akivaHOTSL

# --- Do not remove these libs ---
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
# --------------------------------
import talib.abstract as ta
import numpy as np
import freqtrade.vendor.qtpylib.indicators as qtpylib
import datetime
from technical.util import resample_to_interval, resampled_merge
from datetime import datetime, timedelta
from freqtrade.persistence import Trade
from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter
import technical.indicators as ftt
from freqtrade.exchange import timeframe_to_prev_date

# @Rallipanos

# # Buy hyperspace params:
# buy_params = {
#     "base_nb_candles_buy": 14,
#     "ewo_high": 2.327,
#     "ewo_high_2": -2.327,
#     "ewo_low": -20.988,
#     "low_offset": 0.975,
#     "low_offset_2": 0.955,
#     "rsi_buy": 69
# }

# # Buy hyperspace params:
# buy_params = {
#     "base_nb_candles_buy": 18,
#     "ewo_high": 3.422,
#     "ewo_high_2": -3.436,
#     "ewo_low": -8.562,
#     "low_offset": 0.966,
#     "low_offset_2": 0.959,
#     "rsi_buy": 66,
# }

# # # Sell hyperspace params:
# # sell_params = {
# #     "base_nb_candles_sell": 17,
# #     "high_offset": 0.997,
# #     "high_offset_2": 1.01,
# # }

# # Sell hyperspace params:
# sell_params = {
#     "base_nb_candles_sell": 7,
#     "high_offset": 1.014,
#     "high_offset_2": 0.995,
# }

# # Buy hyperspace params:
# buy_params = {
#     "ewo_high_2": -5.642,
#     "low_offset_2": 0.951,
#     "rsi_buy": 54,
#     "base_nb_candles_buy": 16,  # value loaded from strategy
#     "ewo_high": 3.422,  # value loaded from strategy
#     "ewo_low": -8.562,  # value loaded from strategy
#     "low_offset": 0.966,  # value loaded from strategy
# }

# # Sell hyperspace params:
# sell_params = {
#     "base_nb_candles_sell": 8,
#     "high_offset_2": 1.002,
#     "high_offset": 1.014,  # value loaded from strategy
# }

# Buy hyperspace params:
buy_params = {
    "base_nb_candles_buy": 8,
    "ewo_high": 4.179,
    "ewo_low": -16.917,
    "ewo_high_2": -2.609,  # value loaded from strategy
    "low_offset": 0.986,  # value loaded from strategy
    "low_offset_2": 0.944,  # value loaded from strategy
    "rsi_buy": 58,  # value loaded from strategy
}

# Sell hyperspace params:
sell_params = {
    "base_nb_candles_sell": 16,
    "high_offset": 1.054,
    "high_offset_2": 1.018,  # value loaded from strategy
    "high_offset_ema": 1.012,
    
}


def EWO(dataframe, ema_length=5, ema2_length=35):
    df = dataframe.copy()
    ema1 = ta.EMA(df, timeperiod=ema_length)
    ema2 = ta.EMA(df, timeperiod=ema2_length)
    emadif = (ema1 - ema2) / df['low'] * 100
    return emadif


class akivaHOTSL(IStrategy):
    INTERFACE_VERSION = 2

    # ROI table:
    minimal_roi = {
        # "0": 0.283,
        # "40": 0.086,
        # "99": 0.036,
        "0": 10
    }

    # Stoploss:
    stoploss = -0.04

    # SMAOffset
    high_offset_ema = DecimalParameter(0.99, 1.1, default=1.012, load=True, space='sell', optimize=False)
    base_nb_candles_buy = IntParameter(
        2, 20, default=buy_params['base_nb_candles_buy'], space='buy', optimize=False)
    base_nb_candles_sell = IntParameter(
        10, 40, default=sell_params['base_nb_candles_sell'], space='sell', optimize=False)
    low_offset = DecimalParameter(
        0.9, 0.99, default=buy_params['low_offset'], space='buy', optimize=False)
    low_offset_2 = DecimalParameter(
        0.9, 0.99, default=buy_params['low_offset_2'], space='buy', optimize=False)
    high_offset = DecimalParameter(
        0.95, 1.1, default=sell_params['high_offset'], space='sell', optimize=False)
    high_offset_2 = DecimalParameter(
        0.99, 1.5, default=sell_params['high_offset_2'], space='sell', optimize=False)

    # Protection
    fast_ewo = 50
    slow_ewo = 200
    ewo_low = DecimalParameter(-20.0, -8.0,
                               default=buy_params['ewo_low'], space='buy', optimize=False)
    ewo_high = DecimalParameter(
        2.0, 12.0, default=buy_params['ewo_high'], space='buy', optimize=False)

    ewo_high_2 = DecimalParameter(
        -6.0, 12.0, default=buy_params['ewo_high_2'], space='buy', optimize=False)

    rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=True)

    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.001
    trailing_stop_positive_offset = 0.018
    trailing_only_offset_is_reached = True

    # Sell signal
    use_sell_signal = True
    sell_profit_only = False
    sell_profit_offset = 0.001
    ignore_roi_if_buy_signal = False

