'''
Guru Skippyasurmuni - 
Even though Skippy is designed to be easily modified and hyperopted. However, Skippy has alread spent a lot of time thinking about the best way to trade crypto.
He has come to the conclusing - simple is better.  A couple basic indicators and some simple rules will yield the best results in every condition.

'''

'''
docker-compose run --rm freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --spaces buy sell --strategy GuruSkippyasurmuni -e 2000 --timerange=20220314- --eps
'''

'''
 $1 = 20220314 ; $2 = "day" ;  
 docker-compose run --rm freqtrade backtesting --datadir user_data/data/binanceus --config /freqtrade/user_data/SkippyGod_config.json --export trades  -s SkippyGod --fee 0.00075 --timerange=$1- --breakdown $2 --eps ; 
 docker-compose run --rm freqtrade plot-dataframe -s SkippyGod --config /freqtrade/user_data/SkippyGod_config.json -i 5m --timerange=$1- --indicators2 AROONOSC-5 RSI-5  ; 
 docker-compose run --rm freqtrade plot-profit -s SkippyGod --config /freqtrade/user_data/SkippyGod_config.json -i 5m --timerange=$1-
'''

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


import talib.abstract as ta
import numpy as np
import freqtrade.vendor.qtpylib.indicators as qtpylib
import datetime
from technical.util import resample_to_interval, resampled_merge
from datetime import datetime, timedelta
from freqtrade.persistence import Trade
from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter
import technical.indicators as ftt
from freqtrade.exchange import timeframe_to_prev_date
import warnings
from pandas.core.common import SettingWithCopyWarning
warnings.simplefilter(action="ignore", category=SettingWithCopyWarning)
from random import shuffle


all_god_genes = {
    'Overlap Studies': {
        'BBANDS-0',             # Bollinger Bands
        'BBANDS-1',             # Bollinger Bands
        'BBANDS-2',             # Bollinger Bands
        'DEMA',                 # Double Exponential Moving Average
        'EMA',                  # Exponential Moving Average
        'HT_TRENDLINE',         # Hilbert Transform - Instantaneous Trendline
        'KAMA',                 # Kaufman Adaptive Moving Average
        'MA',                   # Moving average
        'MAMA-0',               # MESA Adaptive Moving Average
        'MAMA-1',               # MESA Adaptive Moving Average


