✨ AI Search 🔒
💬 Forum

BB_RPB_TSL_RNG_VWAP

🏆 League #22 / 1911
♡

davidzr/freqtrade-strategies/strategies/BB_RPB_TSL_RNG_VWAP/BB_RPB_TSL_RNG_VWAP_converted.py · ★563 · ⑂259 · first seen 2026-07-28 · repo updated 2024-02-10

Basics mode: spot timeframe: 5m interface version: 3 5m
Settings stoploss: -0.99 has minimal roi custom stoploss process only new candles startup candle count: 120 hyperopt hyperopt params: 36
Indicators ADX Bollinger_Bands CCI EMA HMA RMI RSI SMA Stoch_RSI Stochastic Williams_R pandas_ta talib technical
Methods EWO custom_stoploss top_percent_change vmap_b williams_r
15 related strategies (⧉ identical code, ≈ similar name)

Each tile is a different kind of check — from an instant code lint to full sandboxed backtests and forward tests on recent data. Not sure what a check actually proves? See the FAQ →

Source

Download Raw 
  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
# --- Do not remove these libs ---
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
import pandas_ta as pta
from freqtrade.persistence import Trade
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame, Series, DatetimeIndex, merge
from datetime import datetime, timedelta
from freqtrade.strategy import merge_informative_pair, CategoricalParameter, DecimalParameter, IntParameter, stoploss_from_open
from functools import reduce
from technical.indicators import RMI, zema
# --------------------------------

def EWO(dataframe, ema_length=5, ema2_length=35):
    df = dataframe.copy()
    ema1 = ta.EMA(df, timeperiod=ema_length)
    ema2 = ta.EMA(df, timeperiod=ema2_length)
    emadif = (ema1 - ema2) / df['low'] * 100
    return emadif
# Williams %R

def williams_r(dataframe: DataFrame, period: int=14) -> Series:
    """Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to the high and low
        of the past N days (for a given N). It was developed by a publisher and promoter of trading materials, Larry Williams.
        Its purpose is to tell whether a stock or commodity limit is trading near the high or the low, or somewhere in between,
        of its recent trading range.
        The oscillator is on a negative scale, from −100 (lowest) up to 0 (highest).
    """
    highest_high = dataframe['high'].rolling(center=False, window=period).max()
    lowest_low = dataframe['low'].rolling(center=False, window=period).min()
    WR = Series((highest_high - dataframe['close']) / (highest_high - lowest_low), name=f'{period} Williams %R')
    return WR * -100
####################################################
# VWAP bands, come from vwap strategy

