Lmao

import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
from functools import reduce
import talib.abstract as ta
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import (
    merge_informative_pair,
    DecimalParameter,
    stoploss_from_open,
    RealParameter,
    IntParameter,
    informative,
)
from pandas import DataFrame, Series
from datetime import datetime
import math
import logging
from freqtrade.persistence import Trade
import pandas_ta as pta
from technical.indicators import RMI

logger = logging.getLogger(__name__)


def ewo(dataframe, sma1_length=5, sma2_length=35):
    sma1 = ta.EMA(dataframe, timeperiod=sma1_length)
    sma2 = ta.EMA(dataframe, timeperiod=sma2_length)
    smadif = (sma1 - sma2) / dataframe["close"] * 100
    return smadif


def top_percent_change_dca(dataframe: DataFrame, length: int) -> float:
    """
    Percentage change of the current close from the range maximum Open price

    :param dataframe: DataFrame The original OHLC dataframe
    :param length: int The length to look back
    """
    if length == 0:
        return (dataframe["open"] - dataframe["close"]) / dataframe["close"]
    else:
        return (
            dataframe["open"].rolling(length).max() - dataframe["close"]
        ) / dataframe["close"]


def EWO(dataframe, ema_length=5, ema2_length=3):
    df = dataframe.copy()
    ema1 = ta.EMA(df, timeperiod=ema_length)
    ema2 = ta.EMA(df, timeperiod=ema2_length)
    emadif = (ema1 - ema2) / df["close"] * 100
    return emadif


def williams_r(dataframe: DataFrame, period: int = 14) -> Series:
    """Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to the high and low
    of the past N days (for a given N). It was developed by a publisher and promoter of trading materials, Larry Williams.
    Its purpose is to tell whether a stock or commodity market is trading near the high or the low, or somewhere in between,
    of its recent trading range.
    The oscillator is on a negative scale, from −100 (lowest) up to 0 (highest).
    """

    highest_high = dataframe["high"].rolling(center=False, window=period).max()
    lowest_low = dataframe["low"].rolling(center=False, window=period).min()

    WR = Series(
        (highest_high - dataframe["close"]) / (highest_high - lowest_low),
        name="{0} Williams %R".format(period),
    )

    return WR * -100


def VWAPB(dataframe, window_size=20, num_of_std=1):
    df = dataframe.copy()
    df["vwap"] = qtpylib.rolling_vwap(df, window=window_size)
    rolling_std = df["vwap"].rolling(window=window_size).std()
    df["vwap_low"] = df["vwap"] - (rolling_std * num_of_std)
    df["vwap_high"] = df["vwap"] + (rolling_std * num_of_std)
    return df["vwap_low"], df["vwap"], df["vwap_high"]


def bollinger_bands(stock_price, window_size, num_of_std):
    rolling_mean = stock_price.rolling(window=window_size).mean()
    rolling_std = stock_price.rolling(window=window_size).std()
    lower_band = rolling_mean - (rolling_std * num_of_std)
    return np.nan_to_num(rolling_mean), np.nan_to_num(lower_band)


def chaikin_money_flow(dataframe, n=20, fillna=False) -> Series:
    """Chaikin Money Flow (CMF)
    It measures the amount of Money Flow Volume over a specific period.
    http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_money_flow_cmf
    Args:
        dataframe(pandas.Dataframe): dataframe containing ohlcv
        n(int): n period.
        fillna(bool): if True, fill nan values.
    Returns:
        pandas.Series: New feature generated.
    """
    mfv = (
        (dataframe["close"] - dataframe["low"])
        - (dataframe["high"] - dataframe["close"])
    ) / (dataframe["high"] - dataframe["low"])
    mfv = mfv.fillna(0.0)  # float division by zero
    mfv *= dataframe["volume"]
    cmf = (
        mfv.rolling(n, min_periods=0).sum()
        / dataframe["volume"].rolling(n, min_periods=0).sum()
    )
    if fillna:
        cmf = cmf.replace([np.inf, -np.inf], np.nan).fillna(0)
    return Series(cmf, name="cmf")


def ha_typical_price(bars):
    res = (bars["ha_high"] + bars["ha_low"] + bars["ha_close"]) / 3.0
    return Series(index=bars.index, data=res)


class Lmao(IStrategy):
    """
    PASTE OUTPUT FROM HYPEROPT HERE
    Can be overridden for specific sub-strategies (stake currencies) at the bottom.
    MaxDrawDownRelativeHyperOptLoss
    31/900:    801 trades. 800/0/1 Wins/Draws/Losses. Avg profit   4.04%. Median profit   3.70%. Total profit 57.02887841 USDT (   5.70%). Avg duration 3:05:00 min. Objective: -14799833083.36105
    """

    can_short = False
    buy_params = {
        "bbdelta_close": 0.00082,
        "bbdelta_tail": 0.85788,
        "close_bblower": 0.00128,
        "closedelta_close": 0.00987,
        "low_offset": 0.991,
        "rocr1_1h": 0.9346,
        "rocr_1h": 0.65666,
        "base_nb_candles_buy": 5,  # value loaded from strategy
        "buy_bb_delta": 0.015,  # value loaded from strategy
        "buy_bb_factor": 0.995,  # value loaded from strategy
        "buy_bb_width": 0.035,  # value loaded from strategy
        "buy_bb_width_1h": 1.074,  # value loaded from strategy
        "buy_cci": -116,  # value loaded from strategy
        "buy_cci_length": 25,  # value loaded from strategy
        "buy_closedelta": 4.0,  # value loaded from strategy
        "buy_clucha_bbdelta_close": 0.049,  # value loaded from strategy
        "buy_clucha_bbdelta_tail": 1.146,  # value loaded from strategy
        "buy_clucha_close_bblower": 0.018,  # value loaded from strategy
        "buy_clucha_closedelta_close": 0.017,  # value loaded from strategy
        "buy_clucha_rocr_1h": 0.526,  # value loaded from strategy
        "buy_ema_diff": 0.025,  # value loaded from strategy
        "buy_rmi": 49,  # value loaded from strategy
        "buy_rmi_length": 17,  # value loaded from strategy
        "buy_roc_1h": 10,  # value loaded from strategy
        "buy_srsi_fk": 32,  # value loaded from strategy
    }

