# source: https://raw.githubusercontent.com/flessner/freqtrade/89d04067a9bdd588029770281eecd7b837fcbad0/user_data/strategies/bz04ho2.py
import freqtrade.vendor.qtpylib.indicators as qtpylib
import talib.abstract as ta
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
from freqtrade.strategy import (
    merge_informative_pair,
    CategoricalParameter,
    DecimalParameter,
)
from functools import reduce

# BigZ04HO2
# source: https://github.com/davidzr/freqtrade-strategies/blob/main/strategies/BigZ04HO2/BigZ04HO2.py


class Github_flessner_freqtrade__bz04ho2__20250222_025553(IStrategy):
    INTERFACE_VERSION = 2

    # ROI table:
    minimal_roi = {"0": 0.066, "11": 0.044, "60": 0.031, "85": 0}

    stoploss = -0.03

    timeframe = "5m"
    inf_1h = "1h"

    # Sell signal
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False

    # Trailing stoploss
    trailing_stop = False
    # trailing_only_offset_is_reached = False
    # trailing_stop_positive = 0.01
    # trailing_stop_positive_offset = 0.025

    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = True

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count = 200

    buy_params = {
        "buy_condition_0_enable": True,
        "buy_condition_1_enable": True,
        "buy_condition_2_enable": True,
        "buy_condition_3_enable": True,
        "buy_condition_4_enable": True,
        "buy_condition_5_enable": True,
        "buy_condition_6_enable": True,
        "buy_condition_7_enable": True,
        "buy_condition_8_enable": True,
        "buy_condition_9_enable": True,
        "buy_condition_10_enable": True,
        "buy_condition_11_enable": True,
        "buy_condition_12_enable": True,
        "buy_condition_13_enable": True,
        "buy_bb20_close_bblowerband_safe_1": 0.951,
        "buy_bb20_close_bblowerband_safe_2": 0.743,
        "buy_volume_drop_1": 10.0,
        "buy_volume_drop_2": 9.7,
        "buy_volume_drop_3": 4.1,
        "buy_volume_pump_1": 0.1,
        "buy_rsi_1h_1": 20.3,
        "buy_rsi_1h_2": 17.6,
        "buy_rsi_1h_3": 21.4,
        "buy_rsi_1h_4": 36.2,
        "buy_rsi_1h_5": 35.7,
        "buy_macd_1": 0.01,
        "buy_macd_2": 0.03,
        "buy_condition_0_close": 1.044,
        "buy_condition_0_rsi": 34,
        "buy_condition_0_rsi_1h": 73,
        "buy_condition_11_close_1": 0.115,
        "buy_condition_11_close_2": 0.015,
        "buy_condition_11_rsi": 49.19,
        "buy_condition_12_bblower_close": 0.994,
        "buy_condition_12_bblower_low": 0.983,
        "buy_condition_12_rsi_1h": 72.5,
    }

    ############################################################################

    buy_condition_0_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True
    )
    buy_condition_1_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True
    )
    buy_condition_2_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True
    )
    buy_condition_3_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True
    )
    buy_condition_4_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True
    )
    buy_condition_5_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True
    )
    buy_condition_6_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True
    )
    buy_condition_7_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True
    )
    buy_condition_8_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True
    )
    buy_condition_9_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True
    )
    buy_condition_10_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True
    )
    buy_condition_11_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True
    )
    buy_condition_12_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True
    )
    buy_condition_13_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True
    )

    buy_bb20_optimize = True
    buy_bb20_close_bblowerband_safe_1 = DecimalParameter(
        0.7,
        1.1,
        default=0.989,
        space="buy",
        optimize=buy_bb20_optimize,
    )
    buy_bb20_close_bblowerband_safe_2 = DecimalParameter(
        0.7,
        1.1,
        default=0.982,
        space="buy",
        optimize=buy_bb20_optimize,
    )

