tesla4

# --- 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
# with help from @stash86 and @Perkmeister

# Buy hyperspace params:
buy_params = {
    "base_nb_candles_buy": 8,
      "ewo_high": 2.675,
      "ewo_high_2": 4.516,
      "ewo_low": -9.263,
      "lookback_candles": 22,
      "low_offset": 0.988,
      "low_offset_2": 0.915,
      "profit_threshold": 1.0408,
      "rsi_buy": 57,
      "rsi_fast_buy": 49
}

# Sell hyperspace params:
sell_params = {
    "base_nb_candles_sell": 24,
      "high_offset": 0.998,
      "high_offset_2": 1,
      "sell_rsi_main": 87.43
}

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']

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 tesla4(IStrategy):
    INTERFACE_VERSION = 2

    # ROI table:
    minimal_roi = {
       "0": 0.215,
        "40": 0.032,
        "64": 0.03,
        "87": 0.016,
        "201": 0

       

        
    }

    # Stoploss:
    stoploss = -0.15

    # SMAOffset
    base_nb_candles_buy = IntParameter(
        2, 20, default=buy_params['base_nb_candles_buy'], space='buy', optimize=False)
    base_nb_candles_sell = IntParameter(
        2, 25, 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)

    
    sell_rsi_main = DecimalParameter(72.0, 90.0, default=sell_params['sell_rsi_main'], space='sell', decimals=2, optimize=False, load=True)
    

    # Protection
    fast_ewo = 50
    slow_ewo = 200

    lookback_candles = IntParameter(
        1, 36, default=buy_params['lookback_candles'], space='buy', optimize=False)

    profit_threshold = DecimalParameter(0.99, 1.05,
                                        default=buy_params['profit_threshold'], space='buy', optimize=False)

    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(10, 80, default=buy_params['rsi_buy'], space='buy', optimize=False)
    rsi_fast_buy = IntParameter(
        10, 50, default=buy_params['rsi_fast_buy'], space='buy', optimize=False)

    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.001
    trailing_stop_positive_offset = 0.010
    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_15m = '15m'
    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'},
        },
        'subplots': {
            'rsi': {
                'rsi': {'color': 'orange'},
                'rsi_fast': {'color': 'red'},
                'rsi_slow': {'color': 'green'},
            },
            'ewo': {
                'EWO': {'color': 'orange'}
            },
        }
    }

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

    

   

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

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

        if 'bb_bull' in trade.buy_tag and current_profit > 0.01:
            return True
            
        if (last_candle is not None):
                if (sell_reason in ['sell_signal']):
                        if (last_candle['hma_50'] > last_candle['ema_100']) and (last_candle['rsi'] < 45): #*1.2
                            return False

        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

        
        if (trade.buy_tag == 'bb_bull'):
            if (sell_reason in ['sell_signal'])or (sell_reason in ['roi']):
                        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, '15m') 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)
        dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200)
        dataframe['sma_200_dec'] = dataframe['sma_200'] < dataframe['sma_200'].shift(20)
        # 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 informative_15m_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        assert self.dp, "DataProvider is required for multiple timeframes."
        # Get the informative pair
        informative_15m = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_15m)
        dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200)
        dataframe['sma_200_dec'] = dataframe['sma_200'] < dataframe['sma_200'].shift(20)
        # 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_15m

    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['ema_10']  = zlema2(dataframe, 10)
        dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200)
        dataframe['sma_200_dec'] = dataframe['sma_200'] < dataframe['sma_200'].shift(20)
        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)
        dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)

        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()

        dataframe['vol_7_max'] = dataframe['volume'].rolling(window=20).max()
        dataframe['vol_14_max'] = dataframe['volume'].rolling(window=14).max()
        dataframe['vol_7_min'] = dataframe['volume'].rolling(window=20).min()
        dataframe['vol_14_min'] = dataframe['volume'].rolling(window=14).min()
        dataframe['roll_7'] = 100*((dataframe['volume']-dataframe['vol_7_max'])/(dataframe['vol_7_max']-dataframe['vol_7_min']))
        dataframe['vol_base']=ta.SMA(dataframe['roll_7'], timeperiod=5)
        dataframe['vol_ma_26'] = ta.EMA(dataframe['volume'], timeperiod=26)
        dataframe['vol_ma_200'] = ta.EMA(dataframe['volume'], timeperiod=200)
        dataframe['vol_ma_26_front'] = ((ta.EMA(dataframe['volume'], timeperiod=26).max())-(ta.EMA(dataframe['volume'], timeperiod=26).min()))/2

      

        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # informative_1h = self.informative_1h_indicators(dataframe, metadata)
        
        informative_15m = self.informative_15m_indicators(dataframe, metadata)
        dataframe = merge_informative_pair(
            dataframe, informative_15m, self.timeframe, self.inf_15m, ffill=True)

        # The indicators for the normal (5m) timeframe
        dataframe = self.normal_tf_indicators(dataframe, metadata)

    

        return dataframe

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

        dont_buy_conditions = []

        dont_buy_conditions.append(
            (
                # don't buy if there isn't 3% profit to be made
                (dataframe['close_15m'].rolling(self.lookback_candles.value).max()
                 < (dataframe['close'] * self.profit_threshold.value))
            )
        )

        dataframe.loc[
            (
                (dataframe['vol_base']<-80) &
                (dataframe['rsi_fast'] < self.rsi_fast_buy.value) &
                (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['vol_base']<-80) &
                (dataframe['rsi_fast'] < self.rsi_fast_buy.value) &
                (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['vol_base']>-96)&
                (dataframe['vol_base']> -20)&
                (dataframe['rsi_fast'] < self.rsi_fast_buy.value) &
                (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, 'ewo3')

        dataframe.loc[
            (
                (dataframe['vol_base']<-80) &
                (dataframe['rsi_fast'] < self.rsi_fast_buy.value) &
                (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')


            # buy in bull market
        dataframe.loc[
        (  
                (dataframe['vol_base']<-80) &
                (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['vol_base']<-80) &
                (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)
        ),
        ['buy', 'buy_tag']] = (1, 'bb_bull')  

        if dont_buy_conditions:
            for condition in dont_buy_conditions:
                dataframe.loc[condition, 'buy'] = 0

        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'])
            )

        )


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

        return dataframe