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

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

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

class BB_RPB_TSL_RNG_VWAP(IStrategy):
    INTERFACE_VERSION = 3
    '\n        BB_RPB_TSL\n        @author jilv220\n        Simple bollinger brand strategy inspired by this blog  ( https://hacks-for-life.blogspot.com/2020/12/freqtrade-notes.html )\n        RPB, which stands for Real Pull Back, taken from ( https://github.com/GeorgeMurAlkh/freqtrade-stuff/blob/main/user_data/strategies/TheRealPullbackV2.py )\n        The trailing custom stoploss taken from BigZ04_TSL from Perkmeister ( modded by ilya )\n        I modified it to better suit my taste and added Hyperopt for this strategy.\n    '
    ##########################################################################
    # Hyperopt result area
    # buy space
    ##
    ##
    ##
    ##
    ##
    ##
    ##
    buy_params = {'buy_btc_safe': -289, 'buy_btc_safe_1d': -0.05, 'buy_threshold': 0.003, 'buy_bb_factor': 0.999, 'buy_bb_delta': 0.025, 'buy_bb_width': 0.095, 'buy_cci': -116, 'buy_cci_length': 25, 'buy_rmi': 49, 'buy_rmi_length': 17, 'buy_srsi_fk': 32, 'buy_closedelta': 12.148, 'buy_ema_diff': 0.022, 'buy_adx': 20, 'buy_fastd': 20, 'buy_fastk': 22, 'buy_ema_cofi': 0.98, 'buy_ewo_high': 4.179, 'buy_ema_high_2': 1.087, 'buy_ema_low_2': 0.97}
    # sell space
    sell_params = {'pHSL': -0.25, 'pPF_1': 0.019, 'pPF_2': 0.065, 'pSL_1': 0.019, 'pSL_2': 0.062, 'sell_btc_safe': -389, 'base_nb_candles_sell': 24, 'high_offset': 0.991, 'high_offset_2': 0.997}
    # really hard to use this
    minimal_roi = {'0': 100}
    # Optimal timeframe for the strategy
    timeframe = '5m'
    inf_1h = '1h'
    # Disabled
    stoploss = -0.99
    # Custom stoploss
    use_custom_stoploss = True
    use_exit_signal = True
    process_only_new_candles = True
    startup_candle_count = 120
    order_types = {'entry': 'limit', 'exit': 'limit', 'emergencysell': 'limit', 'forcebuy': 'limit', 'forcesell': 'limit', 'stoploss': 'limit', 'stoploss_on_exchange': False, 'stoploss_on_exchange_interval': 60, 'stoploss_on_exchange_limit_ratio': 0.99}
    ############################################################################
    ## Buy params
    is_optimize_dip = False
    buy_rmi = IntParameter(30, 50, default=35, optimize=is_optimize_dip)
    buy_cci = IntParameter(-135, -90, default=-133, optimize=is_optimize_dip)
    buy_srsi_fk = IntParameter(30, 50, default=25, optimize=is_optimize_dip)
    buy_cci_length = IntParameter(25, 45, default=25, optimize=is_optimize_dip)
    buy_rmi_length = IntParameter(8, 20, default=8, optimize=is_optimize_dip)
    is_optimize_break = False
    buy_bb_width = DecimalParameter(0.05, 0.2, default=0.15, optimize=is_optimize_break)
    buy_bb_delta = DecimalParameter(0.025, 0.08, default=0.04, optimize=is_optimize_break)
    is_optimize_local_dip = False
    buy_ema_diff = DecimalParameter(0.022, 0.027, default=0.025, optimize=is_optimize_local_dip)
    buy_bb_factor = DecimalParameter(0.99, 0.999, default=0.995, optimize=False)
    buy_closedelta = DecimalParameter(12.0, 18.0, default=15.0, optimize=is_optimize_local_dip)
    is_optimize_ewo = False
    buy_rsi_fast = IntParameter(35, 50, default=45, optimize=False)
    buy_rsi = IntParameter(15, 30, default=35, optimize=False)
    buy_ewo = DecimalParameter(-6.0, 5, default=-5.585, optimize=is_optimize_ewo)
    buy_ema_low = DecimalParameter(0.9, 0.99, default=0.942, optimize=is_optimize_ewo)
    buy_ema_high = DecimalParameter(0.95, 1.2, default=1.084, optimize=is_optimize_ewo)
    is_optimize_ewo_2 = False
    buy_ema_low_2 = DecimalParameter(0.96, 0.978, default=0.96, optimize=is_optimize_ewo_2)
    buy_ema_high_2 = DecimalParameter(1.05, 1.2, default=1.09, optimize=is_optimize_ewo_2)
    is_optimize_cofi = False
    buy_ema_cofi = DecimalParameter(0.96, 0.98, default=0.97, optimize=is_optimize_cofi)
    buy_fastk = IntParameter(20, 30, default=20, optimize=is_optimize_cofi)
    buy_fastd = IntParameter(20, 30, default=20, optimize=is_optimize_cofi)
    buy_adx = IntParameter(20, 30, default=30, optimize=is_optimize_cofi)
    buy_ewo_high = DecimalParameter(2, 12, default=3.553, optimize=is_optimize_cofi)
    is_optimize_btc_safe = False
    buy_btc_safe = IntParameter(-300, 50, default=-200, optimize=is_optimize_btc_safe)
    buy_btc_safe_1d = DecimalParameter(-0.075, -0.025, default=-0.05, optimize=is_optimize_btc_safe)
    buy_threshold = DecimalParameter(0.003, 0.012, default=0.008, optimize=is_optimize_btc_safe)
    # Buy params toggle
    buy_is_dip_enabled = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_is_break_enabled = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    ## Sell params
    sell_btc_safe = IntParameter(-400, -300, default=-365, optimize=True)
    base_nb_candles_sell = IntParameter(5, 80, default=sell_params['base_nb_candles_sell'], space='sell', optimize=True)
    high_offset = DecimalParameter(0.95, 1.1, default=sell_params['high_offset'], space='sell', optimize=True)
    high_offset_2 = DecimalParameter(0.99, 1.5, default=sell_params['high_offset_2'], space='sell', optimize=True)
    ## Trailing params
    # hard stoploss profit
    pHSL = DecimalParameter(-0.2, -0.04, default=-0.08, decimals=3, space='sell', load=True)
    # profit threshold 1, trigger point, SL_1 is used
    pPF_1 = DecimalParameter(0.008, 0.02, default=0.016, decimals=3, space='sell', load=True)
    pSL_1 = DecimalParameter(0.008, 0.02, default=0.011, decimals=3, space='sell', load=True)
    # profit threshold 2, SL_2 is used
    pPF_2 = DecimalParameter(0.04, 0.1, default=0.08, decimals=3, space='sell', load=True)
    pSL_2 = DecimalParameter(0.02, 0.07, default=0.04, decimals=3, space='sell', load=True)
    ############################################################################

