✨ AI Search 🔒
💬 Forum

NFI5MOHO_WIP

♡

Haoyun8/Freqai11.py/NFI5MOHO_WIP_converted.py · ★1 · first seen 2026-07-28 · repo updated 2024-06-25

Basics mode: spot timeframe: 5m interface version: 3
Settings stoploss: -0.15 has minimal roi trailing custom stoploss protections process only new candles startup candle count: 300 hyperopt hyperopt params: 195
Indicators ADX ATR Aroon Bollinger_Bands CCI EMA KAMA MACD MFI PPO RSI SAR SMA Stoch_RSI Stochastic TRIX UO Williams_R talib
Concepts risk_management trailing
Methods EWO confirm_trade_entry custom_exit custom_stoploss get_ticker_indicator informative_tf_indicators normal_tf_indicators
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
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
import logging
from functools import reduce
from typing import Dict
import numpy as np
import talib.abstract as ta
from pandas import DataFrame
from datetime import datetime
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter
from freqtrade.persistence import Trade
import freqtrade.vendor.qtpylib.indicators as qtpylib
logger = logging.getLogger(__name__)

class NFI5MOHO_WIP(IStrategy):
    protections = [{'method': 'LowProfitPairs', 'lookback_period_candles': 60, 'trade_limit': 1, 'stop_duration': 60, 'required_profit': -0.05}, {'method': 'CooldownPeriod', 'stop_duration_candles': 2}]
    INTERFACE_VERSION = 3
    order_types = {'entry': 'limit', 'exit': 'limit', 'trailing_stop_loss': 'limit', 'stoploss': 'limit', 'stoploss_on_exchange': False}
    minimal_roi = {'0': 0.08, '10': 0.04, '30': 0.02, '60': 0.01}
    stoploss = -0.15
    base_nb_candles_buy = IntParameter(5, 80, default=20, load=True, space='buy', optimize=False)
    base_nb_candles_sell = IntParameter(5, 80, default=20, load=True, space='sell', optimize=False)
    low_offset_sma = DecimalParameter(0.9, 0.99, default=0.958, load=True, space='buy', optimize=False)
    high_offset_sma = DecimalParameter(0.99, 1.1, default=1.012, load=True, space='sell', optimize=False)
    low_offset_ema = DecimalParameter(0.9, 0.99, default=0.958, load=True, space='buy', optimize=False)
    high_offset_ema = DecimalParameter(0.99, 1.1, default=1.012, load=True, space='sell', optimize=False)
    low_offset_trima = DecimalParameter(0.9, 0.99, default=0.958, load=True, space='buy', optimize=False)
    high_offset_trima = DecimalParameter(0.99, 1.1, default=1.012, load=True, space='sell', optimize=False)
    low_offset_t3 = DecimalParameter(0.9, 0.99, default=0.958, load=True, space='buy', optimize=False)
    high_offset_t3 = DecimalParameter(0.99, 1.1, default=1.012, load=True, space='sell', optimize=False)
    low_offset_kama = DecimalParameter(0.9, 0.99, default=0.958, load=True, space='buy', optimize=False)
    high_offset_kama = DecimalParameter(0.99, 1.1, default=1.012, load=True, space='sell', optimize=False)
    ewo_low = DecimalParameter(-20.0, -8.0, default=-20.0, load=True, space='buy', optimize=False)
    ewo_high = DecimalParameter(2.0, 12.0, default=6.0, load=True, space='buy', optimize=False)
    fast_ewo = IntParameter(10, 50, default=50, load=True, space='buy', optimize=False)
    slow_ewo = IntParameter(100, 200, default=200, load=True, space='buy', optimize=False)
    ma_types = ['sma', 'ema', 'trima', 't3', 'kama']
    ma_map = {'sma': {'low_offset': low_offset_sma.value, 'high_offset': high_offset_sma.value, 'calculate': ta.SMA}, 'ema': {'low_offset': low_offset_ema.value, 'high_offset': high_offset_ema.value, 'calculate': ta.EMA}, 'trima': {'low_offset': low_offset_trima.value, 'high_offset': high_offset_trima.value, 'calculate': ta.TRIMA}, 't3': {'low_offset': low_offset_t3.value, 'high_offset': high_offset_t3.value, 'calculate': ta.T3}, 'kama': {'low_offset': low_offset_kama.value, 'high_offset': high_offset_kama.value, 'calculate': ta.KAMA}}
    trailing_stop = True
    trailing_only_offset_is_reached = True
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.04
    use_custom_stoploss = False
    timeframe = '5m'
    inf_1h = '1h'
    process_only_new_candles = True
    use_exie_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = True
    startup_candle_count: int = 300
    plot_config = {'main_plot': {'ma_offset_buy': {'color': 'orange'}, 'ma_offset_sell': {'color': 'orange'}}}
    buy_condition_1_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_2_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_3_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_4_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_5_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_6_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_7_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_8_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_9_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_10_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_11_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_12_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_13_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_14_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_15_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_16_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_17_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_18_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_19_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_20_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_21_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_dip_threshold_1 = DecimalParameter(0.001, 0.05, default=0.02, space='buy', decimals=3, optimize=False, load=True)