    sell_params = {
        "high_offset": 1.012,
        "high_offset_2": 1.016,
        "sell_deadfish_bb_factor": 1.089,
        "sell_deadfish_bb_width": 0.11,
        "sell_deadfish_profit": -0.107,
        "sell_deadfish_volume_factor": 1.761,
        "base_nb_candles_sell": 8,  # value loaded from strategy
        "pHSL": -0.397,  # value loaded from strategy
        "pPF_1": 0.012,  # value loaded from strategy
        "pPF_2": 0.07,  # value loaded from strategy
        "pSL_1": 0.015,  # value loaded from strategy
        "pSL_2": 0.068,  # value loaded from strategy
        "sell_bbmiddle_close": 1.09092,  # value loaded from strategy
        "sell_fisher": 0.46406,  # value loaded from strategy
        "sell_trail_down_1": 0.03,  # value loaded from strategy
        "sell_trail_down_2": 0.015,  # value loaded from strategy
        "sell_trail_profit_max_1": 0.4,  # value loaded from strategy
        "sell_trail_profit_max_2": 0.11,  # value loaded from strategy
        "sell_trail_profit_min_1": 0.1,  # value loaded from strategy
        "sell_trail_profit_min_2": 0.04,  # value loaded from strategy
    }

    minimal_roi = {"0": 0.006, "15": 0.003, "30": 0}

    position_adjustment_enable = True

    stoploss = -0.99  # use custom stoploss

    trailing_stop = False
    trailing_stop_positive = 0.003  # povodne 0.001
    trailing_stop_positive_offset = 0.007  # povodne 0.012
    trailing_only_offset_is_reached = True

    """
    END HYPEROPT
    """

    timeframe = "5m"

    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False

    use_custom_stoploss = True

    process_only_new_candles = True
    startup_candle_count = 48

    order_types = {
        "entry": "market",
        "exit": "market",
        "stoploss": "market",
        "stoploss_on_exchange": False,
    }

    def is_support(self, row_data) -> bool:
        conditions = []
        for row in range(len(row_data) - 1):
            if row < len(row_data) / 2:
                conditions.append(row_data[row] > row_data[row + 1])
            else:
                conditions.append(row_data[row] < row_data[row + 1])
        return reduce(lambda x, y: x & y, conditions)

    fast_ewo = 50
    slow_ewo = 200

    buy_44_ma_offset = 0.982
    buy_44_ewo = -18.143
    buy_44_cti = -0.8
    buy_44_r_1h = -75.0

    buy_37_ma_offset = 0.98
    buy_37_ewo = 9.8
    buy_37_rsi = 56.0
    buy_37_cti = -0.7

    buy_ema_open_mult_7 = 0.030
    buy_cti_7 = -0.89

    is_optimize_dip = False
    buy_rmi = IntParameter(30, 50, default=35, optimize=is_optimize_dip)
    buy_cci = IntParameter(-135, -90, default=-133, optimize=is_optimize_dip)
    buy_srsi_fk = IntParameter(30, 50, default=25, optimize=is_optimize_dip)
    buy_cci_length = IntParameter(25, 45, default=25, optimize=is_optimize_dip)
    buy_rmi_length = IntParameter(8, 20, default=8, optimize=is_optimize_dip)

    is_optimize_break = False
    buy_bb_width = DecimalParameter(
        0.065, 0.135, default=0.095, optimize=is_optimize_break
    )
    buy_bb_delta = DecimalParameter(
        0.018, 0.035, default=0.025, optimize=is_optimize_break
    )

    is_optimize_check = False
    buy_roc_1h = IntParameter(-25, 200, default=10, optimize=is_optimize_check)
    buy_bb_width_1h = DecimalParameter(
        0.3, 2.0, default=0.3, optimize=is_optimize_check
    )

    is_optimize_clucha = False
    buy_clucha_bbdelta_close = DecimalParameter(
        0.01, 0.05, default=0.02206, optimize=is_optimize_clucha
    )
    buy_clucha_bbdelta_tail = DecimalParameter(
        0.7, 1.2, default=1.02515, optimize=is_optimize_clucha
    )
    buy_clucha_close_bblower = DecimalParameter(
        0.001, 0.05, default=0.03669, optimize=is_optimize_clucha
    )
    buy_clucha_closedelta_close = DecimalParameter(
        0.001, 0.05, default=0.04401, optimize=is_optimize_clucha
    )
    buy_clucha_rocr_1h = DecimalParameter(
        0.1, 1.0, default=0.47782, optimize=is_optimize_clucha
    )

    is_optimize_local_uptrend = False
    buy_ema_diff = DecimalParameter(
        0.022, 0.027, default=0.025, optimize=is_optimize_local_uptrend
    )
    buy_bb_factor = DecimalParameter(0.990, 0.999, default=0.995, optimize=False)
    buy_closedelta = DecimalParameter(
        12.0, 18.0, default=15.0, optimize=is_optimize_local_uptrend
    )

    rocr_1h = RealParameter(0.5, 1.0, default=0.54904, space="buy", optimize=True)
    rocr1_1h = RealParameter(0.5, 1.0, default=0.72, space="buy", optimize=True)
    bbdelta_close = RealParameter(
        0.0005, 0.02, default=0.01965, space="buy", optimize=True
    )
    closedelta_close = RealParameter(
        0.0005, 0.02, default=0.00556, space="buy", optimize=True
    )
    bbdelta_tail = RealParameter(0.7, 1.0, default=0.95089, space="buy", optimize=True)
    close_bblower = RealParameter(
        0.0005, 0.02, default=0.00799, space="buy", optimize=True
    )

    sell_fisher = RealParameter(0.1, 0.5, default=0.38414, space="sell", optimize=False)
    sell_bbmiddle_close = RealParameter(
        0.97, 1.1, default=1.07634, space="sell", optimize=False
    )

    is_optimize_deadfish = True
    sell_deadfish_bb_width = DecimalParameter(
        0.03, 0.75, default=0.05, space="sell", optimize=is_optimize_deadfish
    )
    sell_deadfish_profit = DecimalParameter(
        -0.15, -0.05, default=-0.08, space="sell", optimize=is_optimize_deadfish
    )
    sell_deadfish_bb_factor = DecimalParameter(
        0.90, 1.20, default=1.0, space="sell", optimize=is_optimize_deadfish
    )
    sell_deadfish_volume_factor = DecimalParameter(
        1, 2.5, default=1.5, space="sell", optimize=is_optimize_deadfish
    )

