# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/strategy003_3.py

from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from hyperopt import hp
from functools import reduce
from pandas import DataFrame


import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy # noqa

class_name = 'CustomStrategy'


class Github_remiotore_freqtrade__strategy003_3__20260111_210550(IStrategy):
    """
    Prod strategy 003
    author@: Gerald Lonlas
    github@: https://github.com/glonlas/freqtrade-strategies
    """


    minimal_roi = {
        "60":  0.01,
        "30":  0.03,
        "20":  0.04,
        "0":  0.05
    }


    stoploss = -0.3

    ticker_interval = 5

    def populate_indicators(self, dataframe: DataFrame) -> 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.
        """

        dataframe['mfi'] = ta.MFI(dataframe)

        stoch_fast = ta.STOCHF(dataframe)
        dataframe['fastd'] = stoch_fast['fastd']
        dataframe['fastk'] = stoch_fast['fastk']

        dataframe['rsi'] = ta.RSI(dataframe)

        rsi = 0.1 * (dataframe['rsi'] - 50)
        dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)

        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']

        dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
        dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
        dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)

        dataframe['sar'] = ta.SAR(dataframe)

        dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
        """
        Based on TA indicators, populates the buy signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                (dataframe['rsi'] < 28) &
                (dataframe['rsi'] > 0) &
                (dataframe['close'] < dataframe['sma']) &
                (dataframe['fisher_rsi'] < -0.94) &
                (dataframe['mfi'] < 16.0) &
                (
                    (dataframe['ema50'] > dataframe['ema100']) |
                    (qtpylib.crossed_above(dataframe['ema5'], dataframe['ema10']))
                ) &
                (dataframe['fastd'] > dataframe['fastk']) &
                (dataframe['fastd'] > 0)
            ),
            'buy'] = 1

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
        """
        Based on TA indicators, populates the sell signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                (dataframe['sar'] > dataframe['close']) &
                (dataframe['fisher_rsi'] > 0.3)
            ),
            'sell'] = 1
        return dataframe