        'MIDPOINT',             # MidPoint over period
        'MIDPRICE',             # Midpoint Price over period
        'SAR',                  # Parabolic SAR
        'SAREXT',               # Parabolic SAR - Extended
        'SMA',                  # Simple Moving Average
        'T3',                   # Triple Exponential Moving Average (T3)
        'TEMA',                 # Triple Exponential Moving Average
        'TRIMA',                # Triangular Moving Average
        'WMA',                  # Weighted Moving Average
    },
    'Momentum Indicators': {
        'ADX',                  # Average Directional Movement Index
        'ADXR',                 # Average Directional Movement Index Rating
        'APO',                  # Absolute Price Oscillator
        'AROON-0',              # Aroon
        'AROON-1',              # Aroon
        'AROONOSC',             # Aroon Oscillator
        'BOP',                  # Balance Of Power
        'CCI',                  # Commodity Channel Index
        'CMO',                  # Chande Momentum Oscillator
        'DX',                   # Directional Movement Index
        'MACD-0',               # Moving Average Convergence/Divergence
        'MACD-1',               # Moving Average Convergence/Divergence
        'MACD-2',               # Moving Average Convergence/Divergence
        'MACDEXT-0',            # MACD with controllable MA type
        'MACDEXT-1',            # MACD with controllable MA type
        'MACDEXT-2',            # MACD with controllable MA type
        'MACDFIX-0',            # Moving Average Convergence/Divergence Fix 12/26
        'MACDFIX-1',            # Moving Average Convergence/Divergence Fix 12/26
        'MACDFIX-2',            # Moving Average Convergence/Divergence Fix 12/26
        'MFI',                  # Money Flow Index
        'MINUS_DI',             # Minus Directional Indicator
        'MINUS_DM',             # Minus Directional Movement
        'MOM',                  # Momentum
        'PLUS_DI',              # Plus Directional Indicator
        'PLUS_DM',              # Plus Directional Movement
        'PPO',                  # Percentage Price Oscillator
        'ROC',                  # Rate of change : ((price/prevPrice)-1)*100
        'ROCP',                 # Rate of change Percentage: (price-prevPrice)/prevPrice
        'ROCR',                 # Rate of change ratio: (price/prevPrice)
        'ROCR100',              # Rate of change ratio 100 scale: (price/prevPrice)*100
        'RSI',                  # Relative Strength Index
        'STOCH-0',              # Stochastic
        'STOCH-1',              # Stochastic
        'STOCHF-0',             # Stochastic Fast
        'STOCHF-1',             # Stochastic Fast
        'STOCHRSI-0',           # Stochastic Relative Strength Index
        'STOCHRSI-1',           # Stochastic Relative Strength Index
        'TRIX',                 # 1-day Rate-Of-Change (ROC) of a Triple Smooth EMA
        'ULTOSC',               # Ultimate Oscillator
        'WILLR',                # Williams' %R
    },
    'Volume Indicators': {
        'AD',                   # Chaikin A/D Line
        'ADOSC',                # Chaikin A/D Oscillator
        'OBV',                  # On Balance Volume
    },
    'Volatility Indicators': {
        'ATR',                  # Average True Range
        'NATR',                 # Normalized Average True Range
        'TRANGE',               # True Range
    },
    'Price Transform': {
        'AVGPRICE',             # Average Price
        'MEDPRICE',             # Median Price
        'TYPPRICE',             # Typical Price
        'WCLPRICE',             # Weighted Close Price
    },
    'Cycle Indicators': {
        'HT_DCPERIOD',          # Hilbert Transform - Dominant Cycle Period
        'HT_DCPHASE',           # Hilbert Transform - Dominant Cycle Phase
        'HT_PHASOR-0',          # Hilbert Transform - Phasor Components
        'HT_PHASOR-1',          # Hilbert Transform - Phasor Components
        'HT_SINE-0',            # Hilbert Transform - SineWave
        'HT_SINE-1',            # Hilbert Transform - SineWave
        'HT_TRENDMODE',         # Hilbert Transform - Trend vs Cycle Mode
    },
    'Pattern Recognition': {
        'CDL2CROWS',            # Two Crows
        'CDL3BLACKCROWS',       # Three Black Crows
        'CDL3INSIDE',           # Three Inside Up/Down
        'CDL3LINESTRIKE',       # Three-Line Strike
        'CDL3OUTSIDE',          # Three Outside Up/Down
        'CDL3STARSINSOUTH',     # Three Stars In The South
        'CDL3WHITESOLDIERS',    # Three Advancing White Soldiers
        'CDLABANDONEDBABY',     # Abandoned Baby
        'CDLADVANCEBLOCK',      # Advance Block
        'CDLBELTHOLD',          # Belt-hold
        'CDLBREAKAWAY',         # Breakaway
        'CDLCLOSINGMARUBOZU',   # Closing Marubozu
        'CDLCONCEALBABYSWALL',  # Concealing Baby Swallow
        'CDLCOUNTERATTACK',     # Counterattack
        'CDLDARKCLOUDCOVER',    # Dark Cloud Cover
        'CDLDOJI',              # Doji
        'CDLDOJISTAR',          # Doji Star
        'CDLDRAGONFLYDOJI',     # Dragonfly Doji
        'CDLENGULFING',         # Engulfing Pattern
        'CDLEVENINGDOJISTAR',   # Evening Doji Star
        'CDLEVENINGSTAR',       # Evening Star
        'CDLGAPSIDESIDEWHITE',  # Up/Down-gap side-by-side white lines
        'CDLGRAVESTONEDOJI',    # Gravestone Doji
        'CDLHAMMER',            # Hammer
        'CDLHANGINGMAN',        # Hanging Man
        'CDLHARAMI',            # Harami Pattern
        'CDLHARAMICROSS',       # Harami Cross Pattern
        'CDLHIGHWAVE',          # High-Wave Candle
        'CDLHIKKAKE',           # Hikkake Pattern
        'CDLHIKKAKEMOD',        # Modified Hikkake Pattern
        'CDLHOMINGPIGEON',      # Homing Pigeon
        'CDLIDENTICAL3CROWS',   # Identical Three Crows
        'CDLINNECK',            # In-Neck Pattern
        'CDLINVERTEDHAMMER',    # Inverted Hammer
        'CDLKICKING',           # Kicking
        'CDLKICKINGBYLENGTH',   # Kicking - bull/bear determined by the longer marubozu
        'CDLLADDERBOTTOM',      # Ladder Bottom
        'CDLLONGLEGGEDDOJI',    # Long Legged Doji
        'CDLLONGLINE',          # Long Line Candle
        'CDLMARUBOZU',          # Marubozu
        'CDLMATCHINGLOW',       # Matching Low
        'CDLMATHOLD',           # Mat Hold
        'CDLMORNINGDOJISTAR',   # Morning Doji Star
        'CDLMORNINGSTAR',       # Morning Star
        'CDLONNECK',            # On-Neck Pattern
        'CDLPIERCING',          # Piercing Pattern
        'CDLRICKSHAWMAN',       # Rickshaw Man
        'CDLRISEFALL3METHODS',  # Rising/Falling Three Methods
        'CDLSEPARATINGLINES',   # Separating Lines
        'CDLSHOOTINGSTAR',      # Shooting Star
        'CDLSHORTLINE',         # Short Line Candle
        'CDLSPINNINGTOP',       # Spinning Top
        'CDLSTALLEDPATTERN',    # Stalled Pattern
        'CDLSTICKSANDWICH',     # Stick Sandwich
        'CDLTAKURI',            # Takuri (Dragonfly Doji with very long lower shadow)
        'CDLTASUKIGAP',         # Tasuki Gap
        'CDLTHRUSTING',         # Thrusting Pattern
        'CDLTRISTAR',           # Tristar Pattern
        'CDLUNIQUE3RIVER',      # Unique 3 River
        'CDLUPSIDEGAP2CROWS',   # Upside Gap Two Crows
        'CDLXSIDEGAP3METHODS',  # Upside/Downside Gap Three Methods

