MultiMA_TSL

import freqtrade.vendor.qtpylib.indicators as qtpylib
from typing import Dict, List
import numpy as np
import talib.abstract as ta
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import merge_informative_pair, DecimalParameter, IntParameter, BooleanParameter, timeframe_to_minutes
from pandas import DataFrame, Series
from functools import reduce
from freqtrade.persistence import Trade
from datetime import datetime, timedelta
from freqtrade.exchange import timeframe_to_prev_date
from technical.indicators import zema, VIDYA
###########################################################################################################
##    MultiMA_TSL, modded by stash86, based on SMAOffsetProtectOptV1 (modded by Perkmeister)             ##
##    Based on @Lamborghini Store's SMAOffsetProtect strat, heavily based on @tirail's original SMAOffset##
##                                                                                                       ##
##    Strategy for Freqtrade https://github.com/freqtrade/freqtrade                                      ##
##                                                                                                       ##
##    Thanks to                                                                                          ##
##    - Perkmeister, for their snippets for the sell signals and decaying EMA sell                       ##
##    - ChangeToTower, for the PMax idea                                                                 ##
##    - JimmyNixx, for their snippet to limit close value from the peak (that I modify into 5m tf check) ##
##    - froggleston, for the Heikinashi check snippet from Cryptofrog                                    ##
##                                                                                                       ##
##                                                                                                       ##
##                                                                                                       ##
###########################################################################################################
# I hope you do enough testing before proceeding, either backtesting and/or dry run.
# Any profits and losses are all your responsibility