    base_nb_candles_buy = IntParameter(
        8, 20, default=buy_params["base_nb_candles_buy"], space="buy", optimize=False
    )
    base_nb_candles_sell = IntParameter(
        8, 20, default=sell_params["base_nb_candles_sell"], space="sell", optimize=False
    )
    low_offset = DecimalParameter(
        0.985, 0.995, default=buy_params["low_offset"], space="buy", optimize=True
    )
    high_offset = DecimalParameter(
        1.005, 1.015, default=sell_params["high_offset"], space="sell", optimize=True
    )
    high_offset_2 = DecimalParameter(
        1.010, 1.020, default=sell_params["high_offset_2"], space="sell", optimize=True
    )

    sell_trail_profit_min_1 = DecimalParameter(
        0.1, 0.25, default=0.1, space="sell", decimals=3, optimize=False, load=True
    )
    sell_trail_profit_max_1 = DecimalParameter(
        0.3, 0.5, default=0.4, space="sell", decimals=2, optimize=False, load=True
    )
    sell_trail_down_1 = DecimalParameter(
        0.04, 0.1, default=0.03, space="sell", decimals=3, optimize=False, load=True
    )

    sell_trail_profit_min_2 = DecimalParameter(
        0.04, 0.1, default=0.04, space="sell", decimals=3, optimize=False, load=True
    )
    sell_trail_profit_max_2 = DecimalParameter(
        0.08, 0.25, default=0.11, space="sell", decimals=2, optimize=False, load=True
    )
    sell_trail_down_2 = DecimalParameter(
        0.04, 0.2, default=0.015, space="sell", decimals=3, optimize=False, load=True
    )

    pHSL = DecimalParameter(
        -0.500,
        -0.040,
        default=-0.08,
        decimals=3,
        space="sell",
        optimize=False,
        load=True,
    )

    pPF_1 = DecimalParameter(
        0.008, 0.020, default=0.016, decimals=3, space="sell", optimize=False, load=True
    )
    pSL_1 = DecimalParameter(
        0.008, 0.020, default=0.011, decimals=3, space="sell", optimize=False, load=True
    )

    pPF_2 = DecimalParameter(
        0.040, 0.100, default=0.080, decimals=3, space="sell", optimize=False, load=True
    )
    pSL_2 = DecimalParameter(
        0.020, 0.070, default=0.040, decimals=3, space="sell", optimize=False, load=True
    )

    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, "15m") for pair in pairs]
        informative_pairs += [(pair, "1h") for pair in pairs]
        return informative_pairs

    def custom_exit(
        self,
        pair: str,
        trade: "Trade",
        current_time: "datetime",
        current_rate: float,
        current_profit: float,
        **kwargs,
    ):
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()
        filled_buys = trade.select_filled_orders("buy")
        count_of_buys = len(filled_buys)

        if last_candle is not None:

            if (
                (current_profit > self.sell_trail_profit_min_1.value)
                & (current_profit < self.sell_trail_profit_max_1.value)
                & (
                    ((trade.max_rate - trade.open_rate) / 100)
                    > (current_profit + self.sell_trail_down_1.value)
                )
            ):
                return "trail_target_1"
            elif (
                (current_profit > self.sell_trail_profit_min_2.value)
                & (current_profit < self.sell_trail_profit_max_2.value)
                & (
                    ((trade.max_rate - trade.open_rate) / 100)
                    > (current_profit + self.sell_trail_down_2.value)
                )
            ):
                return "trail_target_2"
            elif (current_profit > 3) & (last_candle["rsi"] > 85):
                return "RSI-85 target"

            if (
                (current_profit > 0)
                & (count_of_buys < 4)
                & (last_candle["close"] > last_candle["hma_50"])
                & (
                    last_candle["close"]
                    > (
                        last_candle[f"ma_sell_{self.base_nb_candles_sell.value}"]
                        * self.high_offset_2.value
                    )
                )
                & (last_candle["rsi"] > 50)
                & (last_candle["volume"] > 0)
                & (last_candle["rsi_fast"] > last_candle["rsi_slow"])
            ):
                return "sell signal1"
            if (
                (current_profit > 0)
                & (count_of_buys >= 4)
                & (last_candle["close"] > last_candle["hma_50"] * 1.01)
                & (
                    last_candle["close"]
                    > (
                        last_candle[f"ma_sell_{self.base_nb_candles_sell.value}"]
                        * self.high_offset_2.value
                    )
                )
                & (last_candle["rsi"] > 50)
                & (last_candle["volume"] > 0)
                & (last_candle["rsi_fast"] > last_candle["rsi_slow"])
            ):
                return "sell signal1 * 1.01"
            if (
                (current_profit > 0)
                & (last_candle["close"] > last_candle["hma_50"])
                & (
                    last_candle["close"]
                    > (
                        last_candle[f"ma_sell_{self.base_nb_candles_sell.value}"]
                        * self.high_offset.value
                    )
                )
                & (last_candle["volume"] > 0)
                & (last_candle["rsi_fast"] > last_candle["rsi_slow"])
            ):
                return "sell signal2"

                return "sell stoploss1"

            if (
                (current_profit < self.sell_deadfish_profit.value)
                and (last_candle["close"] < last_candle["ema_200"])
                and (last_candle["bb_width"] < self.sell_deadfish_bb_width.value)
                and (
                    last_candle["close"]
                    > last_candle["bb_middleband2"] * self.sell_deadfish_bb_factor.value
                )
                and (
                    last_candle["volume_mean_12"]
                    < last_candle["volume_mean_24"]
                    * self.sell_deadfish_volume_factor.value
                )
                and (last_candle["cmf"] < 0.0)
            ):
                return f"sell_stoploss_deadfish"

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

        # The original hyperopt floor allows deep bag-holding, which is what
        # dominated the recent force-exit losses in broader backtests.
        HSL = max(self.pHSL.value, -0.05)
        PF_1 = self.pPF_1.value
        SL_1 = self.pSL_1.value
        PF_2 = self.pPF_2.value
        SL_2 = self.pSL_2.value

        if current_profit > PF_2:
            sl_profit = SL_2 + (current_profit - PF_2)
        elif current_profit > PF_1:
            sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1))
        else:
            sl_profit = HSL

        if sl_profit >= current_profit:
            return -0.99

        return stoploss_from_open(sl_profit, current_profit)