    def informative_pairs(self):
        informative_pairs = [('BTC/USDT', '5m')]
        return informative_pairs
    ############################################################################
    ## Custom Trailing stoploss ( credit to Perkmeister for this custom stoploss to help the strategy ride a green candle )

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
        # hard stoploss profit
        HSL = self.pHSL.value
        PF_1 = self.pPF_1.value
        SL_1 = self.pSL_1.value
        PF_2 = self.pPF_2.value
        SL_2 = self.pSL_2.value
        enter_tag = ''
        if hasattr(trade, 'enter_tag') and trade.buy_tag is not None:
            enter_tag = trade.buy_tag
        buy_tags = buy_tag.split()
        if len(buy_tags) == 1 and 'vwap' in buy_tags:
            PF_1 = 0.01
            SL_1 = 0.01
            PF_2 = 0.05
            SL_2 = 0.042
        # For profits between PF_1 and PF_2 the stoploss (sl_profit) used is linearly interpolated
        # between the values of SL_1 and SL_2. For all profits above PL_2 the sl_profit value
        # rises linearly with current profit, for profits below PF_1 the hard stoploss profit is used.
        if current_profit > PF_2:
            sl_profit = SL_2 + (current_profit - PF_2)
        elif current_profit > PF_1:
            sl_profit = SL_1 + (current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1)
        else:
            sl_profit = HSL
        # Only for hyperopt invalid return
        if sl_profit >= current_profit:
            return -0.99
        return stoploss_from_open(sl_profit, current_profit)
    ############################################################################

