# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import pandas as pd  # noqa
pd.options.mode.chained_assignment = None  # default='warn'
import technical.indicators as ftt
from functools import reduce
from datetime import datetime, timedelta
from freqtrade.strategy import merge_informative_pair
import numpy as np
from freqtrade.strategy import stoploss_from_open

def ichimoku_edit(
    dataframe, conversion_line_period=9, base_line_periods=26, laggin_span=52, displacement=26, open="open", high="high", low="low", close="close"
):
    """
    Ichimoku cloud indicator
    Note: Do not use chikou_span for backtesting.
        It looks into the future, is not printed by most charting platforms.
        It is only useful for visual analysis
    :param dataframe: Dataframe containing OHLCV data
    :param conversion_line_period: Conversion line Period (defaults to 9)
    :param base_line_periods: Base line Periods (defaults to 26)
    :param laggin_span: Lagging span period
    :param displacement: Displacement (shift) - defaults to 26
    :return: Dict containing the following keys:
        tenkan_sen, kijun_sen, senkou_span_a, senkou_span_b, leading_senkou_span_a,
        leading_senkou_span_b, chikou_span, cloud_green, cloud_red
    """

    tenkan_sen = (
        dataframe[high].rolling(window=conversion_line_period).max()
        + dataframe[low].rolling(window=conversion_line_period).min()
    ) / 2

    kijun_sen = (
        dataframe[high].rolling(window=base_line_periods).max()
        + dataframe[low].rolling(window=base_line_periods).min()
    ) / 2

    leading_senkou_span_a = (tenkan_sen + kijun_sen) / 2

    leading_senkou_span_b = (
        dataframe[high].rolling(window=laggin_span).max()
        + dataframe[low].rolling(window=laggin_span).min()
    ) / 2

    senkou_span_a = leading_senkou_span_a.shift(displacement)

    senkou_span_b = leading_senkou_span_b.shift(displacement)

    chikou_span = dataframe[close].shift(-displacement)

    cloud_green = senkou_span_a > senkou_span_b
    cloud_red = senkou_span_b > senkou_span_a

    return {
        "tenkan_sen": tenkan_sen,
        "kijun_sen": kijun_sen,
        "senkou_span_a": senkou_span_a,
        "senkou_span_b": senkou_span_b,
        "leading_senkou_span_a": leading_senkou_span_a,
        "leading_senkou_span_b": leading_senkou_span_b,
        "chikou_span": chikou_span,
        "cloud_green": cloud_green,
        "cloud_red": cloud_red,
    }

class IchiV1_Fixed(IStrategy):

    # NOTE: settings as of the 25th july 21
    # Buy hyperspace params:
    buy_params = {
        "buy_trend_above_senkou_level": 1,
        "buy_trend_bullish_level": 6,
        "buy_fan_magnitude_shift_value": 3,
        "buy_min_fan_magnitude_gain": 1.002 # NOTE: Good value (Win% ~70%), alot of trades
        #"buy_min_fan_magnitude_gain": 1.008 # NOTE: Very save value (Win% ~90%), only the biggest moves 1.008,
    }

    # Sell hyperspace params:
    # NOTE: was 15m but kept bailing out in dryrun
    sell_params = {
        "sell_trend_indicator": "trend_close_2h",
    }

    # ROI table:
    minimal_roi = {
        "0": 0.059,
        "10": 0.037,
        "41": 0.012,
        "114": 0
    }

    # Stoploss:
    stoploss = -0.275

    # Optimal timeframe for the strategy
    timeframe = '5m'

    startup_candle_count = 96
    process_only_new_candles = False

    trailing_stop = False
    #trailing_stop_positive = 0.002
    #trailing_stop_positive_offset = 0.025
    #trailing_only_offset_is_reached = True

    use_sell_signal = True
    sell_profit_only = False
    ignore_roi_if_buy_signal = False