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Main timeframe indicators
        inf_tf = "5m"
        btc_info_pair = "BTC/USDT"
        if self.config.get("trading_mode", "spot") == "futures":
            stake_currency = self.config.get("stake_currency", "USDT")
            btc_info_pair = f"BTC/{stake_currency}:{stake_currency}"

        informative = self.dp.get_pair_dataframe(btc_info_pair, timeframe=inf_tf)
        if informative.empty or "close" not in informative.columns:
            logger.warning(
                "Missing BTC informative data for %s on %s. Falling back to pair close.",
                btc_info_pair,
                inf_tf,
            )
            dataframe["btc_close"] = dataframe["close"]
            dataframe["btc_ema_fast"] = ta.EMA(dataframe, timeperiod=20)
            dataframe["btc_ema_slow"] = ta.EMA(dataframe, timeperiod=25)
            dataframe["down"] = (
                dataframe["btc_ema_fast"] < dataframe["btc_ema_slow"]
            ).astype("int")
        else:
            informative_btc = informative.copy().shift(1)

            dataframe["btc_close"] = informative_btc["close"]
            dataframe["btc_ema_fast"] = ta.EMA(informative_btc, timeperiod=20)
            dataframe["btc_ema_slow"] = ta.EMA(informative_btc, timeperiod=25)
            dataframe["down"] = (
                dataframe["btc_ema_fast"] < dataframe["btc_ema_slow"]
            ).astype("int")

        for val in self.base_nb_candles_sell.range:
            dataframe[f"ma_sell_{val}"] = ta.EMA(dataframe, timeperiod=val)

        dataframe["volume_mean_12"] = dataframe["volume"].rolling(12).mean().shift(1)
        dataframe["volume_mean_24"] = dataframe["volume"].rolling(24).mean().shift(1)

        dataframe["cmf"] = chaikin_money_flow(dataframe, 20)

        bollinger2 = qtpylib.bollinger_bands(
            qtpylib.typical_price(dataframe), window=20, stds=2
        )
        dataframe["bb_lowerband2"] = bollinger2["lower"]
        dataframe["bb_middleband2"] = bollinger2["mid"]
        dataframe["bb_upperband2"] = bollinger2["upper"]
        dataframe["bb_width"] = (
            dataframe["bb_upperband2"] - dataframe["bb_lowerband2"]
        ) / dataframe["bb_middleband2"]

        bollinger2_40 = qtpylib.bollinger_bands(
            qtpylib.typical_price(dataframe), window=40, stds=2
        )
        dataframe["bb_lowerband2_40"] = bollinger2_40["lower"]
        dataframe["bb_middleband2_40"] = bollinger2_40["mid"]
        dataframe["bb_upperband2_40"] = bollinger2_40["upper"]

        dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200)
        dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50)

        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
        dataframe["rsi_fast"] = ta.RSI(dataframe, timeperiod=4)
        dataframe["rsi_slow"] = ta.RSI(dataframe, timeperiod=20)
        dataframe["rsi_84"] = ta.RSI(dataframe, timeperiod=84)
        dataframe["rsi_112"] = ta.RSI(dataframe, timeperiod=112)

        heikinashi = qtpylib.heikinashi(dataframe)
        dataframe["ha_open"] = heikinashi["open"]
        dataframe["ha_close"] = heikinashi["close"]
        dataframe["ha_high"] = heikinashi["high"]
        dataframe["ha_low"] = heikinashi["low"]

        dataframe["bb_delta_cluc"] = (
            dataframe["bb_middleband2_40"] - dataframe["bb_lowerband2_40"]
        ).abs()
        dataframe["ha_closedelta"] = (
            dataframe["ha_close"] - dataframe["ha_close"].shift()
        ).abs()

        stoch = ta.STOCHRSI(dataframe, 15, 20, 2, 2)
        dataframe["srsi_fk"] = stoch["fastk"]
        dataframe["srsi_fd"] = stoch["fastd"]

        mid, lower = bollinger_bands(
            ha_typical_price(dataframe), window_size=40, num_of_std=2
        )
        dataframe["lower"] = lower
        dataframe["mid"] = mid

        dataframe["bbdelta"] = (mid - dataframe["lower"]).abs()
        dataframe["closedelta"] = (
            dataframe["ha_close"] - dataframe["ha_close"].shift()
        ).abs()
        dataframe["tail"] = (dataframe["ha_close"] - dataframe["ha_low"]).abs()

        dataframe["bb_lowerband"] = dataframe["lower"]
        dataframe["bb_middleband"] = dataframe["mid"]

        bollinger3 = qtpylib.bollinger_bands(
            qtpylib.typical_price(dataframe), window=20, stds=3
        )
        dataframe["bb_lowerband3"] = bollinger3["lower"]
        dataframe["bb_middleband3"] = bollinger3["mid"]
        dataframe["bb_upperband3"] = bollinger3["upper"]
        dataframe["bb_delta"] = (
            dataframe["bb_lowerband2"] - dataframe["bb_lowerband3"]
        ) / dataframe["bb_lowerband2"]

        dataframe["ema_fast"] = ta.EMA(dataframe["ha_close"], timeperiod=3)
        dataframe["ema_slow"] = ta.EMA(dataframe["ha_close"], timeperiod=50)
        dataframe["volume_mean_slow"] = dataframe["volume"].rolling(window=30).mean()
        dataframe["rocr"] = ta.ROCR(dataframe["ha_close"], timeperiod=28)

        vwap_low, vwap, vwap_high = VWAPB(dataframe, 20, 1)
        dataframe["vwap_low"] = vwap_low
        dataframe["vwap_upperband"] = vwap_high
        dataframe["vwap_middleband"] = vwap
        dataframe["vwap_lowerband"] = vwap_low
        dataframe["vwap_width"] = (
            (dataframe["vwap_upperband"] - dataframe["vwap_lowerband"])
            / dataframe["vwap_middleband"]
        ) * 100

        dataframe["ema_vwap_diff_50"] = (
            dataframe["ema_50"] - dataframe["vwap_lowerband"]
        ) / dataframe["ema_50"]

