Strategy_4Hyperopt

# --- 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
from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter

class Strategy_4Hyperopt(IStrategy):
    """
    Strategy 004
    author@: Gerald Lonlas
    github@: https://github.com/freqtrade/freqtrade-strategies

    How to use it?
    > python3 ./freqtrade/main.py -s Strategy004

    ADX - Directional Movement - Average Index
    https://www.programmersought.com/article/69361964343/

    ADX Value	Trend Strength
    0-25	Absent or Weak Trend
    25-50	Strong Trend
    50-75	Very Strong Trend
    75-100	Extremely Strong Trend

    CCI Commodity Channel Index:
    values Oversold:<-100, Overbought:>100
    """

    # Hyperoptable parameter definitions
    # NOTE: The defaults set below in these params
    # will be the onces used in the strategy.
    # So after running the Hyperopt, simply update these
    # defaults to the best values returned by the Hyeropt!

    # BUY PARAMS
    buy_adx = IntParameter(25, 75, default=50)
    buy_slowadx = IntParameter(20, 50, default=26)
    buy_cci = IntParameter(-100, -50, default=-100)
    buy_fastk_fastd = IntParameter(10, 20, default=20)
    buy_slowfastk_slowfastd = IntParameter(10, 30, default=30)
    buy_mean_volume = DecimalParameter(0.7, 0.8, default=0.75)

    # BUY PARAMS ENABLED
    buy_adx_enabled = CategoricalParameter([True, False], default=True)
    buy_cci_enabled = CategoricalParameter([True, False], default=True)

    # SELL PARAMS
    sell_slowadx = IntParameter(15, 35, default=25)
    sell_fastk_fastd = IntParameter(60, 80, default=70)

    # SELL PARAMS ENABLED
    sell_slowadx_enabled = CategoricalParameter([True, False], default=True)

    # 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
    }

    # Optimal stoploss designed for the strategy
    # This attribute will be overridden if the config file contains "stoploss"
    stoploss = -0.10

    # Optimal timeframe for the strategy
    timeframe = '5m'

    # trailing stoploss
    trailing_stop = False
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.02

    # run "populate_indicators" only for new candle
    process_only_new_candles = False

    # Experimental settings (configuration will overide these if set)
    use_sell_signal = True
    sell_profit_only = True
    ignore_roi_if_buy_signal = False

    # Optional order type mapping
    order_types = {
        'buy': 'limit',
        'sell': 'limit',
        'stoploss': 'market',
        'stoploss_on_exchange': False
    }

    def informative_pairs(self):
        """
        Define additional, informative pair/interval combinations to be cached from the exchange.
        These pair/interval combinations are non-tradeable, unless they are part
        of the whitelist as well.
        For more information, please consult the documentation
        :return: List of tuples in the format (pair, interval)
            Sample: return [("ETH/USDT", "5m"),
                            ("BTC/USDT", "15m"),
                            ]
        """
        return []

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

        # ADX
        dataframe['adx'] = ta.ADX(dataframe)
        dataframe['slowadx'] = ta.ADX(dataframe, 35)

        # Commodity Channel Index: values Oversold:<-100, Overbought:>100
        dataframe['cci'] = ta.CCI(dataframe)

        # Stoch
        stoch = ta.STOCHF(dataframe, 5)
        dataframe['fastd'] = stoch['fastd']
        dataframe['fastk'] = stoch['fastk']
        dataframe['fastk-previous'] = dataframe.fastk.shift(1)
        dataframe['fastd-previous'] = dataframe.fastd.shift(1)

        # Slow Stoch
        slowstoch = ta.STOCHF(dataframe, 50)
        dataframe['slowfastd'] = slowstoch['fastd']
        dataframe['slowfastk'] = slowstoch['fastk']
        dataframe['slowfastk-previous'] = dataframe.slowfastk.shift(1)
        dataframe['slowfastd-previous'] = dataframe.slowfastd.shift(1)

        # EMA - Exponential Moving Average
        dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)

        dataframe['mean-volume'] = dataframe['volume'].mean()

        return dataframe

    # def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    #     """
    #     Based on TA indicators, populates the buy signal for the given dataframe
    #     :param dataframe: DataFrame
    #     :return: DataFrame with buy column
    #     """
    #     conditions = []

    #     # GUARDS AND TRENDS
    #     if self.buy_adx_enabled.value:
    #         conditions.append((
    #             (dataframe['adx'] > 50) |
    #             (dataframe['slowadx'] > 26)
    #         ))
    #     if self.buy_cci_enabled.value:
    #         conditions.append(dataframe['cci'] < -100)

    #     conditions.append((
    #         (dataframe['fastk-previous'] < 20) &
    #         (dataframe['fastd-previous'] < 20)
    #     ))

    #     conditions.append((
    #         (dataframe['slowfastk-previous'] < 30) &
    #         (dataframe['slowfastd-previous'] < 30)
    #     ))

    #     conditions.append((dataframe['fastk-previous'] < dataframe['fastd-previous']))

    #     conditions.append((dataframe['fastk'] > dataframe['fastd']))

    #     conditions.append((dataframe['mean-volume'] > 0.75))

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

    #      return dataframe
    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

            conditions = []

            # GUARDS AND TRENDS
            if self.buy_adx_enabled.value:
                conditions.append((
                    (dataframe['adx'] > self.buy_adx.value) |
                    (dataframe['slowadx'] > self.buy_slowadx.value)
                ))
            if self.buy_cci_enabled.value:
                conditions.append(dataframe['cci'] < self.buy_cci.value)

            conditions.append((
                (dataframe['fastk-previous'] < self.buy_fastk_fastd.value) &
                (dataframe['fastd-previous'] < self.buy_fastk_fastd.value)
            ))

            conditions.append((
                (dataframe['slowfastk-previous'] < self.buy_slowfastk_slowfastd.value) &
                (dataframe['slowfastd-previous'] < self.buy_slowfastk_slowfastd.value)
            ))

            conditions.append((dataframe['fastk-previous'] < dataframe['fastd-previous']))

            conditions.append((dataframe['fastk'] > dataframe['fastd']))

            conditions.append((dataframe['mean-volume'] > self.buy_mean_volume.value))

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

            return dataframe

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

    #     if self.sell_slowadx_enabled.value:
    #         conditions.append((dataframe['slowadx'] < 25))

    #     conditions.append((dataframe['fastk'] > 70) | (dataframe['fastd'] > 70))

    #     conditions.append((dataframe['fastk-previous'] < dataframe['fastd-previous']))

    #     conditions.append((dataframe['close'] > dataframe['ema5']))

    #     if conditions:
    #         dataframe.loc[
    #             reduce(lambda x, y: x & y, conditions),
    #             'sell'] = 1

    #     return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
            """
            Based on TA indicators, populates the sell signal for the given dataframe
            :param dataframe: DataFrame
            :return: DataFrame with buy column
            """

            conditions = []

            if self.sell_slowadx_enabled.value:
                conditions.append((dataframe['slowadx'] < self.sell_slowadx.value))

            conditions.append((dataframe['fastk'] > self.sell_fastk_fastd.value) | (dataframe['fastd'] > self.sell_fastk_fastd.value))

            conditions.append((dataframe['fastk-previous'] < dataframe['fastd-previous']))

            conditions.append((dataframe['close'] > dataframe['ema5']))

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

            return dataframe