    buy_volume_pump_optimize = True
    buy_volume_pump_1 = DecimalParameter(
        0.1,
        0.9,
        default=0.4,
        space="buy",
        decimals=1,
        optimize=buy_volume_pump_optimize,
    )
    buy_volume_drop_1 = DecimalParameter(
        1,
        10,
        default=3.8,
        space="buy",
        decimals=1,
        optimize=buy_volume_pump_optimize,
    )
    buy_volume_drop_2 = DecimalParameter(
        1,
        10,
        default=3,
        space="buy",
        decimals=1,
        optimize=buy_volume_pump_optimize,
    )
    buy_volume_drop_3 = DecimalParameter(
        1,
        10,
        default=2.7,
        space="buy",
        decimals=1,
        optimize=buy_volume_pump_optimize,
    )

    buy_rsi_1h_optimize = True
    buy_rsi_1h_1 = DecimalParameter(
        10.0,
        40.0,
        default=16.5,
        space="buy",
        decimals=1,
        optimize=buy_rsi_1h_optimize,
    )
    buy_rsi_1h_2 = DecimalParameter(
        10.0,
        40.0,
        default=15.0,
        space="buy",
        decimals=1,
        optimize=buy_rsi_1h_optimize,
    )
    buy_rsi_1h_3 = DecimalParameter(
        10.0,
        40.0,
        default=20.0,
        space="buy",
        decimals=1,
        optimize=buy_rsi_1h_optimize,
    )
    buy_rsi_1h_4 = DecimalParameter(
        10.0,
        40.0,
        default=35.0,
        space="buy",
        decimals=1,
        optimize=buy_rsi_1h_optimize,
    )
    buy_rsi_1h_5 = DecimalParameter(
        10.0,
        60.0,
        default=39.0,
        space="buy",
        decimals=1,
        optimize=buy_rsi_1h_optimize,
    )

    buy_rsi_optimize = True
    buy_rsi_1 = DecimalParameter(
        10.0,
        40.0,
        default=28.0,
        space="buy",
        decimals=1,
        optimize=buy_rsi_optimize,
    )
    buy_rsi_2 = DecimalParameter(
        7.0,
        40.0,
        default=10.0,
        space="buy",
        decimals=1,
        optimize=buy_rsi_optimize,
    )
    buy_rsi_3 = DecimalParameter(
        7.0,
        40.0,
        default=14.2,
        space="buy",
        decimals=1,
        optimize=buy_rsi_optimize,
    )

    buy_macd_optimize = True
    buy_macd_1 = DecimalParameter(
        0.01,
        0.09,
        default=0.02,
        space="buy",
        decimals=2,
        optimize=buy_macd_optimize,
    )
    buy_macd_2 = DecimalParameter(
        0.01,
        0.09,
        default=0.03,
        space="buy",
        decimals=2,
        optimize=buy_macd_optimize,
    )

    buy_condition_12_optimize = True
    buy_condition_12_bblower_close = DecimalParameter(
        0.95,
        0.995,
        default=0.993,
        space="buy",
        decimals=3,
        optimize=buy_condition_12_optimize,
    )
    buy_condition_12_bblower_low = DecimalParameter(
        0.95,
        0.99,
        default=0.985,
        space="buy",
        decimals=3,
        optimize=buy_condition_12_optimize,
    )
    buy_condition_12_rsi_1h = DecimalParameter(
        60,
        80,
        default=72.8,
        space="buy",
        decimals=1,
        optimize=buy_condition_12_optimize,
    )

    buy_condition_11_optimize = False
    buy_condition_11_close_1 = DecimalParameter(
        0.05,
        0.15,
        default=0.1,
        space="buy",
        decimals=3,
        optimize=buy_condition_11_optimize,
    )
    buy_condition_11_close_2 = DecimalParameter(
        0.01,
        0.03,
        default=0.018,
        space="buy",
        decimals=3,
        optimize=buy_condition_11_optimize,
    )
    buy_condition_11_rsi = DecimalParameter(
        49,
        53,
        default=51,
        space="buy",
        decimals=2,
        optimize=buy_condition_11_optimize,
    )

