DS_Green_5mv2

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, BooleanParameter, DecimalParameter, IntParameter, CategoricalParameter
import math
import logging

logger = logging.getLogger(__name__)



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



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

class DS_Green_5mv2(IStrategy):



    buy_params = {
        "lambo2_enabled": True,
        "ewo1_enabled": True,
        "ewo2_enabled": True,
        "cofi_enabled": True,

        "base_nb_candles_buy": 12,

        "ewo_high": 3.009,
        "low_offset_1": 0.988,
        "high_offset_1": 0.969,
        "rsi_buy": 51,

        "ewo_low": -8.929,
        "low_offset_2": 0.985,
        "high_offset_2": 1.01,

        "lambo2_ema_14_factor": 0.978,
        "lambo2_rsi_14_limit": 53,
        "lambo2_rsi_4_limit": 46,

        "buy_adx": 25,
        "buy_fastd": 20,
        "buy_fastk": 24,
        "buy_ema_cofi": 0.977,
        "buy_ewo_high": 3.767,

        "dca_min_rsi": 64,
    }
    sell_params = {
        "base_nb_candles_sell": 22,

        "high_offset_above": 1.05, 
        "high_offset_below": 1.01,

        "pHSL": -0.397,
        "pPF_1": 0.012,
        "pPF_2": 0.07,
        "pSL_1": 0.015,
        "pSL_2": 0.068,
    }
    minimal_roi = {
        "0": 100,
    }
    stoploss = -0.99
    trailing_stop = True
    trailing_stop_positive = 0.001  # Positive offset for trailing stop.
    trailing_stop_positive_offset = 0.0135  # Offset for triggering the trailing stop.
    trailing_only_offset_is_reached = True  # Only trigger trailing stop if the offset is reached.



    use_custom_stoploss = False
    timeframe = '5m'  # The primary timeframe for analysis.
    inf_1h = '1h'  # Informative timeframe to gather additional data.

    use_exit_signal = True
    exit_profit_only = True
    exit_profit_offset = 0.01  # Offset added to exit signal (profitable threshold).
    ignore_roi_if_entry_signal = False  # If True, ignore ROI when the buy signal is still present.

    process_only_new_candles = True
    startup_candle_count = 400

    initial_safety_order_trigger = -0.018  # Initial trigger for the first safety order.
    max_safety_orders = 8  # Maximum number of safety orders to prevent overexposure.
    safety_order_step_scale = 1.2  # How much to increase the trigger for each additional safety order.
    safety_order_volume_scale = 1.4  # How much to increase the volume of each safety order.

    order_types = {
        'entry': 'limit',
        'exit': 'limit',
        'trailing_stop_loss': 'limit',
        'emergency_exit': 'market',
        'force_entry': 'limit',
        'force_exit': 'market',
        'stoploss': 'limit',
        'stoploss_on_exchange': False,
        'stoploss_on_exchange_interval': 60,
        'stoploss_on_exchange_limit_ratio': 0.99
    }

    plot_config = {
        'main_plot': {
            'ma_buy': {'color': 'orange'},  # Color for the buy moving average.
            'ma_sell': {'color': 'orange'},  # Color for the sell moving average.
        },
    }

    order_time_in_force = {
        'entry': 'gtc',
        'exit': 'gtc'
    }




    is_optimize_remove = False
    lambo2_enabled = BooleanParameter(default=buy_params['lambo2_enabled'], space='buy', optimize=is_optimize_remove)
    ewo1_enabled = BooleanParameter(default=buy_params['ewo1_enabled'], space='buy', optimize=is_optimize_remove)
    ewo2_enabled = BooleanParameter(default=buy_params['ewo2_enabled'], space='buy', optimize=is_optimize_remove)
    cofi_enabled = BooleanParameter(default=buy_params['cofi_enabled'], space='buy', optimize=is_optimize_remove)


    is_optimize_base_nb_candles = False
    base_nb_candles_buy = IntParameter(8, 20, default=buy_params['base_nb_candles_buy'], space='buy', optimize=is_optimize_base_nb_candles)
    base_nb_candles_sell = IntParameter(8, 20, default=sell_params['base_nb_candles_sell'], space='sell', optimize=is_optimize_base_nb_candles)

    fast_ewo = 60
    slow_ewo = 220

    is_optimize_ewo = False
    low_offset_1 = DecimalParameter(0.985, 0.995, default=buy_params['low_offset_1'], space='buy', optimize=is_optimize_ewo)
    rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=is_optimize_ewo)
    ewo_high = DecimalParameter(3.0, 3.4, default=buy_params['ewo_high'], space='buy', optimize=is_optimize_ewo)
    high_offset_1 = DecimalParameter(0.95, 1.10, default=buy_params['high_offset_1'], space='buy', optimize=is_optimize_ewo)

    is_optimize_ewo2 = False
    ewo_low = DecimalParameter(-20.0, -8.0,default=buy_params['ewo_low'], space='buy', optimize=is_optimize_ewo2)
    low_offset_2 = DecimalParameter(0.985, 0.995, default=buy_params['low_offset_2'], space='buy', optimize=is_optimize_ewo2)
    high_offset_2 = DecimalParameter(0.95, 1.10, default=buy_params['high_offset_2'], space='buy', optimize=is_optimize_ewo2)

    is_optimize_lambo2 = True
    lambo2_ema_14_factor = DecimalParameter(0.8, 1.2, decimals=3,  default=buy_params['lambo2_ema_14_factor'], space='buy', optimize=is_optimize_lambo2)
    lambo2_rsi_4_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_4_limit'], space='buy', optimize=is_optimize_lambo2)
    lambo2_rsi_14_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_14_limit'], space='buy', optimize=is_optimize_lambo2)

