ETCG

# --- 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
import math
import logging

logger = logging.getLogger(__name__)

# @Rallipanos # changes by IcHiAT


def EWO(dataframe, ema_length=5, ema2_length=3):
    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 ETCG(IStrategy):
    INTERFACE_VERSION = 3
    # ROI table:
    minimal_roi = {
        "0": 0.05,
        "20": 0.025,
        "40": 0.015,
        "87": 0.01,
        "201": 0.005,
        "202": 0.002
    }

    # Buy hyperspace params:
    buy_params = {
        "base_nb_candles_buy": 12,
        "rsi_buy": 58,
        "ewo_high": 3.001,
        "ewo_low": -10.289,
        "low_offset": 0.987,
        "lambo2_ema_14_factor": 0.981,
        "lambo2_enabled": True,
        "lambo2_rsi_14_limit": 39,
        "lambo2_rsi_4_limit": 44,
        "buy_adx": 20,
        "buy_fastd": 20,
        "buy_fastk": 22,
        "buy_ema_cofi": 0.98,
        "buy_ewo_high": 4.179
    }

    # Sell hyperspace params:
    sell_params = {
        "base_nb_candles_sell": 22,
        "high_offset": 1.014,
        "high_offset_2": 1.01
    }

    @property
    def protections(self):
        return [
            {
                "method": "CooldownPeriod",
                "stop_duration_candles": 5
            },
            {
                "method": "MaxDrawdown",
                "lookback_period_candles": 48,
                "trade_limit": 20,
                "stop_duration_candles": 4,
                "max_allowed_drawdown": 0.2
            },
            {
                "method": "StoplossGuard",
                "lookback_period_candles": 24,
                "trade_limit": 4,
                "stop_duration_candles": 2,
                "only_per_pair": False
            },
            {
                "method": "LowProfitPairs",
                "lookback_period_candles": 6,
                "trade_limit": 2,
                "stop_duration_candles": 60,
                "required_profit": 0.02
            },
            {
                "method": "LowProfitPairs",
                "lookback_period_candles": 24,
                "trade_limit": 4,
                "stop_duration_candles": 2,
                "required_profit": 0.01
            }
        ]

    # Stoploss:
    stoploss = -0.99

    # SMAOffset
    base_nb_candles_buy = IntParameter(8, 20, default=buy_params['base_nb_candles_buy'], space='buy', optimize=False)
    base_nb_candles_sell = IntParameter(8, 20, default=sell_params['base_nb_candles_sell'], space='sell', optimize=False)
    low_offset = DecimalParameter(0.985, 0.995, default=buy_params['low_offset'], space='buy', optimize=True)
    high_offset = DecimalParameter(1.005, 1.015, default=sell_params['high_offset'], space='sell', optimize=True)
    high_offset_2 = DecimalParameter(1.010, 1.020, default=sell_params['high_offset_2'], space='sell', optimize=True)

    # lambo2
    lambo2_ema_14_factor = DecimalParameter(0.8, 1.2, decimals=3,  default=buy_params['lambo2_ema_14_factor'], space='buy', optimize=True)
    lambo2_rsi_4_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_4_limit'], space='buy', optimize=True)
    lambo2_rsi_14_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_14_limit'], space='buy', optimize=True)

    # Protection
    fast_ewo = 50
    slow_ewo = 200

    ewo_low = DecimalParameter(-20.0, -8.0,default=buy_params['ewo_low'], space='buy', optimize=True)
    ewo_high = DecimalParameter(3.0, 3.4, default=buy_params['ewo_high'], space='buy', optimize=True)
    rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=False)

    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.001
    trailing_stop_positive_offset = 0.012
    trailing_only_offset_is_reached = True

    #cofi
    is_optimize_cofi = False
    buy_ema_cofi = DecimalParameter(0.96, 0.98, default=0.97 , optimize = is_optimize_cofi)
    buy_fastk = IntParameter(20, 30, default=20, optimize = is_optimize_cofi)
    buy_fastd = IntParameter(20, 30, default=20, optimize = is_optimize_cofi)
    buy_adx = IntParameter(20, 30, default=30, optimize = is_optimize_cofi)
    buy_ewo_high = DecimalParameter(2, 12, default=3.553, optimize = is_optimize_cofi)