    buy_dip_threshold_2 = DecimalParameter(0.01, 0.2, default=0.14, space='buy', decimals=3, optimize=False, load=True)
    buy_dip_threshold_3 = DecimalParameter(0.05, 0.4, default=0.32, space='buy', decimals=3, optimize=False, load=True)
    buy_dip_threshold_4 = DecimalParameter(0.2, 0.5, default=0.5, space='buy', decimals=3, optimize=False, load=True)
    buy_dip_threshold_5 = DecimalParameter(0.001, 0.05, default=0.015, space='buy', decimals=3, optimize=False, load=True)
    buy_dip_threshold_6 = DecimalParameter(0.01, 0.2, default=0.06, space='buy', decimals=3, optimize=False, load=True)
    buy_dip_threshold_7 = DecimalParameter(0.05, 0.4, default=0.24, space='buy', decimals=3, optimize=False, load=True)
    buy_dip_threshold_8 = DecimalParameter(0.2, 0.5, default=0.4, space='buy', decimals=3, optimize=False, load=True)
    buy_dip_threshold_9 = DecimalParameter(0.001, 0.05, default=0.026, space='buy', decimals=3, optimize=False, load=True)
    buy_dip_threshold_10 = DecimalParameter(0.01, 0.2, default=0.24, space='buy', decimals=3, optimize=False, load=True)
    buy_dip_threshold_11 = DecimalParameter(0.05, 0.4, default=0.42, space='buy', decimals=3, optimize=False, load=True)
    buy_dip_threshold_12 = DecimalParameter(0.2, 0.5, default=0.66, space='buy', decimals=3, optimize=False, load=True)
    buy_pump_pull_threshold_1 = DecimalParameter(1.5, 3.0, default=1.75, space='buy', decimals=2, optimize=False, load=True)
    buy_pump_threshold_1 = DecimalParameter(0.4, 1.0, default=0.5, space='buy', decimals=3, optimize=False, load=True)
    buy_pump_pull_threshold_2 = DecimalParameter(1.5, 3.0, default=1.75, space='buy', decimals=2, optimize=False, load=True)
    buy_pump_threshold_2 = DecimalParameter(0.4, 1.0, default=0.56, space='buy', decimals=3, optimize=False, load=True)
    buy_pump_pull_threshold_3 = DecimalParameter(1.5, 3.0, default=1.75, space='buy', decimals=2, optimize=False, load=True)
    buy_pump_threshold_3 = DecimalParameter(0.4, 1.0, default=0.85, space='buy', decimals=3, optimize=False, load=True)
    buy_pump_pull_threshold_4 = DecimalParameter(1.5, 3.0, default=2.2, space='buy', decimals=2, optimize=False, load=True)
    buy_pump_threshold_4 = DecimalParameter(0.4, 1.0, default=0.4, space='buy', decimals=3, optimize=False, load=True)
    buy_pump_pull_threshold_5 = DecimalParameter(1.5, 3.0, default=2.0, space='buy', decimals=2, optimize=False, load=True)
    buy_pump_threshold_5 = DecimalParameter(0.4, 1.0, default=0.56, space='buy', decimals=3, optimize=False, load=True)
    buy_pump_pull_threshold_6 = DecimalParameter(1.5, 3.0, default=2.0, space='buy', decimals=2, optimize=False, load=True)
    buy_pump_threshold_6 = DecimalParameter(0.4, 1.0, default=0.68, space='buy', decimals=3, optimize=False, load=True)
    buy_pump_pull_threshold_7 = DecimalParameter(1.5, 3.0, default=1.7, space='buy', decimals=2, optimize=False, load=True)
    buy_pump_threshold_7 = DecimalParameter(0.4, 1.0, default=0.66, space='buy', decimals=3, optimize=False, load=True)
    buy_pump_pull_threshold_8 = DecimalParameter(1.5, 3.0, default=1.7, space='buy', decimals=2, optimize=False, load=True)
    buy_pump_threshold_8 = DecimalParameter(0.4, 1.0, default=0.7, space='buy', decimals=3, optimize=False, load=True)
    buy_pump_pull_threshold_9 = DecimalParameter(1.5, 3.0, default=1.4, space='buy', decimals=2, optimize=False, load=True)
    buy_pump_threshold_9 = DecimalParameter(0.4, 1.8, default=1.3, space='buy', decimals=3, optimize=False, load=True)
    buy_min_inc_1 = DecimalParameter(0.01, 0.05, default=0.022, space='buy', decimals=3, optimize=False, load=True)
    buy_rsi_1h_min_1 = DecimalParameter(25.0, 40.0, default=30.0, space='buy', decimals=1, optimize=False, load=True)
    buy_rsi_1h_max_1 = DecimalParameter(70.0, 90.0, default=84.0, space='buy', decimals=1, optimize=False, load=True)
    buy_rsi_1 = DecimalParameter(20.0, 40.0, default=36.0, space='buy', decimals=1, optimize=False, load=True)
    buy_mfi_1 = DecimalParameter(20.0, 40.0, default=26.0, space='buy', decimals=1, optimize=False, load=True)
    buy_volume_2 = DecimalParameter(1.0, 10.0, default=2.6, space='buy', decimals=1, optimize=False, load=True)
    buy_rsi_1h_min_2 = DecimalParameter(30.0, 40.0, default=32.0, space='buy', decimals=1, optimize=False, load=True)
    buy_rsi_1h_max_2 = DecimalParameter(70.0, 95.0, default=84.0, space='buy', decimals=1, optimize=False, load=True)