    plot_config = {
        'main_plot': {
            # fill area between senkou_a and senkou_b
            'senkou_a': {
                'color': 'green', #optional
                'fill_to': 'senkou_b',
                'fill_label': 'Ichimoku Cloud', #optional
                'fill_color': 'rgba(255,76,46,0.2)', #optional
            },
            # plot senkou_b, too. Not only the area to it.
            'senkou_b': {},
            'trend_close_5m': {'color': '#FF5733'},
            'trend_close_15m': {'color': '#FF8333'},
            'trend_close_30m': {'color': '#FFB533'},
            'trend_close_1h': {'color': '#FFE633'},
            'trend_close_2h': {'color': '#E3FF33'},
            'trend_close_4h': {'color': '#C4FF33'},
            'trend_close_6h': {'color': '#61FF33'},
            'trend_close_8h': {'color': '#33FF7D'}
        },
        'subplots': {
            'fan_magnitude': {
                'fan_magnitude': {}
            },
            'fan_magnitude_gain': {
                'fan_magnitude_gain': {}
            }
        }
    }

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

        heikinashi = qtpylib.heikinashi(dataframe)
        dataframe['hk_open'] = heikinashi['open']
        #dataframe['close'] = heikinashi['close']
        dataframe['hk_high'] = heikinashi['high']
        dataframe['hk_low'] = heikinashi['low']

        dataframe['trend_close_5m'] = dataframe['close']
        dataframe['trend_close_15m'] = ta.EMA(dataframe['close'], timeperiod=3)
        dataframe['trend_close_30m'] = ta.EMA(dataframe['close'], timeperiod=6)
        dataframe['trend_close_1h'] = ta.EMA(dataframe['close'], timeperiod=12)
        dataframe['trend_close_2h'] = ta.EMA(dataframe['close'], timeperiod=24)
        dataframe['trend_close_4h'] = ta.EMA(dataframe['close'], timeperiod=48)
        dataframe['trend_close_6h'] = ta.EMA(dataframe['close'], timeperiod=72)
        dataframe['trend_close_8h'] = ta.EMA(dataframe['close'], timeperiod=96)

        dataframe['trend_open_5m'] = dataframe['hk_open']
        dataframe['trend_open_15m'] = ta.EMA(dataframe['hk_open'], timeperiod=3)
        dataframe['trend_open_30m'] = ta.EMA(dataframe['hk_open'], timeperiod=6)
        dataframe['trend_open_1h'] = ta.EMA(dataframe['hk_open'], timeperiod=12)
        dataframe['trend_open_2h'] = ta.EMA(dataframe['hk_open'], timeperiod=24)
        dataframe['trend_open_4h'] = ta.EMA(dataframe['hk_open'], timeperiod=48)
        dataframe['trend_open_6h'] = ta.EMA(dataframe['hk_open'], timeperiod=72)
        dataframe['trend_open_8h'] = ta.EMA(dataframe['hk_open'], timeperiod=96)

        dataframe['fan_magnitude'] = (dataframe['trend_close_1h'] / dataframe['trend_close_8h'])
        dataframe['fan_magnitude_gain'] = dataframe['fan_magnitude'] / dataframe['fan_magnitude'].shift(1)

        ichimoku = ichimoku_edit(dataframe, conversion_line_period=20, base_line_periods=60, laggin_span=120, displacement=30, open="hk_open", high="hk_high", low="hk_low")
        dataframe['chikou_span'] = ichimoku['chikou_span']
        dataframe['tenkan_sen'] = ichimoku['tenkan_sen']
        dataframe['kijun_sen'] = ichimoku['kijun_sen']
        dataframe['senkou_a'] = ichimoku['senkou_span_a']
        dataframe['senkou_b'] = ichimoku['senkou_span_b']
        dataframe['leading_senkou_span_a'] = ichimoku['leading_senkou_span_a']
        dataframe['leading_senkou_span_b'] = ichimoku['leading_senkou_span_b']
        dataframe['cloud_green'] = ichimoku['cloud_green']
        dataframe['cloud_red'] = ichimoku['cloud_red']

        dataframe['atr'] = ta.ATR(dataframe)

        return dataframe


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

        conditions = []

