NotAnotherSMAOffsetStrategy_uzi3

# --- Do not remove these libs ---
from typing import DefaultDict
from freqtrade.strategy.interface import IStrategy
from functools import reduce
from pandas import DataFrame
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import datetime
from datetime import datetime
from freqtrade.persistence import Trade
from freqtrade.strategy import DecimalParameter, IntParameter
# @Rallipanos mod. Uzirox

def zlema2(dataframe, fast):
    df = dataframe.copy()
    zema1 = ta.EMA(df['close'], fast)
    zema2 = ta.EMA(zema1, fast)
    d1 = zema1 - zema2
    df['zlema2'] = zema1 + d1
    return df['zlema2']
order_types = {'entry': 'limit', 'exit': 'market', 'stoploss': 'market', 'stoploss_on_exchange': False}

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['close'] * 100
    return emadif

class NotAnotherSMAOffsetStrategy_uzi3(IStrategy):
    INTERFACE_VERSION = 3
    # ROI table:
    # "201": 0
    minimal_roi = {'0': 0.215, '40': 0.032, '87': 0.016}
    # Stoploss:
    stoploss = -0.1
    # Protection
    fast_ewo = 50
    slow_ewo = 200
    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.005
    trailing_stop_positive_offset = 0.025
    trailing_only_offset_is_reached = True
    # Sell signal
    use_exit_signal = True
    exit_profit_only = False
    exit_profit_offset = 0.005
    ignore_roi_if_entry_signal = False
    # Optimal timeframe for the strategy
    timeframe = '5m'
    process_only_new_candles = True
    startup_candle_count = 400
    slippage_protection = {'retries': 3, 'max_slippage': -0.02}
    entry_signals = {}