    },
    'Statistic Functions': {
        'BETA',                 # Beta
        'CORREL',               # Pearson's Correlation Coefficient (r)
        'LINEARREG',            # Linear Regression
        'LINEARREG_ANGLE',      # Linear Regression Angle
        'LINEARREG_INTERCEPT',  # Linear Regression Intercept
        'LINEARREG_SLOPE',      # Linear Regression Slope
        'STDDEV',               # Standard Deviation
        'TSF',                  # Time Series Forecast
        'VAR',                  # Variance
    }

}
god_genes = set()



god_genes = {
    'RSI',                  # Relative Strength Index
    'AROONOSC',             # Aroon Oscillator
   }









timeperiods = [5, 6, 12, 15, 50, 55, 100, 110]
operators = [
    "D",  # Disabled gene
    ">",  # Indicator, bigger than cross indicator
    "<",  # Indicator, smaller than cross indicator
    "=",  # Indicator, equal with cross indicator
    "C",  # Indicator, crossed the cross indicator
    "CA",  # Indicator, crossed above the cross indicator
    "CB",  # Indicator, crossed below the cross indicator
    ">R",  # Normalized indicator, bigger than real number
    "=R",  # Normalized indicator, equal with real number
    "<R",  # Normalized indicator, smaller than real number
    "/>R",  # Normalized indicator devided to cross indicator, bigger than real number
    "/=R",  # Normalized indicator devided to cross indicator, equal with real number
    "/<R",  # Normalized indicator devided to cross indicator, smaller than real number
    "UT",  # Indicator, is in UpTrend status
    "DT",  # Indicator, is in DownTrend status
    "OT",  # Indicator, is in Off trend status(RANGE)
    "CUT",  # Indicator, Entered to UpTrend status
    "CDT",  # Indicator, Entered to DownTrend status
    "COT"  # Indicator, Entered to Off trend status(RANGE)
]

TREND_CHECK_CANDLES = 4
DECIMALS = 3



god_genes = list(god_genes)


god_genes_with_timeperiod = list()
for god_gene in god_genes:
    for timeperiod in timeperiods:
        god_genes_with_timeperiod.append(f'{god_gene}-{timeperiod}')



if len(god_genes) == 1:
    god_genes = god_genes*2
if len(timeperiods) == 1:
    timeperiods = timeperiods*2
if len(operators) == 1:
    operators = operators*2


def normalize(df):
    df = (df-df.min())/(df.max()-df.min())
    return df


def gene_calculator(dataframe, indicator):

    if 'CDL' in indicator:
        splited_indicator = indicator.split('-')
        splited_indicator[1] = "0"
        new_indicator = "-".join(splited_indicator)

        indicator = new_indicator

    gene = indicator.split("-")

    gene_name = gene[0]
    gene_len = len(gene)

    if indicator in dataframe.keys():


        return dataframe[indicator]
    else:
        result = None

        if gene_len == 1:

            result = getattr(ta, gene_name)(
                dataframe
            )
            return normalize(result)
        elif gene_len == 2:

            gene_timeperiod = int(gene[1])
            result = getattr(ta, gene_name)(
                dataframe,
                timeperiod=gene_timeperiod,
            )
            return normalize(result)

        elif gene_len == 3:

            gene_timeperiod = int(gene[2])
            gene_index = int(gene[1])
            result = getattr(ta, gene_name)(
                dataframe,
                timeperiod=gene_timeperiod,
            ).iloc[:, gene_index]
            return normalize(result)

        elif gene_len == 4:

            gene_timeperiod = int(gene[1])
            sharp_indicator = f'{gene_name}-{gene_timeperiod}'
            dataframe[sharp_indicator] = getattr(ta, gene_name)(
                dataframe,
                timeperiod=gene_timeperiod,
            )
            return normalize(ta.SMA(dataframe[sharp_indicator].fillna(0), TREND_CHECK_CANDLES))

        elif gene_len == 5:

            gene_timeperiod = int(gene[2])
            gene_index = int(gene[1])
            sharp_indicator = f'{gene_name}-{gene_index}-{gene_timeperiod}'
            dataframe[sharp_indicator] = getattr(ta, gene_name)(
                dataframe,
                timeperiod=gene_timeperiod,
            ).iloc[:, gene_index]
            return normalize(ta.SMA(dataframe[sharp_indicator].fillna(0), TREND_CHECK_CANDLES))


def condition_generator(dataframe, operator, indicator, crossed_indicator, real_num):

    condition = (dataframe['volume'] > 10)


    dataframe[indicator] = gene_calculator(dataframe, indicator)
    dataframe[crossed_indicator] = gene_calculator(dataframe, crossed_indicator)

    indicator_trend_sma = f"{indicator}-SMA-{TREND_CHECK_CANDLES}"
    if operator in ["UT", "DT", "OT", "CUT", "CDT", "COT"]:
        dataframe[indicator_trend_sma] = gene_calculator(dataframe, indicator_trend_sma)

    if operator == ">":
        condition = (
            dataframe[indicator] > dataframe[crossed_indicator]
        )
    elif operator == "=":
        condition = (
            np.isclose(dataframe[indicator], dataframe[crossed_indicator])
        )
    elif operator == "<":
        condition = (
            dataframe[indicator] < dataframe[crossed_indicator]
        )
    elif operator == "C":
        condition = (
            (qtpylib.crossed_below(dataframe[indicator], dataframe[crossed_indicator])) |
            (qtpylib.crossed_above(dataframe[indicator], dataframe[crossed_indicator]))
        )
    elif operator == "CA":
        condition = (
            qtpylib.crossed_above(dataframe[indicator], dataframe[crossed_indicator])
        )
    elif operator == "CB":
        condition = (
            qtpylib.crossed_below(
                dataframe[indicator], dataframe[crossed_indicator])
        )
    elif operator == ">R":
        condition = (
            dataframe[indicator] > real_num
        )
    elif operator == "=R":
        condition = (
            np.isclose(dataframe[indicator], real_num)
        )
    elif operator == "<R":
        condition = (
            dataframe[indicator] < real_num
        )
    elif operator == "/>R":
        condition = (
            dataframe[indicator].div(dataframe[crossed_indicator]) > real_num
        )
    elif operator == "/=R":
        condition = (
            np.isclose(dataframe[indicator].div(dataframe[crossed_indicator]), real_num)
        )
    elif operator == "/<R":
        condition = (
            dataframe[indicator].div(dataframe[crossed_indicator]) < real_num
        )
    elif operator == "UT":
        condition = (
            dataframe[indicator] > dataframe[indicator_trend_sma]
        )
    elif operator == "DT":
        condition = (
            dataframe[indicator] < dataframe[indicator_trend_sma]
        )
    elif operator == "OT":
        condition = (

            np.isclose(dataframe[indicator], dataframe[indicator_trend_sma])
        )
    elif operator == "CUT":
        condition = (
            (
                qtpylib.crossed_above(
                    dataframe[indicator],
                    dataframe[indicator_trend_sma]
                )
            ) &
            (
                dataframe[indicator] > dataframe[indicator_trend_sma]
            )
        )
    elif operator == "CDT":
        condition = (
            (
                qtpylib.crossed_below(
                    dataframe[indicator],
                    dataframe[indicator_trend_sma]
                )
            ) &
            (
                dataframe[indicator] < dataframe[indicator_trend_sma]
            )
        )
    elif operator == "COT":
        condition = (
            (
                (
                    qtpylib.crossed_below(
                        dataframe[indicator],
                        dataframe[indicator_trend_sma]
                    )
                ) |
                (
                    qtpylib.crossed_above(
                        dataframe[indicator],
                        dataframe[indicator_trend_sma]
                    )
                )
            ) &
            (
                np.isclose(
                    dataframe[indicator],
                    dataframe[indicator_trend_sma]
                )
            )
        )