        dataframe["tpct_change_0"] = top_percent_change_dca(dataframe, 0)
        dataframe["tpct_change_1"] = top_percent_change_dca(dataframe, 1)
        dataframe["tcp_percent_4"] = top_percent_change_dca(dataframe, 4)

        dataframe["ewo"] = ewo(dataframe, 50, 200)

        for val in self.buy_rmi_length.range:
            dataframe[f"rmi_length_{val}"] = RMI(dataframe, length=val, mom=4)

        for val in self.buy_cci_length.range:
            dataframe[f"cci_length_{val}"] = ta.CCI(dataframe, val)

        dataframe["cti"] = pta.cti(dataframe["close"], length=20)

        dataframe["bb_delta_cluc"] = (
            dataframe["bb_middleband2_40"] - dataframe["bb_lowerband2_40"]
        ).abs()

        dataframe["ema_16"] = ta.EMA(dataframe, timeperiod=16)

        dataframe["EWO"] = EWO(dataframe, self.fast_ewo, self.slow_ewo)

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

        dataframe["r_14"] = williams_r(dataframe, period=14)

        dataframe["ema_5"] = ta.EMA(dataframe, timeperiod=5)
        dataframe["ema_10"] = ta.EMA(dataframe, timeperiod=10)

        dataframe["pm"], dataframe["pmx"] = pmax(
            heikinashi, MAtype=1, length=9, multiplier=27, period=10, src=3
        )
        dataframe["source"] = (
            dataframe["high"]
            + dataframe["low"]
            + dataframe["open"]
            + dataframe["close"]
        ) / 4
        dataframe["pmax_thresh"] = ta.EMA(dataframe["source"], timeperiod=9)
        dataframe["sma_75"] = ta.SMA(dataframe, timeperiod=75)

        rsi = ta.RSI(dataframe)
        dataframe["rsi"] = rsi
        rsi = 0.1 * (rsi - 50)
        dataframe["fisher"] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)

        # --- Informative 1h timeframe
        inf_tf = "1h"
        informative = self.dp.get_pair_dataframe(
            pair=metadata["pair"], timeframe=inf_tf
        )

        inf_heikinashi = qtpylib.heikinashi(informative)

        informative["ha_close"] = inf_heikinashi["close"]
        informative["rocr"] = ta.ROCR(informative["ha_close"], timeperiod=168)
        informative["rsi_14"] = ta.RSI(dataframe, timeperiod=14)
        informative["cmf"] = chaikin_money_flow(dataframe, 20)

        sup_series = (
            informative["low"]
            .rolling(window=5)
            .apply(lambda row: self.is_support(row), raw=True)
        )
        informative["sup_level"] = Series(
            np.where(
                sup_series,
                np.where(
                    informative["close"] < informative["open"],
                    informative["close"],
                    informative["open"],
                ),
                float("NaN"),
            )
        ).ffill()
        informative["roc"] = ta.ROC(informative, timeperiod=9)

        informative["r_480"] = williams_r(informative, period=480)

        bollinger2 = qtpylib.bollinger_bands(
            qtpylib.typical_price(informative), window=20, stds=2
        )
        informative["bb_lowerband2"] = bollinger2["lower"]
        informative["bb_middleband2"] = bollinger2["mid"]
        informative["bb_upperband2"] = bollinger2["upper"]
        informative["bb_width"] = (
            informative["bb_upperband2"] - informative["bb_lowerband2"]
        ) / informative["bb_middleband2"]

        informative["r_84"] = williams_r(informative, period=84)
        informative["cti_40"] = pta.cti(informative["close"], length=40)

        # --- Informative 15m timeframe
        inf_tf_15m = "15m"
        informative_15m = self.dp.get_pair_dataframe(
            pair=metadata["pair"], timeframe=inf_tf_15m
        )
        informative_15m["rsi_14_15m"] = ta.RSI(informative_15m, timeperiod=14)
        informative_15m["cmf_15m"] = chaikin_money_flow(informative_15m, 20)
        informative_15m["ema_15m"] = ta.EMA(informative_15m, timeperiod=50)

        dataframe["hma_50"] = qtpylib.hull_moving_average(dataframe["close"], window=50)

        dataframe = merge_informative_pair(
            dataframe, informative, self.timeframe, inf_tf, ffill=True
        )
        dataframe = merge_informative_pair(
            dataframe, informative_15m, self.timeframe, inf_tf_15m, ffill=True
        )

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (
                    dataframe[f"rmi_length_{self.buy_rmi_length.value}"]
                    < self.buy_rmi.value
                )
                & (
                    dataframe[f"cci_length_{self.buy_cci_length.value}"]
                    <= self.buy_cci.value
                )
                & (dataframe["srsi_fk"] < self.buy_srsi_fk.value)
                & (dataframe["bb_delta"] > self.buy_bb_delta.value)
                & (dataframe["bb_width"] > self.buy_bb_width.value)
                & (dataframe["closedelta"] > 0)  # from BinH
                & (
                    dataframe["close"]
                    < dataframe["bb_lowerband3"] * self.buy_bb_factor.value
                )
                & (dataframe["roc_1h"] < self.buy_roc_1h.value)
                & (dataframe["bb_width_1h"] < self.buy_bb_width_1h.value)
            ),
            ["enter_long", "enter_tag"],
        ] = (1, "DIP signal")

        dataframe.loc[
            (
                (dataframe["bb_delta"] > self.buy_bb_delta.value)
                & (dataframe["bb_width"] > self.buy_bb_width.value)
                & (
                    dataframe["closedelta"]
                    > dataframe["close"] * self.buy_closedelta.value / 1000
                )  # from BinH
                & (
                    dataframe["close"]
                    < dataframe["bb_lowerband3"] * self.buy_bb_factor.value
                )
                & (dataframe["roc_1h"] < self.buy_roc_1h.value)
                & (dataframe["bb_width_1h"] < self.buy_bb_width_1h.value)
            ),
            ["enter_long", "enter_tag"],
        ] = (1, "Break signal")