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        assert self.dp, 'DataProvider is required for multiple timeframes.'
        # Bollinger bands (hyperopt hard to implement)
        bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband2'] = bollinger2['lower']
        dataframe['bb_middleband2'] = bollinger2['mid']
        dataframe['bb_upperband2'] = bollinger2['upper']
        bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3)
        dataframe['bb_lowerband3'] = bollinger3['lower']
        dataframe['bb_middleband3'] = bollinger3['mid']
        dataframe['bb_upperband3'] = bollinger3['upper']
        # vmap indicators
        vwap_low, vwap, vwap_high = vmap_b(dataframe, 20, 1)
        dataframe['vwap_low'] = vwap_low
        dataframe['tcp_percent_4'] = top_percent_change(dataframe, 4)
        ### BTC protection
        # BTC info
        inf_tf = '5m'
        informative = self.dp.get_pair_dataframe('BTC/USDT', timeframe=inf_tf)
        informative_past = informative.copy().shift(1)  # Get recent BTC info
        # BTC 5m dump protection
        informative_past_source = (informative_past['open'] + informative_past['close'] + informative_past['high'] + informative_past['low']) / 4  # Get BTC price
        informative_threshold = informative_past_source * self.buy_threshold.value  # BTC dump n% in 5 min
        informative_past_delta = informative_past['close'].shift(1) - informative_past['close']  # should be positive if dump
        informative_diff = informative_threshold - informative_past_delta  # Need be larger than 0
        dataframe['btc_threshold'] = informative_threshold
        dataframe['btc_diff'] = informative_diff
        # BTC 1d dump protection
        informative_past_1d = informative.copy().shift(288)
        informative_past_source_1d = (informative_past_1d['open'] + informative_past_1d['close'] + informative_past_1d['high'] + informative_past_1d['low']) / 4
        dataframe['btc_5m'] = informative_past_source
        dataframe['btc_1d'] = informative_past_source_1d
        ### Other checks
        dataframe['bb_width'] = (dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2']
        dataframe['bb_delta'] = (dataframe['bb_lowerband2'] - dataframe['bb_lowerband3']) / dataframe['bb_lowerband2']
        dataframe['bb_bottom_cross'] = qtpylib.crossed_below(dataframe['close'], dataframe['bb_lowerband3']).astype('int')
        # CCI hyperopt
        for val in self.buy_cci_length.range:
            dataframe[f'cci_length_{val}'] = ta.CCI(dataframe, val)
        dataframe['cci'] = ta.CCI(dataframe, 26)
        dataframe['cci_long'] = ta.CCI(dataframe, 170)
        # RMI hyperopt
        for val in self.buy_rmi_length.range:
            dataframe[f'rmi_length_{val}'] = RMI(dataframe, length=val, mom=4)
        #dataframe['rmi'] = RMI(dataframe, length=8, mom=4)
        # SRSI hyperopt ?
        stoch = ta.STOCHRSI(dataframe, 15, 20, 2, 2)
        dataframe['srsi_fk'] = stoch['fastk']
        dataframe['srsi_fd'] = stoch['fastd']
        # BinH
        dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
        # SMA
        dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15)
        dataframe['sma_30'] = ta.SMA(dataframe, timeperiod=30)
        # CTI
        dataframe['cti'] = pta.cti(dataframe['close'], length=20)
        # EMA
        dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8)
        dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12)
        dataframe['ema_13'] = ta.EMA(dataframe, timeperiod=13)
        dataframe['ema_16'] = ta.EMA(dataframe, timeperiod=16)
        dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26)
        dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50)
        dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100)
        dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9)
        # RSI
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)
        dataframe['rsi_84'] = ta.RSI(dataframe, timeperiod=84)
        dataframe['rsi_112'] = ta.RSI(dataframe, timeperiod=112)
        # Elliot
        dataframe['EWO'] = EWO(dataframe, 50, 200)
        # Cofi
        stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
        dataframe['fastd'] = stoch_fast['fastd']
        dataframe['fastk'] = stoch_fast['fastk']
        dataframe['adx'] = ta.ADX(dataframe)
        # Williams %R
        dataframe['r_14'] = williams_r(dataframe, period=14)
        # Volume
        dataframe['volume_mean_4'] = dataframe['volume'].rolling(4).mean().shift(1)
        # Calculate all ma_sell values
        for val in self.base_nb_candles_sell.range:
            dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        dataframe.loc[:, 'enter_tag'] = ''
        if self.buy_is_dip_enabled.value:
            is_dip = (dataframe[f'rmi_length_{self.buy_rmi_length.value}'] < self.buy_rmi.value) & (dataframe[f'cci_length_{self.buy_cci_length.value}'] <= self.buy_cci.value) & (dataframe['srsi_fk'] < self.buy_srsi_fk.value)