    return condition, dataframe

class GuruSkippyasurmuni_strategy(IStrategy):
    INTERFACE_VERSION = 2

    DATESTAMP = 0    
    COUNT = 0
    custom_info = { }


    buy_params = {
        "buy_crossed_indicator0": "STOCHRSI-0-15",
        "buy_crossed_indicator1": "MACDFIX-0-15",
        "buy_crossed_indicator2": "STOCHRSI-0-55",
        "buy_indicator0": "MACDFIX-0-15",
        "buy_indicator1": "RSI-5",
        "buy_indicator2": "AROONOSC-5",
        "buy_operator0": "D",
        "buy_operator1": "<R",
        "buy_operator2": "=R",
        "buy_real_num0": 0.35,
        "buy_real_num1": 0.30,
        "buy_real_num2": 0,
    }

    sell_params = {
        "sell_crossed_indicator0": "STOCHRSI-0-15",
        "sell_crossed_indicator1": "MACDFIX-0-15",
        "sell_crossed_indicator2": "STOCHRSI-0-15",
        "sell_indicator0": "MACDFIX-0-15",
        "sell_indicator1": "RSI-5",
        "sell_indicator2": "AROONOSC-5",
        "sell_operator0": "D",
        "sell_operator1": ">R",
        "sell_operator2": "=R",
        "sell_real_num0": 0.65,
        "sell_real_num1": 0.9,
        "sell_real_num2": 1.0,
    }    

    minimal_roi = {
        "0": 1000.0
    }

    stoploss = -1.0

    trailing_stop = False
    trailing_stop_positive = 0.001
    trailing_stop_positive_offset = 0.01
    trailing_only_offset_is_reached = True

    use_sell_signal = True
    sell_profit_only = True

    ignore_roi_if_buy_signal = False

    timeframe = '5m'

    process_only_new_candles = True

    plot_config = {
        'subplots': {
            "buy_1": {
                'buy_1': {'color': 'blue'}
            },
            "sell_1": {
                'sell_1': {'color': 'orange'}
            },
        }
    }

    position_adjustment_enable = True
    max_entry_position_adjustment = 24

    @property
    def protections(self):
        return [
            {
            "method": "CooldownPeriod",
            "stop_duration_candles": 3
            }
        ]


    buy_crossed_indicator0 = CategoricalParameter(
        god_genes_with_timeperiod, default="STOCHRSI-0-15", space='buy')
    buy_crossed_indicator1 = CategoricalParameter(
        god_genes_with_timeperiod, default="MACDFIX-0-15", space='buy')
    buy_crossed_indicator2 = CategoricalParameter(
        god_genes_with_timeperiod, default="STOCHRSI-0-55", space='buy')

    buy_indicator0 = CategoricalParameter(
        god_genes_with_timeperiod, default="MACDFIX-0-15", space='buy')
    buy_indicator1 = CategoricalParameter(
        god_genes_with_timeperiod, default="RSI-5", space='buy')
    buy_indicator2 = CategoricalParameter(
        god_genes_with_timeperiod, default="AROONOSC-5", space='buy')

    buy_operator0 = CategoricalParameter(operators, default="D", space='buy')
    buy_operator1 = CategoricalParameter(operators, default="<R", space='buy')
    buy_operator2 = CategoricalParameter(operators, default="=R", space='buy')

    buy_real_num0 = DecimalParameter(0, 1, decimals=DECIMALS,  default=0.35, space='buy')
    buy_real_num1 = DecimalParameter(0, 1, decimals=DECIMALS, default=0.30, space='buy')
    buy_real_num2 = DecimalParameter(0, 1, decimals=DECIMALS, default=0.00, space='buy')

    sell_crossed_indicator0 = CategoricalParameter(
        god_genes_with_timeperiod, default="STOCHRSI-0-15", space='sell')
    sell_crossed_indicator1 = CategoricalParameter(
        god_genes_with_timeperiod, default="MACDFIX-0-15", space='sell')
    sell_crossed_indicator2 = CategoricalParameter(
        god_genes_with_timeperiod, default="STOCHRSI-0-55", space='sell')