    buy_condition_0_optimize = True
    buy_condition_0_rsi = DecimalParameter(
        26,
        34,
        default=30,
        space="buy",
        decimals=1,
        optimize=buy_condition_0_optimize,
    )
    buy_condition_0_close = DecimalParameter(
        1,
        1.2,
        default=1.024,
        space="buy",
        decimals=3,
        optimize=buy_condition_0_optimize,
    )
    buy_condition_0_rsi_1h = DecimalParameter(
        66,
        76,
        default=71,
        space="buy",
        decimals=1,
        optimize=buy_condition_0_optimize,
    )

    buy_condition_1_optimize = True
    buy_condition_1_rsi_1h = DecimalParameter(
        63,
        75,
        default=69,
        space="buy",
        decimals=1,
        optimize=buy_condition_1_optimize,
    )

    buy_condition_10_optimize = True
    buy_condition_10_rsi = DecimalParameter(
        35,
        45,
        default=40.5,
        space="buy",
        decimals=1,
        optimize=buy_condition_10_optimize,
    )
    buy_condition_10_hist_close = DecimalParameter(
        0.0001,
        0.01,
        default=0.0012,
        space="buy",
        decimals=4,
        optimize=buy_condition_10_optimize,
    )

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

    def informative_1h_indicators(
        self, dataframe: DataFrame, metadata: dict
    ) -> DataFrame:
        assert self.dp, "DataProvider is required for multiple timeframes."
        # Get the informative pair
        informative_1h = self.dp.get_pair_dataframe(
            pair=metadata["pair"], timeframe=self.inf_1h
        )
        # EMA
        informative_1h["ema_50"] = ta.EMA(informative_1h, timeperiod=50)
        informative_1h["ema_200"] = ta.EMA(informative_1h, timeperiod=200)
        # RSI
        informative_1h["rsi"] = ta.RSI(informative_1h, timeperiod=14)

        bollinger = qtpylib.bollinger_bands(
            qtpylib.typical_price(dataframe), window=20, stds=2
        )
        informative_1h["bb_lowerband"] = bollinger["lower"]
        informative_1h["bb_middleband"] = bollinger["mid"]
        informative_1h["bb_upperband"] = bollinger["upper"]

        return informative_1h

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

        bollinger = qtpylib.bollinger_bands(
            qtpylib.typical_price(dataframe), window=20, stds=2
        )
        dataframe["bb_lowerband"] = bollinger["lower"]
        dataframe["bb_middleband"] = bollinger["mid"]
        dataframe["bb_upperband"] = bollinger["upper"]

        dataframe["volume_mean_slow"] = dataframe["volume"].rolling(window=48).mean()

        # EMA
        dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200)

        dataframe["ema_26"] = ta.EMA(dataframe, timeperiod=26)
        dataframe["ema_12"] = ta.EMA(dataframe, timeperiod=12)

        # MACD
        dataframe["macd"], dataframe["signal"], dataframe["hist"] = ta.MACD(
            dataframe["close"], fastperiod=12, slowperiod=26, signalperiod=9
        )

        # SMA
        dataframe["sma_5"] = ta.EMA(dataframe, timeperiod=5)

        # RSI
        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)

        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # The indicators for the 1h informative timeframe
        informative_1h = self.informative_1h_indicators(dataframe, metadata)
        dataframe = merge_informative_pair(
            dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True
        )

        # The indicators for the normal (5m) timeframe
        dataframe = self.normal_tf_indicators(dataframe, metadata)

        return dataframe

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

        conditions = []

        conditions.append(
            (
                self.buy_condition_12_enable.value
                & (dataframe["close"] > dataframe["ema_200"])
                & (dataframe["close"] > dataframe["ema_200_1h"])
                & (
                    dataframe["close"]
                    < dataframe["bb_lowerband"]
                    * self.buy_condition_12_bblower_close.value
                )
                & (
                    dataframe["low"]
                    < dataframe["bb_lowerband"]
                    * self.buy_condition_12_bblower_low.value
                )
                & (dataframe["close"].shift() > dataframe["bb_lowerband"])
                & (dataframe["rsi_1h"] < self.buy_condition_12_rsi_1h.value)
                & (dataframe["open"] > dataframe["close"])
                & (
                    dataframe["volume_mean_slow"]
                    > dataframe["volume_mean_slow"].shift(48)
                    * self.buy_volume_pump_1.value
                )
                & (
                    dataframe["volume_mean_slow"] * self.buy_volume_pump_1.value
                    < dataframe["volume_mean_slow"].shift(48)
                )
                & (
                    dataframe["volume"]
                    < (dataframe["volume"].shift() * self.buy_volume_drop_1.value)
                )
                & (
                    (dataframe["open"] - dataframe["close"])
                    < dataframe["bb_upperband"].shift(2)
                    - dataframe["bb_lowerband"].shift(2)
                )
                & (dataframe["volume"] > 0)
            )
        )