    # 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': 'gtc'
    }

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

    process_only_new_candles = True
    startup_candle_count = 400

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


    def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs):
        # Sell any positions at a loss if they are held for more than 7 days.
        if current_profit < -0.04 and (current_time - trade.open_date_utc).days >= 4:
            return 'unclog'


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

        if self.config['trading_mode'] == "futures":
            btc_info_pair = "BTC/USDT:USDT"
        else:
            btc_info_pair = "BTC/USDT"

        informative_pairs.append((btc_info_pair, self.timeframe))
        informative_pairs.append((btc_info_pair, self.inf_1h))

        return informative_pairs

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

        df36h = dataframe.copy().shift( 432 ) # TODO FIXME: This assumes 5m timeframe
        df24h = dataframe.copy().shift( 288 ) # TODO FIXME: This assumes 5m timeframe

        dataframe['volume_mean_short'] = dataframe['volume'].rolling(4).mean()
        dataframe['volume_mean_long'] = df24h['volume'].rolling(48).mean()
        dataframe['volume_mean_base'] = df36h['volume'].rolling(288).mean()

        dataframe['volume_change_percentage'] = (dataframe['volume_mean_long'] / dataframe['volume_mean_base'])

        dataframe['rsi_mean'] = dataframe['rsi'].rolling(48).mean()

        dataframe['pnd_volume_warn'] = np.where((dataframe['volume_mean_short'] / dataframe['volume_mean_long'] > 5.0), -1, 0)

        return dataframe


    def base_tf_btc_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Indicators
        # -----------------------------------------------------------------------------------------
        dataframe['price_trend_long'] = (dataframe['close'].rolling(8).mean() / dataframe['close'].shift(8).rolling(144).mean())

        # Add prefix
        # -----------------------------------------------------------------------------------------
        ignore_columns = ['date', 'open', 'high', 'low', 'close', 'volume']
        dataframe.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True)

        return dataframe

    def info_tf_btc_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Indicators
        # -----------------------------------------------------------------------------------------
        dataframe['rsi_8'] = ta.RSI(dataframe, timeperiod=8)

        # Add prefix
        # -----------------------------------------------------------------------------------------
        ignore_columns = ['date', 'open', 'high', 'low', 'close', 'volume']
        dataframe.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True)

        return dataframe


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

        if self.config['trading_mode'] == "futures":
            btc_info_pair = "BTC/USDT:USDT"
        else:
            btc_info_pair = "BTC/USDT"

        btc_info_tf = self.dp.get_pair_dataframe(btc_info_pair, self.inf_1h)
        btc_info_tf = self.info_tf_btc_indicators(btc_info_tf, metadata)
        dataframe = merge_informative_pair(dataframe, btc_info_tf, self.timeframe, self.inf_1h, ffill=True)
        drop_columns = [f"{s}_{self.inf_1h}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']]
        dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True)

        btc_base_tf = self.dp.get_pair_dataframe(btc_info_pair, self.timeframe)
        btc_base_tf = self.base_tf_btc_indicators(btc_base_tf, metadata)
        dataframe = merge_informative_pair(dataframe, btc_base_tf, self.timeframe, self.timeframe, ffill=True)
        drop_columns = [f"{s}_{self.timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']]
        dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True)


        # 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['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)

        #lambo2
        dataframe['ema_14'] = ta.EMA(dataframe, timeperiod=14)
        dataframe['rsi_4'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14)


        # Pump strength
        dataframe['zema_30'] = ftt.dema(dataframe, period=30)
        dataframe['zema_200'] = ftt.dema(dataframe, period=200)
        dataframe['pump_strength'] = (dataframe['zema_30'] - dataframe['zema_200']) / dataframe['zema_30']

