diff --git a/user_data/hyperopts/GodStraHo.py b/user_data/hyperopts/GodStraHo.py
new file mode 100644
index 0000000..e7f4cff
--- /dev/null
+++ b/user_data/hyperopts/GodStraHo.py
@@ -0,0 +1,199 @@
+# GodStra Strategy Hyperopt
+# Author: @Mablue (Masoud Azizi)
+# github: https://github.com/mablue/
+# IMPORTANT: INSTALL TA BEFOUR RUN:
+# :~$ pip install ta
+# freqtrade hyperopt --hyperopt GodStraHo --hyperopt-loss SharpeHyperOptLossDaily --spaces all --strategy GodStra --config config.json -e 100
+
+# --- Do not remove these libs ---
+from functools import reduce
+from typing import Any, Callable, Dict, List
+
+import numpy as np  # noqa
+import pandas as pd  # noqa
+from pandas import DataFrame
+from skopt.space import Categorical, Dimension, Integer, Real  # noqa
+
+from freqtrade.optimize.hyperopt_interface import IHyperOpt
+
+# --------------------------------
+# Add your lib to import here
+# import talib.abstract as ta  # noqa
+from ta import add_all_ta_features
+from ta.utils import dropna
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+# this is your trading strategy DNA Size
+# you can change it and see the results...
+DNA_SIZE = 1
+
+
+GodGenes = ["open", "high", "low", "close", "volume", "volume_adi", "volume_obv",
+            "volume_cmf", "volume_fi", "volume_mfi", "volume_em", "volume_sma_em", "volume_vpt",
+            "volume_nvi", "volume_vwap", "volatility_atr", "volatility_bbm", "volatility_bbh",
+            "volatility_bbl", "volatility_bbw", "volatility_bbp", "volatility_bbhi",
+            "volatility_bbli", "volatility_kcc", "volatility_kch", "volatility_kcl",
+            "volatility_kcw", "volatility_kcp", "volatility_kchi", "volatility_kcli",
+            "volatility_dcl", "volatility_dch", "volatility_dcm", "volatility_dcw",
+            "volatility_dcp", "volatility_ui", "trend_macd", "trend_macd_signal",
+            "trend_macd_diff", "trend_sma_fast", "trend_sma_slow", "trend_ema_fast",
+            "trend_ema_slow", "trend_adx", "trend_adx_pos", "trend_adx_neg", "trend_vortex_ind_pos",
+            "trend_vortex_ind_neg", "trend_vortex_ind_diff", "trend_trix",
+            "trend_mass_index", "trend_cci", "trend_dpo", "trend_kst",
+            "trend_kst_sig", "trend_kst_diff", "trend_ichimoku_conv",
+            "trend_ichimoku_base", "trend_ichimoku_a", "trend_ichimoku_b",
+            "trend_visual_ichimoku_a", "trend_visual_ichimoku_b", "trend_aroon_up",
+            "trend_aroon_down", "trend_aroon_ind", "trend_psar_up", "trend_psar_down",
+            "trend_psar_up_indicator", "trend_psar_down_indicator", "trend_stc",
+            "momentum_rsi", "momentum_stoch_rsi", "momentum_stoch_rsi_k",
+            "momentum_stoch_rsi_d", "momentum_tsi", "momentum_uo", "momentum_stoch",
+            "momentum_stoch_signal", "momentum_wr", "momentum_ao", "momentum_kama",
+            "momentum_roc", "momentum_ppo", "momentum_ppo_signal", "momentum_ppo_hist",
+            "others_dr", "others_dlr", "others_cr"]
+
+
+class GodStraHo(IHyperOpt):
+
+    @staticmethod
+    def indicator_space() -> List[Dimension]:
+        """
+        Define your Hyperopt space for searching buy strategy parameters.
