# source: https://raw.githubusercontent.com/papound/freqtradeStrategies/6a8717691a8da0d58108b1d63cf001977ce84b35/jul152022_strategy.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file # --- Do not remove these libs --- from asyncio import base_tasks import imp from h11 import Data import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame from functools import reduce from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter) # -------------------------------- # Add your lib to import here import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib import datetime from typing import Optional # This class is a sample. Feel free to customize it. class github_papound_freqtradeStrategies__jul152022_strategy__20220718_202350(IStrategy): """ This is a sample strategy to inspire you. More information in https://www.freqtrade.io/en/latest/strategy-customization/ You can: :return: a Dataframe with all mandatory indicators for the strategies - Rename the class name (Do not forget to update class_name) - Add any methods you want to build your strategy - Add any lib you need to build your strategy You must keep: - the lib in the section "Do not remove these libs" - the methods: populate_indicators, populate_entry_trend, populate_exit_trend You should keep: - timeframe, minimal_roi, stoploss, trailing_* """ # Strategy interface version - allow new iterations of the strategy interface. # Check the documentation or the Sample strategy to get the latest version. INTERFACE_VERSION = 3 # Can this strategy go short? can_short: bool = False # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". minimal_roi = { "60": 0.01, "30": 0.02, "0": 0.04 } # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". stoploss = -0.10 # Trailing stoploss trailing_stop = True trailing_stop_positive = 0.02 trailing_stop_positive_offset = 0.03 trailing_only_offset_is_reached = True # trailing_only_offset_is_reached = False # trailing_stop_positive = 0.01 # trailing_stop_positive_offset = 0.0 # Disabled / not configured # Optimal timeframe for the strategy. timeframe = '1h' inf_1h = '1h' # Run "populate_indicators()" only for new candle. process_only_new_candles = True # These values can be overridden in the config. use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False # Hyperoptable parameters buy_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True) sell_rsi = IntParameter(low=50, high=100, default=70, space='sell', optimize=True, load=True) short_rsi = IntParameter(low=51, high=100, default=70, space='sell', optimize=True, load=True) exit_short_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True) PeriodF = 10 PeriodS = 55 EnableSmooth = True # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 100 # Optional order type mapping. order_types = { 'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } # Optional order time in force. order_time_in_force = { 'entry': 'gtc', 'exit': 'gtc' } plot_config = { 'main_plot': { 'tema': {}, 'sar': {'color': 'white'}, }, 'subplots': { "MACD": { 'macd': {'color': 'blue'}, 'macdsignal': {'color': 'orange'}, }, "RSI": { 'rsi': {'color': 'red'}, } } } #Strategy ''' Long / Buy Condition 1. Green Line Cross Up White Line (LFS) 2. Green candle 3. Firefly Histogram Value above middle > 50 , Histogram Show Green 4. HARSI RSI Over Lay Above or Cross up Middle Line , RSI Over Lay Value = > 30 Stop Loss = Swing High or White Line Take Profit TP 1 = 30% RR 1.5-3 / TP2 50% 1. RSI Over Lay (Yellow Line) > 38 2. Firefly Histogram Value below middle > 50 , Histogram Show Green 3. Green Candle Take Profit & Close Position 1. Green Line Cross Down White Line (LFS) ''' max_leverage = 25.0 #Set Leverage def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: """ Customize leverage for each new trade. This method is only called in futures mode. :param pair: Pair that's currently analyzed :param current_time: datetime object, containing the current datetime :param current_rate: Rate, calculated based on pricing