        conditions.append(
            (
                self.buy_condition_11_enable.value
                & (dataframe["close"] > dataframe["ema_200"])
                & (dataframe["hist"] > 0)
                & (dataframe["hist"].shift() > 0)
                & (dataframe["hist"].shift(2) > 0)
                & (dataframe["hist"].shift(3) > 0)
                & (dataframe["hist"].shift(5) > 0)
                & (
                    dataframe["bb_middleband"] - dataframe["bb_middleband"].shift(5)
                    > dataframe["close"] / 200
                )
                & (
                    dataframe["bb_middleband"] - dataframe["bb_middleband"].shift(10)
                    > dataframe["close"] / 100
                )
                & (
                    (dataframe["bb_upperband"] - dataframe["bb_lowerband"])
                    < (dataframe["close"] * self.buy_condition_11_close_1.value)
                )
                & (
                    (dataframe["open"].shift() - dataframe["close"].shift())
                    < (dataframe["close"] * self.buy_condition_11_close_2.value)
                )
                & (dataframe["rsi"] > self.buy_condition_11_rsi.value)
                & (dataframe["open"] < dataframe["close"])
                & (dataframe["open"].shift() > dataframe["close"].shift())
                & (dataframe["close"] > dataframe["bb_middleband"])
                & (dataframe["close"].shift() < dataframe["bb_middleband"].shift())
                & (dataframe["low"].shift(2) > dataframe["bb_middleband"].shift(2))
                & (dataframe["volume"] > 0)  # Make sure Volume is not 0
            )
        )

        conditions.append(
            (
                self.buy_condition_0_enable.value
                & (dataframe["close"] > dataframe["ema_200"])
                & (dataframe["rsi"] < self.buy_condition_0_rsi.value)
                & (
                    dataframe["close"] * self.buy_condition_0_close.value
                    < dataframe["open"].shift(3)
                )
                & (dataframe["rsi_1h"] < self.buy_condition_0_rsi_1h.value)
                & (
                    dataframe["volume_mean_slow"]
                    > dataframe["volume_mean_slow"].shift(48)
                    * self.buy_volume_pump_1.value
                )
                & (
                    dataframe["volume_mean_slow"] * self.buy_volume_pump_1.value
                    < dataframe["volume_mean_slow"].shift(48)
                )
                & (dataframe["volume"] > 0)  # Make sure Volume is not 0
            )
        )

        conditions.append(
            (
                self.buy_condition_1_enable.value
                & (dataframe["close"] > dataframe["ema_200"])
                & (dataframe["close"] > dataframe["ema_200_1h"])
                & (
                    dataframe["close"]
                    < dataframe["bb_lowerband"]
                    * self.buy_bb20_close_bblowerband_safe_1.value
                )
                & (dataframe["rsi_1h"] < self.buy_condition_1_rsi_1h.value)
                & (dataframe["open"] > dataframe["close"])
                & (
                    dataframe["volume_mean_slow"]
                    > dataframe["volume_mean_slow"].shift(48)
                    * self.buy_volume_pump_1.value
                )
                & (
                    dataframe["volume_mean_slow"] * self.buy_volume_pump_1.value
                    < dataframe["volume_mean_slow"].shift(48)
                )
                & (
                    dataframe["volume"]
                    < (dataframe["volume"].shift() * self.buy_volume_drop_1.value)
                )
                & (
                    (dataframe["open"] - dataframe["close"])
                    < dataframe["bb_upperband"].shift(2)
                    - dataframe["bb_lowerband"].shift(2)
                )
                & (dataframe["volume"] > 0)
            )
        )

