# Ichess Strategy  ichimoku_score
# Associate various ichimoku signals with a score.
# For example, bullish signal => positive score,
# and bearish signal => negative score. If the total score is above 0, it may indicate a bullish trend .
# Otherwise, if it is below 0, it may indicate a bearish trend .
# We used two smoothed moving averages to find the trend.
# More info:
# https://github.com/freqtrade/freqtrade-strategies/issues/97
# https://www.tradingview.com/script/P1bybHZA-Ichimoku-Cloud-Signal-Score-v2-0-0/
# Author: @Mablue (Masoud Azizi)
# github: https://github.com/mablue/
# --- Do not remove these libs ---
from freqtrade.strategy import IntParameter
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
import numpy as np
# --------------------------------
# Add your lib to import here
# import talib.abstract as ta
import talib as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from functools import reduce

class Ichess(IStrategy):
    INTERFACE_VERSION = 3
    # ROI table:
    minimal_roi = {'0': 0.642, '8834': 0.37, '25392': 0.118, '55146': 0}
    # Stoploss:
    stoploss = -0.314
    # Opt Timeframe
    timeframe = '1d'
    entry_fast_timeperiod = IntParameter(2, 50, default=9, space='entry')
    entry_slow_timeperiod = IntParameter(2, 50, default=10, space='entry')
    exit_fast_timeperiod = IntParameter(2, 50, default=15, space='exit')
    exit_slow_timeperiod = IntParameter(2, 50, default=16, space='exit')

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conversion_line_period = 9
        base_line_periods = 26
        laggin_span = 52
        displacement = 26
        df = dataframe.copy()
        # Heikin Ashi Strategy
        heikinashi = qtpylib.heikinashi(df)
        df['ha_open'] = heikinashi['open']
        df['ha_close'] = heikinashi['close']
        df['ha_high'] = heikinashi['high']
        df['ha_low'] = heikinashi['low']
        df['tenkan'] = (df['ha_high'].rolling(window=conversion_line_period).max() + df['ha_low'].rolling(window=conversion_line_period).min()) / 2
        df['kijun'] = (df['ha_high'].rolling(window=base_line_periods).max() + df['ha_low'].rolling(window=base_line_periods).min()) / 2
        df['senkou_leading_a'] = (df['tenkan'] + df['kijun']) / 2
        df['senkou_leading_b'] = (df['ha_high'].rolling(window=laggin_span).max() + df['ha_low'].rolling(window=laggin_span).min()) / 2
        df['senkou_span_a'] = df['senkou_leading_a'].shift(displacement)
        df['senkou_span_b'] = df['senkou_leading_b'].shift(displacement)
        df['chikou_span'] = df['ha_close'].shift(displacement)
        df['tenkan1'] = df['tenkan'].shift(+1)
        df['kijun1'] = df['kijun'].shift(+1)
        df['senkou_leading_a1'] = df['senkou_leading_a'].shift(+1)
        df['senkou_leading_b1'] = df['senkou_leading_b'].shift(+1)
        df['senkou_span_a1'] = df['senkou_span_a'].shift(+1)
        df['senkou_span_b1'] = df['senkou_span_b'].shift(+1)
        df['chikou_span1'] = df['chikou_span'].shift(+1)
        df['ha_close1'] = df['ha_close'].shift(+1)
        # // == Price and Kijun Sen (standard line) Cross ==

        def calcTkCross(tenkan, kijun, tenkan1, kijun1, senkou_span_a, senkou_span_b):
            if tenkan > kijun and tenkan1 <= kijun1:
                intersect = (tenkan1 * (kijun - kijun1) - kijun1 * (tenkan - tenkan1)) / (kijun - kijun1 - (tenkan - tenkan1))
                if intersect > senkou_span_a and intersect > senkou_span_b:
                    return 2
                elif intersect < senkou_span_a and intersect < senkou_span_b:
                    return 0.5
                else:
                    return 1
            elif tenkan < kijun and tenkan1 >= kijun1:
                intersect = (tenkan1 * (kijun - kijun1) - kijun1 * (tenkan - tenkan1)) / (kijun - kijun1 - (tenkan - tenkan1))
                if intersect > senkou_span_a and intersect > senkou_span_b:
                    return -0.5
                elif intersect < senkou_span_a and intersect < senkou_span_b:
                    return -2
                else:
                    return -1
            else:
                return 0
        # df['tkCrossScore'] = np.vectorize(calcTkCross)()
        df['tkCrossScore'] = np.vectorize(calcTkCross)(df['tenkan'], df['kijun'], df['tenkan1'], df['kijun1'], df['senkou_span_a'], df['senkou_span_b'])
        # // == Price and Kijun Sen (standard line) Cross ==

        def calcPkCross(ha_close, kijun, ha_close1, kijun1, senkou_span_a, senkou_span_b):
            if ha_close > kijun and ha_close1 <= kijun1:
                intersect = (ha_close1 * (kijun - kijun1) - kijun1 * (ha_close - ha_close1)) / (kijun - kijun1 - (ha_close - ha_close1))
                if intersect > senkou_span_a and intersect > senkou_span_b:
                    return 2
                elif intersect < senkou_span_a and intersect < senkou_span_b:
                    return 0.5
                else:
                    return 1
            elif ha_close < kijun and ha_close1 >= kijun1:
                intersect = (ha_close1 * (kijun - kijun1) - kijun1 * (ha_close - ha_close1)) / (kijun - kijun1 - (ha_close - ha_close1))
                if intersect > senkou_span_a and intersect > senkou_span_b:
                    return -0.5
                elif intersect < senkou_span_a and intersect < senkou_span_b:
                    return -2
                else:
                    return -1
            else:
                return 0
        df['pkCrossScore'] = np.vectorize(calcPkCross)(df['ha_close'], df['kijun'], df['ha_close1'], df['kijun1'], df['senkou_span_a'], df['senkou_span_b'])
        # // == Kumo Breakouts ==

