diff --git a/user_data/strategies/GodStraNew.py b/user_data/strategies/GodStraNew.py
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+++ b/user_data/strategies/GodStraNew.py
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+# GodStraNew Strategy
+# Author: @Mablue (Masoud Azizi)
+# github: https://github.com/mablue/
+# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --spaces buy roi trailing sell --strategy GodStraNew
+# --- Do not remove these libs ---
+from freqtrade import data
+from freqtrade.strategy.hyper import CategoricalParameter, DecimalParameter
+
+from numpy.lib import math
+from freqtrade.strategy.interface import IStrategy
+from pandas import DataFrame
+
+# --------------------------------
+
+# Add your lib to import here
+# TODO: talib is fast but have not more indicators
+import talib.abstract as ta
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+from functools import reduce
+import numpy as np
+from random import shuffle
+#  TODO: this gene is removed 'MAVP' cuz or error on periods
+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
+        # TODO: Fix this
+        # 'MAVP',               # Moving average with variable period
+        '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()
+########################### SETTINGS ##############################
+
+god_genes = {'SMA'}
+# god_genes |= all_god_genes['Overlap Studies']
+# god_genes |= all_god_genes['Momentum Indicators']
+# god_genes |= all_god_genes['Volume Indicators']
+# god_genes |= all_god_genes['Volatility Indicators']
+# god_genes |= all_god_genes['Price Transform']
+# god_genes |= all_god_genes['Cycle Indicators']
+# god_genes |= all_god_genes['Pattern Recognition']
+# god_genes |= all_god_genes['Statistic Functions']
+
+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)
+]
+# number of candles to check up,don,off trend.
+TREND_CHECK_CANDLES = 4
+DECIMALS = 1
+########################### END SETTINGS ##########################
+# DATAFRAME = DataFrame()
+
+god_genes = list(god_genes)
+# print('selected indicators for optimzatin: \n', 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}')
+
+# Let give somethings to CatagoricalParam to Play with them
+# When just one thing is inside catagorical lists
+# TODO: its Not True Way :)
+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):
+    # Cuz Timeperiods not effect calculating CDL patterns recognations
+    if 'CDL' in indicator:
+        splited_indicator = indicator.split('-')
+        splited_indicator[1] = "0"
+        new_indicator = "-".join(splited_indicator)
+        # print(indicator, new_indicator)
+        indicator = new_indicator
+
+    gene = indicator.split("-")
+
+    gene_name = gene[0]
+    gene_len = len(gene)
+
+    if indicator in dataframe.keys():
+        # print(f"{indicator}, calculated befoure")
+        # print(len(dataframe.keys()))
+        return dataframe[indicator]
+    else:
+        result = None
+        # For Pattern Recognations
+        if gene_len == 1:
+            # print('gene_len == 1\t', indicator)
+            result = getattr(ta, gene_name)(
+                dataframe
+            )
+            return normalize(result)
+        elif gene_len == 2:
+            # print('gene_len == 2\t', indicator)
+            gene_timeperiod = int(gene[1])
+            result = getattr(ta, gene_name)(
+                dataframe,
+                timeperiod=gene_timeperiod,
+            )
+            return normalize(result)
+        # For
+        elif gene_len == 3:
+            # print('gene_len == 3\t', indicator)
+            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)
+        # For trend operators(MA-5-SMA-4)
+        elif gene_len == 4:
+            # print('gene_len == 4\t', indicator)
+            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))
+        # For trend operators(STOCH-0-4-SMA-4)
+        elif gene_len == 5:
+            # print('gene_len == 5\t', indicator)
+            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)
+
+    # TODO : it ill callculated in populate indicators.
+
+    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 GodStraNew(IStrategy):
+    # #################### RESULTS PASTE PLACE ####################
+
+    # #################### END OF RESULT PLACE ####################
+
+    # TODO: Its not dry code!