+        """
+        gene = list()
+
+        for i in range(DNA_SIZE):
+            gene.append(Categorical(GodGenes, name=f'buy-indicator-{i}'))
+            gene.append(Categorical(GodGenes, name=f'buy-cross-{i}'))
+            gene.append(Integer(-1, 101, name=f'buy-int-{i}'))
+            gene.append(Real(-1.1, 1.1, name=f'buy-real-{i}'))
+            # Operations
+            # CA: Crossed Above, CB: Crossed Below,
+            # I: Integer, R: Real, D: Disabled
+            gene.append(Categorical(["D", ">", "<", "=", "CA", "CB",
+                                     ">I", "=I", "<I", ">R", "=R", "<R"], name=f'buy-oper-{i}'))
+        return gene
+
+    @staticmethod
+    def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
+        """
+        Define the buy strategy parameters to be used by Hyperopt.
+        """
+        def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
+            """
+            Buy strategy Hyperopt will build and use.
+            """
+            conditions = []
+            # GUARDS AND TRENDS
+            for i in range(DNA_SIZE):
+
+                OPR = params[f'buy-oper-{i}']
+                IND = params[f'buy-indicator-{i}']
+                CRS = params[f'buy-cross-{i}']
+                INT = params[f'buy-int-{i}']
+                REAL = params[f'buy-real-{i}']
+                DFIND = dataframe[IND]
+                DFCRS = dataframe[CRS]
+
+                if OPR == ">":
+                    conditions.append(DFIND > DFCRS)
+                elif OPR == "=":
+                    conditions.append(np.isclose(DFIND, DFCRS))
+                elif OPR == "<":
+                    conditions.append(DFIND < DFCRS)
+                elif OPR == "CA":
+                    conditions.append(qtpylib.crossed_above(DFIND, DFCRS))
+                elif OPR == "CB":
+                    conditions.append(qtpylib.crossed_below(DFIND, DFCRS))
+                elif OPR == ">I":
+                    conditions.append(DFIND > INT)
+                elif OPR == "=I":
+                    conditions.append(DFIND == INT)
+                elif OPR == "<I":
+                    conditions.append(DFIND < INT)
+                elif OPR == ">R":
+                    conditions.append(DFIND > REAL)
+                elif OPR == "=R":
+                    conditions.append(np.isclose(DFIND, REAL))
+                elif OPR == "<R":
+                    conditions.append(DFIND < REAL)
+
+            if conditions:
+                dataframe.loc[
+                    reduce(lambda x, y: x & y, conditions),
+                    'buy'] = 1
+
+            return dataframe
+
+        return populate_buy_trend
+
+    @ staticmethod
+    def sell_indicator_space() -> List[Dimension]:
+        """
+        Define your Hyperopt space for searching sell strategy parameters.
+        """
+        gene = list()
+
+        for i in range(DNA_SIZE):
+            gene.append(Categorical(GodGenes, name=f'sell-indicator-{i}'))
+            gene.append(Categorical(GodGenes, name=f'sell-cross-{i}'))
+            gene.append(Integer(-1, 101, name=f'sell-int-{i}'))
+            gene.append(Real(-0.01, 1.01, name=f'sell-real-{i}'))
+            # Operations
+            # CA: Crossed Above, CB: Crossed Below,
+            # I: Integer, R: Real, D: Disabled
+            gene.append(Categorical(["D", ">", "<", "=", "CA", "CB",
+                                     ">I", "=I", "<I", ">R", "=R", "<R"], name=f'sell-oper-{i}'))
+        return gene
+
+    @ staticmethod
+    def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
+        """
+        Define the sell strategy parameters to be used by Hyperopt.
+        """
+        def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
+            """
+            Sell strategy Hyperopt will build and use.