    buy_rsi_1h_diff_2 = DecimalParameter(30.0, 50.0, default=39.0, space='buy', decimals=1, optimize=False, load=True)
    buy_mfi_2 = DecimalParameter(30.0, 56.0, default=49.0, space='buy', decimals=1, optimize=False, load=True)
    buy_bb_offset_2 = DecimalParameter(0.97, 0.999, default=0.983, space='buy', decimals=3, optimize=False, load=True)
    buy_bb40_bbdelta_close_3 = DecimalParameter(0.005, 0.06, default=0.057, space='buy', optimize=False, load=True)
    buy_bb40_closedelta_close_3 = DecimalParameter(0.01, 0.03, default=0.023, space='buy', optimize=False, load=True)
    buy_bb40_tail_bbdelta_3 = DecimalParameter(0.15, 0.45, default=0.418, space='buy', optimize=False, load=True)
    buy_ema_rel_3 = DecimalParameter(0.97, 0.999, default=0.986, space='buy', decimals=3, optimize=False, load=True)
    buy_bb20_close_bblowerband_4 = DecimalParameter(0.96, 0.99, default=0.979, space='buy', optimize=False, load=True)
    buy_bb20_volume_4 = DecimalParameter(1.0, 20.0, default=10.0, space='buy', decimals=2, optimize=False, load=True)
    buy_ema_open_mult_5 = DecimalParameter(0.016, 0.03, default=0.019, space='buy', decimals=3, optimize=False, load=True)
    buy_bb_offset_5 = DecimalParameter(0.98, 1.0, default=0.999, space='buy', decimals=3, optimize=False, load=True)
    buy_ema_rel_5 = DecimalParameter(0.97, 0.999, default=0.982, space='buy', decimals=3, optimize=False, load=True)
    buy_ema_open_mult_6 = DecimalParameter(0.02, 0.03, default=0.025, space='buy', decimals=3, optimize=False, load=True)
    buy_bb_offset_6 = DecimalParameter(0.98, 0.999, default=0.984, space='buy', decimals=3, optimize=False, load=True)
    buy_volume_7 = DecimalParameter(1.0, 10.0, default=2.0, space='buy', decimals=1, optimize=False, load=True)
    buy_ema_open_mult_7 = DecimalParameter(0.02, 0.04, default=0.03, space='buy', decimals=3, optimize=False, load=True)
    buy_rsi_7 = DecimalParameter(24.0, 50.0, default=36.0, space='buy', decimals=1, optimize=False, load=True)
    buy_ema_rel_7 = DecimalParameter(0.97, 0.999, default=0.986, space='buy', decimals=3, optimize=False, load=True)
    buy_volume_8 = DecimalParameter(1.0, 6.0, default=2.0, space='buy', decimals=1, optimize=False, load=True)
    buy_rsi_8 = DecimalParameter(36.0, 40.0, default=20.0, space='buy', decimals=1, optimize=False, load=True)
    buy_tail_diff_8 = DecimalParameter(3.0, 10.0, default=3.5, space='buy', decimals=1, optimize=False, load=True)
    buy_volume_9 = DecimalParameter(1.0, 4.0, default=1.0, space='buy', decimals=2, optimize=False, load=True)
    buy_ma_offset_9 = DecimalParameter(0.94, 0.99, default=0.97, space='buy', decimals=3, optimize=False, load=True)
    buy_bb_offset_9 = DecimalParameter(0.97, 0.99, default=0.985, space='buy', decimals=3, optimize=False, load=True)
    buy_rsi_1h_min_9 = DecimalParameter(26.0, 40.0, default=30.0, space='buy', decimals=1, optimize=False, load=True)
    buy_rsi_1h_max_9 = DecimalParameter(70.0, 90.0, default=88.0, space='buy', decimals=1, optimize=False, load=True)
    buy_mfi_9 = DecimalParameter(36.0, 65.0, default=30.0, space='buy', decimals=1, optimize=False, load=True)
    buy_volume_10 = DecimalParameter(1.0, 8.0, default=2.4, space='buy', decimals=1, optimize=False, load=True)
    buy_ma_offset_10 = DecimalParameter(0.93, 0.97, default=0.944, space='buy', decimals=3, optimize=False, load=True)
    buy_bb_offset_10 = DecimalParameter(0.97, 0.99, default=0.994, space='buy', decimals=3, optimize=False, load=True)
    buy_rsi_1h_10 = DecimalParameter(20.0, 40.0, default=37.0, space='buy', decimals=1, optimize=False, load=True)
    buy_ma_offset_11 = DecimalParameter(0.93, 0.99, default=0.939, space='buy', decimals=3, optimize=False, load=True)
    buy_min_inc_11 = DecimalParameter(0.005, 0.05, default=0.022, space='buy', decimals=3, optimize=False, load=True)
    buy_rsi_1h_min_11 = DecimalParameter(40.0, 60.0, default=56.0, space='buy', decimals=1, optimize=False, load=True)
    buy_rsi_1h_max_11 = DecimalParameter(70.0, 90.0, default=84.0, space='buy', decimals=1, optimize=False, load=True)
    buy_rsi_11 = DecimalParameter(30.0, 48.0, default=48.0, space='buy', decimals=1, optimize=False, load=True)
    buy_mfi_11 = DecimalParameter(36.0, 56.0, default=38.0, space='buy', decimals=1, optimize=False, load=True)
    buy_volume_12 = DecimalParameter(1.0, 10.0, default=1.7, space='buy', decimals=1, optimize=False, load=True)
    buy_ma_offset_12 = DecimalParameter(0.93, 0.97, default=0.936, space='buy', decimals=3, optimize=False, load=True)
    buy_rsi_12 = DecimalParameter(26.0, 40.0, default=30.0, space='buy', decimals=1, optimize=False, load=True)
    buy_ewo_12 = DecimalParameter(2.0, 6.0, default=2.0, space='buy', decimals=1, optimize=False, load=True)
    buy_volume_13 = DecimalParameter(1.0, 10.0, default=1.6, space='buy', decimals=1, optimize=False, load=True)
    buy_ma_offset_13 = DecimalParameter(0.93, 0.98, default=0.978, space='buy', decimals=3, optimize=False, load=True)