        conditions.append(
            (
                self.buy_condition_2_enable.value
                & (dataframe["close"] > dataframe["ema_200"])
                & (
                    dataframe["close"]
                    < dataframe["bb_lowerband"]
                    * self.buy_bb20_close_bblowerband_safe_2.value
                )
                & (
                    dataframe["volume_mean_slow"]
                    > dataframe["volume_mean_slow"].shift(48)
                    * self.buy_volume_pump_1.value
                )
                & (
                    dataframe["volume_mean_slow"] * self.buy_volume_pump_1.value
                    < dataframe["volume_mean_slow"].shift(48)
                )
                & (
                    dataframe["volume"]
                    < (dataframe["volume"].shift() * self.buy_volume_drop_1.value)
                )
                & (
                    dataframe["open"] - dataframe["close"]
                    < dataframe["bb_upperband"].shift(2)
                    - dataframe["bb_lowerband"].shift(2)
                )
                & (dataframe["volume"] > 0)
            )
        )

        conditions.append(
            (
                self.buy_condition_3_enable.value
                & (dataframe["close"] > dataframe["ema_200_1h"])
                & (dataframe["close"] < dataframe["bb_lowerband"])
                & (dataframe["rsi"] < self.buy_rsi_3.value)
                & (
                    dataframe["volume_mean_slow"]
                    > dataframe["volume_mean_slow"].shift(48)
                    * self.buy_volume_pump_1.value
                )
                & (
                    dataframe["volume_mean_slow"] * self.buy_volume_pump_1.value
                    < dataframe["volume_mean_slow"].shift(48)
                )
                & (
                    dataframe["volume"]
                    < (dataframe["volume"].shift() * self.buy_volume_drop_3.value)
                )
                & (dataframe["volume"] > 0)
            )
        )

        conditions.append(
            (
                self.buy_condition_4_enable.value
                & (dataframe["rsi_1h"] < self.buy_rsi_1h_1.value)
                & (dataframe["close"] < dataframe["bb_lowerband"])
                & (
                    dataframe["volume_mean_slow"]
                    > dataframe["volume_mean_slow"].shift(48)
                    * self.buy_volume_pump_1.value
                )
                & (
                    dataframe["volume_mean_slow"] * self.buy_volume_pump_1.value
                    < dataframe["volume_mean_slow"].shift(48)
                )
                & (
                    dataframe["volume"]
                    < (dataframe["volume"].shift() * self.buy_volume_drop_1.value)
                )
                & (dataframe["volume"] > 0)
            )
        )

        conditions.append(
            (
                self.buy_condition_5_enable.value
                & (dataframe["close"] > dataframe["ema_200"])
                & (dataframe["close"] > dataframe["ema_200_1h"])
                & (dataframe["ema_26"] > dataframe["ema_12"])
                & (
                    (dataframe["ema_26"] - dataframe["ema_12"])
                    > (dataframe["open"] * self.buy_macd_1.value)
                )
                & (
                    (dataframe["ema_26"].shift() - dataframe["ema_12"].shift())
                    > (dataframe["open"] / 100)
                )
                & (dataframe["close"] < (dataframe["bb_lowerband"]))
                & (
                    dataframe["volume"]
                    < (dataframe["volume"].shift() * self.buy_volume_drop_1.value)
                )
                & (
                    dataframe["volume_mean_slow"]
                    > dataframe["volume_mean_slow"].shift(48)
                    * self.buy_volume_pump_1.value
                )
                & (
                    dataframe["volume_mean_slow"] * self.buy_volume_pump_1.value
                    < dataframe["volume_mean_slow"].shift(48)
                )
                & (dataframe["volume"] > 0)  # Make sure Volume is not 0
            )
        )