+            """
+            conditions = []
+
+            # GUARDS AND TRENDS
+            for i in range(DNA_SIZE):
+
+                OPR = params[f'sell-oper-{i}']
+                IND = params[f'sell-indicator-{i}']
+                CRS = params[f'sell-cross-{i}']
+                INT = params[f'sell-int-{i}']
+                REAL = params[f'sell-real-{i}']
+                DFIND = dataframe[IND]
+                DFCRS = dataframe[CRS]
+
+                if OPR == ">":
+                    conditions.append(DFIND > DFCRS)
+                elif OPR == "=":
+                    conditions.append(np.isclose(DFIND, DFCRS))
+                elif OPR == "<":
+                    conditions.append(DFIND < DFCRS)
+                elif OPR == "CA":
+                    conditions.append(qtpylib.crossed_above(DFIND, DFCRS))
+                elif OPR == "CB":
+                    conditions.append(qtpylib.crossed_below(DFIND, DFCRS))
+                elif OPR == ">I":
+                    conditions.append(DFIND > INT)
+                elif OPR == "=I":
+                    conditions.append(DFIND == INT)
+                elif OPR == "<I":
+                    conditions.append(DFIND < INT)
+                elif OPR == ">R":
+                    conditions.append(DFIND > REAL)
+                elif OPR == "=R":
+                    conditions.append(np.isclose(DFIND, REAL))
+                elif OPR == "<R":
+                    conditions.append(DFIND < REAL)
+
+            if conditions:
+                dataframe.loc[
+                    reduce(lambda x, y: x & y, conditions),
+                    'sell']=1
+
+            return dataframe
+
+        return populate_sell_trend
diff --git a/user_data/hyperopts/HeraclesHo.py b/user_data/hyperopts/HeraclesHo.py
new file mode 100644
index 0000000..aa174ee
--- /dev/null
+++ b/user_data/hyperopts/HeraclesHo.py
@@ -0,0 +1,118 @@
+# Heracles Strategy Hyperopt
+# Author: @Mablue (Masoud Azizi)
+# github: https://github.com/mablue/
+# IMPORTANT: INSTALL TA BEFOUR RUN:
+# :~$ pip install ta
+# freqtrade hyperopt --hyperopt GodStraHo --hyperopt-loss SharpeHyperOptLossDaily --gene all --strategy GodStra --config config.json -e 100
+
+# --- Do not remove these libs ---
+from functools import reduce
+from typing import Any, Callable, Dict, List
+
+import numpy as np  # noqa
+import pandas as pd  # noqa
+from pandas import DataFrame
+from skopt.space import Categorical, Dimension, Integer, Real  # noqa
+
+from freqtrade.optimize.hyperopt_interface import IHyperOpt
+
+# --------------------------------
+# Add your lib to import here
+# import talib.abstract as ta  # noqa
+from ta import add_all_ta_features
+from ta.utils import dropna
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+# this is your trading strategy DNA Size
+# you can change it and see the results...
+
+
+class HeraclesHo(IHyperOpt):
+
+    @staticmethod
+    def indicator_space() -> List[Dimension]:
+        """
+        Define your Hyperopt space for searching buy strategy parameters.
+        """
+
+        return [
+            Real(-0.1, 1.1, name='buy-div'),
+            Integer(0, 5, name='DFINDShift'),
+            Integer(0, 5, name='DFCRSShift'),
+        ]
+
+    @staticmethod
+    def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
+        """
+        Define the buy strategy parameters to be used by Hyperopt.
+        """
+        def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
+            """
+            Buy strategy Hyperopt will build and use.
+            """
+            conditions = []
+
+            IND = 'volatility_dcp'
+            CRS = 'volatility_kcw'
+            DFIND = dataframe[IND]
+            DFCRS = dataframe[CRS]
+
+            conditions.append(
+                DFIND.shift(params['DFINDShift']).div(
+                    DFCRS.shift(params['DFCRSShift'])
+                ) <= params['buy-div']
+            )
+
+            if conditions:
+                dataframe.loc[
+                    reduce(lambda x, y: x & y, conditions),
+                    'buy'] = 1
+
+            return dataframe
+
+        return populate_buy_trend
+
+    @ staticmethod
+    def sell_indicator_space() -> List[Dimension]:
+        """
+        Define your Hyperopt space for searching sell strategy parameters.