class MultiMA_TSL(IStrategy):
    INTERFACE_VERSION = 3
    DATESTAMP = 0
    SELLMA = 1
    SELL_TRIGGER = 2
    buy_params = {'base_nb_candles_buy_trima': 15, 'base_nb_candles_buy_trima2': 38, 'low_offset_trima': 0.959, 'low_offset_trima2': 0.949, 'base_nb_candles_buy_ema': 9, 'base_nb_candles_buy_ema2': 75, 'low_offset_ema': 1.067, 'low_offset_ema2': 0.973, 'base_nb_candles_buy_zema': 25, 'base_nb_candles_buy_zema2': 53, 'low_offset_zema': 0.958, 'low_offset_zema2': 0.961, 'base_nb_candles_buy_hma': 70, 'base_nb_candles_buy_hma2': 12, 'low_offset_hma': 0.948, 'low_offset_hma2': 0.941, 'ewo_high': 2.615, 'ewo_high2': 2.188, 'ewo_low': -19.632, 'ewo_low2': -19.955, 'rsi_buy': 60, 'rsi_buy2': 45}
    sell_params = {'base_nb_candles_ema_sell': 5, 'high_offset_sell_ema': 0.994}
    # ROI table:
    minimal_roi = {'0': 100}
    stoploss = -0.15
    optimize_sell_ema = False
    base_nb_candles_ema_sell = IntParameter(5, 80, default=20, space='sell', optimize=False)
    high_offset_sell_ema = DecimalParameter(0.99, 1.1, default=1.012, space='sell', optimize=False)
    base_nb_candles_ema_sell2 = IntParameter(5, 80, default=20, space='sell', optimize=False)
    # Multi Offset
    optimize_buy_ema = False
    base_nb_candles_buy_ema = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_ema)
    low_offset_ema = DecimalParameter(0.9, 1.1, default=0.958, space='buy', optimize=optimize_buy_ema)
    base_nb_candles_buy_ema2 = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_ema)
    low_offset_ema2 = DecimalParameter(0.9, 1.1, default=0.958, space='buy', optimize=optimize_buy_ema)
    optimize_buy_trima = False
    base_nb_candles_buy_trima = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_trima)
    low_offset_trima = DecimalParameter(0.9, 0.99, default=0.958, space='buy', optimize=optimize_buy_trima)
    base_nb_candles_buy_trima2 = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_trima)
    low_offset_trima2 = DecimalParameter(0.9, 0.99, default=0.958, space='buy', optimize=optimize_buy_trima)
    optimize_buy_zema = False
    base_nb_candles_buy_zema = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_zema)
    low_offset_zema = DecimalParameter(0.9, 0.99, default=0.958, space='buy', optimize=optimize_buy_zema)
    base_nb_candles_buy_zema2 = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_zema)
    low_offset_zema2 = DecimalParameter(0.9, 0.99, default=0.958, space='buy', optimize=optimize_buy_zema)
    optimize_buy_hma = False
    base_nb_candles_buy_hma = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_hma)
    low_offset_hma = DecimalParameter(0.9, 0.99, default=0.958, space='buy', optimize=optimize_buy_hma)
    base_nb_candles_buy_hma2 = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_hma)
    low_offset_hma2 = DecimalParameter(0.9, 0.99, default=0.958, space='buy', optimize=optimize_buy_hma)
    buy_condition_enable_optimize = False
    buy_condition_trima_enable = BooleanParameter(default=True, space='buy', optimize=buy_condition_enable_optimize)
    buy_condition_zema_enable = BooleanParameter(default=True, space='buy', optimize=buy_condition_enable_optimize)
    buy_condition_hma_enable = BooleanParameter(default=True, space='buy', optimize=buy_condition_enable_optimize)
    # Protection
    ewo_check_optimize = False
    ewo_low = DecimalParameter(-20.0, -8.0, default=-20.0, space='buy', optimize=ewo_check_optimize)
    ewo_high = DecimalParameter(2.0, 12.0, default=6.0, space='buy', optimize=ewo_check_optimize)
    ewo_low2 = DecimalParameter(-20.0, -8.0, default=-20.0, space='buy', optimize=ewo_check_optimize)
    ewo_high2 = DecimalParameter(2.0, 12.0, default=6.0, space='buy', optimize=ewo_check_optimize)
    rsi_buy_optimize = False
    rsi_buy = IntParameter(30, 70, default=50, space='buy', optimize=rsi_buy_optimize)
    rsi_buy2 = IntParameter(30, 70, default=50, space='buy', optimize=rsi_buy_optimize)
    buy_rsi_fast = IntParameter(0, 50, default=35, space='buy', optimize=False)
    fast_ewo = IntParameter(10, 50, default=50, space='buy', optimize=False)
    slow_ewo = IntParameter(100, 200, default=200, space='buy', optimize=False)
    # Trailing stoploss (not used)
    trailing_stop = False
    trailing_only_offset_is_reached = True
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.018
    use_custom_stoploss = True
    # Protection hyperspace params:
    # value loaded from strategy
    # value loaded from strategy
    # value loaded from strategy
    protection_params = {'low_profit_lookback': 48, 'low_profit_min_req': 0.04, 'low_profit_stop_duration': 14, 'cooldown_lookback': 2, 'stoploss_lookback': 72, 'stoploss_stop_duration': 20}
    cooldown_lookback = IntParameter(2, 48, default=2, space='protection', optimize=False)
    low_profit_optimize = False
    low_profit_lookback = IntParameter(2, 60, default=20, space='protection', optimize=low_profit_optimize)
    low_profit_stop_duration = IntParameter(12, 200, default=20, space='protection', optimize=low_profit_optimize)
    low_profit_min_req = DecimalParameter(-0.05, 0.05, default=-0.05, space='protection', decimals=2, optimize=low_profit_optimize)

    @property
    def protections(self):
        prot = []
        prot.append({'method': 'CooldownPeriod', 'stop_duration_candles': self.cooldown_lookback.value})
        prot.append({'method': 'LowProfitPairs', 'lookback_period_candles': self.low_profit_lookback.value, 'trade_limit': 1, 'stop_duration': int(self.low_profit_stop_duration.value), 'required_profit': self.low_profit_min_req.value})
        return prot
    # Optimal timeframe for the strategy.
    timeframe = '5m'
    # storage dict for custom info
    custom_info = {}
    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = True
    # These values can be overridden in the "ask_strategy" section in the config.
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False
    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 200