+    # Buy Hyperoptable Parameters/Spaces.
+    buy_crossed_indicator0 = CategoricalParameter(
+        god_genes_with_timeperiod, default="ADD-20", space='buy')
+    buy_crossed_indicator1 = CategoricalParameter(
+        god_genes_with_timeperiod, default="ASIN-6", space='buy')
+    buy_crossed_indicator2 = CategoricalParameter(
+        god_genes_with_timeperiod, default="CDLEVENINGSTAR-50", space='buy')
+
+    buy_indicator0 = CategoricalParameter(
+        god_genes_with_timeperiod, default="SMA-100", space='buy')
+    buy_indicator1 = CategoricalParameter(
+        god_genes_with_timeperiod, default="WILLR-50", space='buy')
+    buy_indicator2 = CategoricalParameter(
+        god_genes_with_timeperiod, default="CDLHANGINGMAN-20", space='buy')
+
+    buy_operator0 = CategoricalParameter(operators, default="/<R", space='buy')
+    buy_operator1 = CategoricalParameter(operators, default="<R", space='buy')
+    buy_operator2 = CategoricalParameter(operators, default="CB", space='buy')
+
+    buy_real_num0 = DecimalParameter(0, 1, decimals=DECIMALS,  default=0.89009, space='buy')
+    buy_real_num1 = DecimalParameter(0, 1, decimals=DECIMALS, default=0.56953, space='buy')
+    buy_real_num2 = DecimalParameter(0, 1, decimals=DECIMALS, default=0.38365, space='buy')
+
+    # Sell Hyperoptable Parameters/Spaces.
+    sell_crossed_indicator0 = CategoricalParameter(
+        god_genes_with_timeperiod, default="CDLSHOOTINGSTAR-150", space='sell')
+    sell_crossed_indicator1 = CategoricalParameter(
+        god_genes_with_timeperiod, default="MAMA-1-100", space='sell')
+    sell_crossed_indicator2 = CategoricalParameter(
+        god_genes_with_timeperiod, default="CDLMATHOLD-6", space='sell')
+
+    sell_indicator0 = CategoricalParameter(
+        god_genes_with_timeperiod, default="CDLUPSIDEGAP2CROWS-5", space='sell')
+    sell_indicator1 = CategoricalParameter(
+        god_genes_with_timeperiod, default="CDLHARAMICROSS-150", space='sell')
+    sell_indicator2 = CategoricalParameter(
+        god_genes_with_timeperiod, default="CDL2CROWS-5", space='sell')
+
+    sell_operator0 = CategoricalParameter(operators, default="<R", space='sell')
+    sell_operator1 = CategoricalParameter(operators, default="D", space='sell')
+    sell_operator2 = CategoricalParameter(operators, default="/>R", space='sell')
+
+    sell_real_num0 = DecimalParameter(0, 1, decimals=DECIMALS, default=0.09731, space='sell')
+    sell_real_num1 = DecimalParameter(0, 1, decimals=DECIMALS, default=0.81657, space='sell')
+    sell_real_num2 = DecimalParameter(0, 1, decimals=DECIMALS, default=0.87267, space='sell')
+
+    # Stoploss:
+    stoploss = -1
+    # Buy hypers
+    timeframe = '4h'
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        '''
+        It's good to calculate all indicators in all time periods here and so optimize the strategy.
+        But this strategy can take much time to generate anything that may not use in his optimization.
+        I just calculate the specific indicators in specific time period inside buy and sell strategy populator methods if needed.
+        Also, this method (populate_indicators) just calculates default value of hyperoptable params
+        so using this method have not big benefits instade of calculating useable things inside buy and sell trand populators
+        '''
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+
+        conditions = list()
+
+        # TODO: Its not dry code!
+        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)
+        # backup
+        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
+
+        # print(len(dataframe.keys()))
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+
+        conditions = list()
+        # TODO: Its not dry code!
+        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