        # Trending market
        if self.buy_params['buy_trend_above_senkou_level'] >= 1:
            conditions.append(dataframe['trend_close_5m'] > dataframe['senkou_a'])
            conditions.append(dataframe['trend_close_5m'] > dataframe['senkou_b'])

        if self.buy_params['buy_trend_above_senkou_level'] >= 2:
            conditions.append(dataframe['trend_close_15m'] > dataframe['senkou_a'])
            conditions.append(dataframe['trend_close_15m'] > dataframe['senkou_b'])

        if self.buy_params['buy_trend_above_senkou_level'] >= 3:
            conditions.append(dataframe['trend_close_30m'] > dataframe['senkou_a'])
            conditions.append(dataframe['trend_close_30m'] > dataframe['senkou_b'])

        if self.buy_params['buy_trend_above_senkou_level'] >= 4:
            conditions.append(dataframe['trend_close_1h'] > dataframe['senkou_a'])
            conditions.append(dataframe['trend_close_1h'] > dataframe['senkou_b'])

        if self.buy_params['buy_trend_above_senkou_level'] >= 5:
            conditions.append(dataframe['trend_close_2h'] > dataframe['senkou_a'])
            conditions.append(dataframe['trend_close_2h'] > dataframe['senkou_b'])

        if self.buy_params['buy_trend_above_senkou_level'] >= 6:
            conditions.append(dataframe['trend_close_4h'] > dataframe['senkou_a'])
            conditions.append(dataframe['trend_close_4h'] > dataframe['senkou_b'])

        if self.buy_params['buy_trend_above_senkou_level'] >= 7:
            conditions.append(dataframe['trend_close_6h'] > dataframe['senkou_a'])
            conditions.append(dataframe['trend_close_6h'] > dataframe['senkou_b'])

        if self.buy_params['buy_trend_above_senkou_level'] >= 8:
            conditions.append(dataframe['trend_close_8h'] > dataframe['senkou_a'])
            conditions.append(dataframe['trend_close_8h'] > dataframe['senkou_b'])

        # Trends bullish
        if self.buy_params['buy_trend_bullish_level'] >= 1:
            conditions.append(dataframe['trend_close_5m'] > dataframe['trend_open_5m'])

        if self.buy_params['buy_trend_bullish_level'] >= 2:
            conditions.append(dataframe['trend_close_15m'] > dataframe['trend_open_15m'])

        if self.buy_params['buy_trend_bullish_level'] >= 3:
            conditions.append(dataframe['trend_close_30m'] > dataframe['trend_open_30m'])

        if self.buy_params['buy_trend_bullish_level'] >= 4:
            conditions.append(dataframe['trend_close_1h'] > dataframe['trend_open_1h'])

        if self.buy_params['buy_trend_bullish_level'] >= 5:
            conditions.append(dataframe['trend_close_2h'] > dataframe['trend_open_2h'])

        if self.buy_params['buy_trend_bullish_level'] >= 6:
            conditions.append(dataframe['trend_close_4h'] > dataframe['trend_open_4h'])

        if self.buy_params['buy_trend_bullish_level'] >= 7:
            conditions.append(dataframe['trend_close_6h'] > dataframe['trend_open_6h'])

        if self.buy_params['buy_trend_bullish_level'] >= 8:
            conditions.append(dataframe['trend_close_8h'] > dataframe['trend_open_8h'])

        # Trends magnitude
        conditions.append(dataframe['fan_magnitude_gain'] >= self.buy_params['buy_min_fan_magnitude_gain'])
        conditions.append(dataframe['fan_magnitude'] > 1)

        for x in range(self.buy_params['buy_fan_magnitude_shift_value']):
            conditions.append(dataframe['fan_magnitude'].shift(x+1) < dataframe['fan_magnitude'])

        if conditions:
            dataframe.loc[
                reduce(lambda x, y: x & y, conditions),
                'buy'] = 1

        return dataframe


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

        conditions = []

        conditions.append(qtpylib.crossed_below(dataframe['trend_close_5m'], dataframe[self.sell_params['sell_trend_indicator']]))

        if conditions:
            dataframe.loc[
                reduce(lambda x, y: x & y, conditions),
                'sell'] = 1

        return dataframe