ichiV3

# --- 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


class ichiV3(IStrategy):
    """
    This is the next version of ichiVx, and the previous version was ichiV2_5.

    ==============================
    Summary of changes from ichiV2_5:
    1. Does not alter the original OHLC data. The Heikin Ashi data is now only
       for calculating signals.
    2. The buy and sell parameters are the same as in the original ichiV1
       version.
    """
    INTERFACE_VERSION = 2

    # NOTE: settings as of the 25th july 21
    # Buy hyperspace params:
    buy_params = {
        "buy_trend_above_senkou_level": 5,
        "buy_trend_bullish_level": 6,
        "buy_fan_magnitude_shift_value": 3,
        "buy_min_fan_magnitude_gain": 1.002
        # "buy_min_fan_magnitude_gain": 1.0013 # 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": 0.3
    # }

    # Stoploss:
    stoploss = -0.06

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

    startup_candle_count = 96
    process_only_new_candles = False

    trailing_stop = True
    trailing_stop_positive = 0.03
    trailing_stop_positive_offset = 0.4
    trailing_only_offset_is_reached = True

    use_sell_signal = True
    sell_profit_only = False
    # ignore_roi_if_buy_signal = True

    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'},
            'trend_open_5m': {'color': '#4D7BF5'},
            'trend_open_15m': {'color': '#1E8C38'},
            'trend_open_30m': {'color': '#ABA6D0'},
            'trend_open_1h': {'color': '#631B69'},
            'trend_open_2h': {'color': '#A553FF'},
            'trend_open_4h': {'color': '#B10C78'},
            'trend_open_6h': {'color': '#389E03'},
            'trend_open_8h': {'color': '#571E60'}
        },
        'subplots': {
            'fan_magnitude': {
                'fan_magnitude': {}
            },
            'fan_magnitude_gain': {
                'fan_magnitude_gain': {}
            }
        }
    }

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Populate Heikin Ashi candles
        heikinashi = qtpylib.heikinashi(dataframe)
        dataframe['ha_open'] = heikinashi['open']
        # dataframe['close'] = heikinashi['close']
        # dataframe['ha_high'] = heikinashi['high']
        # dataframe['ha_low'] = heikinashi['low']

        dataframe['trend_close_5m'] = dataframe['close']
        dataframe['trend_close_15m'] = ta.EMA(dataframe['trend_close_5m'], timeperiod=3)
        dataframe['trend_close_30m'] = ta.EMA(dataframe['trend_close_5m'], timeperiod=6)
        dataframe['trend_close_1h'] = ta.EMA(dataframe['trend_close_5m'], timeperiod=12)
        dataframe['trend_close_1.5h'] = ta.EMA(dataframe['trend_close_5m'], timeperiod=18)
        dataframe['trend_close_2h'] = ta.EMA(dataframe['trend_close_5m'], timeperiod=24)
        dataframe['trend_close_4h'] = ta.EMA(dataframe['trend_close_5m'], timeperiod=48)
        dataframe['trend_close_6h'] = ta.EMA(dataframe['trend_close_5m'], timeperiod=72)
        dataframe['trend_close_8h'] = ta.EMA(dataframe['trend_close_5m'], timeperiod=96)

        # Use Heikin Ashi open as the open trend tracker: it is the average of
        # the open and close of the last period
        dataframe['trend_open_5m'] = dataframe['ha_open']
        dataframe['trend_open_15m'] = ta.EMA(dataframe['trend_open_5m'], timeperiod=3)
        dataframe['trend_open_30m'] = ta.EMA(dataframe['trend_open_5m'], timeperiod=6)
        dataframe['trend_open_1h'] = ta.EMA(dataframe['trend_open_5m'], timeperiod=12)
        dataframe['trend_open_2h'] = ta.EMA(dataframe['trend_open_5m'], timeperiod=24)
        dataframe['trend_open_4h'] = ta.EMA(dataframe['trend_open_5m'], timeperiod=48)
        dataframe['trend_open_6h'] = ta.EMA(dataframe['trend_open_5m'], timeperiod=72)
        dataframe['trend_open_8h'] = ta.EMA(dataframe['trend_open_5m'], 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 = ftt.ichimoku(dataframe, conversion_line_period=20, base_line_periods=60, laggin_span=120, displacement=30)
        dataframe['chikou_span'] = ichimoku['chikou_span']  # do not use this in live, it has lookahead bias
        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'].shift(1) > dataframe['senkou_a'].shift(1))
            conditions.append(dataframe['trend_close_5m'].shift(1) > dataframe['senkou_b'].shift(1))