    sell_indicator0 = CategoricalParameter(
        god_genes_with_timeperiod, default="MACDFIX-0-15", space='sell')
    sell_indicator1 = CategoricalParameter(
        god_genes_with_timeperiod, default="RSI-5", space='sell')
    sell_indicator2 = CategoricalParameter(
        god_genes_with_timeperiod, default="AROONOSC-5", space='sell')

    sell_operator0 = CategoricalParameter(operators, default="D", space='sell')
    sell_operator1 = CategoricalParameter(operators, default=">R", space='sell')
    sell_operator2 = CategoricalParameter(operators, default="=R", space='sell')

    sell_real_num0 = DecimalParameter(0, 1, decimals=DECIMALS, default=0.65, space='sell')
    sell_real_num1 = DecimalParameter(0, 1, decimals=DECIMALS, default=0.90, space='sell')
    sell_real_num2 = DecimalParameter(0, 1, decimals=DECIMALS, default=0.00, space='sell')



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

        pair = metadata['pair']

        if not pair in self.custom_info:

            self.custom_info[pair] = ['', 0] 
















        return dataframe

    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
                           rate: float, time_in_force: str, sell_reason: str, current_profit: float,
                           **kwargs) -> bool:

        if current_profit < 0.01:
                return False

        return True
    
    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                            proposed_stake: float, min_stake: float, max_stake: float,
                            **kwargs) -> float:
        return proposed_stake

    def adjust_trade_position(self, trade: Trade, current_time: datetime,
                              current_rate: float, current_profit: float, min_stake: float,
                              max_stake: float, **kwargs):
        dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)

        if(len(dataframe) < 1):
            return None

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

        if(self.custom_info[trade.pair][self.DATESTAMP] != last_candle['date']):

            self.custom_info[trade.pair][self.DATESTAMP] = last_candle['date']

            if current_profit > -0.01:
                return None

            if last_candle['buy'] > 0:
                filled_buys = trade.select_filled_orders('buy')
                try:
                    stake_amount = filled_buys[0].cost
                    return stake_amount
                except Exception as exception:
                    return None

        return None
    
    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = list()

        buy_indicator = self.buy_indicator0.value
        buy_crossed_indicator = self.buy_crossed_indicator0.value
        buy_operator = self.buy_operator0.value
        buy_real_num = self.buy_real_num0.value
        condition, dataframe = condition_generator(
            dataframe,
            buy_operator,
            buy_indicator,
            buy_crossed_indicator,
            buy_real_num
        )
        conditions.append(condition)

        buy_indicator = self.buy_indicator1.value
        buy_crossed_indicator = self.buy_crossed_indicator1.value
        buy_operator = self.buy_operator1.value
        buy_real_num = self.buy_real_num1.value

        condition, dataframe = condition_generator(
            dataframe,
            buy_operator,
            buy_indicator,
            buy_crossed_indicator,
            buy_real_num
        )
        conditions.append(condition)

        buy_indicator = self.buy_indicator2.value
        buy_crossed_indicator = self.buy_crossed_indicator2.value
        buy_operator = self.buy_operator2.value
        buy_real_num = self.buy_real_num2.value
        condition, dataframe = condition_generator(
            dataframe,
            buy_operator,
            buy_indicator,
            buy_crossed_indicator,
            buy_real_num
        )
        conditions.append(condition)

        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 = list()

        sell_indicator = self.sell_indicator0.value
        sell_crossed_indicator = self.sell_crossed_indicator0.value
        sell_operator = self.sell_operator0.value
        sell_real_num = self.sell_real_num0.value
        condition, dataframe = condition_generator(
            dataframe,
            sell_operator,
            sell_indicator,
            sell_crossed_indicator,
            sell_real_num
        )
        conditions.append(condition)

        sell_indicator = self.sell_indicator1.value
        sell_crossed_indicator = self.sell_crossed_indicator1.value
        sell_operator = self.sell_operator1.value
        sell_real_num = self.sell_real_num1.value
        condition, dataframe = condition_generator(
            dataframe,
            sell_operator,
            sell_indicator,
            sell_crossed_indicator,
            sell_real_num
        )
        conditions.append(condition)

        sell_indicator = self.sell_indicator2.value
        sell_crossed_indicator = self.sell_crossed_indicator2.value
        sell_operator = self.sell_operator2.value
        sell_real_num = self.sell_real_num2.value
        condition, dataframe = condition_generator(
            dataframe,
            sell_operator,
            sell_indicator,
            sell_crossed_indicator,
            sell_real_num
        )
        conditions.append(condition)

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

        return dataframe