+        """
+        return [
+            Real(1.e-10, 1.e-0, name='sell-rtol'),
+            Real(1.e-16, 1.e-0, name='sell-atol'),
+            Integer(0, 5, name='DFINDShift'),
+            Integer(0, 5, name='DFCRSShift'),
+        ]
+
+    @ staticmethod
+    def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
+        """
+        Define the sell strategy parameters to be used by Hyperopt.
+        """
+        def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
+            """
+            Sell strategy Hyperopt will build and use.
+            """
+            conditions = []
+
+            IND = 'trend_ema_fast'
+            CRS = 'trend_macd_signal'
+            DFIND = dataframe[IND]
+            DFCRS = dataframe[CRS]
+
+            conditions.append(
+                np.isclose(
+                    DFIND.shift(params['DFINDShift']),
+                    DFCRS.shift(params['DFCRSShift']),
+                    rtol=params['sell-rtol'],
+                    atol=params['sell-atol']
+                )
+            )
+
+            if conditions:
+                dataframe.loc[
+                    reduce(lambda x, y: x & y, conditions),
+                    'sell']=1
+
+            return dataframe
+
+        return populate_sell_trend
diff --git a/user_data/strategies/GodStra.py b/user_data/strategies/GodStra.py
new file mode 100644
index 0000000..bf11c70
--- /dev/null
+++ b/user_data/strategies/GodStra.py
@@ -0,0 +1,167 @@
+# GodStra Strategy
+# Author: @Mablue (Masoud Azizi)
+# github: https://github.com/mablue/
+# IMPORTANT:Add to your pairlists inside config.json (Under StaticPairList):
+#   {
+#       "method": "AgeFilter",
+#       "min_days_listed": 30
+#   },
+# IMPORTANT: INSTALL TA BEFOUR RUN(pip install ta)
+# IMPORTANT: Use Smallest "max_open_trades" for getting best results inside config.json
+
+# --- Do not remove these libs ---
+import logging
+
+from numpy.lib import math
+from freqtrade.strategy.interface import IStrategy
+from pandas import DataFrame
+# --------------------------------
+
+# Add your lib to import here
+# import talib.abstract as ta
+import pandas as pd
+# import talib.abstract as ta
+from ta import add_all_ta_features
+from ta.utils import dropna
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+from functools import reduce
+import numpy as np
+
+
+class GodStra(IStrategy):
+    # 5/66:      9 trades. 8/0/1 Wins/Draws/Losses. Avg profit  21.83%. Median profit  35.52%. Total profit  1060.11476586 USDT ( 196.50Σ%). Avg duration 3440.0 min. Objective: -7.06960
+    # +--------+---------+----------+------------------+--------------+-------------------------------+----------------+-------------+
+    # |   Best |   Epoch |   Trades |    Win Draw Loss |   Avg profit |                        Profit |   Avg duration |   Objective |
+    # |--------+---------+----------+------------------+--------------+-------------------------------+----------------+-------------|
+    # | * Best |   1/500 |       11 |      2    1    8 |        5.22% |  280.74230393 USDT   (57.40%) |      2,421.8 m |    -2.85206 |
+    # | * Best |   2/500 |       10 |      7    0    3 |       18.76% |  983.46414442 USDT  (187.58%) |        360.0 m |    -4.32665 |
+    # | * Best |   5/500 |        9 |      8    0    1 |       21.83% | 1,060.11476586 USDT  (196.50%) |      3,440.0 m |     -7.0696 |
+
+    # Buy hyperspace params:
+    buy_params = {
+        'buy-cross-0': 'volatility_kcc',
+        'buy-indicator-0': 'trend_ichimoku_base',
+        'buy-int-0': 42,
+        'buy-oper-0': '<R',
+        'buy-real-0': 0.06295
+    }
+
+    # Sell hyperspace params:
+    sell_params = {
+        'sell-cross-0': 'volume_mfi',
+        'sell-indicator-0': 'trend_kst_diff',
+        'sell-int-0': 98,
+        'sell-oper-0': '=R',
+        'sell-real-0': 0.8779
+    }
+
