev_pivot , current_pivot) = bearish_occurence 
            bearish_prev_pivot = dataframe['close'][prev_pivot]
            bearish_current_pivot = dataframe['close'][current_pivot]
            bearish_ind_prev_pivot = dataframe[indicator_source][prev_pivot]
            bearish_ind_current_pivot = dataframe[indicator_source][current_pivot]
            length = current_pivot - prev_pivot
            bearish_lines_index = 0
            can_exist = True
            while(True):
                can_draw = True
                if bearish_lines_index <= len(bearish_lines):
                    bearish_lines.append(np.empty(len(dataframe['close'])) * np.nan)
                actual_bearish_lines = bearish_lines[bearish_lines_index]
                for i in range(length + 1):
                    point = bearish_prev_pivot + (bearish_current_pivot - bearish_prev_pivot) * i / length
                    indicator_point =  bearish_ind_prev_pivot + (bearish_ind_current_pivot - bearish_ind_prev_pivot) * i / length
                    if i != 0 and i != length:
                        if (point <= dataframe['close'][prev_pivot + i] 
                        or indicator_point <= dataframe[indicator_source][prev_pivot + i]):
                            can_exist = False
                    if not np.isnan(actual_bearish_lines[prev_pivot + i]):
                        can_draw = False
                if not can_exist:
                    break
                if can_draw:
                    for i in range(length + 1):
                        actual_bearish_lines[prev_pivot + i] = bearish_prev_pivot + (bearish_current_pivot - bearish_prev_pivot) * i / length
                    break
                bearish_lines_index = bearish_lines_index + 1
            if can_exist:
                bearish_divergences[index] = row.close
                dataframe["total_bearish_divergences"][index] = row.close
                if index > 30:
                    dataframe["total_bearish_divergences_count"][index-30] = dataframe["total_bearish_divergences_count"][index-30] + 1
                    dataframe["total_bearish_divergences_names"][index-30] = dataframe["total_bearish_divergences_names"][index-30] + indicator_source.upper() + '<br>'

        bullish_occurence = bullish_divergence_finder(dataframe,
            dataframe[indicator_source],
            low_iterator,
            index)
        
        if bullish_occurence != None:
            (prev_pivot , current_pivot) = bullish_occurence
            bullish_prev_pivot = dataframe['close'][prev_pivot]
            bullish_current_pivot = dataframe['close'][current_pivot]
            bullish_ind_prev_pivot = dataframe[indicator_source][prev_pivot]
            bullish_ind_current_pivot = dataframe[indicator_source][current_pivot]
            length = current_pivot - prev_pivot
            bullish_lines_index = 0
            can_exist = True
            while(True):
                can_draw = True
                if bullish_lines_index <= len(bullish_lines):
                    bullish_lines.append(np.empty(len(dataframe['close'])) * np.nan)
                actual_bullish_lines = bullish_lines[bullish_lines_index]
                for i in range(length + 1):
                    point = bullish_prev_pivot + (bullish_current_pivot - bullish_prev_pivot) * i / length
                    indicator_point =  bullish_ind_prev_pivot + (bullish_ind_current_pivot - bullish_ind_prev_pivot) * i / length
                    if i != 0 and i != length:
                        if (point >= dataframe['close'][prev_pivot + i] 
                        or indicator_point >= dataframe[indicator_source][prev_pivot + i]):
                            can_exist = False
                    if not np.isnan(actual_bullish_lines[prev_pivot + i]):
                        can_draw = False
                if not can_exist:
                    break
                if can_draw:
                    for i in range(length + 1):
                        actual_bullish_lines[prev_pivot + i] = bullish_prev_pivot + (bullish_current_pivot - bullish_prev_pivot) * i / length
                    break
                bullish_lines_index = bullish_lines_index + 1
            if can_exist:
                bullish_divergences[index] = row.close
                dataframe["total_bullish_divergences"][index] = row.close
                if index > 30:
                    dataframe["total_bullish_divergences_count"][index-30] = dataframe["total_bullish_divergences_count"][index-30] + 1
                    dataframe["total_bullish_divergences_names"][index-30] = dataframe["total_bullish_divergences_names"][index-30] + indicator_source.upper() + '<br>'
    
    return (bearish_divergences, bearish_lines, bullish_divergences, bullish_lines)

def bearish_divergence_finder(dataframe, indicator, high_iterator, index):
    if high_iterator[index] == index:
        current_pivot = high_iterator[index]
        occurences = list(dict.fromkeys(high_iterator))
        current_index = occurences.index(high_iterator[index])
        for i in range(current_index-1,current_index-6,-1):            
            prev_pivot = occurences[i]
            if np.isnan(prev_pivot):
                return
            if ((dataframe['pivot_highs'][current_pivot] < dataframe['pivot_highs'][prev_pivot] and indicator[current_pivot] > indicator[prev_pivot])
            or (dataframe['pivot_highs'][current_pivot] > dataframe['pivot_highs'][prev_pivot] and indicator[current_pivot] < indicator[prev_pivot])):
                return (prev_pivot , current_pivot)
    return None