    buy_ewo_13 = DecimalParameter(-14.0, -7.0, default=-10.4, space='buy', decimals=1, optimize=False, load=True)
    buy_volume_14 = DecimalParameter(1.0, 10.0, default=2.0, space='buy', decimals=1, optimize=False, load=True)
    buy_ema_open_mult_14 = DecimalParameter(0.01, 0.03, default=0.014, space='buy', decimals=3, optimize=False, load=True)
    buy_bb_offset_14 = DecimalParameter(0.98, 1.0, default=0.986, space='buy', decimals=3, optimize=False, load=True)
    buy_ma_offset_14 = DecimalParameter(0.93, 0.99, default=0.97, space='buy', decimals=3, optimize=False, load=True)
    buy_volume_15 = DecimalParameter(1.0, 10.0, default=2.0, space='buy', decimals=1, optimize=False, load=True)
    buy_ema_open_mult_15 = DecimalParameter(0.02, 0.04, default=0.018, space='buy', decimals=3, optimize=False, load=True)
    buy_ma_offset_15 = DecimalParameter(0.93, 0.99, default=0.954, space='buy', decimals=3, optimize=False, load=True)
    buy_rsi_15 = DecimalParameter(30.0, 50.0, default=28.0, space='buy', decimals=1, optimize=False, load=True)
    buy_ema_rel_15 = DecimalParameter(0.97, 0.999, default=0.988, space='buy', decimals=3, optimize=False, load=True)
    buy_volume_16 = DecimalParameter(1.0, 10.0, default=2.0, space='buy', decimals=1, optimize=False, load=True)
    buy_ma_offset_16 = DecimalParameter(0.93, 0.97, default=0.952, space='buy', decimals=3, optimize=False, load=True)
    buy_rsi_16 = DecimalParameter(26.0, 50.0, default=31.0, space='buy', decimals=1, optimize=False, load=True)
    buy_ewo_16 = DecimalParameter(4.0, 8.0, default=2.8, space='buy', decimals=1, optimize=False, load=True)
    buy_volume_17 = DecimalParameter(0.5, 8.0, default=2.0, space='buy', decimals=1, optimize=False, load=True)
    buy_ma_offset_17 = DecimalParameter(0.93, 0.98, default=0.958, space='buy', decimals=3, optimize=False, load=True)
    buy_ewo_17 = DecimalParameter(-18.0, -10.0, default=-12.0, space='buy', decimals=1, optimize=False, load=True)
    buy_volume_18 = DecimalParameter(1.0, 6.0, default=2.0, space='buy', decimals=1, optimize=False, load=True)
    buy_rsi_18 = DecimalParameter(16.0, 32.0, default=26.0, space='buy', decimals=1, optimize=False, load=True)
    buy_bb_offset_18 = DecimalParameter(0.98, 1.0, default=0.982, space='buy', decimals=3, optimize=False, load=True)
    buy_rsi_1h_min_19 = DecimalParameter(40.0, 70.0, default=50.0, space='buy', decimals=1, optimize=False, load=True)
    buy_chop_min_19 = DecimalParameter(20.0, 60.0, default=24.1, space='buy', decimals=1, optimize=False, load=True)
    buy_volume_20 = DecimalParameter(0.5, 6.0, default=1.2, space='buy', decimals=1, optimize=False, load=True)
    buy_rsi_20 = DecimalParameter(20.0, 36.0, default=26.0, space='buy', decimals=1, optimize=False, load=True)
    buy_rsi_1h_20 = DecimalParameter(14.0, 30.0, default=20.0, space='buy', decimals=1, optimize=False, load=True)
    buy_volume_21 = DecimalParameter(0.5, 6.0, default=3.0, space='buy', decimals=1, optimize=False, load=True)
    buy_rsi_21 = DecimalParameter(10.0, 28.0, default=23.0, space='buy', decimals=1, optimize=False, load=True)
    buy_rsi_1h_21 = DecimalParameter(18.0, 40.0, default=24.0, space='buy', decimals=1, optimize=False, load=True)
    sell_condition_1_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
    sell_condition_2_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
    sell_condition_3_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
    sell_condition_4_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
    sell_condition_5_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
    sell_condition_6_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
    sell_condition_7_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
    sell_condition_8_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
    sell_rsi_bb_1 = DecimalParameter(60.0, 80.0, default=79.5, space='sell', decimals=1, optimize=False, load=True)
    sell_rsi_bb_2 = DecimalParameter(72.0, 90.0, default=81, space='sell', decimals=1, optimize=False, load=True)
    sell_rsi_main_3 = DecimalParameter(77.0, 90.0, default=82, space='sell', decimals=1, optimize=False, load=True)
    sell_dual_rsi_rsi_4 = DecimalParameter(72.0, 84.0, default=73.4, space='sell', decimals=1, optimize=False, load=True)
    sell_dual_rsi_rsi_1h_4 = DecimalParameter(78.0, 92.0, default=79.6, space='sell', decimals=1, optimize=False, load=True)
    sell_ema_relative_5 = DecimalParameter(0.005, 0.05, default=0.024, space='sell', optimize=False, load=True)
    sell_rsi_diff_5 = DecimalParameter(0.0, 20.0, default=4.4, space='sell', optimize=False, load=True)
    sell_rsi_under_6 = DecimalParameter(72.0, 90.0, default=79.0, space='sell', decimals=1, optimize=False, load=True)
    sell_rsi_1h_7 = DecimalParameter(80.0, 95.0, default=81.7, space='sell', decimals=1, optimize=False, load=True)
    sell_bb_relative_8 = DecimalParameter(1.05, 1.3, default=1.1, space='sell', decimals=3, optimize=False, load=True)