+    # ROI table:
+    minimal_roi = {
+        "0": 0.3556,
+        "4818": 0.21275,
+        "6395": 0.09024,
+        "22372": 0
+    }
+
+    # Stoploss:
+    stoploss = -0.34549
+
+    # Trailing stop:
+    trailing_stop = True
+    trailing_stop_positive = 0.22673
+    trailing_stop_positive_offset = 0.2684
+    trailing_only_offset_is_reached = True
+    # Buy hypers
+    timeframe = '12h'
+    print('Add {\n\t"method": "AgeFilter",\n\t"min_days_listed": 30\n},\n to your pairlists in config (Under StaticPairList)')
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # Add all ta features
+        dataframe = dropna(dataframe)
+        dataframe = add_all_ta_features(
+            dataframe, open="open", high="high", low="low", close="close", volume="volume", fillna=True)
+        # dataframe.to_csv("df.csv", index=True)
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        conditions = list()
+        # /5: Cuz We have 5 Group of variables inside buy_param
+        for i in range(int(len(self.buy_params)/5)):
+
+            OPR = self.buy_params[f'buy-oper-{i}']
+            IND = self.buy_params[f'buy-indicator-{i}']
+            CRS = self.buy_params[f'buy-cross-{i}']
+            INT = self.buy_params[f'buy-int-{i}']
+            REAL = self.buy_params[f'buy-real-{i}']
+            DFIND = dataframe[IND]
+            DFCRS = dataframe[CRS]
+
+            if OPR == ">":
+                conditions.append(DFIND > DFCRS)
+            elif OPR == "=":
+                conditions.append(np.isclose(DFIND, DFCRS))
+            elif OPR == "<":
+                conditions.append(DFIND < DFCRS)
+            elif OPR == "CA":
+                conditions.append(qtpylib.crossed_above(DFIND, DFCRS))
+            elif OPR == "CB":
+                conditions.append(qtpylib.crossed_below(DFIND, DFCRS))
+            elif OPR == ">I":
+                conditions.append(DFIND > INT)
+            elif OPR == "=I":
+                conditions.append(DFIND == INT)
+            elif OPR == "<I":
+                conditions.append(DFIND < INT)
+            elif OPR == ">R":
+                conditions.append(DFIND > REAL)
+            elif OPR == "=R":
+                conditions.append(np.isclose(DFIND, REAL))
+            elif OPR == "<R":
+                conditions.append(DFIND < REAL)
+
+        print(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()
+        for i in range(int(len(self.sell_params)/5)):
+            OPR = self.sell_params[f'sell-oper-{i}']
+            IND = self.sell_params[f'sell-indicator-{i}']
+            CRS = self.sell_params[f'sell-cross-{i}']
+            INT = self.sell_params[f'sell-int-{i}']
+            REAL = self.sell_params[f'sell-real-{i}']
+            DFIND = dataframe[IND]
+            DFCRS = dataframe[CRS]
+
+            if OPR == ">":
+                conditions.append(DFIND > DFCRS)
+            elif OPR == "=":
+                conditions.append(np.isclose(DFIND, DFCRS))
+            elif OPR == "<":
+                conditions.append(DFIND < DFCRS)
+            elif OPR == "CA":
+                conditions.append(qtpylib.crossed_above(DFIND, DFCRS))
+            elif OPR == "CB":
+                conditions.append(qtpylib.crossed_below(DFIND, DFCRS))
+            elif OPR == ">I":
+                conditions.append(DFIND > INT)
+            elif OPR == "=I":
+                conditions.append(DFIND == INT)
+            elif OPR == "<I":
+                conditions.append(DFIND < INT)
+            elif OPR == ">R":
+                conditions.append(DFIND > REAL)
+            elif OPR == "=R":
+                conditions.append(np.isclose(DFIND, REAL))
+            elif OPR == "<R":
+                conditions.append(DFIND < REAL)
+
+        dataframe.loc[
+            reduce(lambda x, y: x & y, conditions),
+            'sell'] = 1
+
