# source: https://raw.githubusercontent.com/hankniel/freqtrade/83ba4265808f598d5e1f810daa10ebea1726a645/user_data/strategies/technical/ThreeCandleStrikeLong.py
"""
EDMA Scalping Strategy (Exponentially Deviating Moving Average)
Source: https://www.tradingview.com/script/CJesxF5m-EDMA-Scalping-Strategy-Exponentially-Deviating-Moving-Average/
"""
# --- Do not remove these libs ---
from freqtrade.strategy import IStrategy, merge_informative_pair
import pandas as pd
from datetime import datetime
import numpy as np
# --------------------------------
import freqtrade.vendor.qtpylib.indicators as qtpylib
from user_data.utils.indicators.custom_indicators import SMMA, RMA, barssince

import talib.abstract as ta

class Github_hankniel_freqtrade__ThreeCandleStrikeLong__20251014_102948(IStrategy):
    INTERFACE_VERSION: int = 3
    
    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi"
    # minimal_roi = {
    #     "60":  0.01,
    #     "30":  0.03,
    #     "20":  0.04,
    #     "0":  0.05
    # }
    
    use_custom_roi = True
    use_custom_stoploss = True
    
    # This attribute will be overridden if the config file contains "stoploss"
    stoploss = -0.99
    
    # Optimal timeframe for the strategy
    timeframe = '15m'
    
    # trailing stoploss
    trailing_stop = False
    
    # run "populate_indicators" only for new candle
    process_only_new_candles = True
    
    # Experimental settings (configuration will overide these if set)
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False
    
    # Short
    can_short = True
    
    # Optional order type mapping
    order_types = {
        'entry': 'limit',
        'exit': 'limit',
        'stoploss': 'market',
        'stoploss_on_exchange': True
    }
    
    # Strategy configs
    atr_period: int = 14
    atr_percent: float = 0.05
    fast_ema_period: int = 8
    fast_smma_period: int = 21
    medium_smma_period: int = 50
    slow_smma_period: int = 200
    lr_period: int = 50
    smooth_lr_slope_period: int = 50
    signal_lr_slope_period: int = 14
    lr_threshold: float = 0.03
    ema_cross_smma_bars: int = 10
    
    custom_leverage: int = 1
    
    # Required startup_candles
    startup_candle_count: int = slow_smma_period * 2
    
    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, '1d') for pair in pairs]
        
        return informative_pairs
    
    def populate_indicators(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        """
        Adds several different TA indicators to the given DataFrame
        
        Performance Note: For the best performance be frugal on the number of indicators
        you are using. Let uncomment only the indicator you are using in your strategies
        or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
        """
        atr_period = self.atr_period
        atr_percent = self.atr_percent
        fast_ema_period = self.fast_ema_period
        fast_smma_period = self.fast_smma_period
        medium_smma_period = self.medium_smma_period
        slow_smma_period = self.slow_smma_period
        lr_period = self.lr_period
        smooth_lr_slope_period = self.smooth_lr_slope_period
        # signal_lr_slope_period = self.signal_lr_slope_period
        lr_threshold = self.lr_threshold
        
        inf_tf = '1d'
        # Daily ATR
        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
        informative['true_range'] = ta.TRANGE(informative)
        informative['daily_atr'] = RMA(informative, timeperiod=atr_period, col='true_range')
        informative['daily_atr_percent'] = informative['daily_atr'] * atr_percent
        
        # Merge informative dataframe
        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
        
        # EMA
        dataframe['ema'] = ta.EMA(dataframe, timeperiod=fast_ema_period)
        
        # SMMA
        dataframe['fast_smma'] = SMMA(dataframe, timeperiod=fast_smma_period, col='close')
        dataframe['medium_smma'] = SMMA(dataframe, timeperiod=medium_smma_period, col='close')
        dataframe['slow_smma'] = SMMA(dataframe, timeperiod=slow_smma_period, col='close')
        
