# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/E0V1E_20.py
from datetime import datetime
import talib.abstract as ta
import pandas_ta as pta
from technical import qtpylib

from freqtrade.persistence import Trade
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
from freqtrade.strategy import DecimalParameter, IntParameter, informative
from functools import reduce


class Github_remiotore_freqtrade__E0V1E_20__20260111_210550(IStrategy):
    minimal_roi = {
        "0": 10
    }

    timeframe = '5m'

    process_only_new_candles = True
    startup_candle_count = 120

    order_types = {
        'entry': 'market',
        'exit': 'market',
        'emergency_exit': 'market',
        'force_entry': 'market',
        'force_exit': "market",
        'stoploss': 'market',
        'stoploss_on_exchange': False,

        'stoploss_on_exchange_interval': 60,
        'stoploss_on_exchange_market_ratio': 0.99
    }

    stoploss = -0.99

    use_custom_stoploss = True

    is_optimize_32 = True
    buy_rsi_fast_32 = IntParameter(20, 70, default=46, space='buy', optimize=is_optimize_32)
    buy_rsi_32 = IntParameter(15, 50, default=19, space='buy', optimize=is_optimize_32)
    buy_sma15_32 = DecimalParameter(0.900, 1, default=0.942, decimals=3, space='buy', optimize=is_optimize_32)
    buy_cti_32 = DecimalParameter(-1, 0, default=-0.86, decimals=2, space='buy', optimize=is_optimize_32)

    sell_fastx = IntParameter(50, 100, default=75, space='sell', optimize=True)

    @informative('1d', 'BTC/USDT')
    def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bollmid'] = bollinger['mid']
        dataframe['ma120'] = ta.MA(dataframe, timeperiod=120)

        return dataframe

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

        dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15)
        dataframe['cti'] = pta.cti(dataframe["close"], length=20)
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)

        stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
        dataframe['fastk'] = stoch_fast['fastk']

        return dataframe

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

        conditions = []
        dataframe.loc[:, 'enter_tag'] = ''

        buy_1 = (
                (dataframe['rsi_slow'] < dataframe['rsi_slow'].shift(1)) &
                (dataframe['rsi_fast'] < self.buy_rsi_fast_32.value) &
                (dataframe['rsi'] > self.buy_rsi_32.value) &
                (dataframe['close'] < dataframe['sma_15'] * self.buy_sma15_32.value) &
                (dataframe['cti'] < self.buy_cti_32.value)
        )

        conditions.append(buy_1)
        dataframe.loc[buy_1, 'enter_tag'] += 'buy_1'

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

        return dataframe

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

        stake = self.config['stake_currency'].lower()
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        current_candle = dataframe.iloc[-1].squeeze()

        if current_candle[f"btc_{stake}_close_1d"] > current_candle[f"btc_{stake}_bollmid_1d"] and \
           current_candle[f"btc_{stake}_close_1d"] > current_candle[f"btc_{stake}_ma120_1d"]:
            if current_profit > 0:
                if current_candle["fastk"] > self.sell_fastx.value:
                    return -0.0001
        else:
            if current_profit > 0:
                if current_candle["fastk"] > self.sell_fastx.value:
                    return -0.0001
            else:
                self.stoploss = -0.15

        return self.stoploss

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

        dataframe.loc[(), ['exit_long', 'exit_tag']] = (0, 'long_out')

        return dataframe