def bullish_divergence_finder(dataframe, indicator, low_iterator, index):
    if low_iterator[index] == index:
        current_pivot = low_iterator[index]
        occurences = list(dict.fromkeys(low_iterator))
        current_index = occurences.index(low_iterator[index])
        for i in range(current_index-1,current_index-6,-1):
            prev_pivot = occurences[i]
            if np.isnan(prev_pivot):
                return 
            if ((dataframe['pivot_lows'][current_pivot] < dataframe['pivot_lows'][prev_pivot] and indicator[current_pivot] > indicator[prev_pivot])
            or (dataframe['pivot_lows'][current_pivot] > dataframe['pivot_lows'][prev_pivot] and indicator[current_pivot] < indicator[prev_pivot])):
                return (prev_pivot, current_pivot)
    return None

from enum import Enum
class PivotSource(Enum):
    HighLow = 0
    Close = 1

def pivot_points(dataframe: DataFrame, window: int = 5, pivot_source: PivotSource = PivotSource.Close) -> DataFrame:
    high_source = None
    low_source = None

    if pivot_source == PivotSource.Close:
        high_source = 'close'
        low_source = 'close'
    elif pivot_source == PivotSource.HighLow:
        high_source = 'high'
        low_source = 'low'

    pivot_points_lows = np.empty(len(dataframe['close'])) * np.nan
    pivot_points_highs = np.empty(len(dataframe['close'])) * np.nan
    last_values = deque()

    for index, row in enumerate(dataframe.itertuples(index=True, name='Pandas')):
        last_values.append(row)
        if len(last_values) >= window * 2 + 1:
            current_value = last_values[window]
            is_greater = True
            is_less = True
            for window_index in range(0, window):
                left = last_values[window_index]
                right = last_values[2 * window - window_index]
                local_is_greater, local_is_less = check_if_pivot_is_greater_or_less(current_value, high_source, low_source, left, right)
                is_greater &= local_is_greater
                is_less &= local_is_less
            if is_greater:
                pivot_points_highs[index - window] = getattr(current_value, high_source)
            if is_less:
                pivot_points_lows[index - window] = getattr(current_value, low_source)
            last_values.popleft()

    if len(last_values) >= window + 2:
        current_value = last_values[-2]
        is_greater = True
        is_less = True
        for window_index in range(0, window):
            left = last_values[-2 - window_index - 1]
            right = last_values[-1]
            local_is_greater, local_is_less = check_if_pivot_is_greater_or_less(current_value, high_source, low_source, left, right)
            is_greater &= local_is_greater
            is_less &= local_is_less
        if is_greater:
            pivot_points_highs[index - 1] = getattr(current_value, high_source)
        if is_less:
            pivot_points_lows[index - 1] = getattr(current_value, low_source)

    return pd.DataFrame(index=dataframe.index, data={
        'pivot_lows': pivot_points_lows,
        'pivot_highs': pivot_points_highs
    })

def check_if_pivot_is_greater_or_less(current_value, high_source: str, low_source: str, left, right) -> Tuple[bool, bool]:
    is_greater = True
    is_less = True
    if (getattr(current_value, high_source) < getattr(left, high_source) or
        getattr(current_value, high_source) < getattr(right, high_source)):
        is_greater = False

    if (getattr(current_value, low_source) > getattr(left, low_source) or
        getattr(current_value, low_source) > getattr(right, low_source)):
        is_less = False
    return (is_greater, is_less)

def emaKeltner(dataframe):
    keltner = {}
    atr = qtpylib.atr(dataframe, window=10)
    ema20 = ta.EMA(dataframe, timeperiod=20)
    keltner['upper'] = ema20 + atr
    keltner['mid'] = ema20
    keltner['lower'] = ema20 - atr
    return keltner

def chaikin_money_flow(dataframe, n=20, fillna=False) -> Series:
    """Chaikin Money Flow (CMF)
    It measures the amount of Money Flow Volume over a specific period.
    http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_money_flow_cmf
    Args:
        dataframe(pandas.Dataframe): dataframe containing ohlcv
        n(int): n period.
        fillna(bool): if True, fill nan values.
    Returns:
        pandas.Series: New feature generated.
    """
    df = dataframe.copy()
    mfv = ((df['close'] - df['low']) - (df['high'] - df['close'])) / (df['high'] - df['low'])
    mfv = mfv.fillna(0.0)  # float division by zero
    mfv *= df['volume']
    cmf = (mfv.rolling(n, min_periods=0).sum()
           / df['volume'].rolling(n, min_periods=0).sum())
    if fillna:
        cmf = cmf.replace([np.inf, -np.inf], np.nan).fillna(0)
    return Series(cmf, name='cmf')