    sell_custom_profit_0 = DecimalParameter(0.01, 0.1, default=0.01, space='sell', decimals=3, optimize=False, load=True)
    sell_custom_rsi_0 = DecimalParameter(30.0, 40.0, default=33.0, space='sell', decimals=3, optimize=False, load=True)
    sell_custom_profit_1 = DecimalParameter(0.01, 0.1, default=0.03, space='sell', decimals=3, optimize=False, load=True)
    sell_custom_rsi_1 = DecimalParameter(30.0, 50.0, default=38.0, space='sell', decimals=2, optimize=False, load=True)
    sell_custom_profit_2 = DecimalParameter(0.01, 0.1, default=0.05, space='sell', decimals=3, optimize=False, load=True)
    sell_custom_rsi_2 = DecimalParameter(34.0, 50.0, default=43.0, space='sell', decimals=2, optimize=False, load=True)
    sell_custom_profit_3 = DecimalParameter(0.06, 0.3, default=0.08, space='sell', decimals=3, optimize=False, load=True)
    sell_custom_rsi_3 = DecimalParameter(38.0, 55.0, default=48.0, space='sell', decimals=2, optimize=False, load=True)
    sell_custom_profit_4 = DecimalParameter(0.3, 0.6, default=0.25, space='sell', decimals=3, optimize=False, load=True)
    sell_custom_rsi_4 = DecimalParameter(40.0, 58.0, default=50.0, space='sell', decimals=2, optimize=False, load=True)
    sell_custom_under_profit_1 = DecimalParameter(0.01, 0.1, default=0.02, space='sell', decimals=3, optimize=False, load=True)
    sell_custom_under_rsi_1 = DecimalParameter(36.0, 60.0, default=56.0, space='sell', decimals=1, optimize=False, load=True)
    sell_custom_under_profit_2 = DecimalParameter(0.01, 0.1, default=0.04, space='sell', decimals=3, optimize=False, load=True)
    sell_custom_under_rsi_2 = DecimalParameter(46.0, 66.0, default=60.0, space='sell', decimals=1, optimize=False, load=True)
    sell_custom_under_profit_3 = DecimalParameter(0.01, 0.1, default=0.6, space='sell', decimals=3, optimize=False, load=True)
    sell_custom_under_rsi_3 = DecimalParameter(50.0, 68.0, default=62.0, space='sell', decimals=1, optimize=False, load=True)
    sell_custom_dec_profit_1 = DecimalParameter(0.01, 0.1, default=0.05, space='sell', decimals=3, optimize=False, load=True)
    sell_custom_dec_profit_2 = DecimalParameter(0.05, 0.2, default=0.07, space='sell', decimals=3, optimize=False, load=True)
    sell_trail_profit_min_1 = DecimalParameter(0.1, 0.25, default=0.15, space='sell', decimals=3, optimize=False, load=True)
    sell_trail_profit_max_1 = DecimalParameter(0.3, 0.5, default=0.46, space='sell', decimals=2, optimize=False, load=True)
    sell_trail_down_1 = DecimalParameter(0.04, 0.2, default=0.18, space='sell', decimals=3, optimize=False, load=True)
    sell_trail_profit_min_2 = DecimalParameter(0.01, 0.1, default=0.01, space='sell', decimals=3, optimize=False, load=True)
    sell_trail_profit_max_2 = DecimalParameter(0.08, 0.25, default=0.12, space='sell', decimals=2, optimize=False, load=True)
    sell_trail_down_2 = DecimalParameter(0.04, 0.2, default=0.14, space='sell', decimals=3, optimize=False, load=True)
    sell_trail_profit_min_3 = DecimalParameter(0.01, 0.1, default=0.05, space='sell', decimals=3, optimize=False, load=True)
    sell_trail_profit_max_3 = DecimalParameter(0.08, 0.16, default=0.1, space='sell', decimals=2, optimize=False, load=True)
    sell_trail_down_3 = DecimalParameter(0.01, 0.04, default=0.01, space='sell', decimals=3, optimize=False, load=True)
    sell_custom_profit_under_rel_1 = DecimalParameter(0.01, 0.04, default=0.024, space='sell', optimize=False, load=True)
    sell_custom_profit_under_rsi_diff_1 = DecimalParameter(0.0, 20.0, default=4.4, space='sell', optimize=False, load=True)
    sell_custom_stoploss_under_rel_1 = DecimalParameter(0.001, 0.02, default=0.004, space='sell', optimize=False, load=True)
    sell_custom_stoploss_under_rsi_diff_1 = DecimalParameter(0.0, 20.0, default=8.0, space='sell', optimize=False, load=True)

    def get_ticker_indicator(self):
        return int(self.timeframe[:-1])

    def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs):
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()
        max_profit = trade.calc_profit_ratio(trade.max_rate) * 100
        sell_tag = None
        if current_profit > self.sell_custom_profit_4.value and last_candle['rsi'] < self.sell_custom_rsi_4.value:
            return (f"custom_sell_profit_4_qtpylib_rsi_{last_candle['rsi']}", current_profit)
        if current_profit > self.sell_custom_profit_3.value and last_candle['rsi'] < self.sell_custom_rsi_3.value:
            return (f"custom_sell_profit_3_qtpylib_rsi_{last_candle['rsi']}", current_profit)
        if current_profit > self.sell_custom_profit_2.value and last_candle['rsi'] < self.sell_custom_rsi_2.value:
            return (f"custom_sell_profit_2_qtpylib_rsi_{last_candle['rsi']}", current_profit)
        if current_profit > self.sell_custom_profit_1.value and last_candle['rsi'] < self.sell_custom_rsi_1.value:
            return (f"custom_sell_profit_1_qtpylib_rsi_{last_candle['rsi']}", current_profit)