settings in exit_pricing. :param proposed_leverage: A leverage proposed by the bot. :param max_leverage: Max leverage allowed on this pair :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal. :param side: 'long' or 'short' - indicating the direction of the proposed trade :return: A leverage amount, which is between 1.0 and max_leverage. """ if side=='long': return 1.0 else: return 50.0 def na(self, val): return val != val def nz(self, x, y=None): if isinstance(x, np.generic): return x.fillna(y or 0) if x != x: if y is not None: return y return 0 return x def barssince(self, condition, occurrence=0): cond_len = len(condition) occ = 0 since = 0 res = float('nan') while cond_len - (since+1) >= 0: cond = condition[cond_len-(since+1)] if cond and not cond != cond: if occ == occurrence: res = since break occ += 1 since += 1 return res def valuewhen(self, condition, source, occurrence=0): res = float('nan') since = self.barssince(condition, occurrence) if since is not None: res = source[-(since+1)] return res def informative_pairs(self): """ Define additional, informative pair/interval combinations to be cached from the exchange. These pair/interval combinations are non-tradeable, unless they are part of the whitelist as well. For more information, please consult the documentation :return: List of tuples in the format (pair, interval) Sample: return [("ETH/USDT", "5m"), ("BTC/USDT", "15m"), ] """ return [("ETH/USDT", "5m"), ("BTC/USDT", "15m"),] def calc_zlema(self, s: DataFrame, l) -> DataFrame: ema1=ta.EMA(s, l) ema2=ta.EMA(ema1, l) d=ema1-ema2 return ema1+d def ma(self, s: DataFrame, l, bt) -> DataFrame: d=np.where(bt == False,ta.EMA(s,l),self.calc_zlema(s,l)) return d def calulateFireflyIndicator(self, dataframe: DataFrame, metadata: dict)-> DataFrame: m=10 #Lookback Length n1=3 #Signal Smoothing a_s=False #Double smooth Osc dataframe['bt'] = pd.DataFrame(False, index=range(len(dataframe.index)), columns=range(1), dtype=bool) dataframe['v2']=((dataframe['high']+dataframe['low']+dataframe['close']*2)/4) dataframe['v2']=dataframe['v2'].fillna(0.0) dataframe['v3']=self.ma(dataframe['v2'], m, dataframe['bt']) dataframe['v3']=dataframe['v3'].fillna(0.0) dataframe['v4']=ta.STDDEV(dataframe['v2'],timeperiod=m) dataframe['v4']=dataframe['v4'].fillna(0.0) dataframe['v5']=(dataframe['v2']-dataframe['v3'])*100/np.where(dataframe['v4']==0.0,1.0,dataframe['v4']) dataframe['v5']=dataframe['v5'].fillna(0.0) dataframe['v6']=self.ma(dataframe['v5'],n1, dataframe['bt']) dataframe['v6']=dataframe['v6'].fillna(0.0) dataframe['v7']=np.where(a_s, self.ma(dataframe['v6'], n1, dataframe['bt']), dataframe['v6']) dataframe['ww']=(self.ma(dataframe['v7'],m,dataframe['bt'])+100)/2-4 dataframe['ww'] = dataframe['ww'].fillna(0.0) dataframe['mm']=ta.MAX(dataframe['ww'], timeperiod=n1) dataframe['mm']=dataframe['mm'].fillna(0.0) dataframe['wwmm_min']=np.minimum(dataframe['ww'], dataframe['mm']) dataframe['wwmm_max']=np.maximum(dataframe['ww'],dataframe['mm']) dataframe['d']=np.where(dataframe['ww']>50, dataframe['wwmm_min'], np.where(dataframe['mm']< 50, dataframe['wwmm_max'], 0.0)) dataframe['dc']= np.where(dataframe['d']>50, np.where(dataframe['d']>dataframe['d'].shift(1), 1.0,0.0), np.where(dataframe['d'] DataFrame: # Vars: Fastest = 0.6667 Slowest = 0.0645 AMA = 0.00 dataframe['Price']=dataframe['hl2'] Price = dataframe['Price'] dataframe['Diff'] = np.absolute(Price - Price.shift(1).fillna(0.0)) dataframe['Index']=pd.DataFrame(list(range(len(Price.index)))) dataframe['Signal'] = np.absolute(Price - Price.shift(Period).fillna(0.0)) dataframe['Noise'] = np.add(dataframe['Diff'], Period) dataframe['efRatio'] = dataframe['Signal'] / dataframe['Noise'] dataframe['Smooth'] = np.power(dataframe['efRatio'] * (Fastest - Slowest) + Slowest, 2) dataframe['AdaptMA'] = pd.DataFrame(0.0, index=range(len(Price.index)), columns=range(1), dtype=float64) dataframe['AMA'] = np.where(dataframe['Index']<= Period, Price, dataframe['AdaptMA'].shift(1).fillna(0.0) + dataframe['Smooth'] * (Price - dataframe['AdaptMA'].fillna(0.0).shift(1).fillna(0.0))) return dataframe['AMA'] def calculateBlackCatIndicator(self, dataframe: DataFrame, metadata: dict)-> DataFrame: PeriodF = 13 PeriodS = 55 EnableSmooth = False dataframe['hl2']=(dataframe['high']+dataframe['low'])/2 dataframe['hl2'] = dataframe['hl2'].fillna(0.0) if EnableSmooth: dataframe['AMAValF'] = ta.LINEARREG(self.blackcat_AMA(PeriodF, dataframe, PeriodF, 0)) dataframe['AMAValS'] = ta.LINEARREG(self.blackcat_AMA(PeriodS, dataframe, PeriodS, 0)) else: dataframe['AMAValF'] = self.blackcat_AMA(PeriodF, dataframe) dataframe['AMAValS'] = self.blackcat_AMA(PeriodS, dataframe) dataframe['BlackCat_Status']=np.where(qtpylib.crossed_above(dataframe['AMAValF'], dataframe['AMAValS']),"Long", np.where(qtpylib.crossed_below(dataframe['AMAValF'], dataframe['AMAValS']),"Short", "N/A")) return dataframe def calculateIFTComboIndicator(self, dataframe: DataFrame, metadata: dict)-> DataFrame: #IFTCOMBO ccilength = 5 wmalength = 9 dataframe['cci'] = ta.CCI(dataframe['high'], dataframe['low'], dataframe['close'], window=ccilength, constant=0.015, fillna=False) dataframe['v11'] = (dataframe['cci']/4)*0.1 dataframe['v21'] = ta.WMA(dataframe['v11'], window=wmalength) dataframe['result1'] = np.exp(dataframe['v21']*2) dataframe['iftcombo'] = (dataframe['result1']-1)/(dataframe['result1']+1) dataframe['iftcombo'] = dataframe['iftcombo'].fillna(0.0) return dataframe #HARSI def f_zrsi(self, dataframe: DataFrame, length)-> DataFrame: d=ta.RSI(dataframe, timeperiod=length) return d def f_rsi(self, _source: DataFrame, _length, _mode )-> DataFrame: _source['_zrsi'] = self.f_zrsi( _source, _length) _source['_smoothed']=np.where(np.isnan(_source['_smoothed'].shift(1)), _source['_zrsi'], (_source['_smoothed'].shift(1)+_source['_zrsi'])/2) if _mode : return _source['_smoothed'] else: return _source['_zrsi'] #RSI Heikin-Ashi generation function def f_rsiHeikinAshi(self, dataframe: DataFrame, _length ) -> DataFrame: i_smoothing = 5 dataframe['_closeRSI'] = self.f_zrsi(dataframe['ha_close'], _length) dataframe['_openRSI'] = np.where(np.isnan(dataframe['_closeRSI'].shift(1)), dataframe['_closeRSI'], dataframe['_closeRSI'].shift(1)) dataframe['_highRSI_raw'] = self.f_zrsi(dataframe['ha_high'], _length) dataframe['_lowRSI_raw'] = self.f_zrsi(dataframe['ha_low'], _length) dataframe['_highRSI']=np.maximum(dataframe['_highRSI_raw'],dataframe['_lowRSI_raw']) dataframe['_lowRSI']=np.minimum(dataframe['_highRSI_raw'],dataframe['_lowRSI_raw']) dataframe['_close'] = ( dataframe['_openRSI'] + dataframe['_highRSI'] + dataframe['_lowRSI'] + dataframe['_closeRSI'] ) / 4 dataframe['_open'] = pd.DataFrame(None, index=range(len(dataframe.index)), columns=range(1), dtype=float64) dataframe['_open'] = np.where(np.isnan(dataframe['_open'].shift(i_smoothing)),(dataframe['_openRSI'] + dataframe['_closeRSI'])/2, ( ( dataframe['_open'].shift(1)*i_smoothing ) + dataframe['_close'].shift(1) ) / (i_smoothing+1)) dataframe['_high']= np.maximum(dataframe['_highRSI'], np.maximum( dataframe['_open'], dataframe['_close'])) dataframe['_low']= np.minimum( dataframe['_lowRSI'], np.minimum( dataframe['_open'], dataframe['_close'])) dataframe['f_rsiHeikinAshi_bar_color']=np.where(dataframe['_close'] > dataframe['_open'], 1.0, -1.0) return dataframe def calculateHARSIIndicator(self, dataframe: DataFrame, metadata: dict)-> DataFrame: i_lenHARSI = 14 dataframe = self.f_rsiHeikinAshi(dataframe, i_lenHARSI) return dataframe def calculateLFSIndicator(self, dataframe: DataFrame, metadata: dict)-> DataFrame: longlen=100 keylen=80 shortlen=50 # longema = ta.ema(src, longlen) # shortema = ta.ema(src, shortlen) # keyema= ta.ema(src,keylen) dataframe['long_ema'] = ta.EMA(dataframe, timeperiod=longlen) dataframe['short_ema'] = ta.EMA(dataframe, timeperiod=shortlen) dataframe['key_ema'] = ta.EMA(dataframe, timeperiod=keylen) return dataframe def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # assert self.dp, "DataProvider is