        dataframe.loc[
            (
                (dataframe["rocr_1h"] > self.buy_clucha_rocr_1h.value)
                & (dataframe["bb_lowerband2_40"].shift() > 0)
                & (
                    dataframe["bb_delta_cluc"]
                    > dataframe["ha_close"] * self.buy_clucha_bbdelta_close.value
                )
                & (
                    dataframe["ha_closedelta"]
                    > dataframe["ha_close"] * self.buy_clucha_closedelta_close.value
                )
                & (
                    dataframe["tail"]
                    < dataframe["bb_delta_cluc"] * self.buy_clucha_bbdelta_tail.value
                )
                & (dataframe["ha_close"] < dataframe["bb_lowerband2_40"].shift())
                & (dataframe["close"] > (dataframe["sup_level_1h"] * 0.88))
                & (dataframe["ha_close"] < dataframe["ha_close"].shift())
            ),
            ["enter_long", "enter_tag"],
        ] = (1, "cluc_HA")

        dataframe.loc[
            (
                (dataframe["ema_200"] > (dataframe["ema_200"].shift(12) * 1.01))
                & (dataframe["ema_200"] > (dataframe["ema_200"].shift(48) * 1.07))
                & (dataframe["bb_lowerband2_40"].shift().gt(0))
                & (dataframe["bb_delta_cluc"].gt(dataframe["close"] * 0.056))
                & (dataframe["closedelta"].gt(dataframe["close"] * 0.01))
                & (dataframe["tail"].lt(dataframe["bb_delta_cluc"] * 0.5))
                & (dataframe["close"].lt(dataframe["bb_lowerband2_40"].shift()))
                & (dataframe["close"].le(dataframe["close"].shift()))
                & (dataframe["close"] > dataframe["ema_50"] * 0.912)
            ),
            ["enter_long", "enter_tag"],
        ] = (1, "NFIX39")

        dataframe.loc[
            (
                (dataframe["close"] > (dataframe["sup_level_1h"] * 0.72))
                & (dataframe["close"] < (dataframe["ema_16"] * 0.982))
                & (dataframe["EWO"] < -10.0)
                & (dataframe["cti"] < -0.9)
            ),
            ["enter_long", "enter_tag"],
        ] = (1, "NFIX29")

        dataframe.loc[
            (
                (dataframe["ema_26"] > dataframe["ema_12"])
                & (
                    dataframe["ema_26"] - dataframe["ema_12"]
                    > dataframe["open"] * self.buy_ema_diff.value
                )
                & (
                    dataframe["ema_26"].shift() - dataframe["ema_12"].shift()
                    > dataframe["open"] / 100
                )
                & (
                    dataframe["close"]
                    < dataframe["bb_lowerband2"] * self.buy_bb_factor.value
                )
                & (
                    dataframe["closedelta"]
                    > dataframe["close"] * self.buy_closedelta.value / 1000
                )
            ),
            ["enter_long", "enter_tag"],
        ] = (1, "local_uptrend")

        # --- VWAP signal (restored from prior strategy) ---
        dataframe.loc[
            (
                (dataframe["close"] < dataframe["vwap_low"])
                & (dataframe["tcp_percent_4"] > 0.053)
                & (dataframe["cti"] < -0.8)
                & (dataframe["rsi"] < 35)
                & (dataframe["rsi_84"] < 60)
                & (dataframe["rsi_112"] < 60)
                & (dataframe["volume"] > 0)
            ),
            ["enter_long", "enter_tag"],
        ] = (1, "vwap")

        dataframe.loc[
            (
                (dataframe["bb_width_1h"] > 0.131)
                & (dataframe["r_14"] < -51)
                & (dataframe["r_84_1h"] < -70)
                & (dataframe["cti"] < -0.845)
                & (dataframe["cti_40_1h"] < -0.735)
                & ((dataframe["close"].rolling(48).max() >= (dataframe["close"] * 1.1)))
                & (
                    dataframe["btc_close"].rolling(24).max()
                    >= (dataframe["btc_close"] * 1.03)
                )
            ),
            ["enter_long", "enter_tag"],
        ] = (1, "insta_signal")

        dataframe.loc[
            (
                (dataframe["close"] < (dataframe["ema_16"] * self.buy_44_ma_offset))
                & (dataframe["ewo"] < self.buy_44_ewo)
                & (dataframe["cti"] < self.buy_44_cti)
                & (dataframe["r_480_1h"] < self.buy_44_r_1h)
                & (dataframe["volume"] > 0)
            ),
            ["enter_long", "enter_tag"],
        ] = (1, "NFINext44")

        dataframe.loc[
            (
                (dataframe["pm"] > dataframe["pmax_thresh"])
                & (dataframe["close"] < dataframe["sma_75"] * self.buy_37_ma_offset)
                & (dataframe["ewo"] > self.buy_37_ewo)
                & (dataframe["rsi"] < self.buy_37_rsi)
                & (dataframe["cti"] < self.buy_37_cti)
            ),
            ["enter_long", "enter_tag"],
        ] = (1, "NFINext37")

        dataframe.loc[
            (
                (dataframe["ema_26"] > dataframe["ema_12"])
                & (
                    (dataframe["ema_26"] - dataframe["ema_12"])
                    > (dataframe["open"] * self.buy_ema_open_mult_7)
                )
                & (
                    (dataframe["ema_26"].shift() - dataframe["ema_12"].shift())
                    > (dataframe["open"] / 100)
                )
                & (dataframe["cti"] < self.buy_cti_7)
            ),
            ["enter_long", "enter_tag"],
        ] = (1, "NFINext7")

        # --- Oversold RSI Rebound signal ---
        dataframe.loc[
            (
                (dataframe["rsi"] < 30)
                & (dataframe["rsi_fast"] > dataframe["rsi_slow"])
                & (dataframe["volume"] > 0)
            ),
            ["enter_long", "enter_tag"],
        ] = (1, "oversold_rebound")

        # --- Bounce Reversal at Support signal ---
        dataframe.loc[
            (
                (dataframe["close"] > dataframe["sup_level_1h"] * 1.01)
                & (dataframe["close"] < dataframe["sup_level_1h"] * 1.05)
                & (dataframe["volume"] > dataframe["volume_mean_24"])
            ),
            ["enter_long", "enter_tag"],
        ] = (1, "bounce_support")