    def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs):
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        if len(dataframe) < 1:
            return False
        last_candle = dataframe.iloc[-1]
        if self.custom_info[pair][self.DATESTAMP] != last_candle['date']:
            # new candle, update EMA and check sell
            # smoothing coefficients
            sell_ema = self.custom_info[pair][self.SELLMA]
            if sell_ema == 0:
                sell_ema = last_candle['ema_sell']
            emaLength = 32
            alpha = 2 / (1 + emaLength)
            # update sell_ema
            sell_ema = alpha * last_candle['close'] + (1 - alpha) * sell_ema
            self.custom_info[pair][self.SELLMA] = sell_ema
            self.custom_info[pair][self.DATESTAMP] = last_candle['date']
            if (last_candle['close'] > sell_ema * self.high_offset_sell_ema.value) & (last_candle['buy_copy'] == 0):
                if not self.config['runmode'].value in ('backtest', 'hyperopt'):
                    self.custom_info[pair][self.SELL_TRIGGER] = 1
                    return False
                enter_tag = 'empty'
                if hasattr(trade, 'enter_tag') and trade.buy_tag is not None:
                    enter_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]
                        enter_tag = buy_signal_candle['enter_tag'] if buy_signal_candle['enter_tag'] != '' else 'empty'
                return f'New Sell Signal ({enter_tag})'
        return False
    #credit to Perkmeister for this custom stoploss to help the strategy ride a green candle when the sell signal triggered

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
        sl_new = 1
        if self.custom_info[pair][self.SELL_TRIGGER] == 1:
            if not self.config['runmode'].value in ('backtest', 'hyperopt'):
                sl_new = 0.001
        if current_profit > 0.2:
            sl_new = 0.05
        elif current_profit > 0.1:
            sl_new = 0.03
        elif current_profit > 0.06:
            sl_new = 0.02
        elif current_profit > 0.03:
            sl_new = 0.01
        return sl_new

    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, **kwargs) -> bool:
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        if len(dataframe) < 1:
            return False
        last_candle = dataframe.iloc[-1].squeeze()
        if rate > last_candle['close']:
            return False
        self.custom_info[pair][self.DATESTAMP] = last_candle['date']
        self.custom_info[pair][self.SELLMA] = last_candle['ema_sell']
        return True

    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, **kwargs) -> bool:
        self.custom_info[pair][self.SELL_TRIGGER] = 0
        return True