        # Cofi
        stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
        dataframe['fastd'] = stoch_fast['fastd']
        dataframe['fastk'] = stoch_fast['fastk']
        dataframe['adx'] = ta.ADX(dataframe)
        dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8)



        dataframe = self.pump_dump_protection(dataframe, metadata)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        dataframe.loc[:, 'buy_tag'] = ''

        lambo2 = (
            #bool(self.lambo2_enabled.value) &
            #(dataframe['pump_warning'] == 0) &
            (dataframe['close'] < (dataframe['ema_14'] * self.lambo2_ema_14_factor.value)) &
            (dataframe['rsi_4'] < int(self.lambo2_rsi_4_limit.value)) &
            (dataframe['rsi_14'] < int(self.lambo2_rsi_14_limit.value))
        )
        dataframe.loc[lambo2, 'buy_tag'] += 'lambo2_'
        conditions.append(lambo2)

        buy1ewo = (
                (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))
        )
        dataframe.loc[buy1ewo, 'buy_tag'] += 'buy1eworsi_'
        conditions.append(buy1ewo)

        buy2ewo = (
                (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))
        )
        dataframe.loc[buy2ewo, 'buy_tag'] += 'buy2ewo_'
        conditions.append(buy2ewo)

        is_cofi = (
                (dataframe['open'] < dataframe['ema_8'] * self.buy_ema_cofi.value) &
                (qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd'])) &
                (dataframe['fastk'] < self.buy_fastk.value) &
                (dataframe['fastd'] < self.buy_fastd.value) &
                (dataframe['adx'] > self.buy_adx.value) &
                (dataframe['EWO'] > self.buy_ewo_high.value)
            )
        dataframe.loc[is_cofi, 'buy_tag'] += 'cofi_'
        conditions.append(is_cofi)

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


        dont_buy_conditions = []

        # don't buy if there seems to be a Pump and Dump event.
        dont_buy_conditions.append((dataframe['pnd_volume_warn'] < 0.0))

        # BTC price protection
        dont_buy_conditions.append((dataframe['btc_rsi_8_1h'] < 35.0))

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

        return dataframe



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

        conditions.append(
            (   (dataframe['close']>dataframe['hma_50'])&
                (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
    #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:

    #    trade.sell_reason = sell_reason + "_" + trade.buy_tag

    #    return True

def pct_change(a, b):
    return (b - a) / a

class EI3v2_tag_cofi_dca_green(ETCG):


    initial_safety_order_trigger = -0.018
    max_safety_orders = 8
    safety_order_step_scale = 1.2
    safety_order_volume_scale = 1.4

    buy_params = {
        "dca_min_rsi": 35,
    }

    # append buy_params of parent class
    buy_params.update(ETCG.buy_params)

    dca_min_rsi = IntParameter(35, 75, default=buy_params['dca_min_rsi'], space='buy', optimize=True)

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe = super().populate_indicators(dataframe, metadata)
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        return dataframe

    def adjust_trade_position(self, trade: Trade, current_time: datetime,
                              current_rate: float, current_profit: float, min_stake: float,
                              max_stake: float, **kwargs):
        if current_profit > self.initial_safety_order_trigger:
            return None

        # credits to reinuvader for not blindly executing safety orders
        # Obtain pair dataframe.
        dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
        # Only buy when it seems it's climbing back up
        last_candle = dataframe.iloc[-1].squeeze()
        previous_candle = dataframe.iloc[-2].squeeze()
        if last_candle['close'] < previous_candle['close']:
            return None

        count_of_buys = 0
        for order in trade.orders:
            if order.ft_is_open or order.ft_order_side != 'buy':
                continue
            if order.status == "closed":
                count_of_buys += 1

        if 1 <= count_of_buys <= self.max_safety_orders:

            safety_order_trigger = abs(self.initial_safety_order_trigger) + (abs(self.initial_safety_order_trigger) * self.safety_order_step_scale * (math.pow(self.safety_order_step_scale,(count_of_buys - 1)) - 1) / (self.safety_order_step_scale - 1))

            if current_profit <= (-1 * abs(safety_order_trigger)):
                try:
                    stake_amount = self.wallets.get_trade_stake_amount(trade.pair, None)
                    stake_amount = stake_amount * math.pow(self.safety_order_volume_scale,(count_of_buys - 1))
                    amount = stake_amount / current_rate
                    logger.info(f"Initiating safety order buy #{count_of_buys} for {trade.pair} with stake amount of {stake_amount} which equals {amount}")
                    return stake_amount
                except Exception as exception:
                    logger.info(f'Error occured while trying to get stake amount for {trade.pair}: {str(exception)}')
                    return None

        return None