+        return dataframe
diff --git a/user_data/strategies/Heracles.py b/user_data/strategies/Heracles.py
new file mode 100644
index 0000000..48967f4
--- /dev/null
+++ b/user_data/strategies/Heracles.py
@@ -0,0 +1,133 @@
+# Heracles Strategy: Strongest Son of GodStra
+# ( With just 1 Genome! its a bacteria :D )
+# Author: @Mablue (Masoud Azizi)
+# github: https://github.com/mablue/
+# IMPORTANT:Add to your pairlists inside config.json (Under StaticPairList):
+#   {
+#       "method": "AgeFilter",
+#       "min_days_listed": 100
+#   },
+# IMPORTANT: INSTALL TA BEFOUR RUN(pip install ta)
+# ######################################################################
+# Optimal config settings:
+# "max_open_trades": 100,
+# "stake_amount": "unlimited",
+
+# --- Do not remove these libs ---
+import logging
+
+from numpy.lib import math
+from freqtrade.strategy.interface import IStrategy
+from pandas import DataFrame
+# --------------------------------
+
+# Add your lib to import here
+# import talib.abstract as ta
+import pandas as pd
+import ta
+from ta.utils import dropna
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+from functools import reduce
+import numpy as np
+
+
+class Heracles(IStrategy):
+    # 65/600:   2275 trades. 1438/7/830 W/D/L.
+    # Avg profit   3.10%. Median profit   3.06%.
+    # Total profit  113171 USDT ( 7062 Σ%).
+    # Avg duration 345 min. Objective: -23.0
+
+    # Buy hyperspace params:
+    buy_params = {
+        'buy-cross-0': 'volatility_kcw',
+        'buy-indicator-0': 'volatility_dcp',
+        'buy-oper-0': '<',
+    }
+
+    # Sell hyperspace params:
+    sell_params = {
+        'sell-cross-0': 'trend_macd_signal',
+        'sell-indicator-0': 'trend_ema_fast',
+        'sell-oper-0': '=',
+    }
+
+    # ROI table:
+    minimal_roi = {
+        "0": 0.32836,
+        "1629": 0.17896,
+        "6302": 0.05372,
+        "10744": 0
+    }
+
+    # Stoploss:
+    stoploss = -0.04655
+
+    # Trailing stop:
+    trailing_stop = True
+    trailing_stop_positive = 0.02444
+    trailing_stop_positive_offset = 0.04406
+    trailing_only_offset_is_reached = True
+
+    # Buy hypers
+    timeframe = '12h'
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # Add all ta features
+        dataframe = dropna(dataframe)
+
+        dataframe['volatility_kcw'] = ta.volatility.keltner_channel_wband(
+            dataframe['high'],
+            dataframe['low'],
+            dataframe['close'],
+            window=20,
+            window_atr=10,
+            fillna=False,
+            original_version=True
+        )
+        dataframe['volatility_dcp'] = ta.volatility.donchian_channel_pband(
+            dataframe['high'],
+            dataframe['low'],
+            dataframe['close'],
+            window=10,
+            offset=0,
+            fillna=False
+        )
+        dataframe['trend_macd_signal'] = ta.trend.macd_signal(
+            dataframe['close'],
+            window_slow=26,
+            window_fast=12,
+            window_sign=9,
+            fillna=False
+        )
+
+        dataframe['trend_ema_fast'] = ta.trend.EMAIndicator(
+            close=dataframe['close'], window=12, fillna=False
+        ).ema_indicator()
+
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+
+        IND = self.buy_params['buy-indicator-0']
+        CRS = self.buy_params['buy-cross-0']
+        DFIND = dataframe[IND]
+        DFCRS = dataframe[CRS]
+
+        dataframe.loc[
+            (DFIND < DFCRS),
+            'buy'] = 1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        IND = self.sell_params['sell-indicator-0']
+        CRS = self.sell_params['sell-cross-0']
+
+        DFIND = dataframe[IND]
+        DFCRS = dataframe[CRS]
+
+        dataframe.loc[
+            (qtpylib.crossed_below(DFIND, DFCRS)),
+            'sell'] = 1
+
+        return dataframe