        # SMMA signals
        dataframe['ma_bull'] = (
            (dataframe['fast_smma'] > dataframe['medium_smma']) & 
            (dataframe['fast_smma'] > dataframe['fast_smma'].shift(1))
        )
        dataframe['ma_bear'] = (
            (dataframe['fast_smma'] < dataframe['medium_smma']) & 
            (dataframe['fast_smma'] < dataframe['fast_smma'].shift(1))
        )
        dataframe['ma_bull_macro'] = (
            (dataframe['fast_smma'] > dataframe['slow_smma']) & 
            (dataframe['medium_smma'] > dataframe['slow_smma'])
        )
        dataframe['ma_bear_macro'] = (
            (dataframe['fast_smma'] < dataframe['slow_smma']) & 
            (dataframe['medium_smma'] < dataframe['slow_smma'])
        )
        
        # LR
        dataframe['lr_curve'] = ta.LINEARREG(dataframe, timeperiod=lr_period)
        dataframe['lr_slope'] = ta.LINEARREG_SLOPE(dataframe, timeperiod=lr_period)
        dataframe['smooth_lr_slope'] = ta.EMA(dataframe['lr_slope'], timeperiod=smooth_lr_slope_period)
        # dataframe['signal_lr_slope'] = ta.SMA(dataframe['smooth_lr_slope'], timeperiod=signal_lr_slope_period)
        dataframe['lr_trending'] = np.abs(dataframe['smooth_lr_slope']) > lr_threshold
        
        # 
        dataframe['bull_3s'] = (
            (dataframe['close'].shift(3) <= dataframe['open'].shift(3)) &
            (dataframe['close'].shift(2) <= dataframe['open'].shift(2)) &
            (dataframe['close'].shift(1) <= dataframe['open'].shift(1)) &
            (dataframe['close'] > dataframe['open'].shift(1))
        )
        dataframe['bear_3s'] = (
            (dataframe['close'].shift(3) >= dataframe['open'].shift(3)) &
            (dataframe['close'].shift(2) >= dataframe['open'].shift(2)) &
            (dataframe['close'].shift(1) >= dataframe['open'].shift(1)) &
            (dataframe['close'] < dataframe['open'].shift(1))
        )
        
        # EMA cross fast SMMA
        ema_cross_smma = (
            (qtpylib.crossed_above(dataframe['ema'], dataframe['fast_smma'])) |
            (qtpylib.crossed_below(dataframe['ema'], dataframe['fast_smma']))
        )
        dataframe['barssince_ema_cross_smma'] = barssince(ema_cross_smma.values)
        
        return dataframe
    
    def populate_entry_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        """
        Based on TA indicators, populates the buy signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        ema_cross_smma_bars = self.ema_cross_smma_bars
        
        # Enter long condition
        conditions_1 = (
            (dataframe['bull_3s'] == 1) & 
            (dataframe['ma_bull'] == 1) &
            (dataframe['lr_trending'] == 1)
        )
        dataframe.loc[conditions_1, ['enter_long', 'enter_tag']] = (1, 'exit_3s')
        
        conditions_2 = (
            (dataframe['ma_bear_macro'] == 1) & 
            (dataframe['ema'] > dataframe['fast_smma']) & 
            (dataframe['bull_3s'] == 1) & 
            (dataframe['barssince_ema_cross_smma'] < ema_cross_smma_bars)
        )
        dataframe.loc[conditions_2, ['enter_long', 'enter_tag']] = (1, 'exit_MA_cross')
        
        return dataframe
    
    def populate_exit_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        """
        Based on TA indicators, populates the sell signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        """
        Based on TA indicators, populates the buy signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        ema_cross_smma_bars = self.ema_cross_smma_bars
        
        # Enter long condition
        conditions_1 = (
            (dataframe['bear_3s'] == 1) & 
            (dataframe['ma_bear'] == 1) &
            (dataframe['lr_trending'] == 1)
        )
        dataframe.loc[conditions_1, ['exit_long', 'exit_tag']] = (1, 'exit_3s')
        
        conditions_2 = (
            (dataframe['ma_bull_macro'] == 1) & 
            (dataframe['ema'] < dataframe['fast_smma']) & 
            (dataframe['bear_3s'] == 1) & 
            (dataframe['barssince_ema_cross_smma'] < ema_cross_smma_bars)
        )
        dataframe.loc[conditions_2, ['exit_long', 'exit_tag']] = (1, 'exit_MA_cross')
        
        return dataframe
    
    def leverage(
        self, pair: str, current_time: datetime, current_rate: float,
        proposed_leverage: float, max_leverage: float, entry_tag: str | None, side: str,
        **kwargs
    ) -> float:
        leverage = self.custom_leverage
        
        return leverage