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

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

    is_optimize_offset_sell = True
    high_offset_above = DecimalParameter(1.00, 1.10, default=sell_params['high_offset_above'], space='sell', optimize=is_optimize_offset_sell)
    high_offset_below = DecimalParameter(0.95, 1.05, default=sell_params['high_offset_below'], space='sell', optimize=is_optimize_offset_sell)


    is_optimize_stoploss = False

    pHSL = DecimalParameter(-0.500, -0.040, default=-0.08, decimals=3, space='sell', optimize=is_optimize_stoploss, load=True)

    pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3, space='sell', optimize=is_optimize_stoploss, load=True)
    pSL_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', optimize=is_optimize_stoploss, load=True)

    pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell',optimize=is_optimize_stoploss, load=True)
    pSL_2 = DecimalParameter(0.020, 0.070, default=0.040, decimals=3, space='sell', optimize=is_optimize_stoploss,load=True)



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

        if self.config['stake_currency'] in ['USDT','BUSD','USDC','DAI','TUSD','PAX','USD','EUR','GBP']:
            btc_info_pair = f"BTC/{self.config['stake_currency']}"
        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 base_tf_btc_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:


        dataframe['price_trend_long'] = (dataframe['close'].rolling(8).mean() / dataframe['close'].shift(8).rolling(144).mean())


        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:


        dataframe['rsi_8'] = ta.RSI(dataframe, timeperiod=8)


        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['stake_currency'] in ['USDT','BUSD']:
            btc_info_pair = f"BTC/{self.config['stake_currency']}"
        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)

        for val in self.base_nb_candles_buy.range:
            dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val)

        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)

        dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo)

        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_14'] = ta.EMA(dataframe, timeperiod=14)
        dataframe['rsi_4'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14)

        dataframe['dema_30'] = ta.DEMA(dataframe, period=30)
        dataframe['dema_200'] = ta.DEMA(dataframe, period=200)
        dataframe['pump_strength'] = (dataframe['dema_30'] - dataframe['dema_200']) / dataframe['dema_30']

        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)

        dataframe = super().populate_indicators(dataframe, metadata)
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        
        return dataframe



    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 custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:

        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



        
        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 sl_profit >= current_profit:
            return -0.99
    
        return stoploss_from_open(sl_profit, current_profit)



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

        lambo2 = (
            bool(self.lambo2_enabled) &
            (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, 'enter_tag'] += 'lambo2_'
        conditions.append(lambo2)

        ewo = (
            (dataframe['rsi_fast'] < 35) &
            (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset_1.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_1.value))
        )
        dataframe.loc[ewo, 'enter_tag'] += 'eworsi_'
        conditions.append(ewo)

        ewo2 = (
            (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_low.value) &
            (dataframe['volume'] > 0) &
            (dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value))
        )
        dataframe.loc[ewo2, 'enter_tag'] += 'ewo2_'
        conditions.append(ewo2)

        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[cofi, 'enter_tag'] += 'cofi_'
        conditions.append(cofi)

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

        dont_buy_conditions = []

        dont_buy_conditions.append((dataframe['pnd_volume_warn'] < 0.0))

        dont_buy_conditions.append((dataframe['btc_rsi_8_1h'] < 35.0))
    
        if dont_buy_conditions:
            for condition in dont_buy_conditions:
                dataframe.loc[condition, 'enter_long'] = 0
        
        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


        dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)

        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 != 'enter_long':
                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



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

        dataframe['exit_long'] = 0
        dataframe['exit_tag'] = 'no_exit'  # Default tag

        primary_condition = dataframe['volume'] > 0

        condition_hma50_above = (
            (dataframe['close'] > dataframe['hma_50']) &
            (dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_above.value)) &
            (dataframe['rsi'] > 50) &
            (dataframe['rsi_fast'] > dataframe['rsi_slow'])
        )
        condition_hma50_below = (
            (dataframe['close'] < dataframe['hma_50']) &
            (dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_below.value)) &
            (dataframe['rsi_fast'] > dataframe['rsi_slow'])
        )

        combined_conditions_above = primary_condition & condition_hma50_above
        combined_conditions_below = primary_condition & condition_hma50_below

        dataframe.loc[combined_conditions_above, 'exit_long'] = 1
        dataframe.loc[combined_conditions_below, 'exit_long'] = 1

        dataframe.loc[combined_conditions_above, 'exit_tag'] = 'hma50_above'
        dataframe.loc[combined_conditions_below, 'exit_tag'] = 'hma50_below'
        
        return dataframe



    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:
    
        if trade and trade.exit_reason:
            trade.exit_reason = exit_reason + "_" + trade.enter_tag
    
        return True



    def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs):

        time_held = current_time - trade.open_date_utc
        time_held_in_hours = time_held.total_seconds() / 3600  # Convert seconds to hours
    
        if current_profit < -0.04 and time_held_in_hours >= 6.5:
            return 'unclog'



    @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
            }
        ]