        # --- Range-bound Mean Reversion signal ---
        dataframe.loc[
            (
                (dataframe["bb_width"] < 0.04)
                & (dataframe["close"] < dataframe["bb_middleband"])
                & (dataframe["rsi"] > 40)
                & (dataframe["rsi"] < 60)
            ),
            ["enter_long", "enter_tag"],
        ] = (1, "mean_reversion_range")

        return dataframe

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

        dataframe.loc[
            (dataframe["fisher"] > self.sell_fisher.value)
            & (dataframe["ha_high"].le(dataframe["ha_high"].shift(1)))
            & (dataframe["ha_high"].shift(1).le(dataframe["ha_high"].shift(2)))
            & (dataframe["ha_close"].le(dataframe["ha_close"].shift(1)))
            & (dataframe["ema_fast"] > dataframe["ha_close"])
            & (
                (dataframe["ha_close"] * self.sell_bbmiddle_close.value)
                > dataframe["bb_middleband"]
            )
            & (dataframe["volume"] > 0),
            "exit_long",
        ] = 0

        return dataframe

    initial_safety_order_trigger = -0.018
    max_safety_orders = 8
    safety_order_step_scale = 1.2
    safety_order_volume_scale = 1.4

    def top_percent_change_dca(self, dataframe: DataFrame, length: int) -> float:
        """
        Percentage change of the current close from the range maximum Open price

        :param dataframe: DataFrame The original OHLC dataframe
        :param length: int The length to look back
        """
        if length == 0:
            return (dataframe["open"] - dataframe["close"]) / dataframe["close"]
        else:
            return (
                dataframe["open"].rolling(length).max() - dataframe["close"]
            ) / dataframe["close"]

    def adjust_trade_position(
        self,
        trade: Trade,
        current_time: datetime,
        current_rate: float,
        current_profit: float,
        min_stake: float,
        max_stake: float,
        **kwargs,
    ):
        if current_profit > self.initial_safety_order_trigger:
            return None

        dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)

        last_candle = dataframe.iloc[-1].squeeze()

        filled_buys = trade.select_filled_orders("buy")
        count_of_buys = len(filled_buys)
        if (
            count_of_buys == 1
            and (last_candle["tpct_change_0"] > 0.001)
            and (last_candle["close"] < last_candle["open"])
        ):

            return None
        elif (
            count_of_buys == 2
            and (last_candle["tpct_change_0"] > 0.001)
            and (last_candle["close"] < last_candle["open"])
            and (last_candle["ema_vwap_diff_50"] < 0.5)
        ):

            return None
        elif (
            count_of_buys == 3
            and (last_candle["tpct_change_0"] > 0.001)
            and (last_candle["close"] < last_candle["open"])
            and (last_candle["ema_vwap_diff_50"] < 0.5)
        ):

            return None
        elif (
            count_of_buys == 4
            and (last_candle["tpct_change_0"] > 0.001)
            and (last_candle["close"] < last_candle["open"])
            and (last_candle["ema_vwap_diff_50"] < 0.5)
            and (last_candle["ema_5"]) >= (last_candle["ema_10"])
        ):

            return None
        elif (
            count_of_buys == 5
            and (last_candle["cmf_1h"] < 0.00)
            and (last_candle["close"] < last_candle["open"])
            and (last_candle["rsi_14_1h"] < 30)
            and (last_candle["tpct_change_0"] > 0.018)
            and (last_candle["close"] < last_candle["open"])
            and (last_candle["ema_vwap_diff_50"] < 0.215)
            and (last_candle["ema_5"]) >= (last_candle["ema_10"])
        ):

            logger.info(
                f"DCA for {trade.pair} waiting for cmf_1h ({last_candle['cmf_1h']}) to rise above 0. Waiting for rsi_1h ({last_candle['rsi_14_1h']})to rise above 30"
            )
            return None
        elif (
            count_of_buys == 6
            and (last_candle["cmf_1h"] < 0.00)
            and (last_candle["close"] < last_candle["open"])
            and (last_candle["rsi_14_1h"] < 30)
            and (last_candle["tpct_change_0"] > 0.018)
            and (
                last_candle["close"] < last_candle["open"]
                and (last_candle["ema_vwap_diff_50"] < 0.215)
            )
            and (last_candle["ema_5"]) >= (last_candle["ema_10"])
        ):

            logger.info(
                f"DCA for {trade.pair} waiting for cmf_1h ({last_candle['cmf_1h']}) to rise above 0. Waiting for rsi_1h ({last_candle['rsi_14_1h']})to rise above 30"
            )
            return None
        elif (
            count_of_buys == 7
            and (last_candle["cmf_1h"] < 0.00)
            and (last_candle["close"] < last_candle["open"])
            and (last_candle["rsi_14_1h"] < 30)
            and (last_candle["tpct_change_0"] > 0.018)
            and (
                last_candle["close"] < last_candle["open"]
                and (last_candle["ema_vwap_diff_50"] < 0.215)
            )
            and (last_candle["ema_5"]) >= (last_candle["ema_10"])
        ):

            logger.info(
                f"DCA for {trade.pair} waiting for cmf_1h ({last_candle['cmf_1h']}) to rise above 0. Waiting for rsi_1h ({last_candle['rsi_14_1h']})to rise above 30"
            )
            return None
        elif (
            count_of_buys == 8
            and (last_candle["cmf_1h"] < 0.00)
            and (last_candle["close"] < last_candle["open"])
            and (last_candle["rsi_14_1h"] < 30)
            and (last_candle["tpct_change_0"] > 0.018)
            and (
                last_candle["close"] < last_candle["open"]
                and (last_candle["ema_vwap_diff_50"] < 0.215)
            )
            and (last_candle["ema_5"]) >= (last_candle["ema_10"])
        ):

            logger.info(
                f"DCA for {trade.pair} waiting for cmf_1h ({last_candle['cmf_1h']}) to rise above 0. Waiting for rsi_1h ({last_candle['rsi_14_1h']})to rise above 30"
            )
            return None

        if 1 <= count_of_buys <= self.max_safety_orders:
            safety_order_trigger = (
                abs(self.initial_safety_order_trigger) * count_of_buys
            )
            if self.safety_order_step_scale > 1:
                safety_order_trigger = abs(self.initial_safety_order_trigger) + (
                    abs(self.initial_safety_order_trigger)
                    * self.safety_order_step_scale
                    * (math.pow(self.safety_order_step_scale, (count_of_buys - 1)) - 1)
                    / (self.safety_order_step_scale - 1)
                )
            elif self.safety_order_step_scale < 1:
                safety_order_trigger = abs(self.initial_safety_order_trigger) + (
                    abs(self.initial_safety_order_trigger)
                    * self.safety_order_step_scale
                    * (1 - math.pow(self.safety_order_step_scale, (count_of_buys - 1)))
                    / (1 - self.safety_order_step_scale)
                )

