NASOSv4

# for live trailing_stop = False and use_custom_stoploss = True
# for backtest trailing_stop = True and use_custom_stoploss = False
# --- Do not remove these libs ---
# --- Do not remove these libs ---
from logging import FATAL
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
# @Rallipanos
# @pluxury
# Buy hyperspace params:
buy_params = {'base_nb_candles_buy': 8, 'ewo_high': 2.403, 'ewo_high_2': -5.585, 'ewo_low': -14.378, 'lookback_candles': 3, 'low_offset': 0.984, 'low_offset_2': 0.942, 'profit_threshold': 1.008, 'rsi_buy': 72}
# Sell hyperspace params:
sell_params = {'base_nb_candles_sell': 16, 'high_offset': 1.084, 'high_offset_2': 1.401, 'pHSL': -0.15, 'pPF_1': 0.016, 'pPF_2': 0.024, 'pSL_1': 0.014, 'pSL_2': 0.022}

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 NASOSv4(IStrategy):
    INTERFACE_VERSION = 3
    # ROI table:
    # "0": 0.283,
    # "40": 0.086,
    # "99": 0.036,
    minimal_roi = {'0': 10}
    # Stoploss:
    stoploss = -0.15
    # SMAOffset
    base_nb_candles_buy = IntParameter(2, 20, default=buy_params['base_nb_candles_buy'], space='buy', optimize=True)
    base_nb_candles_sell = IntParameter(2, 25, default=sell_params['base_nb_candles_sell'], space='sell', optimize=True)
    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=True)
    high_offset_2 = DecimalParameter(0.99, 1.5, default=sell_params['high_offset_2'], space='sell', optimize=True)
    # Protection
    fast_ewo = 50
    slow_ewo = 200
    lookback_candles = IntParameter(1, 24, default=buy_params['lookback_candles'], space='buy', optimize=True)
    profit_threshold = DecimalParameter(1.0, 1.03, default=buy_params['profit_threshold'], space='buy', optimize=True)
    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(50, 100, default=buy_params['rsi_buy'], space='buy', optimize=False)
    # trailing stoploss hyperopt parameters
    # hard stoploss profit
    pHSL = DecimalParameter(-0.2, -0.04, default=-0.15, decimals=3, space='sell', optimize=False, load=True)
    # profit threshold 1, trigger point, SL_1 is used
    pPF_1 = DecimalParameter(0.008, 0.02, default=0.016, decimals=3, space='sell', optimize=False, load=True)
    pSL_1 = DecimalParameter(0.008, 0.02, default=0.014, decimals=3, space='sell', optimize=False, load=True)
    # profit threshold 2, SL_2 is used
    pPF_2 = DecimalParameter(0.04, 0.1, default=0.024, decimals=3, space='sell', optimize=False, load=True)
    pSL_2 = DecimalParameter(0.02, 0.07, default=0.022, decimals=3, space='sell', optimize=False, load=True)
    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.001
    trailing_stop_positive_offset = 0.016
    trailing_only_offset_is_reached = True
    # Sell signal
    use_exit_signal = True
    exit_profit_only = False
    exit_profit_offset = 0.01
    ignore_roi_if_entry_signal = False
    # Optional order time in force.
    order_time_in_force = {'entry': 'gtc', 'exit': '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}
    # Custom Trailing Stoploss by Perkmeister

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
        # # hard stoploss profit
        HSL = self.pHSL.value
        PF_1 = self.pPF_1.value
        SL_1 = self.pSL_1.value
        PF_2 = self.pPF_2.value
        SL_2 = self.pSL_2.value
        # For profits between PF_1 and PF_2 the stoploss (sl_profit) used is linearly interpolated
        # between the values of SL_1 and SL_2. For all profits above PL_2 the sl_profit value
        # rises linearly with current profit, for profits below PF_1 the hard stoploss profit is used.
        if current_profit > PF_2:
            sl_profit = SL_2 + (current_profit - PF_2)
        elif current_profit > PF_1:
            sl_profit = SL_1 + (current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1)
        else:
            sl_profit = HSL
        # if current_profit < 0.001 and current_time - timedelta(minutes=600) > trade.open_date_utc:
        #     return -0.005
        return stoploss_from_open(sl_profit, current_profit)

    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_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 exit_reason in ['exit_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
        return True

    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, '1h') for pair in pairs]
        return informative_pairs

    def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        assert self.dp, 'DataProvider is required for multiple timeframes.'
        # Get the informative pair
        informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h)
        # EMA
        # informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50)
        # informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200)
        # # RSI
        # informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14)
        # bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        # informative_1h['bb_lowerband'] = bollinger['lower']
        # informative_1h['bb_middleband'] = bollinger['mid']
        # informative_1h['bb_upperband'] = bollinger['upper']
        return informative_1h

    def normal_tf_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_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        informative_1h = self.informative_1h_indicators(dataframe, metadata)
        dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True)
        # The indicators for the normal (5m) timeframe
        dataframe = self.normal_tf_indicators(dataframe, metadata)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dont_buy_conditions = []
        # don't buy if there isn't 3% profit to be made
        dont_buy_conditions.append(dataframe['close_1h'].rolling(self.lookback_candles.value).max() < dataframe['close'] * self.profit_threshold.value)
        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), ['enter_long', 'enter_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), ['enter_long', 'enter_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), ['enter_long', 'enter_tag']] = (1, 'ewolow')
        if dont_buy_conditions:
            for condition in dont_buy_conditions:
                dataframe.loc[condition, 'enter_long'] = 0
        return dataframe

    def populate_exit_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']))
        if conditions:
            dataframe.loc[reduce(lambda x, y: x | y, conditions), 'exit_long'] = 1
        return dataframe