        if current_profit > self.sell_custom_profit_0.value and last_candle['rsi'] < self.sell_custom_rsi_0.value:
            return (f"custom_sell_profit_0_qtpylib_rsi_{last_candle['rsi']}", current_profit)
        if current_profit < self.sell_custom_under_profit_3.value and last_candle['rsi'] > self.sell_custom_under_rsi_3.value:
            return (f"custom_sell_under_profit_3_qtpylib_rsi_{last_candle['rsi']}", current_profit)
        if current_profit < self.sell_custom_under_profit_2.value and last_candle['rsi'] > self.sell_custom_under_rsi_2.value:
            return (f"custom_sell_under_profit_2_qtpylib_rsi_{last_candle['rsi']}", current_profit)
        if current_profit < self.sell_custom_under_profit_1.value and last_candle['rsi'] > self.sell_custom_under_rsi_1.value:
            return (f"custom_sell_under_profit_1_qtpylib_rsi_{last_candle['rsi']}", current_profit)
        if max_profit > self.sell_trail_profit_min_1.value and current_profit < max_profit - self.sell_trail_down_1.value:
            return (f'custom_sell_trail_qtpylib_profit_max_{max_profit}_current_profit_{current_profit}', current_profit)
        if max_profit > self.sell_trail_profit_min_2.value and current_profit < max_profit - self.sell_trail_down_2.value:
            return (f'custom_sell_trail_qtpylib_profit_max_{max_profit}_current_profit_{current_profit}', current_profit)
        if max_profit > self.sell_trail_profit_min_3.value and current_profit < max_profit - self.sell_trail_down_3.value:
            return (f'custom_sell_trail_qtpylib_profit_max_{max_profit}_current_profit_{current_profit}', current_profit)
        return None

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs):
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()
        exit_reason = None
        if current_profit < self.sell_custom_stoploss_under_rel_1.value:
            if last_candle['rsi'] > self.sell_custom_stoploss_under_rsi_diff_1.value + last_candle['rsi']:
                exit_reason = f'custom_stoploss_qtpylib_profit_max_{current_profit}_current_profit_{current_profit}'
        return exit_reason

    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, current_time, entry_tag, side: str, **kwargs) -> bool:
        df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = df.iloc[-1].squeeze()
        if side == 'long':
            if rate > last_candle['close'] * (1 + 0.0025):
                return False
        elif rate < last_candle['close'] * (1 - 0.0025):
            return False
        return True

    def normal_tf_indicators(self, dataframe: DataFrame, metadata: Dict) -> DataFrame:
        dataframe['sma_offset_buy'] = ta.SMA(dataframe, timeperiod=self.base_nb_candles_buy.value) * self.low_offset_sma.value
        dataframe['sma_offset_sell'] = ta.SMA(dataframe, timeperiod=self.base_nb_candles_sell.value) * self.high_offset_sma.value
        dataframe['ema_offset_buy'] = ta.EMA(dataframe, timeperiod=self.base_nb_candles_buy.value) * self.low_offset_ema.value
        dataframe['ema_offset_sell'] = ta.EMA(dataframe, timeperiod=self.base_nb_candles_sell.value) * self.high_offset_ema.value
        dataframe['trima_offset_buy'] = ta.TRIMA(dataframe, timeperiod=self.base_nb_candles_buy.value) * self.low_offset_trima.value
        dataframe['trima_offset_sell'] = ta.TRIMA(dataframe, timeperiod=self.base_nb_candles_sell.value) * self.high_offset_trima.value
        dataframe['t3_offset_buy'] = ta.T3(dataframe, timeperiod=self.base_nb_candles_buy.value) * self.low_offset_t3.value
        dataframe['t3_offset_sell'] = ta.T3(dataframe, timeperiod=self.base_nb_candles_sell.value) * self.high_offset_t3.value
        dataframe['kama_offset_buy'] = ta.KAMA(dataframe, timeperiod=self.base_nb_candles_buy.value) * self.low_offset_kama.value
        dataframe['kama_offset_sell'] = ta.KAMA(dataframe, timeperiod=self.base_nb_candles_sell.value) * self.high_offset_kama.value
        dataframe['bb_upperband'] = ta.BBANDS(dataframe, timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)[0]
        dataframe['bb_middleband'] = ta.BBANDS(dataframe, timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)[1]
        dataframe['bb_lowerband'] = ta.BBANDS(dataframe, timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)[2]
        dataframe['bbpercent'] = (dataframe['close'] - dataframe['bb_lowerband']) / (dataframe['bb_upperband'] - dataframe['bb_lowerband'])
        dataframe['bb_width'] = (dataframe['bb_upperband'] - dataframe['bb_lowerband']) / dataframe['bb_middleband']
        dataframe['sar'] = ta.SAR(dataframe)
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=50)
        dataframe['mfi'] = ta.MFI(dataframe, timeperiod=14)
        dataframe['plus_dm'] = ta.PLUS_DM(dataframe, timeperiod=14)
        dataframe['minus_dm'] = ta.MINUS_DM(dataframe, timeperiod=14)
        dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
        dataframe['adxr'] = ta.ADXR(dataframe, timeperiod=14)
        dataframe['willr'] = ta.WILLR(dataframe, timeperiod=14)