            if current_profit <= (-1 * abs(safety_order_trigger)):
                try:

                    stake_amount = filled_buys[0].cost

                    stake_amount = stake_amount * math.pow(
                        self.safety_order_volume_scale, (count_of_buys - 1)
                    )
                    amount = stake_amount / current_rate
                    logger.info(
                        f"Initiating safety order buy #{count_of_buys} for {trade.pair} with stake amount of {stake_amount} which equals {amount}"
                    )
                    return stake_amount
                except Exception as exception:
                    logger.info(
                        f"Error occured while trying to get stake amount for {trade.pair}: {str(exception)}"
                    )
                    return None

        return None


class LmaoStoplossClusterOpt(Lmao):
    """
    Narrow sell-space hyperopt target around the current DCA-on winner.

    This keeps the live/default Lmao parameters stable while allowing a focused
    search over the custom-stoploss transition cluster.
    """

    sell_params = {
        "pHSL": -0.050,
        "pPF_1": 0.016,
        "pSL_1": 0.011,
        "pPF_2": 0.080,
        "pSL_2": 0.040,
    }

    pHSL = DecimalParameter(
        -0.065,
        -0.045,
        default=-0.050,
        decimals=3,
        space="sell",
        optimize=True,
        load=True,
    )
    pPF_1 = DecimalParameter(
        0.010, 0.018, default=0.016, decimals=3, space="sell", optimize=True, load=True
    )
    pSL_1 = DecimalParameter(
        0.006, 0.016, default=0.011, decimals=3, space="sell", optimize=True, load=True
    )
    pPF_2 = DecimalParameter(
        0.045, 0.085, default=0.080, decimals=3, space="sell", optimize=True, load=True
    )
    pSL_2 = DecimalParameter(
        0.020, 0.055, default=0.040, decimals=3, space="sell", optimize=True, load=True
    )

    def custom_stoploss(
        self,
        pair: str,
        trade: "Trade",
        current_time: datetime,
        current_rate: float,
        current_profit: float,
        **kwargs,
    ) -> float:
        HSL = self.pHSL.value
        PF_1 = self.pPF_1.value
        SL_1 = self.pSL_1.value
        PF_2 = self.pPF_2.value
        SL_2 = self.pSL_2.value

        if current_profit > PF_2:
            sl_profit = SL_2 + (current_profit - PF_2)
        elif current_profit > PF_1:
            sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1))
        else:
            sl_profit = HSL

        if sl_profit >= current_profit:
            return -0.99

        return stoploss_from_open(sl_profit, current_profit)


# --- pmax function moved outside the class ---


def pmax(df, period, multiplier, length, MAtype, src):
    period = int(period)
    multiplier = int(multiplier)
    length = int(length)
    MAtype = int(MAtype)
    src = int(src)

    mavalue = "MA_" + str(MAtype) + "_" + str(length)
    atr = "ATR_" + str(period)
    pm = (
        "pm_"
        + str(period)
        + "_"
        + str(multiplier)
        + "_"
        + str(length)
        + "_"
        + str(MAtype)
    )
    pmx = (
        "pmX_"
        + str(period)
        + "_"
        + str(multiplier)
        + "_"
        + str(length)
        + "_"
        + str(MAtype)
    )

    if src == 1:
        masrc = df["close"]
    elif src == 2:
        masrc = (df["high"] + df["low"]) / 2
    elif src == 3:
        masrc = (df["high"] + df["low"] + df["close"] + df["open"]) / 4

    if MAtype == 1:
        mavalue = ta.EMA(masrc, timeperiod=length)
    elif MAtype == 2:
        mavalue = ta.DEMA(masrc, timeperiod=length)
    elif MAtype == 3:
        mavalue = ta.T3(masrc, timeperiod=length)
    elif MAtype == 4:
        mavalue = ta.SMA(masrc, timeperiod=length)
    elif MAtype == 5:
        mavalue = VIDYA(df, length=length)
    elif MAtype == 6:
        mavalue = ta.TEMA(masrc, timeperiod=length)
    elif MAtype == 7:
        mavalue = ta.WMA(df, timeperiod=length)
    elif MAtype == 8:
        mavalue = vwma(df, length)
    elif MAtype == 9:
        mavalue = zema(df, period=length)

    df[atr] = ta.ATR(df, timeperiod=period)
    df["basic_ub"] = mavalue + ((multiplier / 10) * df[atr])
    df["basic_lb"] = mavalue - ((multiplier / 10) * df[atr])

    basic_ub = df["basic_ub"].values
    final_ub = np.full(len(df), 0.00)
    basic_lb = df["basic_lb"].values
    final_lb = np.full(len(df), 0.00)

    for i in range(period, len(df)):
        final_ub[i] = (
            basic_ub[i]
            if (basic_ub[i] < final_ub[i - 1] or mavalue[i - 1] > final_ub[i - 1])
            else final_ub[i - 1]
        )
        final_lb[i] = (
            basic_lb[i]
            if (basic_lb[i] > final_lb[i - 1] or mavalue[i - 1] < final_lb[i - 1])
            else final_lb[i - 1]
        )

    df["final_ub"] = final_ub
    df["final_lb"] = final_lb

    pm_arr = np.full(len(df), 0.00)
    for i in range(period, len(df)):
        pm_arr[i] = (
            final_ub[i]
            if (pm_arr[i - 1] == final_ub[i - 1] and mavalue[i] <= final_ub[i])
            else (
                final_lb[i]
                if (pm_arr[i - 1] == final_ub[i - 1] and mavalue[i] > final_ub[i])
                else (
                    final_lb[i]
                    if (pm_arr[i - 1] == final_lb[i - 1] and mavalue[i] >= final_lb[i])
                    else (
                        final_ub[i]
                        if (
                            pm_arr[i - 1] == final_lb[i - 1]
                            and mavalue[i] < final_lb[i]
                        )
                        else 0.00
                    )
                )
            )
        )

    pm = Series(pm_arr)

    pmx = np.where(
        (pm_arr > 0.00), np.where((mavalue < pm_arr), "down", "up"), "neutral"
    )

    return pm, pmx