        conditions.append(
            (
                self.buy_condition_6_enable.value
                & (dataframe["rsi_1h"] < self.buy_rsi_1h_5.value)
                & (dataframe["ema_26"] > dataframe["ema_12"])
                & (
                    (dataframe["ema_26"] - dataframe["ema_12"])
                    > (dataframe["open"] * self.buy_macd_2.value)
                )
                & (
                    (dataframe["ema_26"].shift() - dataframe["ema_12"].shift())
                    > (dataframe["open"] / 100)
                )
                & (dataframe["close"] < (dataframe["bb_lowerband"]))
                & (
                    dataframe["volume_mean_slow"]
                    > dataframe["volume_mean_slow"].shift(48)
                    * self.buy_volume_pump_1.value
                )
                & (
                    dataframe["volume_mean_slow"] * self.buy_volume_pump_1.value
                    < dataframe["volume_mean_slow"].shift(48)
                )
                & (
                    dataframe["volume"]
                    < (dataframe["volume"].shift() * self.buy_volume_drop_1.value)
                )
                & (dataframe["volume"] > 0)
            )
        )

        conditions.append(
            (
                self.buy_condition_7_enable.value
                & (dataframe["rsi_1h"] < self.buy_rsi_1h_2.value)
                & (dataframe["ema_26"] > dataframe["ema_12"])
                & (
                    (dataframe["ema_26"] - dataframe["ema_12"])
                    > (dataframe["open"] * self.buy_macd_1.value)
                )
                & (
                    (dataframe["ema_26"].shift() - dataframe["ema_12"].shift())
                    > (dataframe["open"] / 100)
                )
                & (
                    dataframe["volume"]
                    < (dataframe["volume"].shift() * self.buy_volume_drop_1.value)
                )
                & (
                    dataframe["volume_mean_slow"]
                    > dataframe["volume_mean_slow"].shift(48)
                    * self.buy_volume_pump_1.value
                )
                & (
                    dataframe["volume_mean_slow"] * self.buy_volume_pump_1.value
                    < dataframe["volume_mean_slow"].shift(48)
                )
                & (dataframe["volume"] > 0)
            )
        )

        conditions.append(
            (
                self.buy_condition_8_enable.value
                & (dataframe["rsi_1h"] < self.buy_rsi_1h_3.value)
                & (dataframe["rsi"] < self.buy_rsi_1.value)
                & (
                    dataframe["volume"]
                    < (dataframe["volume"].shift() * self.buy_volume_drop_1.value)
                )
                & (dataframe["volume"] > 0)
            )
        )

        conditions.append(
            (
                self.buy_condition_9_enable.value
                & (dataframe["rsi_1h"] < self.buy_rsi_1h_4.value)
                & (dataframe["rsi"] < self.buy_rsi_2.value)
                & (
                    dataframe["volume"]
                    < (dataframe["volume"].shift() * self.buy_volume_drop_1.value)
                )
                & (
                    dataframe["volume_mean_slow"]
                    > dataframe["volume_mean_slow"].shift(48)
                    * self.buy_volume_pump_1.value
                )
                & (
                    dataframe["volume_mean_slow"] * self.buy_volume_pump_1.value
                    < dataframe["volume_mean_slow"].shift(48)
                )
                & (dataframe["volume"] > 0)
            )
        )

        conditions.append(
            (
                self.buy_condition_10_enable.value
                & (dataframe["rsi_1h"] < self.buy_rsi_1h_4.value)
                & (dataframe["close_1h"] < dataframe["bb_lowerband_1h"])
                & (dataframe["hist"] > 0)
                & (dataframe["hist"].shift(2) < 0)
                & (dataframe["rsi"] < self.buy_condition_10_rsi.value)
                & (
                    dataframe["hist"]
                    > dataframe["close"] * self.buy_condition_10_hist_close.value
                )
                & (dataframe["open"] < dataframe["close"])
                & (dataframe["volume"] > 0)
            )
        )

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

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (
                    dataframe["close"] > dataframe["bb_middleband"] * 1.01
                )  # Don't be gready, sell fast
                & (dataframe["volume"] > 0)  # Make sure Volume is not 0
            ),
            "sell",
        ] = 0
        return dataframe