        dataframe['ultosc'] = ta.ULTOSC(dataframe)
        dataframe['kst'] = ta.KST(dataframe)
        dataframe['macd'] = ta.MACD(dataframe)[0]
        dataframe['macdsignal'] = ta.MACD(dataframe)[1]
        dataframe['macdhist'] = ta.MACD(dataframe)[2]
        dataframe['ppo'] = ta.PPO(dataframe)
        dataframe['pposignal'] = ta.PPO(dataframe)[1]
        dataframe['ppohist'] = ta.PPO(dataframe)[2]
        dataframe['fastk'], dataframe['fastd'] = ta.STOCHRSI(dataframe, timeperiod=14)
        dataframe['slowk'], dataframe['slowd'] = ta.STOCH(dataframe)
        dataframe['fisher'] = 0.5 * np.log((1 + dataframe['fastk']) / (1 - dataframe['fastk']))
        dataframe['fisher'] = dataframe['fisher'].fillna(0)
        dataframe['ao'] = ta.AO(dataframe)
        dataframe['cci'] = ta.CCI(dataframe)
        dataframe['rocp'] = ta.ROCP(dataframe, timeperiod=14)
        dataframe['apo'] = ta.APO(dataframe)
        dataframe['aroonosc'] = ta.AROONOSC(dataframe)
        dataframe['bop'] = ta.BOP(dataframe)
        dataframe['cmo'] = ta.CMO(dataframe)
        dataframe['dx'] = ta.DX(dataframe)
        dataframe['mfi'] = ta.MFI(dataframe)
        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
        dataframe['mom'] = ta.MOM(dataframe)
        dataframe['plus_di'] = ta.PLUS_DI(dataframe)
        dataframe['rvi'] = ta.RVI(dataframe)
        dataframe['stoch_k'] = ta.STOCH(dataframe)[0]
        dataframe['stoch_d'] = ta.STOCH(dataframe)[1]
        dataframe['atr'] = ta.ATR(dataframe)
        dataframe['trix'] = ta.TRIX(dataframe)
        dataframe['ht_trendline'] = ta.HT_TRENDLINE(dataframe)
        dataframe['ht_sine'], dataframe['ht_leadsine'] = ta.HT_SINE(dataframe)
        dataframe['ht_phasor_inphase'], dataframe['ht_phasor_quadrature'] = ta.HT_PHASOR(dataframe)
        return dataframe

    def informative_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        assert isinstance(dataframe, DataFrame)
        assert isinstance(metadata, dict)
        dataframe = self.normal_tf_indicators(dataframe, metadata)
        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h)
        informative = self.informative_tf_indicators(informative, metadata)
        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.inf_1h, ffill=True)
        skip_columns = [(s, s + '_' + self.inf_1h) for s in list(informative.columns) if s != 'date']
        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.inf_1h, ffill=True, skip_columns=skip_columns)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        if self.buy_condition_1_enable.value:
            conditions.append((dataframe['close'] < dataframe['sma_offset_buy']) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe['mfi'] < self.buy_mfi_1.value))
        if self.buy_condition_2_enable.value:
            conditions.append((dataframe['close'] < dataframe['ema_offset_buy']) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe['mfi'] < self.buy_mfi_2.value))
        if self.buy_condition_3_enable.value:
            conditions.append((dataframe['close'] < dataframe['ema_offset_buy']) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe['mfi'] < self.buy_mfi_2.value) & (dataframe['volume'] > self.buy_volume_2.value))
        if self.buy_condition_4_enable.value:
            conditions.append((dataframe['close'] < dataframe['sma_offset_buy']) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe['mfi'] < self.buy_mfi_1.value) & (dataframe['volume'] > self.buy_volume_2.value))
        if self.buy_condition_5_enable.value:
            conditions.append((dataframe['close'] < dataframe['ema_offset_buy']) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe['mfi'] < self.buy_mfi_2.value) & (dataframe['volume'] > self.buy_volume_2.value))
        if self.buy_condition_6_enable.value:
            conditions.append((dataframe['close'] < dataframe['ema_offset_buy']) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe['mfi'] < self.buy_mfi_2.value) & (dataframe['volume'] > self.buy_volume_2.value))
        if self.buy_condition_7_enable.value:
            conditions.append((dataframe['close'] < dataframe['sma_offset_buy']) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe['mfi'] < self.buy_mfi_1.value) & (dataframe['volume'] > self.buy_volume_2.value))
        if self.buy_condition_8_enable.value:
            conditions.append((dataframe['close'] < dataframe['sma_offset_buy']) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe['mfi'] < self.buy_mfi_1.value) & (dataframe['volume'] > self.buy_volume_2.value))
        if self.buy_condition_9_enable.value:
            conditions.append((dataframe['close'] < dataframe['ema_offset_buy']) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe['mfi'] < self.buy_mfi_2.value) & (dataframe['volume'] > self.buy_volume_2.value))
        if self.buy_condition_10_enable.value:
            conditions.append((dataframe['close'] < dataframe['ema_offset_buy']) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe['mfi'] < self.buy_mfi_2.value) & (dataframe['volume'] > self.buy_volume_2.value))