    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 populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        if (dataframe['close'].iloc[-1] - dataframe['close'].iloc[-2]) / dataframe['close'].iloc[-2] * 100 < -2:
            # si scende pesante         
            self.stoploss = -0.3  #stoploss
            # entry params
            self.trailing_stop_positive_offset = 0.03
            self.base_nb_candles_entry = IntParameter(5, 80, default=14, space='entry', optimize=False)
            self.low_offset = DecimalParameter(0.9, 0.99, default=0.975, space='entry', optimize=False)
            self.low_offset_2 = DecimalParameter(0.9, 0.99, default=0.955, space='entry', optimize=False)
            self.ewo_low = DecimalParameter(-20.0, -8.0, default=-20.988, space='entry', optimize=False)
            self.ewo_high = DecimalParameter(2.0, 12.0, default=2.327, space='entry', optimize=False)
            self.ewo_high_2 = DecimalParameter(-6.0, 12.0, default=-2.327, space='entry', optimize=False)
            self.rsi_entry = IntParameter(30, 70, default=69, space='entry', optimize=False)
            # exit params
            self.base_nb_candles_exit = IntParameter(5, 80, default=16, space='exit', optimize=True)
            self.high_offset = DecimalParameter(0.95, 1.1, default=0.991, space='exit', optimize=True)
            self.high_offset_2 = DecimalParameter(0.99, 1.5, default=0.997, space='exit', optimize=True)
        else:
            # normale - si sale
            # entry params
            self.base_nb_candles_entry = IntParameter(5, 80, default=14, space='entry', optimize=False)
            self.low_offset = DecimalParameter(0.9, 0.99, default=0.986, space='entry', optimize=False)
            self.low_offset_2 = DecimalParameter(0.9, 0.99, default=0.944, space='entry', optimize=False)
            self.ewo_low = DecimalParameter(-20.0, -8.0, default=-16.917, space='entry', optimize=False)
            self.ewo_high = DecimalParameter(2.0, 12.0, default=4.179, space='entry', optimize=False)
            self.ewo_high_2 = DecimalParameter(-6.0, 12.0, default=-2.609, space='entry', optimize=False)
            self.rsi_entry = IntParameter(30, 70, default=58, space='entry', optimize=False)
            # exit params
            self.base_nb_candles_exit = IntParameter(5, 80, default=16, space='exit', optimize=True)
            self.high_offset = DecimalParameter(0.95, 1.1, default=1.054, space='exit', optimize=True)
            self.high_offset_2 = DecimalParameter(0.99, 1.5, default=1.018, space='exit', optimize=True)
        # Calculate all ma_entry values
        for val in self.base_nb_candles_entry.range:
            dataframe[f'ma_entry_{val}'] = ta.EMA(dataframe, timeperiod=val)
        # Calculate all ma_exit values
        for val in self.base_nb_candles_exit.range:
            dataframe[f'ma_exit_{val}'] = ta.EMA(dataframe, timeperiod=val)
        # *MAs
        dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50)
        dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100)
        dataframe['ema_10'] = zlema2(dataframe, 10)
        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)
        # strategy BinHV45
        bb_40 = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2)
        dataframe['lower'] = bb_40['lower']
        dataframe['mid'] = bb_40['mid']
        dataframe['bbdelta'] = (bb_40['mid'] - dataframe['lower']).abs()
        dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
        dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()
        # strategy ClucMay72018
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['bb_upperband'] = bollinger['upper']
        dataframe['ema_slow'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean()
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[(dataframe['rsi_fast'] < 35) & (dataframe['close'] < dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset.value) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_entry.value) & (dataframe['volume'] > 0) & (dataframe['close'] < dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value), ['enter_long', 'enter_tag']] = (1, 'ewo1')
        dataframe.loc[(dataframe['rsi_fast'] < 35) & (dataframe['close'] < dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset_2.value) & (dataframe['EWO'] > self.ewo_high_2.value) & (dataframe['rsi'] < self.rsi_entry.value) & (dataframe['volume'] > 0) & (dataframe['close'] < dataframe[f'ma_exit_{self.base_nb_candles_exit.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_entry_{self.base_nb_candles_entry.value}'] * self.low_offset.value) & (dataframe['EWO'] < self.ewo_low.value) & (dataframe['volume'] > 0) & (dataframe['close'] < dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value), ['enter_long', 'enter_tag']] = (1, 'ewolow')
        # entry in bull market
        dataframe.loc[(dataframe['ema_10'].rolling(10).mean() > dataframe['ema_100'].rolling(10).mean()) & dataframe['lower'].shift().gt(0) & dataframe['bbdelta'].gt(dataframe['close'] * 0.031) & dataframe['closedelta'].gt(dataframe['close'] * 0.018) & dataframe['tail'].lt(dataframe['bbdelta'] * 0.233) & dataframe['close'].lt(dataframe['lower'].shift()) & dataframe['close'].le(dataframe['close'].shift()) & (dataframe['volume'] > 0) | (dataframe['ema_10'].rolling(10).mean() > dataframe['ema_100'].rolling(10).mean()) & (dataframe['close'] > dataframe['ema_100']) & (dataframe['close'] < dataframe['ema_slow']) & (dataframe['close'] < 0.993 * dataframe['bb_lowerband']) & (dataframe['volume'] < dataframe['volume_mean_slow'].shift(1) * 21) & (dataframe['volume'] > 0), ['enter_long', 'enter_tag']] = (1, 'bb_bull')
        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_exit_{self.base_nb_candles_exit.value}'] * self.high_offset_2.value) & (dataframe['rsi'] > 50) & (dataframe['volume'] > 0) & (dataframe['rsi_fast'] > dataframe['rsi_slow']) | (dataframe['sma_9'] > dataframe['sma_9'].shift(1) + dataframe['sma_9'].shift(1) * 0.005) & (dataframe['close'] < dataframe['hma_50']) & (dataframe['close'] > dataframe[f'ma_exit_{self.base_nb_candles_exit.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
    plot_config = {'main_plot': {'ema_100': {}, 'ema_10': {}, 'sma_9': {}}}