        #conditions.append(is_dip)
        if self.buy_is_break_enabled.value:  #"buy_bb_delta": 0.025 0.036
            #"buy_bb_width": 0.095 0.133
            # from BinH
            is_break = (dataframe['bb_delta'] > self.buy_bb_delta.value) & (dataframe['bb_width'] > self.buy_bb_width.value) & (dataframe['closedelta'] > dataframe['close'] * self.buy_closedelta.value / 1000) & (dataframe['close'] < dataframe['bb_lowerband3'] * self.buy_bb_factor.value)
            #conditions.append(is_break)  # from NFI next gen
        is_local_uptrend = (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.buy_ema_diff.value) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['close'] < dataframe['bb_lowerband2'] * self.buy_bb_factor.value) & (dataframe['closedelta'] > dataframe['close'] * self.buy_closedelta.value / 1000)  # from SMA offset
        is_ewo = (dataframe['rsi_fast'] < self.buy_rsi_fast.value) & (dataframe['close'] < dataframe['ema_8'] * self.buy_ema_low.value) & (dataframe['EWO'] > self.buy_ewo.value) & (dataframe['close'] < dataframe['ema_16'] * self.buy_ema_high.value) & (dataframe['rsi'] < self.buy_rsi.value)
        is_ewo_2 = (dataframe['rsi_fast'] < self.buy_rsi_fast.value) & (dataframe['close'] < dataframe['ema_8'] * self.buy_ema_low_2.value) & (dataframe['EWO'] > self.buy_ewo_high.value) & (dataframe['close'] < dataframe['ema_16'] * self.buy_ema_high_2.value) & (dataframe['rsi'] < self.buy_rsi.value)
        is_cofi = (dataframe['open'] < dataframe['ema_8'] * self.buy_ema_cofi.value) & qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd']) & (dataframe['fastk'] < self.buy_fastk.value) & (dataframe['fastd'] < self.buy_fastd.value) & (dataframe['adx'] > self.buy_adx.value) & (dataframe['EWO'] > self.buy_ewo_high.value)
        # NFI quick mode
        is_nfi_32 = (dataframe['rsi_slow'] < dataframe['rsi_slow'].shift(1)) & (dataframe['rsi_fast'] < 46) & (dataframe['rsi'] > 19) & (dataframe['close'] < dataframe['sma_15'] * 0.942) & (dataframe['cti'] < -0.86)
        is_nfi_33 = (dataframe['close'] < dataframe['ema_13'] * 0.978) & (dataframe['EWO'] > 8) & (dataframe['cti'] < -0.88) & (dataframe['rsi'] < 32) & (dataframe['r_14'] < -98.0) & (dataframe['volume'] < dataframe['volume_mean_4'] * 2.5)
        is_vwap = (dataframe['close'] < dataframe['vwap_low']) & (dataframe['tcp_percent_4'] > 0.04) & (dataframe['cti'] < -0.8) & (dataframe['rsi'] < 35) & (dataframe['rsi_84'] < 60) & (dataframe['rsi_112'] < 60) & (dataframe['volume'] > 0)
        # is_btc_safe = (
        #         (dataframe['btc_diff'] > self.buy_btc_safe.value)
        #        &(dataframe['btc_5m'] - dataframe['btc_1d'] > dataframe['btc_1d'] * self.buy_btc_safe_1d.value)
        #        &(dataframe['volume'] > 0)           # Make sure Volume is not 0
        #     )
        is_BB_checked = is_dip & is_break
        #print(dataframe['btc_5m'])
        #print(dataframe['btc_1d'])
        #print(dataframe['btc_5m'] - dataframe['btc_1d'])
        #print(dataframe['btc_1d'] * -0.025)
        #print(dataframe['btc_5m'] - dataframe['btc_1d'] > dataframe['btc_1d'] * -0.025)
        ## condition append
        conditions.append(is_BB_checked)  # ~1.7 89%
        dataframe.loc[is_BB_checked, 'enter_tag'] += 'bb '
        conditions.append(is_local_uptrend)  # ~3.84 90.2%
        dataframe.loc[is_local_uptrend, 'enter_tag'] += 'local uptrend '
        conditions.append(is_ewo)  # ~2.26 93.5%
        dataframe.loc[is_ewo, 'enter_tag'] += 'ewo '
        conditions.append(is_ewo_2)  # ~3.68 90.3%
        dataframe.loc[is_ewo_2, 'enter_tag'] += 'ewo2 '
        conditions.append(is_cofi)  # ~3.21 90.8%
        dataframe.loc[is_cofi, 'enter_tag'] += 'cofi '
        conditions.append(is_nfi_32)  # ~2.43 91.3%
        dataframe.loc[is_nfi_32, 'enter_tag'] += 'nfi 32 '
        conditions.append(is_nfi_33)  # ~0.11 100%
        dataframe.loc[is_nfi_33, 'enter_tag'] += 'nfi 33 '
        conditions.append(is_vwap)
        dataframe.loc[is_vwap, 'enter_tag'] += 'vwap '
        if conditions:
            dataframe.loc[reduce(lambda x, y: x | y, conditions), 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        conditions.append((dataframe['close'] > dataframe['sma_9']) & (dataframe['close'] > dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value) & (dataframe['rsi'] > 50) & (dataframe['volume'] > 0) & (dataframe['rsi_fast'] > dataframe['rsi_slow']) | (dataframe['sma_9'] > dataframe['sma_9'].shift(1) + dataframe['sma_9'].shift(1) * 0.005) & (dataframe['close'] < dataframe['hma_50']) & (dataframe['close'] > dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value) & (dataframe['volume'] > 0) & (dataframe['rsi_fast'] > dataframe['rsi_slow']))
        if conditions:
            dataframe.loc[reduce(lambda x, y: x | y, conditions), 'exit_long'] = 1
        return dataframe