        if self.buy_condition_11_enable.value:
            conditions.append((dataframe['close'] < dataframe['ema_offset_buy']) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe['mfi'] < self.buy_mfi_2.value) & (dataframe['volume'] > self.buy_volume_2.value))
        if self.buy_condition_12_enable.value:
            conditions.append((dataframe['close'] < dataframe['sma_offset_buy']) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe['mfi'] < self.buy_mfi_1.value) & (dataframe['volume'] > self.buy_volume_2.value))
        if self.buy_condition_13_enable.value:
            conditions.append((dataframe['close'] < dataframe['ema_offset_buy']) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe['mfi'] < self.buy_mfi_2.value) & (dataframe['volume'] > self.buy_volume_2.value))
        if self.buy_condition_14_enable.value:
            conditions.append((dataframe['close'] < dataframe['ema_offset_buy']) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe['mfi'] < self.buy_mfi_2.value) & (dataframe['volume'] > self.buy_volume_2.value))
        if self.buy_condition_15_enable.value:
            conditions.append((dataframe['close'] < dataframe['sma_offset_buy']) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe['mfi'] < self.buy_mfi_1.value) & (dataframe['volume'] > self.buy_volume_2.value))
        if self.buy_condition_16_enable.value:
            conditions.append((dataframe['close'] < dataframe['sma_offset_buy']) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe['mfi'] < self.buy_mfi_1.value) & (dataframe['volume'] > self.buy_volume_2.value))
        if self.buy_condition_17_enable.value:
            conditions.append((dataframe['close'] < dataframe['ema_offset_buy']) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe['mfi'] < self.buy_mfi_2.value) & (dataframe['volume'] > self.buy_volume_2.value))
        if self.buy_condition_18_enable.value:
            conditions.append((dataframe['close'] < dataframe['ema_offset_buy']) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe['mfi'] < self.buy_mfi_2.value) & (dataframe['volume'] > self.buy_volume_2.value))
        if self.buy_condition_19_enable.value:
            conditions.append((dataframe['close'] < dataframe['sma_offset_buy']) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe['mfi'] < self.buy_mfi_1.value) & (dataframe['volume'] > self.buy_volume_2.value))
        if self.buy_condition_20_enable.value:
            conditions.append((dataframe['close'] < dataframe['ema_offset_buy']) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe['mfi'] < self.buy_mfi_2.value) & (dataframe['volume'] > self.buy_volume_2.value))
        if self.buy_condition_21_enable.value:
            conditions.append((dataframe['close'] < dataframe['ema_offset_buy']) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe['mfi'] < self.buy_mfi_2.value) & (dataframe['volume'] > self.buy_volume_2.value))
        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 = []
        if self.sell_condition_1_enable.value:
            conditions.append((dataframe['close'] > dataframe['sma_offset_sell']) & (dataframe['rsi'] > self.sell_rsi_bb_1.value))
        if self.sell_condition_2_enable.value:
            conditions.append((dataframe['close'] > dataframe['ema_offset_sell']) & (dataframe['rsi'] > self.sell_rsi_bb_2.value))
        if self.sell_condition_3_enable.value:
            conditions.append((dataframe['close'] > dataframe['ema_offset_sell']) & (dataframe['rsi'] > self.sell_rsi_main_3.value))
        if self.sell_condition_4_enable.value:
            conditions.append((dataframe['close'] > dataframe['ema_offset_sell']) & (dataframe['rsi'] > self.sell_dual_rsi_rsi_4.value) & (dataframe['rsi'] > self.sell_dual_rsi_rsi_1h_4.value))
        if self.sell_condition_5_enable.value:
            conditions.append((dataframe['close'] > dataframe['ema_offset_sell']) & (dataframe['rsi'] > self.sell_ema_relative_5.value) & (dataframe['rsi'] > self.sell_rsi_diff_5.value))
        if self.sell_condition_6_enable.value:
            conditions.append((dataframe['close'] > dataframe['sma_offset_sell']) & (dataframe['rsi'] > self.sell_rsi_under_6.value))
        if self.sell_condition_7_enable.value:
            conditions.append((dataframe['close'] > dataframe['ema_offset_sell']) & (dataframe['rsi'] > self.sell_rsi_1h_7.value))
        if self.sell_condition_8_enable.value:
            conditions.append((dataframe['close'] > dataframe['sma_offset_sell']) & (dataframe['rsi'] > self.sell_bb_relative_8.value))
        if conditions:
            dataframe.loc[reduce(lambda x, y: x | y, conditions), 'exit_long'] = 1
        return dataframe
# Elliot Wave Oscillator

def EWO(dataframe, sma1_length=5, sma2_length=35):
    df = dataframe.copy()
    sma1 = ta.EMA(df, timeperiod=sma1_length)
    sma2 = ta.EMA(df, timeperiod=sma2_length)
    smadif = (sma1 - sma2) / df['close'] * 100
    return smadif