ichiV2_5

# --- 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 ichiV2_5(IStrategy):
    """
    This is the next version of ichiVx, and the previous version was ichiV1.

    ==============================
    Summary of changes from ichiV2:
    1. Buy parameter change: buy_fan_magnitude_shift_value is now 2
    2. In the config file: all order types are now of type "market", because
       the transaction costs are the same, and missed orders are less likely.
    3. The trailing stoploss now activates at 40% above original price, and the
       trailing stop positive is now 0.03.
    4. The stoploss is now 0.06.
    5. The "buy_min_fan_magnitude_gain" is now 1.0007. This catches smaller
       waves, and is safe enough to minimise false signals.
    6. The maximum open trades is now 4.
    """
    INTERFACE_VERSION = 2

    # NOTE: settings as of the 25th july 21
    # Buy hyperspace params:
    buy_params = {
        "buy_trend_above_senkou_level": 1,
        "buy_trend_bullish_level": 4,
        "buy_fan_magnitude_shift_value": 2,
        "buy_min_fan_magnitude_gain": 1.0007
        # "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_1.5h",
    }

    # # 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'}
        },
        '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['open'] = heikinashi['open']
        # dataframe['close'] = heikinashi['close']
        dataframe['high'] = heikinashi['high']
        dataframe['low'] = heikinashi['low']

        # Shift close price by 1 candle to avoid lookahead bias, so the close is
        # the previous period's close
        dataframe['trend_close_5m'] = dataframe['close'].shift(1)
        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)

        # Shift open price by 1 candle to avoid lookahead bias, so the open is
        # the previous period's open
        dataframe['trend_open_5m'] = dataframe['open'].shift(1)
        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.shift(1), 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'] > 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