required for multiple timeframes." # # Get the informative pair informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h) # # EMA # informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50) # informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200) # informative_1h['ema_25'] = ta.EMA(informative_1h, timeperiod=25) # informative_1h['ema_10'] = ta.EMA(informative_1h, timeperiod=10) # informative_1h['ema_5'] = ta.EMA(informative_1h, timeperiod=5) # # RSI # informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14) # Bollinger Band # bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) # informative_1h['bb_lowerband'] = bollinger['lower'] # informative_1h['bb_middleband'] = bollinger['mid'] # informative_1h['bb_upperband'] = bollinger['upper'] return informative_1h def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Adds several different TA indicators to the given DataFrame Performance Note: For the best performance be frugal on the number of indicators you are using. Let uncomment only the indicator you are using in your strategies or your hyperopt configuration, otherwise you will waste your memory and CPU usage. :param dataframe: Dataframe with data from the exchange :param metadata: Additional information, like the currently traded pair :return: a Dataframe with all mandatory indicators for the strategies """ # The indicators for the 1h informative timeframe # informative_1h = self.informative_1h_indicators(dataframe, metadata) # dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True) # The indicators for the normal (5m) timeframe # dataframe = self.normal_tf_indicators(dataframe, metadata) # # Chart type # # ------------------------------------ # Heikin Ashi Strategy heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] # Momentum Indicators # ------------------------------------ dataframe = self.calulateFireflyIndicator(dataframe, metadata) dataframe = self.calculateBlackCatIndicator(dataframe, metadata) dataframe = self.calculateIFTComboIndicator(dataframe, metadata) dataframe = self.calculateHARSIIndicator(dataframe, metadata) dataframe = self.calculateLFSIndicator(dataframe, metadata) # #print("Orig Dataframe") # with pd.option_context('display.max_rows', 30, # 'display.max_columns', None, # 'display.precision', 3, # ): # #print(dataframe) # ADX dataframe['adx'] = ta.ADX(dataframe) # RSI dataframe['rsi'] = ta.RSI(dataframe) # # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy) # rsi = 0.1 * (dataframe['rsi'] - 50) # dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) # # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy) # dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) # # Stochastic Slow # stoch = ta.STOCH(dataframe) # dataframe['slowd'] = stoch['slowd'] # dataframe['slowk'] = stoch['slowk'] # Stochastic Fast stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] # # Stochastic RSI # Please read https://github.com/freqtrade/freqtrade/issues/2961 before using this. # STOCHRSI is NOT aligned with tradingview, which may result in non-expected results. # stoch_rsi = ta.STOCHRSI(dataframe) # dataframe['fastd_rsi'] = stoch_rsi['fastd'] # dataframe['fastk_rsi'] = stoch_rsi['fastk'] # MACD macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] # MFI dataframe['mfi'] = ta.MFI(dataframe) # # ROC # dataframe['roc'] = ta.ROC(dataframe) # Overlap Studies # ------------------------------------ # Bollinger Bands bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] dataframe["bb_percent"] = ( (dataframe["close"] - dataframe["bb_lowerband"]) / (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) ) dataframe["bb_width"] = ( (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"] ) # Retrieve best bid and best ask from the orderbook # ------------------------------------ """ # first check if dataprovider is available if self.dp: if self.dp.runmode.value in ('live', 'dry_run'): ob = self.dp.orderbook(metadata['pair'], 1) dataframe['best_bid'] = ob['bids'][0][0] dataframe['best_ask'] = ob['asks'][0][0] """ return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the entry signal for the given dataframe :param dataframe: DataFrame :param metadata: Additional information, like the currently traded pair :return: DataFrame with entry columns populated """ ''' Long / Buy Condition 1. Green Line Cross Up White Line >>LFS / Yellow Line (AMAValF) Cross Up Purple Line (AMAValS) >> Blackcat 2. Green candle 3. Firefly Histogram Value above middle > 50 , Histogram Show Green 4. HARSI RSI Over Lay Above Median Line (Default Value = 0 ) / Bar Green / RSI Over Lay Value > 20 (OB = Over Bought : Default Value = 20) 5. IFTCOMBO > 0.60 Stop Loss = Swing Low Short / Sell Condition 1. Green Line Cross Up White Line >>LFS / Yellow Line (AMAValF) Cross Down Purple Line (AMAValS) >> Blackcat 2. Red Candle 3. Firefly >> Histogram Value Below middle < 50 , Histogram Show Red 4. HARSI RSI Over Lay Below Median Line (Default Value = 0 ) / Bar RED / RSI Over Lay Value < -20 (OS = Over Sell : Default Value = -20) 6. IFTCOMBO < -0.60 Stop Loss = Swing H ''' conditionsLong = [] conditionsShort = [] conditionsLong.append( ( #1 (qtpylib.crossed_above(dataframe['short_ema'], dataframe['key_ema']) | qtpylib.crossed_above(dataframe['AMAValF'], dataframe['AMAValS'])) & #2 (dataframe['open'] < dataframe['close']) & #3 (dataframe['fireflyHistogramColor'] == 1.0) & (dataframe['fireflyHistogramValue'] > 50) & #4 (dataframe['_closeRSI'] > 20) & (dataframe['f_rsiHeikinAshi_bar_color'] == 1.0) & #5 (dataframe['iftcombo'] > 0.6) & #etc (dataframe['volume'] > 0) # Make sure Volume is not 0 ) # 'enter_long'] = 1 ) if conditionsLong: dataframe.loc[ reduce(lambda x, y: x | y, conditionsLong), ['enter_long', 'enter_tag'] ] = (1, 'enter_long') conditionsShort.append( ( #1 (qtpylib.crossed_below(dataframe['short_ema'], dataframe['key_ema']) | qtpylib.crossed_below(dataframe['AMAValF'], dataframe['AMAValS'])) & #2 (dataframe['open'] > dataframe['close']) & #3 (dataframe['fireflyHistogramColor'] == -1.0) & (dataframe['fireflyHistogramValue'] < 50) & #4 (dataframe['_closeRSI'] < -20) & (dataframe['f_rsiHeikinAshi_bar_color'] == -1.0) & #5 (dataframe['iftcombo'] < -0.6) & #etc (dataframe['volume'] > 0) # Make sure Volume is not 0 ), ) if conditionsShort: dataframe.loc[ reduce(lambda x, y: x | y, conditionsShort), ['enter_short', 'enter_tag'] ] = (1, 'enter_short') # 'enter_short'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the exit signal for the given dataframe :param dataframe: DataFrame :param metadata: Additional information, like the currently traded pair :return: DataFrame with exit columns populated """ ''' Take Profit - Long TP 1 = 30% RR 1.5-3 / TP2 50% 1. Green Candle 2. HARSI : Bar green / RSI Over Lay (Yellow Line) > 35 (OB Extreme = Overbought Extreme : Default Value = 30) 3. Firefly Histogram Value Above middle > 50 , Histogram Show Green 4. IFTCOMBO > 0.60 Take Profit & Close Position Green Line Cross Down White Line (LFS) / Yellow Line (AMAValF) Cross Down Purple Line (AMAValS) >> Blackcat Take Profit - Short TP 1 = 30% RR 1.5-3 / TP2 50% 1. Red Candle 2. HARSI : Bar Red / RSI Over Lay (Yellow Line) < 35 (OS Extreme = OverSell Extreme : Default Value = -30) 3. Firefly : Histogram Value below middle < 50 , Histogram Show Red 4. IFTCOMBO < -0.60 Take Profit & Close Position Green Line Cross Up White Line (LFS) / / Yellow Line (AMAValF) Cross Up Purple Line (AMAValS) >> Blackcat ''' dataframe.loc[ ( #1 (qtpylib.crossed_below(dataframe['short_ema'], dataframe['key_ema']) | qtpylib.crossed_below(dataframe['AMAValF'], dataframe['AMAValS'])) & #etc (dataframe['volume'] > 0) # Make sure Volume is not 0 ), ['exit_long', 'exit_tag']] = (1, 'exit_long') dataframe.loc[ ( #1 (qtpylib.crossed_above(dataframe['short_ema'], dataframe['key_ema']) | qtpylib.crossed_above(dataframe['AMAValF'], dataframe['AMAValS'])) & #etc (dataframe['volume'] > 0) # Make sure Volume is not 0 ), ['exit_short', 'exit_tag']] = (1, 'exit_short') return dataframe