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

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # EWO
        dataframe['ewo'] = EWO(dataframe, self.fast_ewo.value, self.slow_ewo.value)
        # RSI
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_84'] = ta.RSI(dataframe, timeperiod=84)
        dataframe['rsi_112'] = ta.RSI(dataframe, timeperiod=112)
        # Heiken Ashi
        heikinashi = qtpylib.heikinashi(dataframe)
        heikinashi['volume'] = dataframe['volume']
        # Profit Maximizer - PMAX
        dataframe['pm'], dataframe['pmx'] = pmax(heikinashi, MAtype=1, length=9, multiplier=27, period=10, src=3)
        dataframe['source'] = (dataframe['high'] + dataframe['low'] + dataframe['open'] + dataframe['close']) / 4
        dataframe['pmax_thresh'] = ta.EMA(dataframe['source'], timeperiod=9)
        dataframe = HA(dataframe, 4)
        if self.config['runmode'].value in ('live', 'dry_run'):
            # Exchange downtime protection
            dataframe['live_data_ok'] = dataframe['volume'].rolling(window=72, min_periods=72).min() > 0
        else:
            dataframe['live_data_ok'] = True
        # Check if the entry already exists
        if not metadata['pair'] in self.custom_info:
            # Create empty entry for this pair {datestamp, sellma, sell_trigger}
            self.custom_info[metadata['pair']] = ['', 0, 0]
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        dataframe['ema_offset_buy'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema.value)) * self.low_offset_ema.value
        dataframe['ema_offset_buy2'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema2.value)) * self.low_offset_ema2.value
        dataframe['ema_sell'] = ta.EMA(dataframe, int(self.base_nb_candles_ema_sell.value))
        dataframe.loc[:, 'enter_tag'] = ''
        dataframe.loc[:, 'buy_copy'] = 0
        dataframe.loc[:, 'enter_long'] = 0
        if self.buy_condition_trima_enable.value:
            dataframe['trima_offset_buy'] = ta.TRIMA(dataframe, int(self.base_nb_candles_buy_trima.value)) * self.low_offset_trima.value
            dataframe['trima_offset_buy2'] = ta.TRIMA(dataframe, int(self.base_nb_candles_buy_trima2.value)) * self.low_offset_trima2.value
            buy_offset_trima = (dataframe['close'] < dataframe['trima_offset_buy']) & (dataframe['pm'] <= dataframe['pmax_thresh']) | (dataframe['close'] < dataframe['trima_offset_buy2']) & (dataframe['pm'] > dataframe['pmax_thresh'])
            dataframe.loc[buy_offset_trima, 'enter_tag'] += 'trima '
            conditions.append(buy_offset_trima)
        if self.buy_condition_zema_enable.value:
            dataframe['zema_offset_buy'] = zema(dataframe, int(self.base_nb_candles_buy_zema.value)) * self.low_offset_zema.value
            dataframe['zema_offset_buy2'] = zema(dataframe, int(self.base_nb_candles_buy_zema2.value)) * self.low_offset_zema2.value
            buy_offset_zema = (dataframe['close'] < dataframe['zema_offset_buy']) & (dataframe['pm'] <= dataframe['pmax_thresh']) | (dataframe['close'] < dataframe['zema_offset_buy2']) & (dataframe['pm'] > dataframe['pmax_thresh'])
            dataframe.loc[buy_offset_zema, 'enter_tag'] += 'zema '
            conditions.append(buy_offset_zema)
        if self.buy_condition_hma_enable.value:
            dataframe['hma_offset_buy'] = qtpylib.hull_moving_average(dataframe['close'], window=int(self.base_nb_candles_buy_hma.value)) * self.low_offset_hma.value
            dataframe['hma_offset_buy2'] = qtpylib.hull_moving_average(dataframe['close'], window=int(self.base_nb_candles_buy_hma2.value)) * self.low_offset_hma2.value
            buy_offset_hma = ((dataframe['close'] < dataframe['hma_offset_buy']) & (dataframe['pm'] <= dataframe['pmax_thresh']) & (dataframe['rsi'] < 35) | (dataframe['close'] < dataframe['hma_offset_buy2']) & (dataframe['pm'] > dataframe['pmax_thresh']) & (dataframe['rsi'] < 30)) & (dataframe['rsi_fast'] < 30)
            dataframe.loc[buy_offset_hma, 'enter_tag'] += 'hma '
            conditions.append(buy_offset_hma)
        add_check = dataframe['live_data_ok'] & (dataframe['close'] < dataframe['Smooth_HA_L']) & (dataframe['close'] < dataframe['ema_sell'] * self.high_offset_sell_ema.value) & (dataframe['close'].rolling(288).max() >= dataframe['close'] * 1.1) & (dataframe['Smooth_HA_O'].shift(1) < dataframe['Smooth_HA_H'].shift(1)) & (dataframe['rsi_fast'] < self.buy_rsi_fast.value) & (dataframe['rsi_84'] < 60) & (dataframe['rsi_112'] < 60) & ((dataframe['close'] < dataframe['ema_offset_buy']) & (dataframe['pm'] <= dataframe['pmax_thresh']) & ((dataframe['ewo'] < self.ewo_low.value) | (dataframe['ewo'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_buy.value)) | (dataframe['close'] < dataframe['ema_offset_buy2']) & (dataframe['pm'] > dataframe['pmax_thresh']) & ((dataframe['ewo'] < self.ewo_low2.value) | (dataframe['ewo'] > self.ewo_high2.value) & (dataframe['rsi'] < self.rsi_buy2.value))) & (dataframe['volume'] > 0)
        if conditions:
            dataframe.loc[add_check & reduce(lambda x, y: x | y, conditions), ['buy_copy', 'enter_long']] = (1, 1)
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[:, 'exit_long'] = 0
        return dataframe
# Elliot Wave Oscillator