    # Optional order time in force.
    order_time_in_force = {
        'buy': 'gtc',
        'sell': 'ioc'
    }

    # Optimal timeframe for the strategy
    timeframe = '5m'
    inf_1h = '1h'

    process_only_new_candles = True
    startup_candle_count = 200
    use_custom_stoploss = False

    plot_config = {
        'main_plot': {
            'ma_buy': {'color': 'orange'},
            'ma_sell': {'color': 'orange'},
        },
    }

    slippage_protection = {
        'retries': 3,
        'max_slippage': -0.02
    }

    buy_signals = {}

   

    # Custom Trailing Stoploss by Perkmeister
    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                        current_rate: float, current_profit: float, **kwargs) -> float:

        if (current_profit > 0.3):
            return 0.05
        elif (current_profit > 0.2):
            return 0.04
        elif (current_profit > 0.1):
            return 0.03
        elif (current_profit > 0.06):
            return 0.02
        elif (current_profit > 0.03):
            return 0.01
        elif (current_profit > 0.018):
            return 0.005

        return 0.99

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

    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)
        buy_tag = 'empty'
        if hasattr(trade, 'buy_tag') and trade.buy_tag is not None:
            buy_tag = trade.buy_tag
        else:
            trade_open_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc)
            buy_signal = dataframe.loc[dataframe['date'] < trade_open_date]
            if not buy_signal.empty:
                buy_signal_candle = buy_signal.iloc[-1]
                buy_tag = buy_signal_candle['buy_tag'] if buy_signal_candle['buy_tag'] != '' else 'empty'
        buy_tags = buy_tag.split()

        last_candle = dataframe.iloc[-1].squeeze()

        if (last_candle['close'] > (last_candle['ema_offset_sell'])) :
            return 'sell signal (' + buy_tag +')'

        return None

    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
                           rate: float, time_in_force: str, sell_reason: str,
                           current_time: datetime, **kwargs) -> bool:

        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1]

        if (last_candle is not None):
            if (sell_reason in ['sell_signal']):
                if (last_candle['hma_50']*1.149 > last_candle['ema_100']) and (last_candle['close'] < last_candle['ema_100']*0.951):  # *1.2
                    return False

        # slippage
        try:
            state = self.slippage_protection['__pair_retries']
        except KeyError:
            state = self.slippage_protection['__pair_retries'] = {}

        candle = dataframe.iloc[-1].squeeze()

        slippage = (rate / candle['close']) - 1
        if slippage < self.slippage_protection['max_slippage']:
            pair_retries = state.get(pair, 0)
            if pair_retries < self.slippage_protection['retries']:
                state[pair] = pair_retries + 1
                return False

        state[pair] = 0
        current_profit = trade.calc_profit_ratio(rate)
        if (sell_reason.startswith('sell signal (') and (current_profit > 0.018)):
            # Reject sell signal when trailing stoplosses
            return False
        return True

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

        # Calculate all ma_buy values
        for val in self.base_nb_candles_buy.range:
            dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val)

        # Calculate all ma_sell values
        for val in self.base_nb_candles_sell.range:
            dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val)

        dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50)
        dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100)

        dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9)
        # Elliot
        dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo)

        # RSI
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)

        return dataframe

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

        dataframe.loc[
            (
                (dataframe['rsi_fast'] < 35) &
                (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) &
                (dataframe['EWO'] > self.ewo_high.value) &
                (dataframe['rsi'] < self.rsi_buy.value) &
                (dataframe['volume'] > 0) &
                (dataframe['close'] < (
                    dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value))
            ),
            ['buy', 'buy_tag']] = (1, 'ewo1')

        dataframe.loc[
            (
                (dataframe['rsi_fast'] < 35) &
                (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset_2.value)) &
                (dataframe['EWO'] > self.ewo_high_2.value) &
                (dataframe['rsi'] < self.rsi_buy.value) &
                (dataframe['volume'] > 0) &
                (dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) &
                (dataframe['rsi'] < 25)
            ),
            ['buy', 'buy_tag']] = (1, 'ewo2')

        dataframe.loc[
            (
                (dataframe['rsi_fast'] < 35) &
                (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) &
                (dataframe['EWO'] < self.ewo_low.value) &
                (dataframe['volume'] > 0) &
                (dataframe['close'] < (
                    dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value))
            ),
            ['buy', 'buy_tag']] = (1, 'ewolow')

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []

        conditions.append(
            ((dataframe['close'] > dataframe['sma_9']) &
                (dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value)) &
                (dataframe['rsi'] > 50) &
                (dataframe['volume'] > 0) &
                (dataframe['rsi_fast'] > dataframe['rsi_slow'])
             )
            |
            (
                (dataframe['close'] < dataframe['hma_50']) &
                (dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) &
                (dataframe['volume'] > 0) &
                (dataframe['rsi_fast'] > dataframe['rsi_slow'])
            )

        )

        dataframe['ema_offset_sell'] = ta.EMA(dataframe, int(self.base_nb_candles_sell.value)) *self.high_offset_ema.value

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

        return dataframe

def EWO(dataframe, sma1_length=5, sma2_length=35):
    df = dataframe.copy()
    sma1 = ta.EMA(df, timeperiod=sma1_length)
    sma2 = ta.EMA(df, timeperiod=sma2_length)
    smadif = (sma1 - sma2) / df['close'] * 100
    return smadif