        if self.buy_params['buy_trend_above_senkou_level'] >= 2:
            conditions.append(dataframe['trend_close_15m'].shift(1) > dataframe['senkou_a'].shift(1))
            conditions.append(dataframe['trend_close_15m'].shift(1) > dataframe['senkou_b'].shift(1))

        if self.buy_params['buy_trend_above_senkou_level'] >= 3:
            conditions.append(dataframe['trend_close_30m'].shift(1) > dataframe['senkou_a'].shift(1))
            conditions.append(dataframe['trend_close_30m'].shift(1) > dataframe['senkou_b'].shift(1))

        if self.buy_params['buy_trend_above_senkou_level'] >= 4:
            conditions.append(dataframe['trend_close_1h'].shift(1) > dataframe['senkou_a'].shift(1))
            conditions.append(dataframe['trend_close_1h'].shift(1) > dataframe['senkou_b'].shift(1))

        if self.buy_params['buy_trend_above_senkou_level'] >= 5:
            conditions.append(dataframe['trend_close_2h'].shift(1) > dataframe['senkou_a'].shift(1))
            conditions.append(dataframe['trend_close_2h'].shift(1) > dataframe['senkou_b'].shift(1))

        if self.buy_params['buy_trend_above_senkou_level'] >= 6:
            conditions.append(dataframe['trend_close_4h'].shift(1) > dataframe['senkou_a'].shift(1))
            conditions.append(dataframe['trend_close_4h'].shift(1) > dataframe['senkou_b'].shift(1))

        if self.buy_params['buy_trend_above_senkou_level'] >= 7:
            conditions.append(dataframe['trend_close_6h'].shift(1) > dataframe['senkou_a'].shift(1))
            conditions.append(dataframe['trend_close_6h'].shift(1) > dataframe['senkou_b'].shift(1))

        if self.buy_params['buy_trend_above_senkou_level'] >= 8:
            conditions.append(dataframe['trend_close_8h'].shift(1) > dataframe['senkou_a'].shift(1))
            conditions.append(dataframe['trend_close_8h'].shift(1) > dataframe['senkou_b'].shift(1))

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

        if self.buy_params['buy_trend_bullish_level'] >= 2:
            conditions.append(dataframe['trend_close_15m'].shift(1) > dataframe['trend_open_15m'])

        if self.buy_params['buy_trend_bullish_level'] >= 3:
            conditions.append(dataframe['trend_close_30m'].shift(1) > dataframe['trend_open_30m'])

        if self.buy_params['buy_trend_bullish_level'] >= 4:
            conditions.append(dataframe['trend_close_1h'].shift(1) > dataframe['trend_open_1h'])

        if self.buy_params['buy_trend_bullish_level'] >= 5:
            conditions.append(dataframe['trend_close_2h'].shift(1) > dataframe['trend_open_2h'])

        if self.buy_params['buy_trend_bullish_level'] >= 6:
            conditions.append(dataframe['trend_close_4h'].shift(1) > dataframe['trend_open_4h'])

        if self.buy_params['buy_trend_bullish_level'] >= 7:
            conditions.append(dataframe['trend_close_6h'].shift(1) > dataframe['trend_open_6h'])

        if self.buy_params['buy_trend_bullish_level'] >= 8:
            conditions.append(dataframe['trend_close_8h'].shift(1) > dataframe['trend_open_8h'])

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

        for x in range(self.buy_params['buy_fan_magnitude_shift_value']):
            conditions.append(dataframe['fan_magnitude'].shift(x+2) < dataframe['fan_magnitude'].shift(1))

        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'].shift(1),
                dataframe[self.sell_params['sell_trend_indicator']]
            )
        )

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

        return dataframe