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
# PMAX

def pmax(df, period, multiplier, length, MAtype, src):
    period = int(period)
    multiplier = int(multiplier)
    length = int(length)
    MAtype = int(MAtype)
    src = int(src)
    mavalue = f'MA_{MAtype}_{length}'
    atr = f'ATR_{period}'
    pm = f'pm_{period}_{multiplier}_{length}_{MAtype}'
    pmx = f'pmX_{period}_{multiplier}_{length}_{MAtype}'
    # MAtype==1 --> EMA
    # MAtype==2 --> DEMA
    # MAtype==3 --> T3
    # MAtype==4 --> SMA
    # MAtype==5 --> VIDYA
    # MAtype==6 --> TEMA
    # MAtype==7 --> WMA
    # MAtype==8 --> VWMA
    # MAtype==9 --> zema
    if src == 1:
        masrc = df['close']
    elif src == 2:
        masrc = (df['high'] + df['low']) / 2
    elif src == 3:
        masrc = (df['high'] + df['low'] + df['close'] + df['open']) / 4
    if MAtype == 1:
        mavalue = ta.EMA(masrc, timeperiod=length)
    elif MAtype == 2:
        mavalue = ta.DEMA(masrc, timeperiod=length)
    elif MAtype == 3:
        mavalue = ta.T3(masrc, timeperiod=length)
    elif MAtype == 4:
        mavalue = ta.SMA(masrc, timeperiod=length)
    elif MAtype == 5:
        mavalue = VIDYA(df, length=length)
    elif MAtype == 6:
        mavalue = ta.TEMA(masrc, timeperiod=length)
    elif MAtype == 7:
        mavalue = ta.WMA(df, timeperiod=length)
    elif MAtype == 8:
        mavalue = vwma(df, length)
    elif MAtype == 9:
        mavalue = zema(df, period=length)
    df[atr] = ta.ATR(df, timeperiod=period)
    df['basic_ub'] = mavalue + multiplier / 10 * df[atr]
    df['basic_lb'] = mavalue - multiplier / 10 * df[atr]
    basic_ub = df['basic_ub'].values
    final_ub = np.full(len(df), 0.0)
    basic_lb = df['basic_lb'].values
    final_lb = np.full(len(df), 0.0)
    for i in range(period, len(df)):
        final_ub[i] = basic_ub[i] if basic_ub[i] < final_ub[i - 1] or mavalue[i - 1] > final_ub[i - 1] else final_ub[i - 1]
        final_lb[i] = basic_lb[i] if basic_lb[i] > final_lb[i - 1] or mavalue[i - 1] < final_lb[i - 1] else final_lb[i - 1]
    df['final_ub'] = final_ub
    df['final_lb'] = final_lb
    pm_arr = np.full(len(df), 0.0)
    for i in range(period, len(df)):
        pm_arr[i] = final_ub[i] if pm_arr[i - 1] == final_ub[i - 1] and mavalue[i] <= final_ub[i] else final_lb[i] if pm_arr[i - 1] == final_ub[i - 1] and mavalue[i] > final_ub[i] else final_lb[i] if pm_arr[i - 1] == final_lb[i - 1] and mavalue[i] >= final_lb[i] else final_ub[i] if pm_arr[i - 1] == final_lb[i - 1] and mavalue[i] < final_lb[i] else 0.0
    pm = Series(pm_arr)
    # Mark the trend direction up/down
    pmx = np.where(pm_arr > 0.0, np.where(mavalue < pm_arr, 'down', 'up'), np.nan)
    return (pm, pmx)
# smoothed Heiken Ashi

def HA(dataframe, smoothing=None):
    df = dataframe.copy()
    df['HA_Close'] = (df['open'] + df['high'] + df['low'] + df['close']) / 4
    df.reset_index(inplace=True)
    ha_open = [(df['open'][0] + df['close'][0]) / 2]
    [ha_open.append((ha_open[i] + df['HA_Close'].values[i]) / 2) for i in range(0, len(df) - 1)]
    df['HA_Open'] = ha_open
    df.set_index('index', inplace=True)
    df['HA_High'] = df[['HA_Open', 'HA_Close', 'high']].max(axis=1)
    df['HA_Low'] = df[['HA_Open', 'HA_Close', 'low']].min(axis=1)
    if smoothing is not None:
        sml = abs(int(smoothing))
        if sml > 0:
            df['Smooth_HA_O'] = ta.EMA(df['HA_Open'], sml)
            df['Smooth_HA_C'] = ta.EMA(df['HA_Close'], sml)
            df['Smooth_HA_H'] = ta.EMA(df['HA_High'], sml)
            df['Smooth_HA_L'] = ta.EMA(df['HA_Low'], sml)
    return df