        def calcKumoBreakout(ha_close, senkou_span_a, ha_close1, senkou_span_a1, senkou_span_b, senkou_span_b1):
            if ha_close > senkou_span_a and ha_close1 <= senkou_span_a1 and (senkou_span_a > senkou_span_b) or (ha_close > senkou_span_b and ha_close1 <= senkou_span_b1 and (senkou_span_a < senkou_span_b)):
                return 2
            elif ha_close < senkou_span_a and ha_close1 >= senkou_span_a1 and (senkou_span_a < senkou_span_b) or (ha_close < senkou_span_b and ha_close1 >= senkou_span_b1 and (senkou_span_a > senkou_span_b)):
                return -2
            else:
                return 0
        df['kumoBreakoutScore'] = np.vectorize(calcKumoBreakout)(df['ha_close'], df['senkou_span_a'], df['ha_close1'], df['senkou_span_a1'], df['senkou_span_b'], df['senkou_span_b1'])
        # // == Senkou Span Cross ==

        def calcSenkouCross(senkou_leading_a, senkou_leading_b, senkou_leading_a1, senkou_leading_b1, ha_close, senkou_span_a, senkou_span_b):
            if senkou_leading_a > senkou_leading_b and senkou_leading_a1 <= senkou_leading_b1:
                if ha_close > senkou_span_a and ha_close > senkou_span_b:
                    return 2
                elif ha_close < senkou_span_a and ha_close < senkou_span_b:
                    return 0.5
                else:
                    return 1
            elif senkou_leading_a < senkou_leading_b and senkou_leading_a1 >= senkou_leading_b1:
                if ha_close > senkou_span_a and ha_close > senkou_span_b:
                    return -0.5
                elif ha_close < senkou_span_a and ha_close < senkou_span_b:
                    return -2
                else:
                    return -1
            else:
                return 0
        df['senkouCrossScore'] = np.vectorize(calcSenkouCross)(df['senkou_leading_a'], df['senkou_leading_b'], df['senkou_leading_a1'], df['senkou_leading_b1'], df['ha_close'], df['senkou_span_a'], df['senkou_span_b'])
        # // == Chikou Span Cross ==

        def calcChikouCross(ha_close, chikou_span, ha_close1, chikou_span1, senkou_span_a, senkou_span_b):
            if ha_close > chikou_span and ha_close1 <= chikou_span1:
                intersect = (ha_close1 * (chikou_span - chikou_span1) - chikou_span1 * (ha_close - ha_close1)) / (chikou_span - chikou_span1 - (ha_close - ha_close1))
                if intersect > senkou_span_a and intersect > senkou_span_b:
                    return 2
                elif intersect < senkou_span_a and intersect < senkou_span_b:
                    return 0.5
                else:
                    return 1
            elif ha_close < chikou_span and ha_close1 >= chikou_span1:
                intersect = (ha_close1 * (chikou_span - chikou_span1) - chikou_span1 * (ha_close - ha_close1)) / (chikou_span - chikou_span1 - (ha_close - ha_close1))
                if intersect > senkou_span_a and intersect > senkou_span_b:
                    return -0.5
                elif intersect < senkou_span_a and intersect < senkou_span_b:
                    return -2
                else:
                    return -1
            else:
                return 0
        df['chikouCrossScore'] = np.vectorize(calcChikouCross)(df['ha_close'], df['chikou_span'], df['ha_close1'], df['chikou_span1'], df['senkou_span_a'], df['senkou_span_b'])
        # // == price relative to cloud ==

        def calcPricePlacement(ha_close, senkou_span_a, senkou_span_b):
            if ha_close > senkou_span_a and ha_close > senkou_span_b:
                return 2
            elif ha_close < senkou_span_a and ha_close < senkou_span_b:
                return -2
            else:
                return 0
        df['pricePlacementScore'] = np.vectorize(calcPricePlacement)(df['ha_close'], df['senkou_span_a'], df['senkou_span_b'])
        # // == lag line releative to cloud ==

        def calcChikouPlacement(ha_close, senkou_leading_a, senkou_leading_b):
            if ha_close > senkou_leading_a and ha_close > senkou_leading_b:
                return 2
            elif ha_close < senkou_leading_a and ha_close < senkou_leading_b:
                return -2
            else:
                return 0
        df['chikouPlacementScore'] = np.vectorize(calcChikouPlacement)(df['ha_close'], df['senkou_leading_a'], df['senkou_leading_b'])
        # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # //
        dataframe['Ichimoku_Score'] = (df['tkCrossScore'] + df['pkCrossScore'] + df['kumoBreakoutScore'] + df['senkouCrossScore'] + df['chikouCrossScore']).rolling(1).sum().cumsum()
        print(metadata['pair'], dataframe['Ichimoku_Score'].median())
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # exit()
        # (dataframe['Ichimoku_Score']>0)&
        dataframe.loc[qtpylib.crossed_above(ta.SMA(dataframe['Ichimoku_Score'], self.entry_fast_timeperiod.value), ta.SMA(dataframe['Ichimoku_Score'], self.entry_slow_timeperiod.value)), 'entry'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # (dataframe['Ichimoku_Score']<0)|
        dataframe.loc[qtpylib.crossed_below(ta.SMA(dataframe['Ichimoku_Score'], self.exit_fast_timeperiod.value), ta.SMA(dataframe['Ichimoku_Score'], self.exit_slow_timeperiod.value)), 'exit'] = 1
        return dataframe